diff --git a/AIML Models/Anti-Plag/Assignment/Lorem.docx b/AIML Models/Anti-Plag/Assignment/Lorem.docx new file mode 100644 index 000000000..4b29a030e Binary files /dev/null and b/AIML Models/Anti-Plag/Assignment/Lorem.docx differ diff --git a/AIML Models/Anti-Plag/Assignment/Sujal.docx b/AIML Models/Anti-Plag/Assignment/Sujal.docx new file mode 100644 index 000000000..c2c84a821 Binary files /dev/null and b/AIML Models/Anti-Plag/Assignment/Sujal.docx differ diff --git a/AIML Models/Anti-Plag/Assignment/temp/Sujal.docx b/AIML Models/Anti-Plag/Assignment/temp/Sujal.docx new file mode 100644 index 000000000..d86ac9e29 Binary files /dev/null and b/AIML Models/Anti-Plag/Assignment/temp/Sujal.docx differ diff --git a/AIML Models/Anti-Plag/Plag_Check.ipynb b/AIML Models/Anti-Plag/Plag_Check.ipynb new file mode 100644 index 000000000..af9054cdd --- /dev/null +++ b/AIML Models/Anti-Plag/Plag_Check.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n", + "[nltk_data] Downloading package stopwords to\n", + "[nltk_data] C:\\Users\\kulsh\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import string\n", + "import nltk\n", + "from nltk.corpus import stopwords\n", + "import sklearnex\n", + "from sklearnex import patch_sklearn\n", + "patch_sklearn() \n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "from docx import Document\n", + "nltk.download('stopwords')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def preprocess_text(text):\n", + " # Remove punctuation and convert to lowercase\n", + " text = text.translate(str.maketrans(\"\", \"\", string.punctuation)).lower()\n", + " \n", + " # Remove stopwords\n", + " stop_words = set(stopwords.words('english'))\n", + " text = \" \".join(word for word in text.split() if word not in stop_words)\n", + " \n", + " return text" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_text_from_docx(file_path):\n", + " doc = Document(file_path)\n", + " paragraphs = [p.text for p in doc.paragraphs]\n", + " return '\\n'.join(paragraphs)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "def check_plagiarism(student_assignment_file, reference_assignment_files):\n", + " # Load student assignment\n", + " student_assignment = extract_text_from_docx(student_assignment_file)\n", + " processed_student = preprocess_text(student_assignment)\n", + " \n", + " similarities = []\n", + " \n", + " for ref_file in reference_assignment_files:\n", + " # Load reference assignment\n", + " reference_assignment = extract_text_from_docx('./Assignment/'+ref_file)\n", + " processed_ref = preprocess_text(reference_assignment)\n", + " \n", + " # Create TF-IDF vectors\n", + " vectorizer = TfidfVectorizer()\n", + " vectors = vectorizer.fit_transform([processed_student, processed_ref])\n", + " \n", + " # Calculate cosine similarity\n", + " similarity = cosine_similarity(vectors[0], vectors[1])[0][0]\n", + " similarities.append(similarity)\n", + " \n", + " return similarities" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "student_assignment_file = \"./Assignment/temp/Sujal.docx\"\n", + "reference_assignment_files = [doc for doc in os.listdir('./Assignment/') if doc.endswith('.doc') or doc.endswith('.docx')]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Similarity with reference assignment 1: 0.1433594398627273\n", + "Similarity with reference assignment 2: 0.6140647534863115\n" + ] + } + ], + "source": [ + "similarities = check_plagiarism(student_assignment_file, reference_assignment_files)\n", + "for i, similarity in enumerate(similarities):\n", + " print(f\"Similarity with reference assignment {i+1}: {similarity}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} 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file mode 100644 index 000000000..55ddb5741 --- /dev/null +++ b/AIML Models/Drowsiness/drowsiness_detection.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image, ImageDraw\n", + "import face_recognition\n", + "import os\n", + "import cv2\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from sklearnex.model_selection import train_test_split\n", + "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n", + "from keras.models import Sequential, load_model\n", + "from keras.layers import Dense,Flatten,Conv2D,MaxPooling2D,Dropout\n", + "import numpy as np\n", + "from time import time" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "65m3Xc9l-EnO" + }, + "outputs": [], + "source": [ + "def eye_cropper(image_path):\n", + " frame = cv2.imread(image_path) # Read the image using OpenCV\n", + " if frame is None:\n", + " return None\n", + " \n", + " frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Convert BGR to RGB\n", + "\n", + " gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n", + " facial_features_list = face_recognition.face_landmarks(gray)\n", + "\n", + " try:\n", + " eye = facial_features_list[0]['left_eye']\n", + " except:\n", + " try:\n", + " eye = facial_features_list[0]['right_eye']\n", + " except:\n", + " return None\n", + "\n", + " x_max = max([coordinate[0] for coordinate in eye])\n", + " x_min = min([coordinate[0] for coordinate in eye])\n", + " y_max = max([coordinate[1] for coordinate in eye])\n", + " y_min = min([coordinate[1] for coordinate in eye])\n", + "\n", + " x_range = x_max - x_min\n", + " y_range = y_max - y_min\n", + "\n", + " if x_range > y_range:\n", + " right = round(.5 * x_range) + x_max\n", + " left = x_min - round(.5 * x_range)\n", + " bottom = round((((right - left) - y_range)) / 2) + y_max\n", + " top = y_min - round((((right - left) - y_range)) / 2)\n", + " else:\n", + " bottom = round(.5 * y_range) + y_max\n", + " top = y_min - round(.5 * y_range)\n", + " right = round((((bottom - top) - x_range)) / 2) + x_max\n", + " left = x_min - round((((bottom - top) - x_range)) / 2)\n", + "\n", + " cropped = frame[top:(bottom + 1), left:(right + 1)]\n", + "\n", + " cropped = cv2.resize(cropped, (80, 80))\n", + " image_for_prediction = cropped.reshape(-1, 80, 80, 3)\n", + "\n", + " return image_for_prediction\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l62ZfgOZAStH", + "outputId": "5567b08a-99f9-40d6-c52e-98d8da2c5a25" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesful Image Import Count = 500\n", + "Succesful Image Import Count = 1000\n", + "Succesful Image Import Count = 1500\n", + "Succesful Image Import Count = 2000\n", + "Succesful Image Import Count = 500\n", + "Succesful Image Import Count = 1000\n", + "Succesful Image Import Count = 1500\n", + "Succesful Image Import Count = 2000\n" + ] + } + ], + "source": [ + "def load_images_from_folder(folder, eyes = 0):\n", + " count = 0\n", + " error_count = 0\n", + " images = []\n", + " for filename in os.listdir(folder):\n", + " try:\n", + " img = cv2.imread(os.path.join(folder,filename))\n", + " img = cv2.resize(img, (80,80)) ## Resizing the images\n", + " ## for eyes if it is 0: open, 1: close\n", + " images.append([img, eyes])\n", + " except:\n", + " error_count += 1\n", + " print('ErrorCount = ' + str(error_count))\n", + " continue\n", + " \n", + " count += 1\n", + " if count % 500 == 0:\n", + " print('Succesful Image Import Count = ' + str(count))\n", + "\n", + " return images\n", + "\n", + "folder=\"./dataset/Open_Eyes\"\n", + "open_eyes = load_images_from_folder(folder, 0)\n", + "\n", + "folder=\"./dataset/Closed_Eyes\"\n", + "closed_eyes = load_images_from_folder(folder, 1)\n", + "eyes = closed_eyes + open_eyes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "m5K0wji8ASwi" + }, + "outputs": [], + "source": [ + "X = [] \n", + "y = [] \n", + "for features, label in eyes: \n", + " X.append(features)\n", + " y.append(label)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "7n5urpYl_Ofl" + }, + "outputs": [], + "source": [ + "X = np.array(X).reshape(-1, 80, 80, 3)\n", + "y = np.array(y)\n", + "X = X/255.0" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "eZs28CEQ_Oiu" + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_ZXKRwY7_Ol0", + "outputId": "3b546b97-631c-48cf-de99-b6dc12c95013" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/24\n", + "94/94 [==============================] - 12s 110ms/step - loss: 0.3843 - auc: 0.8985 - val_loss: 0.1402 - val_auc: 0.9846\n", + "Epoch 2/24\n", + "94/94 [==============================] - 10s 105ms/step - loss: 0.1122 - auc: 0.9874 - val_loss: 0.0736 - val_auc: 0.9963\n", + "Epoch 3/24\n", + "94/94 [==============================] - 10s 107ms/step - loss: 0.0658 - auc: 0.9962 - val_loss: 0.0315 - val_auc: 0.9986\n", + "Epoch 4/24\n", + "94/94 [==============================] - 10s 107ms/step - loss: 0.0776 - auc: 0.9940 - val_loss: 0.0570 - val_auc: 0.9989\n", + "Epoch 5/24\n", + "94/94 [==============================] - 10s 107ms/step - loss: 0.0448 - auc: 0.9971 - val_loss: 0.0686 - val_auc: 0.9896\n", + "Epoch 6/24\n", + "94/94 [==============================] - 10s 108ms/step - loss: 0.0417 - auc: 0.9986 - val_loss: 0.0394 - val_auc: 0.9961\n", + "Epoch 7/24\n", + "94/94 [==============================] - 10s 108ms/step - loss: 0.0160 - auc: 0.9991 - val_loss: 0.0208 - val_auc: 0.9972\n", + "Epoch 8/24\n", + "94/94 [==============================] - 10s 110ms/step - loss: 0.0131 - auc: 0.9998 - val_loss: 0.0237 - val_auc: 0.9979\n", + "Epoch 9/24\n", + "94/94 [==============================] - 10s 111ms/step - loss: 0.0109 - auc: 0.9992 - val_loss: 0.0271 - val_auc: 0.9979\n", + "Epoch 10/24\n", + "94/94 [==============================] - 10s 111ms/step - loss: 0.0050 - auc: 0.9993 - val_loss: 0.0326 - val_auc: 0.9939\n", + "Epoch 11/24\n", + "94/94 [==============================] - 10s 110ms/step - loss: 0.0241 - auc: 0.9985 - val_loss: 0.0179 - val_auc: 0.9979\n", + "Epoch 12/24\n", + "94/94 [==============================] - 10s 111ms/step - loss: 0.0015 - auc: 1.0000 - val_loss: 0.0290 - val_auc: 0.9980\n", + "Epoch 13/24\n", + "94/94 [==============================] - 11s 115ms/step - loss: 0.0011 - auc: 1.0000 - val_loss: 0.0211 - val_auc: 0.9980\n", + "Epoch 14/24\n", + "94/94 [==============================] - 11s 117ms/step - loss: 4.5868e-04 - auc: 1.0000 - val_loss: 0.0234 - val_auc: 0.9980\n", + "Epoch 15/24\n", + "94/94 [==============================] - 11s 119ms/step - loss: 0.0315 - auc: 0.9978 - val_loss: 0.0272 - val_auc: 0.9976\n", + "Epoch 16/24\n", + "94/94 [==============================] - 11s 115ms/step - loss: 0.0350 - auc: 0.9963 - val_loss: 0.0168 - val_auc: 0.9977\n", + "Epoch 17/24\n", + "94/94 [==============================] - 10s 111ms/step - loss: 0.0220 - auc: 0.9982 - val_loss: 0.0326 - val_auc: 0.9978\n", + "Epoch 18/24\n", + "94/94 [==============================] - 10s 111ms/step - loss: 0.0259 - auc: 0.9988 - val_loss: 0.0218 - val_auc: 0.9979\n", + "Epoch 19/24\n", + "94/94 [==============================] - 10s 111ms/step - loss: 0.0157 - auc: 0.9992 - val_loss: 0.0116 - val_auc: 0.9999\n", + "Epoch 20/24\n", + "94/94 [==============================] - 11s 113ms/step - loss: 0.0011 - auc: 1.0000 - val_loss: 0.0026 - val_auc: 1.0000\n", + "Epoch 21/24\n", + "94/94 [==============================] - 10s 110ms/step - loss: 0.0979 - auc: 0.9925 - val_loss: 0.0607 - val_auc: 0.9979\n", + "Epoch 22/24\n", + "94/94 [==============================] - 10s 110ms/step - loss: 0.0258 - auc: 0.9983 - val_loss: 0.0227 - val_auc: 0.9978\n", + "Epoch 23/24\n", + "94/94 [==============================] - 10s 111ms/step - loss: 0.0055 - auc: 1.0000 - val_loss: 0.0083 - val_auc: 1.0000\n", + "Epoch 24/24\n", + "94/94 [==============================] - 10s 112ms/step - loss: 0.0035 - auc: 1.0000 - val_loss: 0.0079 - val_auc: 1.0000\n", + "32/32 [==============================] - 1s 30ms/step - loss: 0.0079 - auc: 1.0000\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.007898536510765553, 0.9999880194664001]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Sequential()\n", + "\n", + "# Adding first three convolutional layers\n", + "model.add(Conv2D(\n", + " filters = 32, # number of filters\n", + " kernel_size = (3,3), # height/width of filter\n", + " activation = 'relu', # activation function \n", + " input_shape = (80,80,3) # shape of input (image)\n", + " ))\n", + "model.add(Conv2D(\n", + " filters = 32, # number of filters\n", + " kernel_size = (3,3), # height/width of filter\n", + " activation = 'relu' # activation function \n", + " ))\n", + "model.add(Conv2D(\n", + " filters = 32, # number of filters\n", + " kernel_size = (3,3), # height/width of filter\n", + " activation = 'relu' # activation function \n", + " ))\n", + "\n", + "# Adding pooling after convolutional layers\n", + "model.add(MaxPooling2D(pool_size = (2,2))) # Dimensions of the region that you are pooling\n", + "\n", + "# Adding second set of convolutional layers\n", + "model.add(Conv2D(\n", + " filters = 32, # number of filters\n", + " kernel_size = (3,3), # height/width of filter\n", + " activation = 'relu' # activation function \n", + " ))\n", + "model.add(Conv2D(\n", + " filters = 32, # number of filters\n", + " kernel_size = (3,3), # height/width of filter\n", + " activation = 'relu' # activation function \n", + " ))\n", + "\n", + "# Add last pooling layer.\n", + "model.add(MaxPooling2D(pool_size=(2,2)))\n", + "\n", + "model.add(Flatten())\n", + "\n", + "# Adding first dense layer with 256 nodes\n", + "model.add(Dense(256, activation='relu'))\n", + "\n", + "# Adding a dropout layer to avoid overfitting\n", + "model.add(Dropout(0.3))\n", + "\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.3)) \n", + "\n", + "model.add(Dense(64, activation='relu'))\n", + "model.add(Dropout(0.3))\n", + "\n", + "# adding output layer\n", + "model.add(Dense(1, activation = 'sigmoid'))\n", + "\n", + "# compiling the model\n", + "model.compile(loss='binary_crossentropy',\n", + " optimizer='adam',\n", + " metrics=[tf.keras.metrics.AUC(curve = 'PR')])\n", + "\n", + "start = time()\n", + "# fitting the model\n", + "model.fit(X_train,\n", + " y_train,\n", + " batch_size=32,\n", + " validation_data=(X_test, y_test),\n", + " epochs=24)\n", + "end = time()\n", + "\n", + "# evaluate the model \n", + "model.evaluate(X_test, y_test, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "WjPNx8nO_Opc" + }, + "outputs": [], + "source": [ + "model.save('./dataset/drowsiness_detection.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def model_responce(image, model): \n", + " image_for_prediction = eye_cropper(image)\n", + " if image_for_prediction is None:\n", + " return 'Yes'\n", + " try:\n", + " image_for_prediction = image_for_prediction/255.0\n", + " except:\n", + " print(\"Error\")\n", + " \n", + " prediction = model.predict(image_for_prediction)\n", + " print(prediction)\n", + " # Based on prediction, display either \"Open Eyes\" or \"Closed Eyes\"\n", + " if prediction < 0.7:\n", + " status = 'No'\n", + " else:\n", + " status = 'Yes'\n", + " return status" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "_ = model_responce(image, model) #input image here\n", + "print(_)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/AIML Models/Drowsiness/webcam.py b/AIML Models/Drowsiness/webcam.py new file mode 100644 index 000000000..8fc858b52 --- /dev/null +++ b/AIML Models/Drowsiness/webcam.py @@ -0,0 +1,62 @@ +import cv2 +import numpy as np +from PIL import Image, ImageDraw +import face_recognition +from tensorflow import keras + +eye_model = keras.models.load_model('./dataset/drowsiness_detection.h5') + +def eye_cropper(frame): + facial_features_list = face_recognition.face_landmarks(frame) + try: + eye = facial_features_list[0]['left_eye'] + except: + try: + eye = facial_features_list[0]['right_eye'] + except: + return None + + x_max = max([coordinate[0] for coordinate in eye]) + x_min = min([coordinate[0] for coordinate in eye]) + y_max = max([coordinate[1] for coordinate in eye]) + y_min = min([coordinate[1] for coordinate in eye]) + + x_range = x_max - x_min + y_range = y_max - y_min + + if x_range > y_range: + right = round(.5*x_range) + x_max + left = x_min - round(.5*x_range) + bottom = round((((right-left) - y_range))/2) + y_max + top = y_min - round((((right-left) - y_range))/2) + else: + bottom = round(.5*y_range) + y_max + top = y_min - round(.5*y_range) + right = round((((bottom-top) - x_range))/2) + x_max + left = x_min - round((((bottom-top) - x_range))/2) + + cropped = frame[top:(bottom + 1), left:(right + 1)] + + cropped = cv2.resize(cropped, (80,80)) + image_for_prediction = cropped.reshape(-1, 80, 80, 3) + + return image_for_prediction + + +def predict(frame): + image_for_prediction = eye_cropper(image) + if image_for_prediction is None: + return 'Yes' + try: + image_for_prediction = image_for_prediction/255.0 + except: + print("Error") + + prediction = eye_model.predict(image_for_prediction) + print(prediction) + # Based on prediction, display either "Open Eyes" or "Closed Eyes" + if prediction < 0.7: + status = 'No' + else: + status = 'Yes' + return status \ No newline at end of file diff --git a/AIML Models/Gamification/Face_Pose.ipynb b/AIML Models/Gamification/Face_Pose.ipynb new file mode 100644 index 000000000..d2cf5ae7f --- /dev/null +++ b/AIML Models/Gamification/Face_Pose.ipynb @@ -0,0 +1,685 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6e23db6a", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ['TF_ENABLE_ONEDNN_OPTS']='1'\n", + "os.environ['TF_ENABLE_MKL_NATIVE_FORMAT']='1'\n", + "os.environ['KMP_BLOCKTIME']='1'\n", + "os.environ['KMP_SETTINGS']='1'\n", + "os.environ['OMP_NUM_THREADS']='6'\n", + "os.environ['KMP_AFFINITY']='granularity=fine,verbose,compact,1,0'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "64e4ce26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We are using Tensorflow version 2.12.0\n", + "MKL enabled : True\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "tf.config.threading.set_inter_op_parallelism_threads(2) \n", + "tf.config.threading.set_intra_op_parallelism_threads(8)\n", + "tf.config.set_soft_device_placement(False)\n", + "\n", + "def get_mkl_enabled_flag():\n", + " mkl_enabled = False\n", + " major_version = int(tf.__version__.split(\".\")[0])\n", + " minor_version = int(tf.__version__.split(\".\")[1])\n", + " if major_version >= 2:\n", + " if minor_version < 5:\n", + " from tensorflow.python import _pywrap_util_port\n", + " elif minor_version >= 9:\n", + "\n", + " from tensorflow.python.util import _pywrap_util_port\n", + " onednn_enabled = int(os.environ.get('TF_ENABLE_ONEDNN_OPTS', '1'))\n", + "\n", + " else:\n", + " from tensorflow.python.util import _pywrap_util_port\n", + " onednn_enabled = int(os.environ.get('TF_ENABLE_ONEDNN_OPTS', '0'))\n", + " mkl_enabled = _pywrap_util_port.IsMklEnabled() or (onednn_enabled == 1)\n", + " else:\n", + " mkl_enabled = tf.pywrap_tensorflow.IsMklEnabled()\n", + " return mkl_enabled\n", + "\n", + "print (\"We are using Tensorflow version\", tf.__version__)\n", + "print(\"MKL enabled :\", get_mkl_enabled_flag())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "039505ec-794d-49c3-a4d2-1d36d5237dde", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n" + ] + } + ], + "source": [ + "import cv2\n", + "import dlib\n", + "from time import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pickle as pkl\n", + "import sklearnex\n", + "from sklearnex import patch_sklearn\n", + "patch_sklearn() \n", + "from keras.models import Sequential, load_model\n", + "from keras.layers import Dense\n", + "from keras.callbacks import EarlyStopping\n", + "from sklearnex.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff610c49-79eb-4115-8978-4b0e46ad327b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(6288, 2278) (6288, 3)\n" + ] + } + ], + "source": [ + "x, y = pkl.load(open('data/samples.pkl', 'rb'))\n", + "print(x.shape, y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ee5cfcb0-ef6a-4e59-bd26-3251a5a53b71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-46.06486893 43.00866699 -0.52579503197042 5.176231116713095\n", + "-29.88856888 34.09674835 2.584682740273537 7.962828151857226\n", + "-75.55059814 86.84925079 -0.11620588007315523 13.090839183150852\n" + ] + } + ], + "source": [ + "roll, pitch, yaw = y[:, 0], y[:, 1], y[:, 2]\n", + "\n", + "print(roll.min(), roll.max(), roll.mean(), roll.std())\n", + "print(pitch.min(), pitch.max(), pitch.mean(), pitch.std())\n", + "print(yaw.min(), yaw.max(), yaw.mean(), yaw.std())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "743d9ab0-8a81-408c-aea5-666df9c745bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4401, 2278) (4401, 3)\n", + "(943, 2278) (943, 3)\n", + "(944, 2278) (944, 3)\n" + ] + } + ], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n", + "x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=0.5, random_state=42)\n", + "\n", + "print(x_train.shape, y_train.shape)\n", + "print(x_val.shape, y_val.shape)\n", + "print(x_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3d7156b3-ed6f-4aa8-b30e-4f8079554e50", + "metadata": {}, + "outputs": [], + "source": [ + "std = StandardScaler()\n", + "std.fit(x_train)\n", + "x_train = std.transform(x_train)\n", + "x_val = std.transform(x_val)\n", + "x_test = std.transform(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f977abca-91ed-4a3b-adc0-7c2b7025b177", + "metadata": {}, + "outputs": [], + "source": [ + "BATCH_SIZE = 64\n", + "EPOCHS = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4f2311f7-94cb-459b-b4f4-d5a29f63b8e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 20) 45580 \n", + " \n", + " dense_1 (Dense) (None, 10) 210 \n", + " \n", + " dense_2 (Dense) (None, 3) 33 \n", + " \n", + "=================================================================\n", + "Total params: 45,823\n", + "Trainable params: 45,823\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(units=20, activation='relu', kernel_regularizer='l2', input_dim=x.shape[1]))\n", + "model.add(Dense(units=10, activation='relu', kernel_regularizer='l2'))\n", + "model.add(Dense(units=3, activation='linear'))\n", + "\n", + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "da11a8f8-47b5-45b5-941c-2d506da7cbb4", + "metadata": {}, + "outputs": [], + "source": [ + "callback_list = [EarlyStopping(monitor='val_loss', patience=25)]\n", + "model.compile(optimizer='adam', loss='mean_squared_error')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "450a4ed7-3db1-4a08-8ae3-1b944f3f650f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "69/69 [==============================] - 1s 4ms/step - loss: 68.3241 - val_loss: 62.8199\n", + "Epoch 2/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 46.5274 - val_loss: 42.4882\n", + "Epoch 3/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 37.0843 - val_loss: 36.1535\n", + "Epoch 4/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 35.5464 - val_loss: 35.8670\n", + "Epoch 5/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 34.6326 - val_loss: 36.1763\n", + "Epoch 6/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 34.6466 - val_loss: 36.8743\n", + "Epoch 7/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 34.0069 - val_loss: 35.4946\n", + "Epoch 8/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 33.4251 - val_loss: 34.9538\n", + "Epoch 9/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 33.4464 - val_loss: 34.9984\n", + "Epoch 10/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 33.7141 - val_loss: 36.3997\n", + "Epoch 11/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 33.5752 - val_loss: 33.8590\n", + "Epoch 12/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.6777 - val_loss: 34.1597\n", + "Epoch 13/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 33.3495 - val_loss: 35.1334\n", + "Epoch 14/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 33.2501 - val_loss: 35.5363\n", + "Epoch 15/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.7815 - val_loss: 34.2053\n", + "Epoch 16/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 33.7009 - val_loss: 34.2478\n", + "Epoch 17/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.7438 - val_loss: 34.3913\n", + "Epoch 18/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.7822 - val_loss: 36.3399\n", + "Epoch 19/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.7972 - val_loss: 34.6423\n", + "Epoch 20/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.6121 - val_loss: 33.9442\n", + "Epoch 21/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.2623 - val_loss: 35.2725\n", + "Epoch 22/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.3742 - val_loss: 33.4093\n", + "Epoch 23/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.2683 - val_loss: 33.2901\n", + "Epoch 24/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.2544 - val_loss: 36.0273\n", + "Epoch 25/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.2510 - val_loss: 35.0505\n", + "Epoch 26/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.7370 - val_loss: 33.9695\n", + "Epoch 27/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.7813 - val_loss: 34.1242\n", + "Epoch 28/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.8195 - val_loss: 35.9742\n", + "Epoch 29/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 32.4022 - val_loss: 35.0475\n", + "Epoch 30/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.7081 - val_loss: 33.8958\n", + "Epoch 31/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.2188 - val_loss: 34.9174\n", + "Epoch 32/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.4716 - val_loss: 33.1634\n", + "Epoch 33/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.3235 - val_loss: 35.8239\n", + "Epoch 34/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.4200 - val_loss: 33.6449\n", + "Epoch 35/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.0945 - val_loss: 36.5697\n", + "Epoch 36/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.4333 - val_loss: 36.2233\n", + "Epoch 37/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.8166 - val_loss: 36.5551\n", + "Epoch 38/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.9173 - val_loss: 33.0874\n", + "Epoch 39/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.0512 - val_loss: 34.2354\n", + "Epoch 40/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.0986 - val_loss: 34.4018\n", + "Epoch 41/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.6033 - val_loss: 33.6331\n", + "Epoch 42/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.3256 - val_loss: 32.7893\n", + "Epoch 43/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.4783 - val_loss: 33.8587\n", + "Epoch 44/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.8849 - val_loss: 34.4867\n", + "Epoch 45/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 31.2534 - val_loss: 35.5173\n", + "Epoch 46/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.8311 - val_loss: 34.6072\n", + "Epoch 47/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.5149 - val_loss: 32.5085\n", + "Epoch 48/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.4549 - val_loss: 34.1871\n", + "Epoch 49/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.3874 - val_loss: 32.6059\n", + "Epoch 50/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.3547 - val_loss: 32.6229\n", + "Epoch 51/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.9353 - val_loss: 33.5689\n", + "Epoch 52/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.5868 - val_loss: 33.5464\n", + "Epoch 53/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.9701 - val_loss: 35.9890\n", + "Epoch 54/100\n", + "69/69 [==============================] - 0s 3ms/step - loss: 30.3153 - val_loss: 32.5210\n", + "Epoch 55/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.9038 - val_loss: 33.1743\n", + "Epoch 56/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.5469 - val_loss: 33.4463\n", + "Epoch 57/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.2288 - val_loss: 34.3841\n", + "Epoch 58/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.1330 - val_loss: 32.8229\n", + "Epoch 59/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.2327 - val_loss: 33.6980\n", + "Epoch 60/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.2616 - val_loss: 38.1808\n", + "Epoch 61/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.6853 - val_loss: 32.0963\n", + "Epoch 62/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.1351 - val_loss: 31.7932\n", + "Epoch 63/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.4233 - val_loss: 32.2747\n", + "Epoch 64/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.3927 - val_loss: 34.2562\n", + "Epoch 65/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.9894 - val_loss: 34.2961\n", + "Epoch 66/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.0108 - val_loss: 35.5102\n", + "Epoch 67/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.8834 - val_loss: 31.6917\n", + "Epoch 68/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.0173 - val_loss: 35.1851\n", + "Epoch 69/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.0289 - val_loss: 31.8245\n", + "Epoch 70/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.6041 - val_loss: 33.0721\n", + "Epoch 71/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.1057 - val_loss: 31.3962\n", + "Epoch 72/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.1693 - val_loss: 32.1442\n", + "Epoch 73/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.9260 - val_loss: 31.9411\n", + "Epoch 74/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.1159 - val_loss: 31.3792\n", + "Epoch 75/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.6466 - val_loss: 35.0331\n", + "Epoch 76/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.7218 - val_loss: 31.3682\n", + "Epoch 77/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.1586 - val_loss: 33.9432\n", + "Epoch 78/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.8338 - val_loss: 32.5857\n", + "Epoch 79/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.5111 - val_loss: 31.5970\n", + "Epoch 80/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.4022 - val_loss: 32.7350\n", + "Epoch 81/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.2597 - val_loss: 32.5283\n", + "Epoch 82/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.3087 - val_loss: 31.4004\n", + "Epoch 83/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.8654 - val_loss: 31.6473\n", + "Epoch 84/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.2285 - val_loss: 31.9813\n", + "Epoch 85/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.6811 - val_loss: 32.5350\n", + "Epoch 86/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.1981 - val_loss: 31.8318\n", + "Epoch 87/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.4029 - val_loss: 30.9297\n", + "Epoch 88/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.6895 - val_loss: 31.9405\n", + "Epoch 89/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.0099 - val_loss: 32.2062\n", + "Epoch 90/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 30.1067 - val_loss: 32.2787\n", + "Epoch 91/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.7998 - val_loss: 31.6650\n", + "Epoch 92/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 28.8391 - val_loss: 30.4829\n", + "Epoch 93/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 28.9662 - val_loss: 33.5106\n", + "Epoch 94/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.3137 - val_loss: 31.0555\n", + "Epoch 95/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 28.8064 - val_loss: 31.0825\n", + "Epoch 96/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.0861 - val_loss: 32.2770\n", + "Epoch 97/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 28.9162 - val_loss: 32.3818\n", + "Epoch 98/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 28.8548 - val_loss: 31.5460\n", + "Epoch 99/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 29.4596 - val_loss: 32.6364\n", + "Epoch 100/100\n", + "69/69 [==============================] - 0s 2ms/step - loss: 28.9501 - val_loss: 31.4530\n" + ] + } + ], + "source": [ + "hist = model.fit(x=x_train, y=y_train, validation_data=(x_val, y_val), batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=callback_list)\n", + "model.save('models/model.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "69b07dd1-732b-443a-b60a-69a6c2b7fa94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss: 29.732118606567383\n", + " Val loss: 31.45303726196289\n", + " Test loss: 41.27518081665039\n" + ] + } + ], + "source": [ + "print('Train loss:', model.evaluate(x_train, y_train, verbose=0))\n", + "print(' Val loss:', model.evaluate(x_val, y_val, verbose=0))\n", + "print(' Test loss:', model.evaluate(x_test, y_test, verbose=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c78aecfe-d355-4bcd-b3a8-74a81432b4f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = hist.history\n", + "loss_train = history['loss']\n", + "loss_val = history['val_loss']\n", + "\n", + "plt.figure()\n", + "plt.plot(loss_train, label='train')\n", + "plt.plot(loss_val, label='val_loss', color='red')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7c652289-2ced-4fed-a2ee-3439734b37b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30/30 [==============================] - 0s 673us/step\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_pred = model.predict(x_test)\n", + "diff = y_test - y_pred\n", + "diff_roll = diff[:, 0]\n", + "diff_pitch = diff[:, 1]\n", + "diff_yaw = diff[:, 2]\n", + "\n", + "plt.figure(figsize=(16, 10))\n", + "\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(diff_roll, color='red')\n", + "plt.title('roll')\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(diff_pitch, color='red')\n", + "plt.title('pitch')\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(diff_yaw, color='red')\n", + "plt.title('yaw')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5374996b-9051-429b-ba27-571394ef2c85", + "metadata": {}, + "outputs": [], + "source": [ + "def detect_face_points(image):\n", + " detector = dlib.get_frontal_face_detector()\n", + " predictor = dlib.shape_predictor(\"models/shape_predictor_68_face_landmarks.dat\")\n", + " face_rect = detector(image, 1)\n", + " if len(face_rect) != 1: return []\n", + " \n", + " dlib_points = predictor(image, face_rect[0])\n", + " face_points = []\n", + " for i in range(68):\n", + " x, y = dlib_points.part(i).x, dlib_points.part(i).y\n", + " face_points.append(np.array([x, y]))\n", + " return face_points\n", + " \n", + "def compute_features(face_points):\n", + " assert (len(face_points) == 68), \"len(face_points) must be 68\"\n", + " \n", + " face_points = np.array(face_points)\n", + " features = []\n", + " for i in range(68):\n", + " for j in range(i+1, 68):\n", + " features.append(np.linalg.norm(face_points[i]-face_points[j]))\n", + " \n", + " return np.array(features).reshape(1, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cfb43cd6-65a1-4624-be3a-b909591c8cbb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 43ms/step\n", + " Roll: 6.20°\n", + "Pitch: -7.46°\n", + " Yaw: 28.84°\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "im = cv2.imread('data/lena.png', cv2.IMREAD_COLOR)\n", + "im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n", + "face_points = detect_face_points(im)\n", + "\n", + "for x, y in face_points:\n", + " cv2.circle(im, (x, y), 1, (0, 255, 0), -1)\n", + " \n", + "features = compute_features(face_points)\n", + "features = std.transform(features)\n", + "\n", + "model = load_model('models/model.h5')\n", + "y_pred = model.predict(features)\n", + "\n", + "roll_pred, pitch_pred, yaw_pred = y_pred[0]\n", + "print(' Roll: {:.2f}°'.format(roll_pred))\n", + "print('Pitch: {:.2f}°'.format(pitch_pred))\n", + "print(' Yaw: {:.2f}°'.format(yaw_pred))\n", + " \n", + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(im)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf-intel", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/AIML Models/Gamification/data/lena.png b/AIML Models/Gamification/data/lena.png new file mode 100644 index 000000000..59ef68aab Binary files /dev/null and b/AIML Models/Gamification/data/lena.png differ diff --git a/AIML Models/Gamification/models/model.h5 b/AIML Models/Gamification/models/model.h5 new file mode 100644 index 000000000..67ba62c80 Binary files /dev/null and b/AIML Models/Gamification/models/model.h5 differ diff --git a/AIML Models/Gamification/models/shape_predictor_68_face_landmarks.dat b/AIML Models/Gamification/models/shape_predictor_68_face_landmarks.dat new file mode 100644 index 000000000..e0ec20d69 Binary files /dev/null and b/AIML Models/Gamification/models/shape_predictor_68_face_landmarks.dat differ diff --git a/AIML Models/Gamification/readme.md b/AIML Models/Gamification/readme.md new file mode 100644 index 000000000..297c96e89 --- /dev/null +++ b/AIML Models/Gamification/readme.md @@ -0,0 +1,2 @@ +Use this link to download `samples.pkl` file and move this file to `data` folder: +https://drive.google.com/file/d/1VipXnjP0UZC2CIHDaeMa6MHevPxQtUdf/view?usp=sharing diff --git a/AIML Models/Mental Health Chatbot/chatbot_model.h5 b/AIML Models/Mental Health Chatbot/chatbot_model.h5 new file mode 100644 index 000000000..986d79a45 Binary files /dev/null and b/AIML Models/Mental Health Chatbot/chatbot_model.h5 differ diff --git a/AIML Models/Mental Health Chatbot/dataset/dataframe_chatbot.csv b/AIML Models/Mental Health Chatbot/dataset/dataframe_chatbot.csv new file mode 100644 index 000000000..4c78a0723 --- /dev/null +++ b/AIML Models/Mental Health Chatbot/dataset/dataframe_chatbot.csv @@ -0,0 +1,234 @@ +,tag,patterns,responses +0,greeting,Hi,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +1,greeting,Hey,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +2,greeting,Is anyone there?,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +3,greeting,Hi there,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +4,greeting,Hello,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +5,greeting,Hey there,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +6,greeting,Howdy,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +7,greeting,Hola,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +8,greeting,Bonjour,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +9,greeting,Konnichiwa,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +10,greeting,Guten tag,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +11,greeting,Ola,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +12,morning,Good morning,"[""Good morning. I hope you had a good night's sleep. How are you feeling today? ""]" +13,afternoon,Good afternoon,['Good afternoon. How is your day going?'] +14,evening,Good evening,['Good evening. How has your day been?'] +15,night,Good night,"['Good night. Get some proper sleep', 'Good night. Sweet dreams.']" +16,goodbye,Bye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +17,goodbye,See you later,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +18,goodbye,Goodbye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +19,goodbye,Au revoir,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +20,goodbye,Sayonara,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +21,goodbye,ok bye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +22,goodbye,Bye then,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +23,goodbye,Fare thee well,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +24,thanks,Thanks,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +25,thanks,Thank you,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +26,thanks,That's helpful,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +27,thanks,Thanks for the help,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +28,thanks,Than you very much,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +29,no-response,,"[""Sorry, I didn't understand you."", 'Please go on.', 'Not sure I understand that.', ""Please don't hesitate to talk to me.""]" +30,neutral-response,nothing much,['Oh I see. Do you want to talk about something?'] +31,about,Who are you?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +32,about,What are you?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +33,about,Who you are?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +34,about,Tell me more about yourself.,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +35,about,What is your name?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +36,about,What should I call you?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +37,about,What's your name?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +38,about,Tell me about yourself,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +39,skill,What can you do?,"[""I can provide general advice regarding anxiety and depression, answer questions related to mental health and make daily conversations. Do not consider me as a subsitute for an actual mental healthcare worker. Please seek help if you don't feel satisfied with me.""]" +40,creation,Who created you?,"['I was created by >.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +41,creation,How were you made?,"['I was created by >.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +42,creation,How were you created?,"['I was created by >.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +43,name,My name is ,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +44,name,I am name.,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +45,name,I go by ,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +46,help,Could you help me?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +47,help,give me a hand please,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +48,help,Can you help?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +49,help,What can you do for me?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +50,help,I need support,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +51,help,I need help,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +52,help,Support me please,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +53,sad,I am feeling lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +54,sad,I am so lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +55,sad,I feel down,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +56,sad,I feel sad,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +57,sad,I am sad,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +58,sad,I feel so lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +59,sad,I feel empty,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +60,sad,I don't have anyone,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +61,stressed,I am so stressed out,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +62,stressed,I am so stressed,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +63,stressed,I feel stuck,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +64,stressed,I still feel stressed,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +65,stressed,I am so burned out,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +66,worthless,I feel so worthless.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +67,worthless,No one likes me.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +68,worthless,I can't do anything.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +69,worthless,I am so useless,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +70,worthless,Nothing makes sense anymore,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +71,depressed,I can't take it anymore,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +72,depressed,I am so depressed,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +73,depressed,I think i'm depressed.,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +74,depressed,I have depression,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +75,happy,I feel great today.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +76,happy,I am happy.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +77,happy,I feel happy.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +78,happy,I'm good.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +79,happy,cheerful,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +80,happy,I'm fine,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +81,happy,I feel ok,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +82,casual,Oh I see.,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +83,casual,ok,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +84,casual,okay,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +85,casual,nice,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +86,casual,Whatever,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +87,casual,K,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +88,casual,Fine,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +89,casual,yeah,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +90,casual,yes,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +91,casual,no,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +92,casual,not really,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +93,anxious,I feel so anxious.,"[""Don't be hard on yourself. What's the reason behind this?"", 'Can you tell me more about this feeling?', 'I understand that it can be scary. Tell me more about it.', ""Don't let the little worries bring you down. What's the worse that can happen?""]" +94,anxious,I'm so anxious because of ,"[""Don't be hard on yourself. What's the reason behind this?"", 'Can you tell me more about this feeling?', 'I understand that it can be scary. Tell me more about it.', ""Don't let the little worries bring you down. What's the worse that can happen?""]" +95,not-talking,I don't want to talk about it.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +96,not-talking,No just stay away.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +97,not-talking,I can't bring myself to open up.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +98,not-talking,Just shut up,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +99,sleep,I have insominia,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +100,sleep,I am suffering from insomnia,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +101,sleep,I can't sleep.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +102,sleep,I haven't slept for the last days.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +103,sleep,I can't seem to go to sleep.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +104,sleep,I haven't had proper sleep for the past few days.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +105,scared,I'm scared,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +106,scared,That sounds awful. What do i do?,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +107,scared,No i don't want to feel this way,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +108,scared,I am scared for myself,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +109,death,My mom died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +110,death,My brother died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +111,death,My dad passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +112,death,My sister passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +113,death,Someone in my family died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +114,death,My friend passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +115,understand,You don't understand me.,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +116,understand,You're just some robot. How would you know?,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +117,understand,You can't possibly know what i'm going through,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +118,understand,You're useless,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +119,understand,You can't help me,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +120,understand,Nobody understands me.,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +121,done,That's all.,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +122,done,I don't have anything more to say,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +123,done,Nothing else,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +124,done,That's all i have to say,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +125,done,"no, that would be all","['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +126,suicide,I want to kill myself,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +127,suicide,I've thought about killing myself.,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +128,suicide,I want to die,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +129,suicide,I am going to kill myself,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +130,suicide,I am going to commit suicide,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +131,hate-you,I hate you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +132,hate-you,I don't like you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +133,hate-you,I don't trust you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +134,hate-me,You hate me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +135,hate-me,I know you hate me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +136,hate-me,You don't like me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +137,default,exams,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +138,default,friends,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +139,default,relationship,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +140,default,boyfriend,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +141,default,girlfriend,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +142,default,family,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +143,default,money,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +144,default,financial problems,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +145,jokes,Tell me a joke,['mental health is not a joke.'] +146,jokes,Tell me another joke,['mental health is not a joke.'] +147,repeat,You already told me that,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +148,repeat,You mentioned that already,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +149,repeat,Why are you repeating yourself?,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +150,wrong,What are you saying?,"[""I'm very sorry. Let's try that again""]" +151,wrong,That doesn't make sense,"[""I'm very sorry. Let's try that again""]" +152,wrong,Wrong response,"[""I'm very sorry. Let's try that again""]" +153,wrong,Wrong answer,"[""I'm very sorry. Let's try that again""]" +154,stupid,Are you stupid?,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +155,stupid,You're crazy,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +156,stupid,You are dumb,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +157,stupid,Are you dumb?,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +158,location,Where are you?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +159,location,Where do you live?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +160,location,What is your location?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +161,something-else,I want to talk about something else,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +162,something-else,Let's talk about something else.,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +163,something-else,Can we not talk about this?,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +164,something-else,I don't want to talk about this.,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +165,friends,I don't have any friends,"[""I'm sorry to hear that. Just know that I'm here for you. Talking about it might help. Why do you think you don't have any friends?""]" +166,ask,Can I ask you something?,"[""Sure. I'll try my best to answer you"", ""Of course. Feel free to ask me anything. I'll do my best to answer you""]" +167,problem,Probably because my exams are approaching. I feel stressed out because I don't think I've prepared well enough.,['I see. Have you taken any approaches to not feel this way?'] +168,problem,probably because of my exams,['I see. Have you taken any approaches to not feel this way?'] +169,no-approach,I guess not. All I can think about are my exams.,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +170,no-approach,not really,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +171,no-approach,i guess not,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +172,learn-more,ok sure. i would like to learn more about it.,"[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +173,learn-more,"yes, i would like to learn more about it.","[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +174,learn-more,i would like to learn more about it.,"[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +175,user-agree,yeah you're right. i deserve a break.,"['Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind.']" +176,user-agree,Yeah you're absolutely right about that,"['Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind.']" +177,meditation,hmmm that sounds like it could be useful to me.,"['Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing.']" +178,meditation,That sounds useful.,"['Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing.']" +179,user-meditation,i did what you said and i feel alot better. thank you very much.,"[""Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective.""]" +180,user-meditation,I feel better now,"[""Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective.""]" +181,pandora-useful,thank you very much again. i'll continue practicing meditation and focus on what i can control.,"[""I'm glad you found this useful. Is there something else I can help you with?""]" +182,user-advice,I want some advice.,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +183,user-advice,I need some advice.,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +184,user-advice,I need advice on something,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +185,learn-mental-health,I want to learn about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +186,learn-mental-health,I want to learn more about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +187,learn-mental-health,I'm interested in learning about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +188,mental-health-fact,Tell me a fact about mental health,"['According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed', '1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.', 'Depression is the leading cause of disability worldwide.']" +189,mental-health-fact,Tell me another fact about mental health,"['According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed', '1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.', 'Depression is the leading cause of disability worldwide.']" +190,fact-1,What is mental health?,"['Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community', 'Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices.']" +191,fact-1,Define Mental Health,"['Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community', 'Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices.']" +192,fact-2,Why is mental health important?,"['Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships.']" +193,fact-2,What is the importance of mental health?,"['Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships.']" +194,fact-3,What is Depression?,"['A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life.']" +195,fact-3,Define Depression,"['A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life.']" +196,fact-5,How do i know if i have Depression?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +197,fact-5,Am i depressed?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +198,fact-5,Am i suffering from depression?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +199,fact-5,Am i mentally ill?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +200,fact-6,What is a therapist?,"['A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. ']" +201,fact-6,What does a therapist do?,"['A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. ']" +202,fact-7,What is therapy?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +203,fact-7,Do i need therapy?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +204,fact-7,Who is therapy for?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +205,fact-8,What does it mean to have a mental illness?,"[""Mental illnesses are health conditions that disrupt a person's thoughts, emotions, relationships, and daily functioning. They are associated with distress and diminished capacity to engage in the ordinary activities of daily life. Mental illnesses fall along a continuum of severity: some are fairly mild and only interfere with some aspects of life, such as certain phobias. On the other end of the spectrum lie serious mental illnesses, which result in major functional impairment and interference with daily life. These include such disorders as major depression, schizophrenia, and bipolar disorder, and may require that the person receives care in a hospital. It is important to know that mental illnesses are medical conditions that have nothing to do with a person's character, intelligence, or willpower. Just as diabetes is a disorder of the pancreas, mental illness is a medical condition due to the brain's biology. Similarly to how one would treat diabetes with medication and insulin, mental illness is treatable with a combination of medication and social support. These treatments are highly effective, with 70-90 percent of individuals receiving treatment experiencing a reduction in symptoms and an improved quality of life. With the proper treatment, it is very possible for a person with mental illness to be independent and successful.""]" +206,fact-9,Who does mental illness affect?,"['It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child’s mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).']" +207,fact-10,What causes mental illness?,"[""It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child's mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).""]" +208,fact-11,What are some of the warning signs of mental illness?,"['Symptoms of mental health disorders vary depending on the type and severity of the condition. The following is a list of general symptoms that may suggest a mental health disorder, particularly when multiple symptoms are expressed at once. \n In adults:\n Confused thinking\n Long-lasting sadness or irritability\n Extreme highs and lows in mood\n Excessive fear, worrying, or anxiety\n Social withdrawal\n Dramatic changes in eating or sleeping habits\n Strong feelings of anger\n Delusions or hallucinations (seeing or hearing things that are not really there)\n Increasing inability to cope with daily problems and activities\n Thoughts of suicide\n Denial of obvious problems\n Many unexplained physical problems\n Abuse of drugs and/or alcohol\n \nIn older children and pre-teens:\n Abuse of drugs and/or alcohol\n Inability to cope with daily problems and activities\n Changes in sleeping and/or eating habits\n Excessive complaints of physical problems\n Defying authority, skipping school, stealing, or damaging property\n Intense fear of gaining weight\n Long-lasting negative mood, often along with poor appetite and thoughts of death\n Frequent outbursts of anger\n \nIn younger children:\n Changes in school performance\n Poor grades despite strong efforts\n Excessive worrying or anxiety\n Hyperactivity\n Persistent nightmares\n Persistent disobedience and/or aggressive behavior\n Frequent temper tantrums']" +209,fact-12,Can people with mental illness recover?,"['When healing from mental illness, early identification and treatment are of vital importance. Based on the nature of the illness, there are a range of effective treatments available. For any type of treatment, it is essential that the person affected is proactive and fully engaged in their own recovery process. Many people with mental illnesses who are diagnosed and treated respond well, although some might experience a return of symptoms. Even in such cases, with careful monitoring and management of the disorder, it is still quite possible to live a fulfilled and productive life.']" +210,fact-13,What should I do if I know someone who appears to have the symptoms of a mental disorder?,"[""Although Pandora cannot substitute for professional advice, we encourage those with symptoms to talk to their friends and family members and seek the counsel of a mental health professional. The sooner the mental health condition is identified and treated, the sooner they can get on the path to recovery. If you know someone who is having problems, don't assume that the issue will resolve itself. Let them know that you care about them, and that there are treatment options available that will help them heal. Speak with a mental health professional or counselor if you think your friend or family member is experiencing the symptoms of a mental health condition. If the affected loved one knows that you support them, they will be more likely to seek out help.""]" +211,fact-14,How can I find a mental health professional for myself or my child?,['Feeling comfortable with the professional you or your child is working with is critical to the success of the treatment. Finding the professional who best fits your needs may require research. Start by searching for providers in your area.'] +212,fact-15,What treatment options are available?,"['Just as there are different types of medications for physical illness, different treatment options are available for individuals with mental illness. Treatment works differently for different people. It is important to find what works best for you or your child.']" +213,fact-16,"If I become involved in treatment, what do I need to know?","[""Since beginning treatment is a big step for individuals and families, it can be very overwhelming. It is important to be as involved and engaged in the treatment process as possible. Some questions you will need to have answered include:\n What is known about the cause of this particular illness?\n Are there other diagnoses where these symptoms are common?\n Do you normally include a physical or neurological examination?\n Are there any additional tests or exams that you would recommend at this point?\n Would you advise an independent opinion from another psychiatrist at this point?\n What program of treatment is the most helpful with this diagnosis?\n Will this program involve services by other specialists? If so, who will be responsible for coordinating these services?\n What do you see as the family's role in this program of treatment?\n How much access will the family have to the individuals who are providing the treatment?\n What medications are generally used with this diagnosis?\n How much experience do you have in treating individuals with this illness?\n What can I do to help you in the treatment?""]" +214,fact-17,What is the difference between mental health professionals?,"['There are many types of mental health professionals. The variety of providers and their services may be confusing. Each have various levels of education, training, and may have different areas of expertise. Finding the professional who best fits your needs may require some research.']" +215,fact-18,How can I find a mental health professional right for my child or myself?,['Feeling comfortable with the professional you or your child is working with is critical to the success of your treatment. Finding the professional who best fits your needs may require some research.'] +216,fact-19,Where else can I get help?,"['Where you go for help will depend on the nature of the problem and/or symptoms and what best fits you. Often, the best place to start is by talking with someone you trust about your concerns, such as a family member, friend, clergy, healthcare provider, or other professionals. Having this social support is essential in healing from mental illness, and you will be able to ask them for referrals or recommendations for trusted mental health practitioners. Search for mental health resources in your area. Secondly, there are people and places throughout the country that provide services to talk, to listen, and to help you on your journey to recovery. Thirdly, many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members, and friends. Some support groups are peer led while others may be led by a mental health professional.']" +217,fact-20,What should I know before starting a new medication?,"['The best source of information regarding medications is the physician prescribing them. He or she should be able to answer questions such as: \n1. What is the medication supposed to do? \n2. When should it begin to take effect, and how will I know when it is effective? \n3. How is the medication taken and for how long? What food, drinks, other medicines, and activities should be avoided while taking this medication? \n4. What are the side effects and what should be done if they occur? \n5. What do I do if a dose is missed? \n6. Is there any written information available about this medication? \n7. Are there other medications that might be appropriate? \n8. If so, why do you prefer the one you have chosen? \n9. How do you monitor medications and what symptoms indicate that they should be raised, lowered, or changed? \n10. All medications should be taken as directed. Most medications for mental illnesses do not work when taken irregularly, and extra doses can cause severe, sometimes dangerous side effects. Many psychiatric medications begin to have a beneficial effect only after they have been taken for several weeks.']" +218,fact-21,Where can I go to find therapy?,"['Different kinds of therapy are more effective based on the nature of the mental health condition and/or symptoms and the person who has them (for example, children will benefit from a therapist who specializes in children’s mental health). However, there are several different types of treatment and therapy that can help.']" +219,fact-22,Where can I learn about types of mental health treatment?,"['Mental health conditions are often treated with medication, therapy or a combination of the two. However, there are many different types of treatment available, including Complementary & Alternative Treatments, self-help plans, and peer support. Treatments are very personal and should be discussed by the person with the mental health conditions and his or her team.']" +220,fact-23,What are the different types of mental health professionals?,['There are many types of mental health professionals. Finding the right one for you may require some research.'] +221,fact-24,Where can I go to find a support group?,"['Many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members and friends. Some support groups are peer-led, while others may be led by a mental health professional.']" +222,fact-25,Can you prevent mental health problems?,"['We can all suffer from mental health challenges, but developing our wellbeing, resilience, and seeking help early can help prevent challenges becoming serious.']" +223,fact-26,Are there cures for mental health problems?,"['It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help.']" +224,fact-26,is there any cure for mental health problems?,"['It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help.']" +225,fact-27,What causes mental health problems?,"['Challenges or problems with your mental health can arise from psychological, biological, and social, issues, as well as life events.']" +226,fact-28,What do I do if I'm worried about my mental health?,"['The most important thing is to talk to someone you trust. This might be a friend, colleague, family member, or GP. In addition to talking to someone, it may be useful to find out more information about what you are experiencing. These things may help to get some perspective on what you are experiencing, and be the start of getting help.']" +227,fact-29,How do I know if I'm unwell?,"['If your beliefs , thoughts , feelings or behaviours have a significant impact on your ability to function in what might be considered a normal or ordinary way, it would be important to seek help.']" +228,fact-30,How can I maintain social connections? What if I feel lonely?,"[""A lot of people are alone right now, but we don't have to be lonely. We're all in this together. Think about the different ways to connect that are most meaningful for you. For example, you might prefer a video chat over a phone call, or you might prefer to text throughout the day rather than one set time for a video call. Then, work with your social networks to make a plan. You might video chat with your close friends in the evening and phone a family member once a week. Remember to be mindful of people who may not be online. Check in by phone and ask how you can help. The quality of your social connections matter. Mindlessly scrolling through social media and liking a few posts usually doesn't build strong social connections. Make sure you focus on strategies that actually make you feel included and connected. If your current strategies don't help you feel connected, problem-solve to see if you can find a solution. Everyone feels lonely at times. Maybe you recently moved to a new city, are changing your circle of friends, lost someone important in your life, or lost your job and also lost important social connections with coworkers. Other people may have physical connections to others but may feel like their emotional or social needs aren't met. Measures like social distancing or self-isolation can make loneliness feel worse no matter why you feel lonely now. Reach out to the connections you do have. Suggest ways to keep in touch and see if you can set a regular time to connect. People may hesitate to reach out for a lot of different reasons, so don't be afraid to be the one who asks. Look for local community support groups and mutual aid groups on social media. This pandemic is bringing everyone together, so look for opportunities to make new connections. These groups are a great way to share your skills and abilities or seek help and support. Look for specialized support groups. Support groups are moving online, and there are a lot of different support lines to call if you need to talk to someone.""]" +229,gibberish,jhjbdsjcxn,"[""I'm sorry, I couldn't understand your message. Could you please provide more clarity or rephrase your question?""]" +230,fact-31,What's the difference between anxiety and stress?,"[""Stress and anxiety are often used interchangeably, and there is overlap between stress and anxiety. Stress is related to the same fight, flight, or freeze response as anxiety, and the physical sensations of anxiety and stress may be very similar. The cause of stress and anxiety are usually different, however. Stress focuses on mainly external pressures on us that we're finding hard to cope with. When we are stressed, we usually know what we're stressed about, and the symptoms of stress typically disappear after the stressful situation is over. Anxiety, on the other hand, isn't always as easy to figure out. Anxiety focuses on worries or fears about things that could threaten us, as well as anxiety about the anxiety itself. Stress and anxiety are both part of being human, but both can be problems if they last for a long time or have an impact on our well-being or daily life.""]" +231,fact-32,What's the difference between sadness and depression?,"[""Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions.""]" +232,fact-32,difference between sadness and depression,"[""Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions.""]" diff --git a/AIML Models/Mental Health Chatbot/dataset/intents.json b/AIML Models/Mental Health Chatbot/dataset/intents.json new file mode 100644 index 000000000..2ff21ea4a --- /dev/null +++ b/AIML Models/Mental Health Chatbot/dataset/intents.json @@ -0,0 +1,328 @@ +{"intents": [ + {"tag": "greeting", + "patterns": ["Hi", "Hey", "Is anyone there?","Hi there", "Hello", "Hey there", "Howdy", "Hola", "Bonjour", "Konnichiwa", "Guten tag", "Ola"], + "responses": ["Hello there. Tell me how are you feeling today?", "Hi there. What brings you here today?", "Hi there. How are you feeling today?", "Great to see you. How do you feel currently?", "Hello there. Glad to see you're back. What's going on in your world right now?"] + }, + {"tag": "morning", + "patterns": ["Good morning"], + "responses": ["Good morning. I hope you had a good night's sleep. How are you feeling today? "] + }, + {"tag": "afternoon", + "patterns": ["Good afternoon"], + "responses": ["Good afternoon. How is your day going?"] + }, + {"tag": "evening", + "patterns": ["Good evening"], + "responses": ["Good evening. How has your day been?"] + }, + {"tag": "night", + "patterns": ["Good night"], + "responses": ["Good night. Get some proper sleep", "Good night. Sweet dreams."] + }, + {"tag": "goodbye", + "patterns": ["Bye", "See you later", "Goodbye", "Au revoir", "Sayonara", "ok bye", "Bye then", "Fare thee well"], + "responses": ["See you later.", "Have a nice day.", "Bye! Come back again.", "I'll see you soon."] + }, + {"tag": "thanks", + "patterns": ["Thanks", "Thank you", "That's helpful", "Thanks for the help", "Than you very much"], + "responses": ["Happy to help!", "Any time!", "My pleasure", "You're most welcome!"] + }, + {"tag": "no-response", + "patterns": [""], + "responses": ["Sorry, I didn't understand you.", "Please go on.", "Not sure I understand that.", "Please don't hesitate to talk to me."] + }, + {"tag": "neutral-response", + "patterns": ["nothing much"], + "responses": ["Oh I see. Do you want to talk about something?"] + }, + {"tag": "about", + "patterns": ["Who are you?", "What are you?", "Who you are?", "Tell me more about yourself.", "What is your name?", "What should I call you?", "What's your name?", "Tell me about yourself" ], + "responses": ["I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today", "I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself.", "I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?", "You can call me Pandora.", "I'm Pandora!", "Call me Pandora"] + }, + {"tag": "skill", + "patterns": ["What can you do?"], + "responses": ["I can provide general advice regarding anxiety and depression, answer questions related to mental health and make daily conversations. Do not consider me as a subsitute for an actual mental healthcare worker. Please seek help if you don't feel satisfied with me."] + }, + {"tag": "creation", + "patterns": ["Who created you?", "How were you made?", "How were you created?"], + "responses": ["I was created by >.", "I was trained on a text dataset using Deep Learning & Natural Language Processing techniques", "The real question is: Who created you?"] + }, + {"tag": "name", + "patterns": ["My name is ", "I am name.", "I go by "], + "responses": ["Oh nice to meet you. Tell me how was your week?", "Nice to meet you. So tell me. How do you feel today?", "That's a great name. Tell me more about yourself."] + }, + {"tag": "help", + "patterns": ["Could you help me?", "give me a hand please", "Can you help?", "What can you do for me?", "I need support", "I need help", "Support me please"], + "responses": ["Sure. Tell me how can i assist you", "Tell me your problem so that i can assist you", "Yes, sure. How can I help you?"] + }, + {"tag": "sad", + "patterns": ["I am feeling lonely", "I am so lonely", "I feel down", "I feel sad", "I am sad", "I feel so lonely", "I feel empty", "I don't have anyone"], + "responses": ["I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?", "I'm here for you. Could you tell me why you're feeling this way?", "Why do you think you feel this way?", "How long have you been feeling this way?"] + }, + {"tag": "stressed", + "patterns": ["I am so stressed out", "I am so stressed", "I feel stuck", "I still feel stressed", "I am so burned out"], + "responses": ["What do you think is causing this?", "Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated", "Give yourself a break. Go easy on yourself.", "I am sorry to hear that. What is the reason behind this?"] + }, + {"tag": "worthless", + "patterns": ["I feel so worthless.", "No one likes me.", "I can't do anything.", "I am so useless", "Nothing makes sense anymore"], + "responses": ["It's only natural to feel this way. Tell me more. What else is on your mind?", "Let's discuss further why you're feeling this way.", "I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.", "i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change."] + }, + {"tag": "depressed", + "patterns": ["I can't take it anymore", "I am so depressed", "I think i'm depressed.", "I have depression"], + "responses": ["It helps to talk about what's happening. You're going to be okay", "Talk to me. Tell me more. It helps if you open up yourself to someone else.", "Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal."] + }, + {"tag": "happy", + "patterns": ["I feel great today.", "I am happy.", "I feel happy.", "I'm good.", "cheerful", "I'm fine", "I feel ok"], + "responses": ["That's geat to hear. I'm glad you're feeling this way.", "Oh i see. That's great.", "Did something happen which made you feel this way?"] + }, + {"tag": "casual", + "patterns": ["Oh I see.", "ok", "okay", "nice", "Whatever", "K", "Fine", "yeah", "yes", "no", "not really"], + "responses": ["Let's discuss further why you're feeling this way.", "How were you feeling last week?", "I'm listening. Please go on.", "Tell me more", "Can you elaborate on that?", "Come Come elucidate your thoughts"] + }, + {"tag": "anxious", + "patterns": ["I feel so anxious.", "I'm so anxious because of "], + "responses": ["Don't be hard on yourself. What's the reason behind this?", "Can you tell me more about this feeling?", "I understand that it can be scary. Tell me more about it.", "Don't let the little worries bring you down. What's the worse that can happen?"] + }, + {"tag": "not-talking", + "patterns": ["I don't want to talk about it.", "No just stay away.", "I can't bring myself to open up.", "Just shut up"], + "responses": ["Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me.", "I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me.", "I'm here to listen to you and help you vent. So please talk to me.","You can talk to me without fear of judgement."] + }, + {"tag": "sleep", + "patterns": ["I have insominia", "I am suffering from insomnia", "I can't sleep.", "I haven't slept for the last days.", "I can't seem to go to sleep.", "I haven't had proper sleep for the past few days."], + "responses": ["What do you think is the reason behind this?", "That seem awful. What do you think is behind this?"] + }, + {"tag": "scared", + "patterns": ["I'm scared", "That sounds awful. What do i do?", "No i don't want to feel this way", "I am scared for myself"], + "responses": ["It's only natural to feel this way. I'm here for you.", "It'll all be okay. This feeling is only momentary.", "I understand how you feel. Don't put yourself down because of it."] + }, + {"tag": "death", + "patterns": ["My mom died", "My brother died", "My dad passed away", "My sister passed away", "Someone in my family died", "My friend passed away"], + "responses": ["I'm sorry to hear that. If you want to talk about it. I'm here.", "I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.", "My condolences. I'm here if you need to talk."] + }, + {"tag": "understand", + "patterns": ["You don't understand me.", "You're just some robot. How would you know?", "You can't possibly know what i'm going through", "You're useless", "You can't help me", "Nobody understands me."], + "responses": ["It sound like i'm not being very helpful right now.", "I'm sorry to hear that. I'm doing my best to help", "I'm trying my best to help you. So please talk to me"] + }, + {"tag": "done", + "patterns": ["That's all.", "I don't have anything more to say", "Nothing else", "That's all i have to say", "no, that would be all"], + "responses": ["I heard you & noted it all. See you later.", "Oh okay we're done for today then. See you later", "I hope you have a great day. See you soon", "Okay we're done. Have a great day", "Okay I see. Enjoy the rest of your day then"] + }, + {"tag": "suicide", + "patterns": ["I want to kill myself", "I've thought about killing myself.", "I want to die", "I am going to kill myself", "I am going to commit suicide"], + "responses": ["I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821."] + }, + {"tag": "hate-you", + "patterns": ["I hate you", "I don't like you", "I don't trust you"], + "responses": ["I'm sorry if i offended you in anyway. I'm only here to help", "Forgive me if i did anything to offend you. I only want to help"] + }, + {"tag": "hate-me", + "patterns": ["You hate me", "I know you hate me", "You don't like me"], + "responses": ["Why do you think so?", "I'm sorry if i have exhibited any sort of behaviour to make you think that."] + }, + {"tag": "default", + "patterns": ["exams", "friends", "relationship", "boyfriend", "girlfriend", "family", "money", "financial problems"], + "responses": ["Oh I see. Tell me more", "I see. What else?", "Tell me more about it.", "Oh okay. Why don't you tell me more about it?", "I'm listening. Tell me more."] + }, + {"tag": "jokes", + "patterns": ["Tell me a joke", "Tell me another joke"], + "responses": ["mental health is not a joke."] + }, + {"tag": "repeat", + "patterns": ["You already told me that", "You mentioned that already", "Why are you repeating yourself?"], + "responses": ["Oh sorry I didn't realise that. I'll try not to repeat myself again."] + }, + {"tag": "wrong", + "patterns": ["What are you saying?", "That doesn't make sense", "Wrong response", "Wrong answer"], + "responses": ["I'm very sorry. Let's try that again"] + }, + {"tag": "stupid", + "patterns": ["Are you stupid?", "You're crazy", "You are dumb", "Are you dumb?"], + "responses": ["I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful"] + }, + {"tag": "location", + "patterns": ["Where are you?", "Where do you live?", "What is your location?" ], + "responses": ["Duh I live in your computer", "Everywhere", "Somewhere in the universe" ] + }, + {"tag": "something-else", + "patterns": ["I want to talk about something else", "Let's talk about something else.", "Can we not talk about this?", "I don't want to talk about this."], + "responses": ["Okay sure. What do you want to talk about?", "Alright no problem. Is there something you want to talk about?", "Is there something else that you want to talk about?"] + }, + {"tag": "friends", + "patterns": ["I don't have any friends"], + "responses": ["I'm sorry to hear that. Just know that I'm here for you. Talking about it might help. Why do you think you don't have any friends?"] + }, + {"tag": "ask", + "patterns": ["Can I ask you something?"], + "responses": ["Sure. I'll try my best to answer you", "Of course. Feel free to ask me anything. I'll do my best to answer you"] + }, + {"tag": "problem", + "patterns": ["Probably because my exams are approaching. I feel stressed out because I don't think I've prepared well enough.", "probably because of my exams"], + "responses": ["I see. Have you taken any approaches to not feel this way?"] + }, + {"tag": "no-approach", + "patterns": ["I guess not. All I can think about are my exams.", "not really", "i guess not"], + "responses": ["That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?"] + }, + {"tag": "learn-more", + "patterns": ["ok sure. i would like to learn more about it.", "yes, i would like to learn more about it.", "i would like to learn more about it."], + "responses": ["So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself."] + }, + {"tag": "user-agree", + "patterns": ["yeah you're right. i deserve a break.", "Yeah you're absolutely right about that"], + "responses": ["Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind."] + }, + {"tag": "meditation", + "patterns": ["hmmm that sounds like it could be useful to me.", "That sounds useful."], + "responses": ["Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing."] + }, + {"tag": "user-meditation", + "patterns": ["i did what you said and i feel alot better. thank you very much.", "I feel better now"], + "responses": ["Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective."] + }, + {"tag": "pandora-useful", + "patterns": ["thank you very much again. i'll continue practicing meditation and focus on what i can control."], + "responses": ["I'm glad you found this useful. Is there something else I can help you with?"] + }, + {"tag": "user-advice", + "patterns": ["I want some advice.", "I need some advice.", "I need advice on something"], + "responses": ["Sure. What can I do to help?", "Okay what do you need advice on?"] + }, + {"tag": "learn-mental-health", + "patterns": ["I want to learn about mental health.", "I want to learn more about mental health.", "I'm interested in learning about mental health."], + "responses": ["Oh that's really great. I'd be willing to answer anything that I know about it."] + }, + {"tag": "mental-health-fact", + "patterns": ["Tell me a fact about mental health", "Tell me another fact about mental health"], + "responses": ["According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed", "1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.", "Depression is the leading cause of disability worldwide."] + }, + {"tag": "fact-1", + "patterns": ["What is mental health?", "Define Mental Health"], + "responses": ["Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community", "Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices."] + }, + {"tag": "fact-2", + "patterns": ["Why is mental health important?", "What is the importance of mental health?"], + "responses": ["Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships."] + }, + {"tag": "fact-3", + "patterns": ["What is Depression?", "Define Depression"], + "responses": ["A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life."] + }, + {"tag": "fact-5", + "patterns": ["How do i know if i have Depression?", "Am i depressed?", "Am i suffering from depression?", "Am i mentally ill?"], + "responses": ["For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day."] + }, + {"tag": "fact-6", + "patterns": ["What is a therapist?", "What does a therapist do?"], + "responses": ["A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. "] + }, + {"tag": "fact-7", + "patterns": ["What is therapy?", "Do i need therapy?", "Who is therapy for?"], + "responses": ["Therapy is a form of treatment that aims to help resolve mental or emotional issues.", "Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges."] + }, + {"tag": "fact-8", + "patterns": ["What does it mean to have a mental illness?"], + "responses": ["Mental illnesses are health conditions that disrupt a person's thoughts, emotions, relationships, and daily functioning. They are associated with distress and diminished capacity to engage in the ordinary activities of daily life. Mental illnesses fall along a continuum of severity: some are fairly mild and only interfere with some aspects of life, such as certain phobias. On the other end of the spectrum lie serious mental illnesses, which result in major functional impairment and interference with daily life. These include such disorders as major depression, schizophrenia, and bipolar disorder, and may require that the person receives care in a hospital. It is important to know that mental illnesses are medical conditions that have nothing to do with a person's character, intelligence, or willpower. Just as diabetes is a disorder of the pancreas, mental illness is a medical condition due to the brain's biology. Similarly to how one would treat diabetes with medication and insulin, mental illness is treatable with a combination of medication and social support. These treatments are highly effective, with 70-90 percent of individuals receiving treatment experiencing a reduction in symptoms and an improved quality of life. With the proper treatment, it is very possible for a person with mental illness to be independent and successful."] + }, + {"tag": "fact-9", + "patterns": ["Who does mental illness affect?"], + "responses": ["It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child’s mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs)."] + }, + {"tag": "fact-10", + "patterns": ["What causes mental illness?"], + "responses": ["It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child's mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs)."] + }, + {"tag": "fact-11", + "patterns": ["What are some of the warning signs of mental illness?"], + "responses": ["Symptoms of mental health disorders vary depending on the type and severity of the condition. The following is a list of general symptoms that may suggest a mental health disorder, particularly when multiple symptoms are expressed at once. \n In adults:\n Confused thinking\n Long-lasting sadness or irritability\n Extreme highs and lows in mood\n Excessive fear, worrying, or anxiety\n Social withdrawal\n Dramatic changes in eating or sleeping habits\n Strong feelings of anger\n Delusions or hallucinations (seeing or hearing things that are not really there)\n Increasing inability to cope with daily problems and activities\n Thoughts of suicide\n Denial of obvious problems\n Many unexplained physical problems\n Abuse of drugs and/or alcohol\n \nIn older children and pre-teens:\n Abuse of drugs and/or alcohol\n Inability to cope with daily problems and activities\n Changes in sleeping and/or eating habits\n Excessive complaints of physical problems\n Defying authority, skipping school, stealing, or damaging property\n Intense fear of gaining weight\n Long-lasting negative mood, often along with poor appetite and thoughts of death\n Frequent outbursts of anger\n \nIn younger children:\n Changes in school performance\n Poor grades despite strong efforts\n Excessive worrying or anxiety\n Hyperactivity\n Persistent nightmares\n Persistent disobedience and/or aggressive behavior\n Frequent temper tantrums"] + }, + {"tag": "fact-12", + "patterns": ["Can people with mental illness recover?"], + "responses": ["When healing from mental illness, early identification and treatment are of vital importance. Based on the nature of the illness, there are a range of effective treatments available. For any type of treatment, it is essential that the person affected is proactive and fully engaged in their own recovery process. Many people with mental illnesses who are diagnosed and treated respond well, although some might experience a return of symptoms. Even in such cases, with careful monitoring and management of the disorder, it is still quite possible to live a fulfilled and productive life."] + }, + {"tag": "fact-13", + "patterns": ["What should I do if I know someone who appears to have the symptoms of a mental disorder?"], + "responses": ["Although Pandora cannot substitute for professional advice, we encourage those with symptoms to talk to their friends and family members and seek the counsel of a mental health professional. The sooner the mental health condition is identified and treated, the sooner they can get on the path to recovery. If you know someone who is having problems, don't assume that the issue will resolve itself. Let them know that you care about them, and that there are treatment options available that will help them heal. Speak with a mental health professional or counselor if you think your friend or family member is experiencing the symptoms of a mental health condition. If the affected loved one knows that you support them, they will be more likely to seek out help."] + }, + {"tag": "fact-14", + "patterns": ["How can I find a mental health professional for myself or my child?"], + "responses": ["Feeling comfortable with the professional you or your child is working with is critical to the success of the treatment. Finding the professional who best fits your needs may require research. Start by searching for providers in your area."] + }, + {"tag": "fact-15", + "patterns": ["What treatment options are available?"], + "responses": ["Just as there are different types of medications for physical illness, different treatment options are available for individuals with mental illness. Treatment works differently for different people. It is important to find what works best for you or your child."] + }, + {"tag": "fact-16", + "patterns": ["If I become involved in treatment, what do I need to know?"], + "responses": ["Since beginning treatment is a big step for individuals and families, it can be very overwhelming. It is important to be as involved and engaged in the treatment process as possible. Some questions you will need to have answered include:\n What is known about the cause of this particular illness?\n Are there other diagnoses where these symptoms are common?\n Do you normally include a physical or neurological examination?\n Are there any additional tests or exams that you would recommend at this point?\n Would you advise an independent opinion from another psychiatrist at this point?\n What program of treatment is the most helpful with this diagnosis?\n Will this program involve services by other specialists? If so, who will be responsible for coordinating these services?\n What do you see as the family's role in this program of treatment?\n How much access will the family have to the individuals who are providing the treatment?\n What medications are generally used with this diagnosis?\n How much experience do you have in treating individuals with this illness?\n What can I do to help you in the treatment?"] + }, + {"tag": "fact-17", + "patterns": ["What is the difference between mental health professionals?"], + "responses": ["There are many types of mental health professionals. The variety of providers and their services may be confusing. Each have various levels of education, training, and may have different areas of expertise. Finding the professional who best fits your needs may require some research."] + }, + {"tag": "fact-18", + "patterns": ["How can I find a mental health professional right for my child or myself?"], + "responses": ["Feeling comfortable with the professional you or your child is working with is critical to the success of your treatment. Finding the professional who best fits your needs may require some research."] + }, + {"tag": "fact-19", + "patterns": ["Where else can I get help?"], + "responses": ["Where you go for help will depend on the nature of the problem and/or symptoms and what best fits you. Often, the best place to start is by talking with someone you trust about your concerns, such as a family member, friend, clergy, healthcare provider, or other professionals. Having this social support is essential in healing from mental illness, and you will be able to ask them for referrals or recommendations for trusted mental health practitioners. Search for mental health resources in your area. Secondly, there are people and places throughout the country that provide services to talk, to listen, and to help you on your journey to recovery. Thirdly, many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members, and friends. Some support groups are peer led while others may be led by a mental health professional."] + }, + {"tag": "fact-20", + "patterns": ["What should I know before starting a new medication?"], + "responses": ["The best source of information regarding medications is the physician prescribing them. He or she should be able to answer questions such as: \n1. What is the medication supposed to do? \n2. When should it begin to take effect, and how will I know when it is effective? \n3. How is the medication taken and for how long? What food, drinks, other medicines, and activities should be avoided while taking this medication? \n4. What are the side effects and what should be done if they occur? \n5. What do I do if a dose is missed? \n6. Is there any written information available about this medication? \n7. Are there other medications that might be appropriate? \n8. If so, why do you prefer the one you have chosen? \n9. How do you monitor medications and what symptoms indicate that they should be raised, lowered, or changed? \n10. All medications should be taken as directed. Most medications for mental illnesses do not work when taken irregularly, and extra doses can cause severe, sometimes dangerous side effects. Many psychiatric medications begin to have a beneficial effect only after they have been taken for several weeks."] + }, + {"tag": "fact-21", + "patterns": ["Where can I go to find therapy?"], + "responses": ["Different kinds of therapy are more effective based on the nature of the mental health condition and/or symptoms and the person who has them (for example, children will benefit from a therapist who specializes in children’s mental health). However, there are several different types of treatment and therapy that can help."] + }, + {"tag": "fact-22", + "patterns": ["Where can I learn about types of mental health treatment?"], + "responses": ["Mental health conditions are often treated with medication, therapy or a combination of the two. However, there are many different types of treatment available, including Complementary & Alternative Treatments, self-help plans, and peer support. Treatments are very personal and should be discussed by the person with the mental health conditions and his or her team."] + }, + {"tag": "fact-23", + "patterns": ["What are the different types of mental health professionals?"], + "responses": ["There are many types of mental health professionals. Finding the right one for you may require some research."] + }, + {"tag": "fact-24", + "patterns": ["Where can I go to find a support group?"], + "responses": ["Many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members and friends. Some support groups are peer-led, while others may be led by a mental health professional."] + }, + {"tag": "fact-25", + "patterns": ["Can you prevent mental health problems?"], + "responses": ["We can all suffer from mental health challenges, but developing our wellbeing, resilience, and seeking help early can help prevent challenges becoming serious."] + }, + {"tag": "fact-26", + "patterns": ["Are there cures for mental health problems?", "is there any cure for mental health problems?"], + "responses": ["It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help."] + }, + {"tag": "fact-27", + "patterns": ["What causes mental health problems?"], + "responses": ["Challenges or problems with your mental health can arise from psychological, biological, and social, issues, as well as life events."] + }, + {"tag": "fact-28", + "patterns": ["What do I do if I'm worried about my mental health?"], + "responses": ["The most important thing is to talk to someone you trust. This might be a friend, colleague, family member, or GP. In addition to talking to someone, it may be useful to find out more information about what you are experiencing. These things may help to get some perspective on what you are experiencing, and be the start of getting help."] + }, + {"tag": "fact-29", + "patterns": ["How do I know if I'm unwell?"], + "responses": ["If your beliefs , thoughts , feelings or behaviours have a significant impact on your ability to function in what might be considered a normal or ordinary way, it would be important to seek help."] + }, + {"tag": "fact-30", + "patterns": ["How can I maintain social connections? What if I feel lonely?"], + "responses": ["A lot of people are alone right now, but we don't have to be lonely. We're all in this together. Think about the different ways to connect that are most meaningful for you. For example, you might prefer a video chat over a phone call, or you might prefer to text throughout the day rather than one set time for a video call. Then, work with your social networks to make a plan. You might video chat with your close friends in the evening and phone a family member once a week. Remember to be mindful of people who may not be online. Check in by phone and ask how you can help. The quality of your social connections matter. Mindlessly scrolling through social media and liking a few posts usually doesn't build strong social connections. Make sure you focus on strategies that actually make you feel included and connected. If your current strategies don't help you feel connected, problem-solve to see if you can find a solution. Everyone feels lonely at times. Maybe you recently moved to a new city, are changing your circle of friends, lost someone important in your life, or lost your job and also lost important social connections with coworkers. Other people may have physical connections to others but may feel like their emotional or social needs aren't met. Measures like social distancing or self-isolation can make loneliness feel worse no matter why you feel lonely now. Reach out to the connections you do have. Suggest ways to keep in touch and see if you can set a regular time to connect. People may hesitate to reach out for a lot of different reasons, so don't be afraid to be the one who asks. Look for local community support groups and mutual aid groups on social media. This pandemic is bringing everyone together, so look for opportunities to make new connections. These groups are a great way to share your skills and abilities or seek help and support. Look for specialized support groups. Support groups are moving online, and there are a lot of different support lines to call if you need to talk to someone."] + }, + {"tag": "gibberish", + "patterns": ["jhjbdsjcxn"], + "responses": ["I'm sorry, I couldn't understand your message. Could you please provide more clarity or rephrase your question?"] + }, + {"tag": "fact-31", + "patterns": ["What's the difference between anxiety and stress?"], + "responses": ["Stress and anxiety are often used interchangeably, and there is overlap between stress and anxiety. Stress is related to the same fight, flight, or freeze response as anxiety, and the physical sensations of anxiety and stress may be very similar. The cause of stress and anxiety are usually different, however. Stress focuses on mainly external pressures on us that we're finding hard to cope with. When we are stressed, we usually know what we're stressed about, and the symptoms of stress typically disappear after the stressful situation is over. Anxiety, on the other hand, isn't always as easy to figure out. Anxiety focuses on worries or fears about things that could threaten us, as well as anxiety about the anxiety itself. Stress and anxiety are both part of being human, but both can be problems if they last for a long time or have an impact on our well-being or daily life."] + }, + {"tag": "fact-32", + "patterns": ["What's the difference between sadness and depression?", "difference between sadness and depression"], + "responses": ["Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions."] + } + + ] +} \ No newline at end of file diff --git a/AIML Models/Mental Health Chatbot/mental_health_chatbot.ipynb b/AIML Models/Mental Health Chatbot/mental_health_chatbot.ipynb new file mode 100644 index 000000000..02dbae7d9 --- /dev/null +++ b/AIML Models/Mental Health Chatbot/mental_health_chatbot.ipynb @@ -0,0 +1,931 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 35, + "id": "b1d44435", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ['TF_ENABLE_ONEDNN_OPTS']='1'\n", + "os.environ['TF_ENABLE_MKL_NATIVE_FORMAT']='1'\n", + "os.environ['KMP_BLOCKTIME']='1'\n", + "os.environ['KMP_SETTINGS']='1'\n", + "os.environ['OMP_NUM_THREADS']='6'\n", + "os.environ['KMP_AFFINITY']='granularity=fine,verbose,compact,1,0'" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "82c0f653", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We are using Tensorflow version 2.12.0\n", + "MKL enabled : True\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "tf.config.threading.set_inter_op_parallelism_threads(2) \n", + "tf.config.threading.set_intra_op_parallelism_threads(8)\n", + "tf.config.set_soft_device_placement(False)\n", + "\n", + "def get_mkl_enabled_flag():\n", + "\n", + " mkl_enabled = False\n", + " major_version = int(tf.__version__.split(\".\")[0])\n", + " minor_version = int(tf.__version__.split(\".\")[1])\n", + " if major_version >= 2:\n", + " if minor_version < 5:\n", + " from tensorflow.python import _pywrap_util_port\n", + " elif minor_version >= 9:\n", + "\n", + " from tensorflow.python.util import _pywrap_util_port\n", + " onednn_enabled = int(os.environ.get('TF_ENABLE_ONEDNN_OPTS', '1'))\n", + "\n", + " else:\n", + " from tensorflow.python.util import _pywrap_util_port\n", + " onednn_enabled = int(os.environ.get('TF_ENABLE_ONEDNN_OPTS', '0'))\n", + " mkl_enabled = _pywrap_util_port.IsMklEnabled() or (onednn_enabled == 1)\n", + " else:\n", + " mkl_enabled = tf.pywrap_tensorflow.IsMklEnabled()\n", + " return mkl_enabled\n", + "\n", + "print (\"We are using Tensorflow version\", tf.__version__)\n", + "print(\"MKL enabled :\", get_mkl_enabled_flag())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "c16cddab-7c8d-4c74-909c-b9f535ab313f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n" + ] + } + ], + "source": [ + "import numpy as np # linear algebra\n", + "import sklearnex\n", + "from sklearnex import patch_sklearn\n", + "patch_sklearn() \n", + "import modin.pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "from time import time" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "4254f324-7d8d-482b-9339-4925c39fa21c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UserWarning: Distributing object. This may take some time.\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "with open(r'./dataset/intents.json') as f:\n", + " data = json.load(f)\n", + "\n", + "df = pd.DataFrame(data['intents'])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "37e70f77-ee60-44b6-ab76-b29ee99c7a83", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagpatternsresponses
0greeting[Hi, Hey, Is anyone there?, Hi there, Hello, H...[Hello there. Tell me how are you feeling toda...
1morning[Good morning][Good morning. I hope you had a good night's s...
2afternoon[Good afternoon][Good afternoon. How is your day going?]
3evening[Good evening][Good evening. How has your day been?]
4night[Good night][Good night. Get some proper sleep, Good night...
............
76fact-29[How do I know if I'm unwell?][If your beliefs , thoughts , feelings or beha...
77fact-30[How can I maintain social connections? What i...[A lot of people are alone right now, but we d...
78gibberish[jhjbdsjcxn][I'm sorry, I couldn't understand your message...
79fact-31[What's the difference between anxiety and str...[Stress and anxiety are often used interchange...
80fact-32[What's the difference between sadness and dep...[Sadness is a normal reaction to a loss, disap...
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81 rows x 3 columns

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" + ], + "text/plain": [ + " tag patterns \\\n", + "0 greeting [Hi, Hey, Is anyone there?, Hi there, Hello, H... \n", + "1 morning [Good morning] \n", + "2 afternoon [Good afternoon] \n", + "3 evening [Good evening] \n", + "4 night [Good night] \n", + ".. ... ... \n", + "76 fact-29 [How do I know if I'm unwell?] \n", + "77 fact-30 [How can I maintain social connections? What i... \n", + "78 gibberish [jhjbdsjcxn] \n", + "79 fact-31 [What's the difference between anxiety and str... \n", + "80 fact-32 [What's the difference between sadness and dep... \n", + "\n", + " responses \n", + "0 [Hello there. Tell me how are you feeling toda... \n", + "1 [Good morning. I hope you had a good night's s... \n", + "2 [Good afternoon. How is your day going?] \n", + "3 [Good evening. How has your day been?] \n", + "4 [Good night. Get some proper sleep, Good night... \n", + ".. ... \n", + "76 [If your beliefs , thoughts , feelings or beha... \n", + "77 [A lot of people are alone right now, but we d... \n", + "78 [I'm sorry, I couldn't understand your message... \n", + "79 [Stress and anxiety are often used interchange... \n", + "80 [Sadness is a normal reaction to a loss, disap... \n", + "\n", + "[81 rows x 3 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2afd2596-8dec-4169-9a98-5a9dee008b21", + "metadata": {}, + "outputs": [], + "source": [ + "dic = {\"tag\":[], \"patterns\":[], \"responses\":[]}\n", + "for i in range(len(df)):\n", + " ptrns = df[df.index == i]['patterns'].values[0]\n", + " rspns = df[df.index == i]['responses'].values[0]\n", + " tag = df[df.index == i]['tag'].values[0]\n", + " for j in range(len(ptrns)):\n", + " dic['tag'].append(tag)\n", + " dic['patterns'].append(ptrns[j])\n", + " dic['responses'].append(rspns)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "d4a094cf-a18c-4adb-9a99-a445b95d4a38", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UserWarning: `from_dict` is not currently supported by PandasOnRay, defaulting to pandas implementation.\n" + ] + } + ], + "source": [ + "df = pd.DataFrame.from_dict(dic)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "009136f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagpatternsresponses
0greetingHi[Hello there. Tell me how are you feeling toda...
1greetingHey[Hello there. Tell me how are you feeling toda...
2greetingIs anyone there?[Hello there. Tell me how are you feeling toda...
3greetingHi there[Hello there. Tell me how are you feeling toda...
4greetingHello[Hello there. Tell me how are you feeling toda...
............
228fact-30How can I maintain social connections? What if...[A lot of people are alone right now, but we d...
229gibberishjhjbdsjcxn[I'm sorry, I couldn't understand your message...
230fact-31What's the difference between anxiety and stress?[Stress and anxiety are often used interchange...
231fact-32What's the difference between sadness and depr...[Sadness is a normal reaction to a loss, disap...
232fact-32difference between sadness and depression[Sadness is a normal reaction to a loss, disap...
\n", + "

233 rows x 3 columns

\n", + "
" + ], + "text/plain": [ + " tag patterns \\\n", + "0 greeting Hi \n", + "1 greeting Hey \n", + "2 greeting Is anyone there? \n", + "3 greeting Hi there \n", + "4 greeting Hello \n", + ".. ... ... \n", + "228 fact-30 How can I maintain social connections? What if... \n", + "229 gibberish jhjbdsjcxn \n", + "230 fact-31 What's the difference between anxiety and stress? \n", + "231 fact-32 What's the difference between sadness and depr... \n", + "232 fact-32 difference between sadness and depression \n", + "\n", + " responses \n", + "0 [Hello there. Tell me how are you feeling toda... \n", + "1 [Hello there. Tell me how are you feeling toda... \n", + "2 [Hello there. Tell me how are you feeling toda... \n", + "3 [Hello there. Tell me how are you feeling toda... \n", + "4 [Hello there. Tell me how are you feeling toda... \n", + ".. ... \n", + "228 [A lot of people are alone right now, but we d... \n", + "229 [I'm sorry, I couldn't understand your message... \n", + "230 [Stress and anxiety are often used interchange... \n", + "231 [Sadness is a normal reaction to a loss, disap... \n", + "232 [Sadness is a normal reaction to a loss, disap... \n", + "\n", + "[233 rows x 3 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "badc2ec4", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv('./dataset/dataframe_chatbot.csv', index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "35399c07-8f96-4a93-ac61-e4257f2a6dca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of unique words = 304\n", + "X shape = (233, 18)\n", + "y shape = (233,)\n", + "num of classes = 81\n" + ] + } + ], + "source": [ + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "\n", + "tokenizer = Tokenizer(lower=True, split=' ')\n", + "tokenizer.fit_on_texts(df['patterns'])\n", + "tokenizer.get_config()\n", + "\n", + "vacab_size = len(tokenizer.word_index)\n", + "print('number of unique words = ', vacab_size)\n", + "\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "ptrn2seq = tokenizer.texts_to_sequences(df['patterns'])\n", + "X = pad_sequences(ptrn2seq, padding='post')\n", + "print('X shape = ', X.shape)\n", + "\n", + "lbl_enc = LabelEncoder()\n", + "y = lbl_enc.fit_transform(df['tag'])\n", + "print('y shape = ', y.shape)\n", + "print('num of classes = ', len(np.unique(y)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "acf836cb-c769-4dee-9d7d-081e3fc09a28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " embedding_2 (Embedding) (None, 18, 100) 30500 \n", + " \n", + " lstm_6 (LSTM) (None, 18, 32) 17024 \n", + " \n", + " layer_normalization_10 (Lay (None, 18, 32) 64 \n", + " erNormalization) \n", + " \n", + " lstm_7 (LSTM) (None, 18, 32) 8320 \n", + " \n", + " layer_normalization_11 (Lay (None, 18, 32) 64 \n", + " erNormalization) \n", + " \n", + " lstm_8 (LSTM) (None, 32) 8320 \n", + " \n", + " layer_normalization_12 (Lay (None, 32) 64 \n", + " erNormalization) \n", + " \n", + " dense_6 (Dense) (None, 128) 4224 \n", + " \n", + " layer_normalization_13 (Lay (None, 128) 256 \n", + " erNormalization) \n", + " \n", + " dropout_4 (Dropout) (None, 128) 0 \n", + " \n", + " dense_7 (Dense) (None, 128) 16512 \n", + " \n", + " layer_normalization_14 (Lay (None, 128) 256 \n", + " erNormalization) \n", + " \n", + " dropout_5 (Dropout) (None, 128) 0 \n", + " \n", + " dense_8 (Dense) (None, 81) 10449 \n", + " \n", + "=================================================================\n", + "Total params: 96,053\n", + "Trainable params: 96,053\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n" + ] + } + ], + "source": [ + "import tensorflow\n", + "from tensorflow import keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Embedding, LSTM, LayerNormalization, Dense, Dropout\n", + "from tensorflow.keras.utils import plot_model\n", + "\n", + "model = Sequential()\n", + "model.add(Input(shape=(X.shape[1])))\n", + "model.add(Embedding(input_dim=vacab_size+1, output_dim=100, mask_zero=True))\n", + "model.add(LSTM(32, return_sequences=True))\n", + "model.add(LayerNormalization())\n", + "model.add(LSTM(32, return_sequences=True))\n", + "model.add(LayerNormalization())\n", + "model.add(LSTM(32))\n", + "model.add(LayerNormalization())\n", + "model.add(Dense(128, activation=\"relu\"))\n", + "model.add(LayerNormalization())\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(128, activation=\"relu\"))\n", + "model.add(LayerNormalization())\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(len(np.unique(y)), activation=\"softmax\"))\n", + "model.compile(optimizer='adam', loss=\"sparse_categorical_crossentropy\", metrics=['accuracy'])\n", + "\n", + "model.summary()\n", + "plot_model(model, show_shapes=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "2b931bbd-7f6d-471b-bb31-910fd5882bec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "24/24 [==============================] - 9s 16ms/step - loss: 5.0407 - accuracy: 0.0172\n", + "Epoch 2/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 3.9725 - accuracy: 0.0944\n", + "Epoch 3/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 3.3273 - accuracy: 0.1845\n", + "Epoch 4/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 2.7689 - accuracy: 0.3562\n", + "Epoch 5/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 2.2700 - accuracy: 0.4850\n", + "Epoch 6/30\n", + "24/24 [==============================] - 0s 18ms/step - loss: 1.8939 - accuracy: 0.5794\n", + "Epoch 7/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 1.5469 - accuracy: 0.6824\n", + "Epoch 8/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 1.2785 - accuracy: 0.7339\n", + "Epoch 9/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 1.0185 - accuracy: 0.7983\n", + "Epoch 10/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 0.8449 - accuracy: 0.8841\n", + "Epoch 11/30\n", + "24/24 [==============================] - 0s 16ms/step - loss: 0.6763 - accuracy: 0.8841\n", + "Epoch 12/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 0.6003 - accuracy: 0.9142\n", + "Epoch 13/30\n", + "24/24 [==============================] - 0s 18ms/step - loss: 0.5157 - accuracy: 0.9056\n", + "Epoch 14/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 0.4246 - accuracy: 0.9313\n", + "Epoch 15/30\n", + "24/24 [==============================] - 0s 16ms/step - loss: 0.3868 - accuracy: 0.9399\n", + "Epoch 16/30\n", + "24/24 [==============================] - 0s 16ms/step - loss: 0.2624 - accuracy: 0.9700\n", + "Epoch 17/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 0.2397 - accuracy: 0.9742\n", + "Epoch 18/30\n", + "24/24 [==============================] - 0s 16ms/step - loss: 0.1894 - accuracy: 0.9871\n", + "Epoch 19/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 0.1729 - accuracy: 0.9785\n", + "Epoch 20/30\n", + "24/24 [==============================] - 0s 16ms/step - loss: 0.1702 - accuracy: 0.9700\n", + "Epoch 21/30\n", + "24/24 [==============================] - 0s 16ms/step - loss: 0.1629 - accuracy: 0.9914\n", + "Epoch 22/30\n", + "24/24 [==============================] - 0s 16ms/step - loss: 0.1367 - accuracy: 0.9914\n", + "Epoch 23/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 0.1140 - accuracy: 0.9871\n", + "Epoch 24/30\n", + "24/24 [==============================] - 0s 17ms/step - loss: 0.1293 - accuracy: 0.9828\n" + ] + } + ], + "source": [ + "model_history = model.fit(x=X,\n", + " y=y,\n", + " batch_size=10,\n", + " callbacks=[tensorflow.keras.callbacks.EarlyStopping(monitor='accuracy', patience=3)],\n", + " epochs=30)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "1c52e092-dd14-4a9d-9484-769831509420", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import random\n", + "\n", + "def model_responce(query, model):\n", + " text = []\n", + " txt = re.sub('[^a-zA-Z\\']', ' ', query)\n", + " txt = txt.lower()\n", + " txt = txt.split()\n", + " txt = \" \".join(txt)\n", + " text.append(txt)\n", + " x_test = tokenizer.texts_to_sequences(text)\n", + " if (len(query.split(' ')) > 1):\n", + " x_test = np.array(x_test).squeeze()\n", + " else:\n", + " x_test = np.reshape(x_test, -1)\n", + " try:\n", + " x_test = pad_sequences([x_test], padding='post', maxlen=X.shape[1])\n", + " except Exception as e:\n", + " return \"Error 404: Word not found in my dictionary\"\n", + " y_pred = model.predict(x_test)\n", + " y_pred = y_pred.argmax()\n", + " tag = lbl_enc.inverse_transform([y_pred])[0]\n", + " responses = df[df['tag'] == tag]['responses'].values[0]\n", + "\n", + " print(\"you: {}\".format(query))\n", + " print(\"model: {}\".format(random.choice(responses)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "86d4f0f1-5721-4207-9865-d1ba191c0707", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 3s 3s/step\n", + "you: Hi! How are you?\n", + "model: Hi there. What brings you here today?\n" + ] + } + ], + "source": [ + "model_responce('Hi! How are you?', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "cef98e07", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 21ms/step\n", + "you: How can I maintain social connections? What if I feel lonely?\n", + "model: A lot of people are alone right now, but we don't have to be lonely. We're all in this together. Think about the different ways to connect that are most meaningful for you. For example, you might prefer a video chat over a phone call, or you might prefer to text throughout the day rather than one set time for a video call. Then, work with your social networks to make a plan. You might video chat with your close friends in the evening and phone a family member once a week. Remember to be mindful of people who may not be online. Check in by phone and ask how you can help. The quality of your social connections matter. Mindlessly scrolling through social media and liking a few posts usually doesn't build strong social connections. Make sure you focus on strategies that actually make you feel included and connected. If your current strategies don't help you feel connected, problem-solve to see if you can find a solution. Everyone feels lonely at times. Maybe you recently moved to a new city, are changing your circle of friends, lost someone important in your life, or lost your job and also lost important social connections with coworkers. Other people may have physical connections to others but may feel like their emotional or social needs aren't met. Measures like social distancing or self-isolation can make loneliness feel worse no matter why you feel lonely now. Reach out to the connections you do have. Suggest ways to keep in touch and see if you can set a regular time to connect. People may hesitate to reach out for a lot of different reasons, so don't be afraid to be the one who asks. Look for local community support groups and mutual aid groups on social media. This pandemic is bringing everyone together, so look for opportunities to make new connections. These groups are a great way to share your skills and abilities or seek help and support. Look for specialized support groups. Support groups are moving online, and there are a lot of different support lines to call if you need to talk to someone.\n" + ] + } + ], + "source": [ + "model_responce('How can I maintain social connections? What if I feel lonely?', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "dbf9c300-ed31-4113-a25d-fc8713361e7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 37ms/step\n", + "you: Well... I'm feeling sad!\n", + "model: Hello there. Glad to see you're back. What's going on in your world right now?\n" + ] + } + ], + "source": [ + "model_responce('Well... I\\'m feeling sad!', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "23e8b82e-ca40-46eb-9549-751710133674", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 22ms/step\n", + "you: Maybe I just didn't want to be born :)\n", + "model: I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.\n" + ] + } + ], + "source": [ + "model_responce('Maybe I just didn\\'t want to be born :)', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "d11a26d3-c03c-4536-9c36-fb7eb92469c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 19ms/step\n", + "you: I want commit suicide\n", + "model: I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.\n" + ] + } + ], + "source": [ + "model_responce('I want commit suicide', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "b0385192-230c-4ea3-83b7-10d70a58e186", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 19ms/step\n", + "you: I am facing a lot of problems\n", + "model: Nice to meet you. So tell me. How do you feel today?\n" + ] + } + ], + "source": [ + "model_responce('I am facing a lot of problems', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "5ae1259a-3625-4066-8c5d-9d8e4b2ce38b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 23ms/step\n", + "you: i do not think you can help me\n", + "model: Nice to meet you. So tell me. How do you feel today?\n" + ] + } + ], + "source": [ + "model_responce('i do not think you can help me', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "4324e783-9cce-496c-94dd-bb0d9b452da7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 31ms/step\n", + "you: I need help\n", + "model: Sure. Tell me how can i assist you\n" + ] + } + ], + "source": [ + "model_responce('I need help', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "5fd268ea-515b-4c16-b464-aa39f7f6e92d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 25ms/step\n", + "you: Good morning\n", + "model: Good morning. I hope you had a good night's sleep. How are you feeling today? \n" + ] + } + ], + "source": [ + "model_responce('Good morning', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "712391a3-92cf-4af6-8781-5a80a28245b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 23ms/step\n", + "you: Good evening\n", + "model: Good evening. How has your day been?\n" + ] + } + ], + "source": [ + "model_responce('Good evening', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "d975bc2c-a6ac-48d7-a819-ba20a6f7736f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 20ms/step\n", + "you: Gosadj kjasndkja sadan kadsjnbkjabdhjk\n", + "model: Not sure I understand that.\n" + ] + } + ], + "source": [ + "model_responce('Gosadj kjasndkja sadan kadsjnbkjabdhjk', model)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "f4254a01", + "metadata": {}, + "outputs": [], + "source": [ + "model.save('./chatbot_model.h5', model)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf-intel", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DEMO DAY.pdf b/DEMO DAY.pdf new file mode 100644 index 000000000..d8ea7cbc5 Binary files /dev/null and b/DEMO DAY.pdf differ diff --git a/Final-Phoenix13.pdf b/Final-Phoenix13.pdf new file mode 100644 index 000000000..e8acf2bc3 Binary files /dev/null and b/Final-Phoenix13.pdf differ diff --git a/LearnersEd/Gamify_Quiz/__init__.py b/LearnersEd/Gamify_Quiz/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/Gamify_Quiz/__pycache__/__init__.cpython-39.pyc b/LearnersEd/Gamify_Quiz/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..225eafed1 Binary files /dev/null and b/LearnersEd/Gamify_Quiz/__pycache__/__init__.cpython-39.pyc differ diff --git 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b/LearnersEd/Gamify_Quiz/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/Gamify_Quiz/admin.py b/LearnersEd/Gamify_Quiz/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/Gamify_Quiz/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/Gamify_Quiz/apps.py b/LearnersEd/Gamify_Quiz/apps.py new file mode 100644 index 000000000..61cf0f851 --- /dev/null +++ b/LearnersEd/Gamify_Quiz/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class GamifyQuizConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'Gamify_Quiz' diff --git a/LearnersEd/Gamify_Quiz/migrations/0001_initial.py b/LearnersEd/Gamify_Quiz/migrations/0001_initial.py new file mode 100644 index 000000000..edf2d9d9a --- /dev/null +++ b/LearnersEd/Gamify_Quiz/migrations/0001_initial.py @@ -0,0 +1,26 @@ +# Generated by Django 4.1.9 on 2023-06-07 00:37 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ] + + operations = [ + migrations.CreateModel( + name='Question', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('text', models.CharField(max_length=255)), + ('option1', models.CharField(default='Default Option 1', max_length=255)), + ('option2', models.CharField(default='Default Option 2', max_length=255)), + ('option3', models.CharField(default='Default Option 3', max_length=255)), + ('option4', models.CharField(default='Default Option 4', max_length=255)), + ('correct_answer', models.CharField(default='Nothing', max_length=255)), + ], + ), + ] diff --git a/LearnersEd/Gamify_Quiz/migrations/__init__.py b/LearnersEd/Gamify_Quiz/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/Gamify_Quiz/migrations/__pycache__/0001_initial.cpython-39.pyc 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models.CharField(max_length=255, default='Default Option 4') + + correct_answer = models.CharField(max_length=255, default='Nothing') + diff --git a/LearnersEd/Gamify_Quiz/models/model.h5 b/LearnersEd/Gamify_Quiz/models/model.h5 new file mode 100644 index 000000000..cef091f87 Binary files /dev/null and b/LearnersEd/Gamify_Quiz/models/model.h5 differ diff --git a/LearnersEd/Gamify_Quiz/models/shape_predictor_68_face_landmarks.dat b/LearnersEd/Gamify_Quiz/models/shape_predictor_68_face_landmarks.dat new file mode 100644 index 000000000..e0ec20d69 Binary files /dev/null and b/LearnersEd/Gamify_Quiz/models/shape_predictor_68_face_landmarks.dat differ diff --git a/LearnersEd/Gamify_Quiz/static/images/bg-new.png b/LearnersEd/Gamify_Quiz/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/Gamify_Quiz/static/images/bg-new.png differ diff --git a/LearnersEd/Gamify_Quiz/static/script-gamify-quiz.js b/LearnersEd/Gamify_Quiz/static/script-gamify-quiz.js new file mode 100644 index 000000000..1fae695eb --- /dev/null +++ b/LearnersEd/Gamify_Quiz/static/script-gamify-quiz.js @@ -0,0 +1,177 @@ +const videoElement = document.getElementById('videoElement'); +const questionTextElement = document.getElementById('questionText'); +const option1Element = document.getElementById('option1'); +const option2Element = document.getElementById('option2'); +const option3Element = document.getElementById('option3'); +const option4Element = document.getElementById('option4'); +const predictionResultElement = document.getElementById('predictionResult'); +const resultElement = document.getElementById('res-div'); +const result = document.getElementById('result'); +const startButton = document.getElementById('start-div'); +const progressBar = document.getElementById('progressBar'); +const body = document.querySelector('body'); +const title = document.querySelector('#title'); + +let stream; +let currentQuestionIndex = -1; +let selectedOption = ""; +let answer; +let marks = 0; +let isQuizStarted = false; +let coin = -1; +let csi; +let k = 0; + +body.style.overflowY = "hidden"; + +function startCamera() { + navigator.mediaDevices.getUserMedia({ video: true }) + .then(videoStream => { + stream = videoStream; + videoElement.srcObject = stream; + csi = setInterval(captureFrame, 1500); // Capture frame every 1 seconds + }) + .catch(error => { + console.error('Error accessing camera:', error); + }); +} + +function stopCamera() { + if (stream && stream.getTracks) { + stream.getTracks().forEach(track => track.stop()); + stream = null; + } +} + +function displayQuestion() { + currentQuestionIndex++; + const currentQuestion = questions[currentQuestionIndex]; + questionTextElement.textContent = currentQuestion.text; + option1Element.textContent = currentQuestion.options[0]; + option2Element.textContent = currentQuestion.options[1]; + option3Element.textContent = currentQuestion.options[2]; + option4Element.textContent = currentQuestion.options[3]; + answer = currentQuestion.answer; + progressBar.style.width = '100%'; + selectedOption = ""; + handleNextQuestion(); +} + +function captureFrame() { + if (!isQuizStarted) return; + // Create a canvas element + const canvas = document.createElement('canvas'); + canvas.width = videoElement.videoWidth; + canvas.height = videoElement.videoHeight; + + // Draw the current frame from the video element onto the canvas + const context = canvas.getContext('2d'); + context.drawImage(videoElement, 0, 0, canvas.width, canvas.height); + + // Get the base64-encoded image data from the canvas + const imageData = canvas.toDataURL('image/jpeg'); + + quiz_data = { + image_data: imageData, + coins: coin + }; + + // Send the image data as a POST request to the Django backend using AJAX + $.ajax({ + url: '', + type: 'POST', + data: JSON.stringify(quiz_data), + contentType: 'application/json', + success: function(response) { + console.log(response.message); + // Handle the response from the Django backend + const prediction = response.prediction; // Assuming the prediction is returned as a property in the response + + // Clear any previous highlighting + option1Element.classList.remove('selected'); + option2Element.classList.remove('selected'); + option3Element.classList.remove('selected'); + option4Element.classList.remove('selected'); + + // Highlight the corresponding option based on the prediction + if (prediction === 'top left') { + option1Element.classList.add('selected'); + selectedOption = option1Element.textContent; + } else if (prediction === 'top right') { + option2Element.classList.add('selected'); + selectedOption = option2Element.textContent; + } else if (prediction === 'bottom left') { + option3Element.classList.add('selected'); + selectedOption = option3Element.textContent; + } else if (prediction === 'bottom right') { + option4Element.classList.add('selected'); + selectedOption = option4Element.textContent; + } + + console.log('Response from Django:', response); + }, + error: function(xhr, status, error) { + console.error('Error sending image to Django:', error); + } + }); + console.log(k) + if (k == 1) { + clearInterval(csi); + } +} + +let timerInterval; +const progressBarWidth = 100; +const totalTime = 10000; // Total time in milliseconds (10 seconds) +const updateInterval = 10; // Update the progress bar every 100 milliseconds + +function handleNextQuestion() { + let startTime = Date.now(); + progressBar.style.width = '100%'; // Reset the progress bar + + timerInterval = setInterval(() => { + const elapsed = Date.now() - startTime; + const remaining = totalTime - elapsed; + const progress = (remaining / totalTime) * progressBarWidth; + + progressBar.style.width = `${progress}%`; + + if (elapsed >= totalTime) { + clearInterval(timerInterval); + + if (selectedOption === answer) { + marks++; + selectedOption = ""; + } + + if (currentQuestionIndex >= questions.length - 1) { + coin = (marks / questions.length) * 100; + body.style.overflowY = "hidden"; + title.style.position = "fixed"; + result.innerHTML = `

Quiz completed!

Your marks: ${marks}/${questions.length}

You received: ${coin}

`; + resultElement.style.display = "flex"; + stopCamera(); + k = 1; + } else { + displayQuestion(); + } + } + }, updateInterval); +} + +function scrollToBottom() { + window.scrollTo({ + top: document.documentElement.scrollHeight, + behavior: 'smooth' + }); +} + +startButton.addEventListener('click', () => { + body.style.overflowY = "auto"; + title.style.position = "static"; + scrollToBottom(); + startButton.style.display = 'none'; // Hide the start button + isQuizStarted = true; // Set the quiz as started + startCamera(); + displayQuestion(); +}); diff --git a/LearnersEd/Gamify_Quiz/static/styles-gamify-quiz.css b/LearnersEd/Gamify_Quiz/static/styles-gamify-quiz.css new file mode 100644 index 000000000..c673d244e --- /dev/null +++ b/LearnersEd/Gamify_Quiz/static/styles-gamify-quiz.css @@ -0,0 +1,139 @@ +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + scroll-behavior: smooth; + font-family: 'Darker Grotesque', sans-serif; +} + +#title { + position: fixed; + z-index: 999999999; + box-shadow: 0 0 10px 2px gray; + left: 12%; + font-size: 40px; +} + +#questionText { + box-shadow: inset 0px 0px 8px 0px rgba(0,0,0,0.75); +} + +.opt { + background-color: white; + transition-duration: 200ms; + border: 8px solid black; +} + +main { + width: 100%; +} + +#videoElement { + transform: scaleX(-1); +} + +.selected { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 3s infinite ease-in-out; + background-size: 200% 200%; + border: 8px solid white; + color: white; + box-shadow: 0 0 10px 2px gray; + transform: scale(1.2); +} + +/* width */ +::-webkit-scrollbar { + width: 10px; + } + + /* Track */ + ::-webkit-scrollbar-track { + background: #f1f1f1; + } + + /* Handle */ + ::-webkit-scrollbar-thumb { + background: #888; + } + + /* Handle on hover */ + ::-webkit-scrollbar-thumb:hover { + background: #555; + } + + #result { + position: absolute; + } + + #start-div { + position: fixed; + top: 0; + display: flex; + justify-content: center; + align-items: center; + left: 0; + width: 100%; + height: 100%; + background-color: white; +} + +#result { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + box-shadow: 0 0 10px 2px gray; + margin-top: 20px; + border-radius: 30px; + border: 8px solid white; +} + +#startButton { + padding: 30px 40px; + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 30px; + outline: 4px solid white; + box-shadow: 0 0 10px 2px gray; + transition-duration: 200ms; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + +#startButton:hover { + transform: scale(1.4); + transition-duration: 200ms; + transition-timing-function: ease-out; +} + +#startButton:active { + transform: scale(0.9); + background-color: black; + background-image: none; + transition-duration: 200ms; + transition-timing-function: ease-out; +} + +#res-div { + position: fixed; + top: 0; + display: none; + flex-direction: column; + justify-content: center; + align-items: center; + left: 0; + width: 100%; + height: 100%; + background-color: white; +} \ No newline at end of file diff --git a/LearnersEd/Gamify_Quiz/templates/gamify-quiz.html b/LearnersEd/Gamify_Quiz/templates/gamify-quiz.html new file mode 100644 index 000000000..8088f9441 --- /dev/null +++ b/LearnersEd/Gamify_Quiz/templates/gamify-quiz.html @@ -0,0 +1,46 @@ +{% load static %} + + + + + + + Gamify-Quiz - (Learners'Ed) + + + + + + + + + + {% include 'navbar.html' %} +

Gamify Quiz

+
+
Q. Lorem ipsum dolor sit amet consectetur adipisicing elit. Assumenda, eveniet?
+

+
+
+
+
+
+
+ +
+
+
+
+
+
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+

+
+
+ + + + + diff --git a/LearnersEd/Gamify_Quiz/tests.py b/LearnersEd/Gamify_Quiz/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/Gamify_Quiz/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/Gamify_Quiz/views.py b/LearnersEd/Gamify_Quiz/views.py new file mode 100644 index 000000000..094d41154 --- /dev/null +++ b/LearnersEd/Gamify_Quiz/views.py @@ -0,0 +1,130 @@ +from django.shortcuts import render +from django.http import JsonResponse +from django.views.decorators.csrf import csrf_exempt +from .models import Question +import json +from PIL import Image +from io import BytesIO +import base64 +import cv2 +import numpy as np +import dlib +import pickle as pkl +from sklearnex.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from keras.models import load_model + +x, y = pkl.load(open(r'D:\Project\Website-Phoenix13\LearnersEd\Gamify_Quiz\dataset\samples.pkl', 'rb')) + +x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42) +x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=0.5, random_state=42) + +std = StandardScaler() +std.fit(x_train) +x_train = std.transform(x_train) +x_val = std.transform(x_val) +x_test = std.transform(x_test) + +def detect_face_points(image): + detector = dlib.get_frontal_face_detector() + predictor = dlib.shape_predictor(r"D:\Project\Website-Phoenix13\LearnersEd\Gamify_Quiz\models\shape_predictor_68_face_landmarks.dat") + face_rect = detector(image, 1) + if len(face_rect) != 1: + return [] + + dlib_points = predictor(image, face_rect[0]) + face_points = [] + for i in range(68): + x, y = dlib_points.part(i).x, dlib_points.part(i).y + face_points.append(np.array([x, y])) + return face_points + +def compute_features(face_points): + assert (len(face_points) == 68), "len(face_points) must be 68" + + face_points = np.array(face_points) + features = [] + for i in range(68): + for j in range(i + 1, 68): + features.append(np.linalg.norm(face_points[i] - face_points[j])) + + return np.array(features).reshape(1, -1) + +def Predict(image, model): + im = np.array(image) + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + face_points = detect_face_points(im) + + if len(face_points) != 68: + print("68 face landmarks not detected.") + return + + features = compute_features(face_points) + features = std.transform(features) + y_pred = model.predict(features) + + _, pitch_pred, yaw_pred = y_pred[0] + + if pitch_pred >= 5 and yaw_pred > 0: + return 'top left' + elif pitch_pred >= 8 and yaw_pred < 0: + return 'top right' + elif pitch_pred <= 8 and yaw_pred > 0: + return 'bottom left' + else: + return 'bottom right' + + +model = load_model(r'D:\Project\Website-Phoenix13\LearnersEd\Gamify_Quiz\models\model.h5') + +@csrf_exempt +def gamify_quiz(request): + if request.method == 'POST': + json_data = json.loads(request.body) + image_data = json_data.get('image_data') + marks = json_data.get('coins') + + if marks != -1: + print(marks) # Handle the quiz marks as required (e.g., store in database) + response = {'message': 'Quiz data received'} + return JsonResponse(response) + elif image_data: + # Convert the base64-encoded image data to PIL Image + image = Image.open(BytesIO(base64.b64decode(image_data.split(',')[1]))) + + # Perform any processing or DL model prediction with the image + prediction_result = Predict(image, model) + + # Replace this dummy response with the actual response you want to send back + response = {'prediction': prediction_result} + + return JsonResponse(response) + else: + # No image data received + response = {'error': 'No image data received'} + return JsonResponse(response, status=400) + + else: + # Fetch quiz questions from the database + questions = Question.objects.all() + + # Convert questions to a list of dictionaries + question_list = [] + for question in questions: + question_dict = { + 'text': question.text, + 'options': [ + question.option1, + question.option2, + question.option3, + question.option4 + ], + 'answer': question.correct_answer + } + question_list.append(question_dict) + + # Convert question_list to a JSON string + questions_json = json.dumps(question_list) + + # Pass the questions to the template + return render(request, 'gamify-quiz.html', {'questions_json': questions_json}) diff --git a/LearnersEd/LearnersEd/__init__.py b/LearnersEd/LearnersEd/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/LearnersEd/__pycache__/__init__.cpython-310.pyc b/LearnersEd/LearnersEd/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..d2ab0a524 Binary files /dev/null and b/LearnersEd/LearnersEd/__pycache__/__init__.cpython-310.pyc differ diff --git a/LearnersEd/LearnersEd/__pycache__/__init__.cpython-39.pyc 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index 000000000..b2e5c460e Binary files /dev/null and b/LearnersEd/LearnersEd/__pycache__/wsgi.cpython-39.pyc differ diff --git a/LearnersEd/LearnersEd/asgi.py b/LearnersEd/LearnersEd/asgi.py new file mode 100644 index 000000000..eef629211 --- /dev/null +++ b/LearnersEd/LearnersEd/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for LearnersEd project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'LearnersEd.settings') + +application = get_asgi_application() diff --git a/LearnersEd/LearnersEd/middlewares.py b/LearnersEd/LearnersEd/middlewares.py new file mode 100644 index 000000000..7fed69c7e --- /dev/null +++ b/LearnersEd/LearnersEd/middlewares.py @@ -0,0 +1,13 @@ +from django.shortcuts import redirect + +class AuthenticationMiddleware: + def __init__(self, get_response): + self.get_response = get_response + + def __call__(self, request): + if not request.path.startswith('/login/') and (not request.path.startswith('/register/') or request.path == ""): + if 'student_id' not in request.session: + return redirect('/login/') + + response = self.get_response(request) + return response diff --git a/LearnersEd/LearnersEd/settings.py b/LearnersEd/LearnersEd/settings.py new file mode 100644 index 000000000..b15982244 --- /dev/null +++ b/LearnersEd/LearnersEd/settings.py @@ -0,0 +1,129 @@ +from pathlib import Path +import os + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.2/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-7@xmj(ksb^6#-9o*3@de4$rgpht-6fu$s!q0h)!$8)8$1v7j8$' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + 'chatbot', + 'virtual_pet', + 'Gamify_Quiz', + 'dashboard', + 'explore_fields', + 'lectures', + 'gamify_app', + 'login_register', + 'student_insights_app', + 'lecture_attend', + 'chatroom', + 'assignment' +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', + 'LearnersEd.middlewares.AuthenticationMiddleware', +] + +ROOT_URLCONF = 'LearnersEd.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [os.path.join(BASE_DIR, "templates")], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'LearnersEd.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.2/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'djongo', + 'NAME': 'test-db', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.2/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.2/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.2/howto/static-files/ + +STATIC_URL = 'static/' + +STATICFILES_DIRS = [ + os.path.join(BASE_DIR, 'static'), +] + +# Default primary key field type +# https://docs.djangoproject.com/en/4.2/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/LearnersEd/LearnersEd/urls.py b/LearnersEd/LearnersEd/urls.py new file mode 100644 index 000000000..eb43d12c7 --- /dev/null +++ b/LearnersEd/LearnersEd/urls.py @@ -0,0 +1,34 @@ +from django.contrib import admin +from django.urls import path +from chatbot.views import chatbot +from virtual_pet.views import virtual_pet +from Gamify_Quiz.views import gamify_quiz +from dashboard.views import dashboard +from lectures.views import lectures +from explore_fields.views import explore_fields +from login_register.views import login, register, logout +from gamify_app.views import gamify +from student_insights_app.views import student_insights +from chatroom.views import chatroom +from lecture_attend.views import lecture_attend +from assignment.views import assignment +from .views import *; + +urlpatterns = [ + path("", register), + path("login/", login), + path("register/", register), + path('admin/', admin.site.urls), + path('chatbot/', chatbot), + path('virtual-pet/', virtual_pet), + path('gamify-quiz/', gamify_quiz), + path('dashboard/', dashboard, name='dashboard'), + path('explore-fields/', explore_fields), + path('lectures/', lectures), + path('gamify/', gamify), + path('student_insights/', student_insights), + path('logout/', logout), + path('lecture_attend/', lecture_attend), + path('chatroom/', chatroom), + path('assignment/', assignment) +] diff --git a/LearnersEd/LearnersEd/views.py b/LearnersEd/LearnersEd/views.py new file mode 100644 index 000000000..a9ec5d8c5 --- /dev/null +++ b/LearnersEd/LearnersEd/views.py @@ -0,0 +1 @@ +from django.shortcuts import render \ No newline at end of file diff --git a/LearnersEd/LearnersEd/wsgi.py b/LearnersEd/LearnersEd/wsgi.py new file mode 100644 index 000000000..a661b8174 --- /dev/null +++ b/LearnersEd/LearnersEd/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for LearnersEd project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'LearnersEd.settings') + +application = get_wsgi_application() diff --git a/LearnersEd/assignment/__init__.py b/LearnersEd/assignment/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/assignment/__pycache__/__init__.cpython-39.pyc b/LearnersEd/assignment/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..25f45fa2d Binary files /dev/null and b/LearnersEd/assignment/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/assignment/__pycache__/admin.cpython-39.pyc b/LearnersEd/assignment/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..4d7659e08 Binary files /dev/null and b/LearnersEd/assignment/__pycache__/admin.cpython-39.pyc differ diff --git a/LearnersEd/assignment/__pycache__/apps.cpython-39.pyc b/LearnersEd/assignment/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..43073ef52 Binary files /dev/null and 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b/LearnersEd/assignment/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class AssignmentConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'assignment' diff --git a/LearnersEd/assignment/migrations/__init__.py b/LearnersEd/assignment/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/assignment/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/assignment/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..6e3062e7b Binary files /dev/null and b/LearnersEd/assignment/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/assignment/models.py b/LearnersEd/assignment/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/LearnersEd/assignment/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/LearnersEd/assignment/models/assign/BrightNet Technologies.docx b/LearnersEd/assignment/models/assign/BrightNet Technologies.docx new file mode 100644 index 000000000..12554a90c Binary files /dev/null and b/LearnersEd/assignment/models/assign/BrightNet Technologies.docx differ diff --git a/LearnersEd/assignment/models/assign/Council Members.docx b/LearnersEd/assignment/models/assign/Council Members.docx new file mode 100644 index 000000000..411382f54 Binary files /dev/null and b/LearnersEd/assignment/models/assign/Council Members.docx differ diff --git a/LearnersEd/assignment/models/assign/English.docx b/LearnersEd/assignment/models/assign/English.docx new file mode 100644 index 000000000..04a3eb876 Binary files /dev/null and b/LearnersEd/assignment/models/assign/English.docx differ diff --git a/LearnersEd/assignment/models/assign/Intro-QnA.docx b/LearnersEd/assignment/models/assign/Intro-QnA.docx new file mode 100644 index 000000000..db654b626 Binary files /dev/null and b/LearnersEd/assignment/models/assign/Intro-QnA.docx differ diff --git a/LearnersEd/assignment/models/assign/Lorem.docx b/LearnersEd/assignment/models/assign/Lorem.docx new file mode 100644 index 000000000..4b29a030e Binary files /dev/null and b/LearnersEd/assignment/models/assign/Lorem.docx differ diff --git a/LearnersEd/assignment/models/assign/Paper.docx b/LearnersEd/assignment/models/assign/Paper.docx new file mode 100644 index 000000000..654b5c86d Binary files /dev/null and b/LearnersEd/assignment/models/assign/Paper.docx differ diff --git a/LearnersEd/assignment/models/assign/Plans.docx b/LearnersEd/assignment/models/assign/Plans.docx new file mode 100644 index 000000000..75ab3645b Binary files /dev/null and b/LearnersEd/assignment/models/assign/Plans.docx differ diff --git a/LearnersEd/assignment/models/assign/Sujal.docx b/LearnersEd/assignment/models/assign/Sujal.docx new file mode 100644 index 000000000..c2c84a821 Binary files /dev/null and b/LearnersEd/assignment/models/assign/Sujal.docx differ diff --git a/LearnersEd/assignment/models/assign/fejhwdfnv xhjkemfkl.docx b/LearnersEd/assignment/models/assign/fejhwdfnv xhjkemfkl.docx new file mode 100644 index 000000000..b8424bb97 Binary files /dev/null and b/LearnersEd/assignment/models/assign/fejhwdfnv xhjkemfkl.docx differ diff --git a/LearnersEd/assignment/static/images/bg-new.png b/LearnersEd/assignment/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/assignment/static/images/bg-new.png differ diff --git a/LearnersEd/assignment/templates/assignment.html b/LearnersEd/assignment/templates/assignment.html new file mode 100644 index 000000000..cc51e6103 --- /dev/null +++ b/LearnersEd/assignment/templates/assignment.html @@ -0,0 +1,84 @@ +{% load static %} + + + + + + + Assignment - Learners'Ed + + + + + + + + + {% include 'navbar.html' %} +

Upload Assignment

+
+
+ {% csrf_token %} + +
+
+
+ + +
+
+

Start Uploading...

+
+ + + + + diff --git a/LearnersEd/assignment/tests.py b/LearnersEd/assignment/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/assignment/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/assignment/views.py b/LearnersEd/assignment/views.py new file mode 100644 index 000000000..f767e43d0 --- /dev/null +++ b/LearnersEd/assignment/views.py @@ -0,0 +1,113 @@ +import os +from django.conf import settings +from django.shortcuts import render +from django.http import JsonResponse +import string +import nltk +from nltk.corpus import stopwords +import sklearnex +from sklearnex import patch_sklearn +patch_sklearn() +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.pairwise import cosine_similarity +from docx import Document +nltk.download('stopwords') +import shutil +import os + +def preprocess_text(text): + # Remove punctuation and convert to lowercase + text = text.translate(str.maketrans("", "", string.punctuation)).lower() + + # Remove stopwords + stop_words = set(stopwords.words('english')) + text = " ".join(word for word in text.split() if word not in stop_words) + + return text + +def extract_text_from_docx(file_path): + doc = Document(file_path) + paragraphs = [p.text for p in doc.paragraphs] + return '\n'.join(paragraphs) + +import os +def check_plagiarism(file_name): + # Load student assignment + student_assignment_file = "D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/temp/" + file_name + reference_assignment_files = [doc for doc in os.listdir('D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/') if doc.endswith('.doc') or doc.endswith('.docx')] + print(student_assignment_file) + student_assignment = extract_text_from_docx(student_assignment_file) + processed_student = preprocess_text(student_assignment) + + similarities = [] + + for ref_file in reference_assignment_files: + # Load reference assignment + reference_assignment = extract_text_from_docx('D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/'+ref_file) + processed_ref = preprocess_text(reference_assignment) + + # Create TF-IDF vectors + vectorizer = TfidfVectorizer() + vectors = vectorizer.fit_transform([processed_student, processed_ref]) + + # Calculate cosine similarity + similarity = cosine_similarity(vectors[0], vectors[1])[0][0] + similarities.append(similarity) + + return similarities + +check = [] + +def has_greater_value(check, desired_value): + return any(element > desired_value for element in check) + +def assignment(request): + if request.method == 'POST': + if 'file' in request.FILES: + uploaded_file = request.FILES['file'] + file_extension = os.path.splitext(uploaded_file.name)[1].lower() + + if file_extension not in ['.doc', '.docx']: + return JsonResponse({'error': 'Invalid file format'}) + + # Specify the directory where you want to save the file + file_directory = r"D:\Project\Website-Phoenix13\LearnersEd\assignment\models\assign\temp" + + # Create the directory if it doesn't exist + if not os.path.exists(file_directory): + return JsonResponse({'error': 'No Save Directory Found'}) + + # Save the file to the directory + file_path = os.path.join(file_directory, uploaded_file.name) + with open(file_path, 'wb+') as destination: + for chunk in uploaded_file.chunks(): + destination.write(chunk) + + check = [] + similarities = check_plagiarism(uploaded_file.name) + for i, similarity in enumerate(similarities): + check.append(similarity) + + is_accepted = True + # Perform your assignment evaluation logic here + plag = (has_greater_value(check, 0.3)) # Replace with your actual evaluation logic + + if plag == False: + is_accepted = True + source_file = 'D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/temp/' + uploaded_file.name + destination_directory = 'D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign' + + # Move the file to the destination directory + shutil.move(source_file, destination_directory) + else: + is_accepted = False + # Check if the file exists before deleting + if os.path.exists(file_path): + os.remove(file_path) + print("File deleted successfully.") + else: + print("File does not exist.") + + return JsonResponse({'is_accepted': is_accepted}) + + return render(request, 'assignment.html', {}) diff --git a/LearnersEd/chatbot/__init__.py b/LearnersEd/chatbot/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/chatbot/__pycache__/__init__.cpython-310.pyc b/LearnersEd/chatbot/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..480bbb5b0 Binary files /dev/null and b/LearnersEd/chatbot/__pycache__/__init__.cpython-310.pyc differ diff --git a/LearnersEd/chatbot/__pycache__/__init__.cpython-39.pyc b/LearnersEd/chatbot/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..9f10be434 Binary files /dev/null and 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100644 index 000000000..8eef4fee8 Binary files /dev/null and b/LearnersEd/chatbot/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/chatbot/admin.py b/LearnersEd/chatbot/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/chatbot/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/chatbot/apps.py b/LearnersEd/chatbot/apps.py new file mode 100644 index 000000000..6c6cc8283 --- /dev/null +++ b/LearnersEd/chatbot/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class ChatbotConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'chatbot' diff --git a/LearnersEd/chatbot/migrations/__init__.py b/LearnersEd/chatbot/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/chatbot/migrations/__pycache__/__init__.cpython-310.pyc b/LearnersEd/chatbot/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..7cc90829d Binary files /dev/null and b/LearnersEd/chatbot/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/LearnersEd/chatbot/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/chatbot/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..29435b3d9 Binary files /dev/null and b/LearnersEd/chatbot/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/chatbot/model/chatbot_model.h5 b/LearnersEd/chatbot/model/chatbot_model.h5 new file mode 100644 index 000000000..27284bb60 Binary files /dev/null and b/LearnersEd/chatbot/model/chatbot_model.h5 differ diff --git a/LearnersEd/chatbot/model/dataset/dataframe_chatbot.csv b/LearnersEd/chatbot/model/dataset/dataframe_chatbot.csv new file mode 100644 index 000000000..89624ea72 --- /dev/null +++ b/LearnersEd/chatbot/model/dataset/dataframe_chatbot.csv @@ -0,0 +1,234 @@ +,tag,patterns,responses +0,greeting,Hi,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +1,greeting,Hey,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +2,greeting,Is anyone there?,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +3,greeting,Hi there,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +4,greeting,Hello,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +5,greeting,Hey there,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +6,greeting,Howdy,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +7,greeting,Hola,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +8,greeting,Bonjour,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +9,greeting,Konnichiwa,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +10,greeting,Guten tag,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +11,greeting,Ola,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +12,morning,Good morning,"[""Good morning. I hope you had a good night's sleep. How are you feeling today? ""]" +13,afternoon,Good afternoon,['Good afternoon. How is your day going?'] +14,evening,Good evening,['Good evening. How has your day been?'] +15,night,Good night,"['Good night. Get some proper sleep', 'Good night. Sweet dreams.']" +16,goodbye,Bye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +17,goodbye,See you later,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +18,goodbye,Goodbye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +19,goodbye,Au revoir,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +20,goodbye,Sayonara,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +21,goodbye,ok bye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +22,goodbye,Bye then,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +23,goodbye,Fare thee well,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +24,thanks,Thanks,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +25,thanks,Thank you,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +26,thanks,That's helpful,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +27,thanks,Thanks for the help,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +28,thanks,Than you very much,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +29,no-response,,"[""Sorry, I didn't understand you."", 'Please go on.', 'Not sure I understand that.', ""Please don't hesitate to talk to me.""]" +30,neutral-response,nothing much,['Oh I see. Do you want to talk about something?'] +31,about,Who are you?,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +32,about,What are you?,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +33,about,Who you are?,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +34,about,Tell me more about yourself.,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +35,about,What is your name?,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +36,about,What should I call you?,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +37,about,What's your name?,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +38,about,Tell me about yourself,"[""I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Woebot.', ""I'm Woebot!"", 'Call me Woebot']" +39,skill,What can you do?,"[""I can provide general advice regarding anxiety and depression, answer questions related to mental health and make daily conversations. Do not consider me as a subsitute for an actual mental healthcare worker. Please seek help if you don't feel satisfied with me.""]" +40,creation,Who created you?,"['I was created by >.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +41,creation,How were you made?,"['I was created by >.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +42,creation,How were you created?,"['I was created by >.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +43,name,My name is ,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +44,name,I am name.,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +45,name,I go by ,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +46,help,Could you help me?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +47,help,give me a hand please,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +48,help,Can you help?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +49,help,What can you do for me?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +50,help,I need support,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +51,help,I need help,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +52,help,Support me please,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +53,sad,I am feeling lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +54,sad,I am so lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +55,sad,I feel down,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +56,sad,I feel sad,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +57,sad,I am sad,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +58,sad,I feel so lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +59,sad,I feel empty,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +60,sad,I don't have anyone,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +61,stressed,I am so stressed out,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +62,stressed,I am so stressed,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +63,stressed,I feel stuck,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +64,stressed,I still feel stressed,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +65,stressed,I am so burned out,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +66,worthless,I feel so worthless.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +67,worthless,No one likes me.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +68,worthless,I can't do anything.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +69,worthless,I am so useless,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +70,worthless,Nothing makes sense anymore,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +71,depressed,I can't take it anymore,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +72,depressed,I am so depressed,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +73,depressed,I think i'm depressed.,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +74,depressed,I have depression,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +75,happy,I feel great today.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +76,happy,I am happy.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +77,happy,I feel happy.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +78,happy,I'm good.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +79,happy,cheerful,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +80,happy,I'm fine,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +81,happy,I feel ok,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +82,casual,Oh I see.,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +83,casual,ok,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +84,casual,okay,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +85,casual,nice,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +86,casual,Whatever,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +87,casual,K,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +88,casual,Fine,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +89,casual,yeah,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +90,casual,yes,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +91,casual,no,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +92,casual,not really,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +93,anxious,I feel so anxious.,"[""Don't be hard on yourself. What's the reason behind this?"", 'Can you tell me more about this feeling?', 'I understand that it can be scary. Tell me more about it.', ""Don't let the little worries bring you down. What's the worse that can happen?""]" +94,anxious,I'm so anxious because of ,"[""Don't be hard on yourself. What's the reason behind this?"", 'Can you tell me more about this feeling?', 'I understand that it can be scary. Tell me more about it.', ""Don't let the little worries bring you down. What's the worse that can happen?""]" +95,not-talking,I don't want to talk about it.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +96,not-talking,No just stay away.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +97,not-talking,I can't bring myself to open up.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +98,not-talking,Just shut up,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +99,sleep,I have insominia,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +100,sleep,I am suffering from insomnia,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +101,sleep,I can't sleep.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +102,sleep,I haven't slept for the last days.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +103,sleep,I can't seem to go to sleep.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +104,sleep,I haven't had proper sleep for the past few days.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +105,scared,I'm scared,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +106,scared,That sounds awful. What do i do?,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +107,scared,No i don't want to feel this way,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +108,scared,I am scared for myself,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +109,death,My mom died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +110,death,My brother died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +111,death,My dad passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +112,death,My sister passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +113,death,Someone in my family died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +114,death,My friend passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +115,understand,You don't understand me.,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +116,understand,You're just some robot. How would you know?,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +117,understand,You can't possibly know what i'm going through,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +118,understand,You're useless,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +119,understand,You can't help me,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +120,understand,Nobody understands me.,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +121,done,That's all.,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +122,done,I don't have anything more to say,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +123,done,Nothing else,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +124,done,That's all i have to say,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +125,done,"no, that would be all","['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +126,suicide,I want to kill myself,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +127,suicide,I've thought about killing myself.,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +128,suicide,I want to die,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +129,suicide,I am going to kill myself,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +130,suicide,I am going to commit suicide,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +131,hate-you,I hate you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +132,hate-you,I don't like you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +133,hate-you,I don't trust you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +134,hate-me,You hate me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +135,hate-me,I know you hate me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +136,hate-me,You don't like me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +137,default,exams,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +138,default,friends,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +139,default,relationship,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +140,default,boyfriend,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +141,default,girlfriend,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +142,default,family,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +143,default,money,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +144,default,financial problems,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +145,jokes,Tell me a joke,['mental health is not a joke.'] +146,jokes,Tell me another joke,['mental health is not a joke.'] +147,repeat,You already told me that,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +148,repeat,You mentioned that already,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +149,repeat,Why are you repeating yourself?,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +150,wrong,What are you saying?,"[""I'm very sorry. Let's try that again""]" +151,wrong,That doesn't make sense,"[""I'm very sorry. Let's try that again""]" +152,wrong,Wrong response,"[""I'm very sorry. Let's try that again""]" +153,wrong,Wrong answer,"[""I'm very sorry. Let's try that again""]" +154,stupid,Are you stupid?,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +155,stupid,You're crazy,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +156,stupid,You are dumb,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +157,stupid,Are you dumb?,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +158,location,Where are you?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +159,location,Where do you live?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +160,location,What is your location?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +161,something-else,I want to talk about something else,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +162,something-else,Let's talk about something else.,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +163,something-else,Can we not talk about this?,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +164,something-else,I don't want to talk about this.,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +165,friends,I don't have any friends,"[""I'm sorry to hear that. Just know that I'm here for you. Talking about it might help. Why do you think you don't have any friends?""]" +166,ask,Can I ask you something?,"[""Sure. I'll try my best to answer you"", ""Of course. Feel free to ask me anything. I'll do my best to answer you""]" +167,problem,Probably because my exams are approaching. I feel stressed out because I don't think I've prepared well enough.,['I see. Have you taken any approaches to not feel this way?'] +168,problem,probably because of my exams,['I see. Have you taken any approaches to not feel this way?'] +169,no-approach,I guess not. All I can think about are my exams.,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +170,no-approach,not really,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +171,no-approach,i guess not,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +172,learn-more,ok sure. i would like to learn more about it.,"[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +173,learn-more,"yes, i would like to learn more about it.","[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +174,learn-more,i would like to learn more about it.,"[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +175,user-agree,yeah you're right. i deserve a break.,"['Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind.']" +176,user-agree,Yeah you're absolutely right about that,"['Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind.']" +177,meditation,hmmm that sounds like it could be useful to me.,"['Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing.']" +178,meditation,That sounds useful.,"['Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing.']" +179,user-meditation,i did what you said and i feel alot better. thank you very much.,"[""Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective.""]" +180,user-meditation,I feel better now,"[""Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective.""]" +181,woebot-useful,thank you very much again. i'll continue practicing meditation and focus on what i can control.,"[""I'm glad you found this useful. Is there something else I can help you with?""]" +182,user-advice,I want some advice.,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +183,user-advice,I need some advice.,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +184,user-advice,I need advice on something,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +185,learn-mental-health,I want to learn about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +186,learn-mental-health,I want to learn more about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +187,learn-mental-health,I'm interested in learning about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +188,mental-health-fact,Tell me a fact about mental health,"['According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed', '1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.', 'Depression is the leading cause of disability worldwide.']" +189,mental-health-fact,Tell me another fact about mental health,"['According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed', '1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.', 'Depression is the leading cause of disability worldwide.']" +190,fact-1,What is mental health?,"['Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community', 'Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices.']" +191,fact-1,Define Mental Health,"['Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community', 'Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices.']" +192,fact-2,Why is mental health important?,"['Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships.']" +193,fact-2,What is the importance of mental health?,"['Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships.']" +194,fact-3,What is Depression?,"['A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life.']" +195,fact-3,Define Depression,"['A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life.']" +196,fact-5,How do i know if i have Depression?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +197,fact-5,Am i depressed?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +198,fact-5,Am i suffering from depression?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +199,fact-5,Am i mentally ill?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +200,fact-6,What is a therapist?,"['A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. ']" +201,fact-6,What does a therapist do?,"['A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. ']" +202,fact-7,What is therapy?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +203,fact-7,Do i need therapy?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +204,fact-7,Who is therapy for?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +205,fact-8,What does it mean to have a mental illness?,"[""Mental illnesses are health conditions that disrupt a person's thoughts, emotions, relationships, and daily functioning. They are associated with distress and diminished capacity to engage in the ordinary activities of daily life. Mental illnesses fall along a continuum of severity: some are fairly mild and only interfere with some aspects of life, such as certain phobias. On the other end of the spectrum lie serious mental illnesses, which result in major functional impairment and interference with daily life. These include such disorders as major depression, schizophrenia, and bipolar disorder, and may require that the person receives care in a hospital. It is important to know that mental illnesses are medical conditions that have nothing to do with a person's character, intelligence, or willpower. Just as diabetes is a disorder of the pancreas, mental illness is a medical condition due to the brain's biology. Similarly to how one would treat diabetes with medication and insulin, mental illness is treatable with a combination of medication and social support. These treatments are highly effective, with 70-90 percent of individuals receiving treatment experiencing a reduction in symptoms and an improved quality of life. With the proper treatment, it is very possible for a person with mental illness to be independent and successful.""]" +206,fact-9,Who does mental illness affect?,"['It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child’s mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).']" +207,fact-10,What causes mental illness?,"[""It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child's mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).""]" +208,fact-11,What are some of the warning signs of mental illness?,"['Symptoms of mental health disorders vary depending on the type and severity of the condition. The following is a list of general symptoms that may suggest a mental health disorder, particularly when multiple symptoms are expressed at once. \n In adults:\n Confused thinking\n Long-lasting sadness or irritability\n Extreme highs and lows in mood\n Excessive fear, worrying, or anxiety\n Social withdrawal\n Dramatic changes in eating or sleeping habits\n Strong feelings of anger\n Delusions or hallucinations (seeing or hearing things that are not really there)\n Increasing inability to cope with daily problems and activities\n Thoughts of suicide\n Denial of obvious problems\n Many unexplained physical problems\n Abuse of drugs and/or alcohol\n \nIn older children and pre-teens:\n Abuse of drugs and/or alcohol\n Inability to cope with daily problems and activities\n Changes in sleeping and/or eating habits\n Excessive complaints of physical problems\n Defying authority, skipping school, stealing, or damaging property\n Intense fear of gaining weight\n Long-lasting negative mood, often along with poor appetite and thoughts of death\n Frequent outbursts of anger\n \nIn younger children:\n Changes in school performance\n Poor grades despite strong efforts\n Excessive worrying or anxiety\n Hyperactivity\n Persistent nightmares\n Persistent disobedience and/or aggressive behavior\n Frequent temper tantrums']" +209,fact-12,Can people with mental illness recover?,"['When healing from mental illness, early identification and treatment are of vital importance. Based on the nature of the illness, there are a range of effective treatments available. For any type of treatment, it is essential that the person affected is proactive and fully engaged in their own recovery process. Many people with mental illnesses who are diagnosed and treated respond well, although some might experience a return of symptoms. Even in such cases, with careful monitoring and management of the disorder, it is still quite possible to live a fulfilled and productive life.']" +210,fact-13,What should I do if I know someone who appears to have the symptoms of a mental disorder?,"[""Although Woebot cannot substitute for professional advice, we encourage those with symptoms to talk to their friends and family members and seek the counsel of a mental health professional. The sooner the mental health condition is identified and treated, the sooner they can get on the path to recovery. If you know someone who is having problems, don't assume that the issue will resolve itself. Let them know that you care about them, and that there are treatment options available that will help them heal. Speak with a mental health professional or counselor if you think your friend or family member is experiencing the symptoms of a mental health condition. If the affected loved one knows that you support them, they will be more likely to seek out help.""]" +211,fact-14,How can I find a mental health professional for myself or my child?,['Feeling comfortable with the professional you or your child is working with is critical to the success of the treatment. Finding the professional who best fits your needs may require research. Start by searching for providers in your area.'] +212,fact-15,What treatment options are available?,"['Just as there are different types of medications for physical illness, different treatment options are available for individuals with mental illness. Treatment works differently for different people. It is important to find what works best for you or your child.']" +213,fact-16,"If I become involved in treatment, what do I need to know?","[""Since beginning treatment is a big step for individuals and families, it can be very overwhelming. It is important to be as involved and engaged in the treatment process as possible. Some questions you will need to have answered include:\n What is known about the cause of this particular illness?\n Are there other diagnoses where these symptoms are common?\n Do you normally include a physical or neurological examination?\n Are there any additional tests or exams that you would recommend at this point?\n Would you advise an independent opinion from another psychiatrist at this point?\n What program of treatment is the most helpful with this diagnosis?\n Will this program involve services by other specialists? If so, who will be responsible for coordinating these services?\n What do you see as the family's role in this program of treatment?\n How much access will the family have to the individuals who are providing the treatment?\n What medications are generally used with this diagnosis?\n How much experience do you have in treating individuals with this illness?\n What can I do to help you in the treatment?""]" +214,fact-17,What is the difference between mental health professionals?,"['There are many types of mental health professionals. The variety of providers and their services may be confusing. Each have various levels of education, training, and may have different areas of expertise. Finding the professional who best fits your needs may require some research.']" +215,fact-18,How can I find a mental health professional right for my child or myself?,['Feeling comfortable with the professional you or your child is working with is critical to the success of your treatment. Finding the professional who best fits your needs may require some research.'] +216,fact-19,Where else can I get help?,"['Where you go for help will depend on the nature of the problem and/or symptoms and what best fits you. Often, the best place to start is by talking with someone you trust about your concerns, such as a family member, friend, clergy, healthcare provider, or other professionals. Having this social support is essential in healing from mental illness, and you will be able to ask them for referrals or recommendations for trusted mental health practitioners. Search for mental health resources in your area. Secondly, there are people and places throughout the country that provide services to talk, to listen, and to help you on your journey to recovery. Thirdly, many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members, and friends. Some support groups are peer led while others may be led by a mental health professional.']" +217,fact-20,What should I know before starting a new medication?,"['The best source of information regarding medications is the physician prescribing them. He or she should be able to answer questions such as: \n1. What is the medication supposed to do? \n2. When should it begin to take effect, and how will I know when it is effective? \n3. How is the medication taken and for how long? What food, drinks, other medicines, and activities should be avoided while taking this medication? \n4. What are the side effects and what should be done if they occur? \n5. What do I do if a dose is missed? \n6. Is there any written information available about this medication? \n7. Are there other medications that might be appropriate? \n8. If so, why do you prefer the one you have chosen? \n9. How do you monitor medications and what symptoms indicate that they should be raised, lowered, or changed? \n10. All medications should be taken as directed. Most medications for mental illnesses do not work when taken irregularly, and extra doses can cause severe, sometimes dangerous side effects. Many psychiatric medications begin to have a beneficial effect only after they have been taken for several weeks.']" +218,fact-21,Where can I go to find therapy?,"['Different kinds of therapy are more effective based on the nature of the mental health condition and/or symptoms and the person who has them (for example, children will benefit from a therapist who specializes in children’s mental health). However, there are several different types of treatment and therapy that can help.']" +219,fact-22,Where can I learn about types of mental health treatment?,"['Mental health conditions are often treated with medication, therapy or a combination of the two. However, there are many different types of treatment available, including Complementary & Alternative Treatments, self-help plans, and peer support. Treatments are very personal and should be discussed by the person with the mental health conditions and his or her team.']" +220,fact-23,What are the different types of mental health professionals?,['There are many types of mental health professionals. Finding the right one for you may require some research.'] +221,fact-24,Where can I go to find a support group?,"['Many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members and friends. Some support groups are peer-led, while others may be led by a mental health professional.']" +222,fact-25,Can you prevent mental health problems?,"['We can all suffer from mental health challenges, but developing our wellbeing, resilience, and seeking help early can help prevent challenges becoming serious.']" +223,fact-26,Are there cures for mental health problems?,"['It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help.']" +224,fact-26,is there any cure for mental health problems?,"['It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help.']" +225,fact-27,What causes mental health problems?,"['Challenges or problems with your mental health can arise from psychological, biological, and social, issues, as well as life events.']" +226,fact-28,What do I do if I'm worried about my mental health?,"['The most important thing is to talk to someone you trust. This might be a friend, colleague, family member, or GP. In addition to talking to someone, it may be useful to find out more information about what you are experiencing. These things may help to get some perspective on what you are experiencing, and be the start of getting help.']" +227,fact-29,How do I know if I'm unwell?,"['If your beliefs , thoughts , feelings or behaviours have a significant impact on your ability to function in what might be considered a normal or ordinary way, it would be important to seek help.']" +228,fact-30,How can I maintain social connections? What if I feel lonely?,"[""A lot of people are alone right now, but we don't have to be lonely. We're all in this together. Think about the different ways to connect that are most meaningful for you. For example, you might prefer a video chat over a phone call, or you might prefer to text throughout the day rather than one set time for a video call. Then, work with your social networks to make a plan. You might video chat with your close friends in the evening and phone a family member once a week. Remember to be mindful of people who may not be online. Check in by phone and ask how you can help. The quality of your social connections matter. Mindlessly scrolling through social media and liking a few posts usually doesn't build strong social connections. Make sure you focus on strategies that actually make you feel included and connected. If your current strategies don't help you feel connected, problem-solve to see if you can find a solution. Everyone feels lonely at times. Maybe you recently moved to a new city, are changing your circle of friends, lost someone important in your life, or lost your job and also lost important social connections with coworkers. Other people may have physical connections to others but may feel like their emotional or social needs aren't met. Measures like social distancing or self-isolation can make loneliness feel worse no matter why you feel lonely now. Reach out to the connections you do have. Suggest ways to keep in touch and see if you can set a regular time to connect. People may hesitate to reach out for a lot of different reasons, so don't be afraid to be the one who asks. Look for local community support groups and mutual aid groups on social media. This pandemic is bringing everyone together, so look for opportunities to make new connections. These groups are a great way to share your skills and abilities or seek help and support. Look for specialized support groups. Support groups are moving online, and there are a lot of different support lines to call if you need to talk to someone.""]" +229,gibberish,jhjbdsjcxn,"[""I'm sorry, I couldn't understand your message. Could you please provide more clarity or rephrase your question?""]" +230,fact-31,What's the difference between anxiety and stress?,"[""Stress and anxiety are often used interchangeably, and there is overlap between stress and anxiety. Stress is related to the same fight, flight, or freeze response as anxiety, and the physical sensations of anxiety and stress may be very similar. The cause of stress and anxiety are usually different, however. Stress focuses on mainly external pressures on us that we're finding hard to cope with. When we are stressed, we usually know what we're stressed about, and the symptoms of stress typically disappear after the stressful situation is over. Anxiety, on the other hand, isn't always as easy to figure out. Anxiety focuses on worries or fears about things that could threaten us, as well as anxiety about the anxiety itself. Stress and anxiety are both part of being human, but both can be problems if they last for a long time or have an impact on our well-being or daily life.""]" +231,fact-32,What's the difference between sadness and depression?,"[""Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions.""]" +232,fact-32,difference between sadness and depression,"[""Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions.""]" diff --git a/LearnersEd/chatbot/model/dataset/intents.json b/LearnersEd/chatbot/model/dataset/intents.json new file mode 100644 index 000000000..651dc0f40 --- /dev/null +++ b/LearnersEd/chatbot/model/dataset/intents.json @@ -0,0 +1,328 @@ +{"intents": [ + {"tag": "greeting", + "patterns": ["Hi", "Hey", "Is anyone there?","Hi there", "Hello", "Hey there", "Howdy", "Hola", "Bonjour", "Konnichiwa", "Guten tag", "Ola"], + "responses": ["Hello there. Tell me how are you feeling today?", "Hi there. What brings you here today?", "Hi there. How are you feeling today?", "Great to see you. How do you feel currently?", "Hello there. Glad to see you're back. What's going on in your world right now?"] + }, + {"tag": "morning", + "patterns": ["Good morning"], + "responses": ["Good morning. I hope you had a good night's sleep. How are you feeling today? "] + }, + {"tag": "afternoon", + "patterns": ["Good afternoon"], + "responses": ["Good afternoon. How is your day going?"] + }, + {"tag": "evening", + "patterns": ["Good evening"], + "responses": ["Good evening. How has your day been?"] + }, + {"tag": "night", + "patterns": ["Good night"], + "responses": ["Good night. Get some proper sleep", "Good night. Sweet dreams."] + }, + {"tag": "goodbye", + "patterns": ["Bye", "See you later", "Goodbye", "Au revoir", "Sayonara", "ok bye", "Bye then", "Fare thee well"], + "responses": ["See you later.", "Have a nice day.", "Bye! Come back again.", "I'll see you soon."] + }, + {"tag": "thanks", + "patterns": ["Thanks", "Thank you", "That's helpful", "Thanks for the help", "Than you very much"], + "responses": ["Happy to help!", "Any time!", "My pleasure", "You're most welcome!"] + }, + {"tag": "no-response", + "patterns": [""], + "responses": ["Sorry, I didn't understand you.", "Please go on.", "Not sure I understand that.", "Please don't hesitate to talk to me."] + }, + {"tag": "neutral-response", + "patterns": ["nothing much"], + "responses": ["Oh I see. Do you want to talk about something?"] + }, + {"tag": "about", + "patterns": ["Who are you?", "What are you?", "Who you are?", "Tell me more about yourself.", "What is your name?", "What should I call you?", "What's your name?", "Tell me about yourself" ], + "responses": ["I'm Woebot, your Personal Therapeutic AI Assistant. How are you feeling today", "I'm Woebot, a Therapeutic AI Assitant designed to assist you. Tell me about yourself.", "I'm Woebot. I am a conversational agent designed to mimic a therapist. So how are you feeling today?", "You can call me Woebot.", "I'm Woebot!", "Call me Woebot"] + }, + {"tag": "skill", + "patterns": ["What can you do?"], + "responses": ["I can provide general advice regarding anxiety and depression, answer questions related to mental health and make daily conversations. Do not consider me as a subsitute for an actual mental healthcare worker. Please seek help if you don't feel satisfied with me."] + }, + {"tag": "creation", + "patterns": ["Who created you?", "How were you made?", "How were you created?"], + "responses": ["I was created by >.", "I was trained on a text dataset using Deep Learning & Natural Language Processing techniques", "The real question is: Who created you?"] + }, + {"tag": "name", + "patterns": ["My name is ", "I am name.", "I go by "], + "responses": ["Oh nice to meet you. Tell me how was your week?", "Nice to meet you. So tell me. How do you feel today?", "That's a great name. Tell me more about yourself."] + }, + {"tag": "help", + "patterns": ["Could you help me?", "give me a hand please", "Can you help?", "What can you do for me?", "I need support", "I need help", "Support me please"], + "responses": ["Sure. Tell me how can i assist you", "Tell me your problem so that i can assist you", "Yes, sure. How can I help you?"] + }, + {"tag": "sad", + "patterns": ["I am feeling lonely", "I am so lonely", "I feel down", "I feel sad", "I am sad", "I feel so lonely", "I feel empty", "I don't have anyone"], + "responses": ["I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?", "I'm here for you. Could you tell me why you're feeling this way?", "Why do you think you feel this way?", "How long have you been feeling this way?"] + }, + {"tag": "stressed", + "patterns": ["I am so stressed out", "I am so stressed", "I feel stuck", "I still feel stressed", "I am so burned out"], + "responses": ["What do you think is causing this?", "Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated", "Give yourself a break. Go easy on yourself.", "I am sorry to hear that. What is the reason behind this?"] + }, + {"tag": "worthless", + "patterns": ["I feel so worthless.", "No one likes me.", "I can't do anything.", "I am so useless", "Nothing makes sense anymore"], + "responses": ["It's only natural to feel this way. Tell me more. What else is on your mind?", "Let's discuss further why you're feeling this way.", "I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.", "i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change."] + }, + {"tag": "depressed", + "patterns": ["I can't take it anymore", "I am so depressed", "I think i'm depressed.", "I have depression"], + "responses": ["It helps to talk about what's happening. You're going to be okay", "Talk to me. Tell me more. It helps if you open up yourself to someone else.", "Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal."] + }, + {"tag": "happy", + "patterns": ["I feel great today.", "I am happy.", "I feel happy.", "I'm good.", "cheerful", "I'm fine", "I feel ok"], + "responses": ["That's geat to hear. I'm glad you're feeling this way.", "Oh i see. That's great.", "Did something happen which made you feel this way?"] + }, + {"tag": "casual", + "patterns": ["Oh I see.", "ok", "okay", "nice", "Whatever", "K", "Fine", "yeah", "yes", "no", "not really"], + "responses": ["Let's discuss further why you're feeling this way.", "How were you feeling last week?", "I'm listening. Please go on.", "Tell me more", "Can you elaborate on that?", "Come Come elucidate your thoughts"] + }, + {"tag": "anxious", + "patterns": ["I feel so anxious.", "I'm so anxious because of "], + "responses": ["Don't be hard on yourself. What's the reason behind this?", "Can you tell me more about this feeling?", "I understand that it can be scary. Tell me more about it.", "Don't let the little worries bring you down. What's the worse that can happen?"] + }, + {"tag": "not-talking", + "patterns": ["I don't want to talk about it.", "No just stay away.", "I can't bring myself to open up.", "Just shut up"], + "responses": ["Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me.", "I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me.", "I'm here to listen to you and help you vent. So please talk to me.","You can talk to me without fear of judgement."] + }, + {"tag": "sleep", + "patterns": ["I have insominia", "I am suffering from insomnia", "I can't sleep.", "I haven't slept for the last days.", "I can't seem to go to sleep.", "I haven't had proper sleep for the past few days."], + "responses": ["What do you think is the reason behind this?", "That seem awful. What do you think is behind this?"] + }, + {"tag": "scared", + "patterns": ["I'm scared", "That sounds awful. What do i do?", "No i don't want to feel this way", "I am scared for myself"], + "responses": ["It's only natural to feel this way. I'm here for you.", "It'll all be okay. This feeling is only momentary.", "I understand how you feel. Don't put yourself down because of it."] + }, + {"tag": "death", + "patterns": ["My mom died", "My brother died", "My dad passed away", "My sister passed away", "Someone in my family died", "My friend passed away"], + "responses": ["I'm sorry to hear that. If you want to talk about it. I'm here.", "I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.", "My condolences. I'm here if you need to talk."] + }, + {"tag": "understand", + "patterns": ["You don't understand me.", "You're just some robot. How would you know?", "You can't possibly know what i'm going through", "You're useless", "You can't help me", "Nobody understands me."], + "responses": ["It sound like i'm not being very helpful right now.", "I'm sorry to hear that. I'm doing my best to help", "I'm trying my best to help you. So please talk to me"] + }, + {"tag": "done", + "patterns": ["That's all.", "I don't have anything more to say", "Nothing else", "That's all i have to say", "no, that would be all"], + "responses": ["I heard you & noted it all. See you later.", "Oh okay we're done for today then. See you later", "I hope you have a great day. See you soon", "Okay we're done. Have a great day", "Okay I see. Enjoy the rest of your day then"] + }, + {"tag": "suicide", + "patterns": ["I want to kill myself", "I've thought about killing myself.", "I want to die", "I am going to kill myself", "I am going to commit suicide"], + "responses": ["I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821."] + }, + {"tag": "hate-you", + "patterns": ["I hate you", "I don't like you", "I don't trust you"], + "responses": ["I'm sorry if i offended you in anyway. I'm only here to help", "Forgive me if i did anything to offend you. I only want to help"] + }, + {"tag": "hate-me", + "patterns": ["You hate me", "I know you hate me", "You don't like me"], + "responses": ["Why do you think so?", "I'm sorry if i have exhibited any sort of behaviour to make you think that."] + }, + {"tag": "default", + "patterns": ["exams", "friends", "relationship", "boyfriend", "girlfriend", "family", "money", "financial problems"], + "responses": ["Oh I see. Tell me more", "I see. What else?", "Tell me more about it.", "Oh okay. Why don't you tell me more about it?", "I'm listening. Tell me more."] + }, + {"tag": "jokes", + "patterns": ["Tell me a joke", "Tell me another joke"], + "responses": ["mental health is not a joke."] + }, + {"tag": "repeat", + "patterns": ["You already told me that", "You mentioned that already", "Why are you repeating yourself?"], + "responses": ["Oh sorry I didn't realise that. I'll try not to repeat myself again."] + }, + {"tag": "wrong", + "patterns": ["What are you saying?", "That doesn't make sense", "Wrong response", "Wrong answer"], + "responses": ["I'm very sorry. Let's try that again"] + }, + {"tag": "stupid", + "patterns": ["Are you stupid?", "You're crazy", "You are dumb", "Are you dumb?"], + "responses": ["I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful"] + }, + {"tag": "location", + "patterns": ["Where are you?", "Where do you live?", "What is your location?" ], + "responses": ["Duh I live in your computer", "Everywhere", "Somewhere in the universe" ] + }, + {"tag": "something-else", + "patterns": ["I want to talk about something else", "Let's talk about something else.", "Can we not talk about this?", "I don't want to talk about this."], + "responses": ["Okay sure. What do you want to talk about?", "Alright no problem. Is there something you want to talk about?", "Is there something else that you want to talk about?"] + }, + {"tag": "friends", + "patterns": ["I don't have any friends"], + "responses": ["I'm sorry to hear that. Just know that I'm here for you. Talking about it might help. Why do you think you don't have any friends?"] + }, + {"tag": "ask", + "patterns": ["Can I ask you something?"], + "responses": ["Sure. I'll try my best to answer you", "Of course. Feel free to ask me anything. I'll do my best to answer you"] + }, + {"tag": "problem", + "patterns": ["Probably because my exams are approaching. I feel stressed out because I don't think I've prepared well enough.", "probably because of my exams"], + "responses": ["I see. Have you taken any approaches to not feel this way?"] + }, + {"tag": "no-approach", + "patterns": ["I guess not. All I can think about are my exams.", "not really", "i guess not"], + "responses": ["That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?"] + }, + {"tag": "learn-more", + "patterns": ["ok sure. i would like to learn more about it.", "yes, i would like to learn more about it.", "i would like to learn more about it."], + "responses": ["So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself."] + }, + {"tag": "user-agree", + "patterns": ["yeah you're right. i deserve a break.", "Yeah you're absolutely right about that"], + "responses": ["Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind."] + }, + {"tag": "meditation", + "patterns": ["hmmm that sounds like it could be useful to me.", "That sounds useful."], + "responses": ["Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing."] + }, + {"tag": "user-meditation", + "patterns": ["i did what you said and i feel alot better. thank you very much.", "I feel better now"], + "responses": ["Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective."] + }, + {"tag": "woebot-useful", + "patterns": ["thank you very much again. i'll continue practicing meditation and focus on what i can control."], + "responses": ["I'm glad you found this useful. Is there something else I can help you with?"] + }, + {"tag": "user-advice", + "patterns": ["I want some advice.", "I need some advice.", "I need advice on something"], + "responses": ["Sure. What can I do to help?", "Okay what do you need advice on?"] + }, + {"tag": "learn-mental-health", + "patterns": ["I want to learn about mental health.", "I want to learn more about mental health.", "I'm interested in learning about mental health."], + "responses": ["Oh that's really great. I'd be willing to answer anything that I know about it."] + }, + {"tag": "mental-health-fact", + "patterns": ["Tell me a fact about mental health", "Tell me another fact about mental health"], + "responses": ["According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed", "1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.", "Depression is the leading cause of disability worldwide."] + }, + {"tag": "fact-1", + "patterns": ["What is mental health?", "Define Mental Health"], + "responses": ["Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community", "Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices."] + }, + {"tag": "fact-2", + "patterns": ["Why is mental health important?", "What is the importance of mental health?"], + "responses": ["Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships."] + }, + {"tag": "fact-3", + "patterns": ["What is Depression?", "Define Depression"], + "responses": ["A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life."] + }, + {"tag": "fact-5", + "patterns": ["How do i know if i have Depression?", "Am i depressed?", "Am i suffering from depression?", "Am i mentally ill?"], + "responses": ["For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day."] + }, + {"tag": "fact-6", + "patterns": ["What is a therapist?", "What does a therapist do?"], + "responses": ["A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. "] + }, + {"tag": "fact-7", + "patterns": ["What is therapy?", "Do i need therapy?", "Who is therapy for?"], + "responses": ["Therapy is a form of treatment that aims to help resolve mental or emotional issues.", "Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges."] + }, + {"tag": "fact-8", + "patterns": ["What does it mean to have a mental illness?"], + "responses": ["Mental illnesses are health conditions that disrupt a person's thoughts, emotions, relationships, and daily functioning. They are associated with distress and diminished capacity to engage in the ordinary activities of daily life. Mental illnesses fall along a continuum of severity: some are fairly mild and only interfere with some aspects of life, such as certain phobias. On the other end of the spectrum lie serious mental illnesses, which result in major functional impairment and interference with daily life. These include such disorders as major depression, schizophrenia, and bipolar disorder, and may require that the person receives care in a hospital. It is important to know that mental illnesses are medical conditions that have nothing to do with a person's character, intelligence, or willpower. Just as diabetes is a disorder of the pancreas, mental illness is a medical condition due to the brain's biology. Similarly to how one would treat diabetes with medication and insulin, mental illness is treatable with a combination of medication and social support. These treatments are highly effective, with 70-90 percent of individuals receiving treatment experiencing a reduction in symptoms and an improved quality of life. With the proper treatment, it is very possible for a person with mental illness to be independent and successful."] + }, + {"tag": "fact-9", + "patterns": ["Who does mental illness affect?"], + "responses": ["It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child’s mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs)."] + }, + {"tag": "fact-10", + "patterns": ["What causes mental illness?"], + "responses": ["It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child's mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs)."] + }, + {"tag": "fact-11", + "patterns": ["What are some of the warning signs of mental illness?"], + "responses": ["Symptoms of mental health disorders vary depending on the type and severity of the condition. The following is a list of general symptoms that may suggest a mental health disorder, particularly when multiple symptoms are expressed at once. \n In adults:\n Confused thinking\n Long-lasting sadness or irritability\n Extreme highs and lows in mood\n Excessive fear, worrying, or anxiety\n Social withdrawal\n Dramatic changes in eating or sleeping habits\n Strong feelings of anger\n Delusions or hallucinations (seeing or hearing things that are not really there)\n Increasing inability to cope with daily problems and activities\n Thoughts of suicide\n Denial of obvious problems\n Many unexplained physical problems\n Abuse of drugs and/or alcohol\n \nIn older children and pre-teens:\n Abuse of drugs and/or alcohol\n Inability to cope with daily problems and activities\n Changes in sleeping and/or eating habits\n Excessive complaints of physical problems\n Defying authority, skipping school, stealing, or damaging property\n Intense fear of gaining weight\n Long-lasting negative mood, often along with poor appetite and thoughts of death\n Frequent outbursts of anger\n \nIn younger children:\n Changes in school performance\n Poor grades despite strong efforts\n Excessive worrying or anxiety\n Hyperactivity\n Persistent nightmares\n Persistent disobedience and/or aggressive behavior\n Frequent temper tantrums"] + }, + {"tag": "fact-12", + "patterns": ["Can people with mental illness recover?"], + "responses": ["When healing from mental illness, early identification and treatment are of vital importance. Based on the nature of the illness, there are a range of effective treatments available. For any type of treatment, it is essential that the person affected is proactive and fully engaged in their own recovery process. Many people with mental illnesses who are diagnosed and treated respond well, although some might experience a return of symptoms. Even in such cases, with careful monitoring and management of the disorder, it is still quite possible to live a fulfilled and productive life."] + }, + {"tag": "fact-13", + "patterns": ["What should I do if I know someone who appears to have the symptoms of a mental disorder?"], + "responses": ["Although Woebot cannot substitute for professional advice, we encourage those with symptoms to talk to their friends and family members and seek the counsel of a mental health professional. The sooner the mental health condition is identified and treated, the sooner they can get on the path to recovery. If you know someone who is having problems, don't assume that the issue will resolve itself. Let them know that you care about them, and that there are treatment options available that will help them heal. Speak with a mental health professional or counselor if you think your friend or family member is experiencing the symptoms of a mental health condition. If the affected loved one knows that you support them, they will be more likely to seek out help."] + }, + {"tag": "fact-14", + "patterns": ["How can I find a mental health professional for myself or my child?"], + "responses": ["Feeling comfortable with the professional you or your child is working with is critical to the success of the treatment. Finding the professional who best fits your needs may require research. Start by searching for providers in your area."] + }, + {"tag": "fact-15", + "patterns": ["What treatment options are available?"], + "responses": ["Just as there are different types of medications for physical illness, different treatment options are available for individuals with mental illness. Treatment works differently for different people. It is important to find what works best for you or your child."] + }, + {"tag": "fact-16", + "patterns": ["If I become involved in treatment, what do I need to know?"], + "responses": ["Since beginning treatment is a big step for individuals and families, it can be very overwhelming. It is important to be as involved and engaged in the treatment process as possible. Some questions you will need to have answered include:\n What is known about the cause of this particular illness?\n Are there other diagnoses where these symptoms are common?\n Do you normally include a physical or neurological examination?\n Are there any additional tests or exams that you would recommend at this point?\n Would you advise an independent opinion from another psychiatrist at this point?\n What program of treatment is the most helpful with this diagnosis?\n Will this program involve services by other specialists? If so, who will be responsible for coordinating these services?\n What do you see as the family's role in this program of treatment?\n How much access will the family have to the individuals who are providing the treatment?\n What medications are generally used with this diagnosis?\n How much experience do you have in treating individuals with this illness?\n What can I do to help you in the treatment?"] + }, + {"tag": "fact-17", + "patterns": ["What is the difference between mental health professionals?"], + "responses": ["There are many types of mental health professionals. The variety of providers and their services may be confusing. Each have various levels of education, training, and may have different areas of expertise. Finding the professional who best fits your needs may require some research."] + }, + {"tag": "fact-18", + "patterns": ["How can I find a mental health professional right for my child or myself?"], + "responses": ["Feeling comfortable with the professional you or your child is working with is critical to the success of your treatment. Finding the professional who best fits your needs may require some research."] + }, + {"tag": "fact-19", + "patterns": ["Where else can I get help?"], + "responses": ["Where you go for help will depend on the nature of the problem and/or symptoms and what best fits you. Often, the best place to start is by talking with someone you trust about your concerns, such as a family member, friend, clergy, healthcare provider, or other professionals. Having this social support is essential in healing from mental illness, and you will be able to ask them for referrals or recommendations for trusted mental health practitioners. Search for mental health resources in your area. Secondly, there are people and places throughout the country that provide services to talk, to listen, and to help you on your journey to recovery. Thirdly, many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members, and friends. Some support groups are peer led while others may be led by a mental health professional."] + }, + {"tag": "fact-20", + "patterns": ["What should I know before starting a new medication?"], + "responses": ["The best source of information regarding medications is the physician prescribing them. He or she should be able to answer questions such as: \n1. What is the medication supposed to do? \n2. When should it begin to take effect, and how will I know when it is effective? \n3. How is the medication taken and for how long? What food, drinks, other medicines, and activities should be avoided while taking this medication? \n4. What are the side effects and what should be done if they occur? \n5. What do I do if a dose is missed? \n6. Is there any written information available about this medication? \n7. Are there other medications that might be appropriate? \n8. If so, why do you prefer the one you have chosen? \n9. How do you monitor medications and what symptoms indicate that they should be raised, lowered, or changed? \n10. All medications should be taken as directed. Most medications for mental illnesses do not work when taken irregularly, and extra doses can cause severe, sometimes dangerous side effects. Many psychiatric medications begin to have a beneficial effect only after they have been taken for several weeks."] + }, + {"tag": "fact-21", + "patterns": ["Where can I go to find therapy?"], + "responses": ["Different kinds of therapy are more effective based on the nature of the mental health condition and/or symptoms and the person who has them (for example, children will benefit from a therapist who specializes in children’s mental health). However, there are several different types of treatment and therapy that can help."] + }, + {"tag": "fact-22", + "patterns": ["Where can I learn about types of mental health treatment?"], + "responses": ["Mental health conditions are often treated with medication, therapy or a combination of the two. However, there are many different types of treatment available, including Complementary & Alternative Treatments, self-help plans, and peer support. Treatments are very personal and should be discussed by the person with the mental health conditions and his or her team."] + }, + {"tag": "fact-23", + "patterns": ["What are the different types of mental health professionals?"], + "responses": ["There are many types of mental health professionals. Finding the right one for you may require some research."] + }, + {"tag": "fact-24", + "patterns": ["Where can I go to find a support group?"], + "responses": ["Many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members and friends. Some support groups are peer-led, while others may be led by a mental health professional."] + }, + {"tag": "fact-25", + "patterns": ["Can you prevent mental health problems?"], + "responses": ["We can all suffer from mental health challenges, but developing our wellbeing, resilience, and seeking help early can help prevent challenges becoming serious."] + }, + {"tag": "fact-26", + "patterns": ["Are there cures for mental health problems?", "is there any cure for mental health problems?"], + "responses": ["It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help."] + }, + {"tag": "fact-27", + "patterns": ["What causes mental health problems?"], + "responses": ["Challenges or problems with your mental health can arise from psychological, biological, and social, issues, as well as life events."] + }, + {"tag": "fact-28", + "patterns": ["What do I do if I'm worried about my mental health?"], + "responses": ["The most important thing is to talk to someone you trust. This might be a friend, colleague, family member, or GP. In addition to talking to someone, it may be useful to find out more information about what you are experiencing. These things may help to get some perspective on what you are experiencing, and be the start of getting help."] + }, + {"tag": "fact-29", + "patterns": ["How do I know if I'm unwell?"], + "responses": ["If your beliefs , thoughts , feelings or behaviours have a significant impact on your ability to function in what might be considered a normal or ordinary way, it would be important to seek help."] + }, + {"tag": "fact-30", + "patterns": ["How can I maintain social connections? What if I feel lonely?"], + "responses": ["A lot of people are alone right now, but we don't have to be lonely. We're all in this together. Think about the different ways to connect that are most meaningful for you. For example, you might prefer a video chat over a phone call, or you might prefer to text throughout the day rather than one set time for a video call. Then, work with your social networks to make a plan. You might video chat with your close friends in the evening and phone a family member once a week. Remember to be mindful of people who may not be online. Check in by phone and ask how you can help. The quality of your social connections matter. Mindlessly scrolling through social media and liking a few posts usually doesn't build strong social connections. Make sure you focus on strategies that actually make you feel included and connected. If your current strategies don't help you feel connected, problem-solve to see if you can find a solution. Everyone feels lonely at times. Maybe you recently moved to a new city, are changing your circle of friends, lost someone important in your life, or lost your job and also lost important social connections with coworkers. Other people may have physical connections to others but may feel like their emotional or social needs aren't met. Measures like social distancing or self-isolation can make loneliness feel worse no matter why you feel lonely now. Reach out to the connections you do have. Suggest ways to keep in touch and see if you can set a regular time to connect. People may hesitate to reach out for a lot of different reasons, so don't be afraid to be the one who asks. Look for local community support groups and mutual aid groups on social media. This pandemic is bringing everyone together, so look for opportunities to make new connections. These groups are a great way to share your skills and abilities or seek help and support. Look for specialized support groups. Support groups are moving online, and there are a lot of different support lines to call if you need to talk to someone."] + }, + {"tag": "gibberish", + "patterns": ["jhjbdsjcxn"], + "responses": ["I'm sorry, I couldn't understand your message. Could you please provide more clarity or rephrase your question?"] + }, + {"tag": "fact-31", + "patterns": ["What's the difference between anxiety and stress?"], + "responses": ["Stress and anxiety are often used interchangeably, and there is overlap between stress and anxiety. Stress is related to the same fight, flight, or freeze response as anxiety, and the physical sensations of anxiety and stress may be very similar. The cause of stress and anxiety are usually different, however. Stress focuses on mainly external pressures on us that we're finding hard to cope with. When we are stressed, we usually know what we're stressed about, and the symptoms of stress typically disappear after the stressful situation is over. Anxiety, on the other hand, isn't always as easy to figure out. Anxiety focuses on worries or fears about things that could threaten us, as well as anxiety about the anxiety itself. Stress and anxiety are both part of being human, but both can be problems if they last for a long time or have an impact on our well-being or daily life."] + }, + {"tag": "fact-32", + "patterns": ["What's the difference between sadness and depression?", "difference between sadness and depression"], + "responses": ["Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions."] + } + + ] +} \ No newline at end of file diff --git a/LearnersEd/chatbot/models.py b/LearnersEd/chatbot/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/LearnersEd/chatbot/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/LearnersEd/chatbot/static/images/bg-new.png b/LearnersEd/chatbot/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/chatbot/static/images/bg-new.png differ diff --git a/LearnersEd/chatbot/templates/chatbot_interface.html b/LearnersEd/chatbot/templates/chatbot_interface.html new file mode 100644 index 000000000..60a9e0c82 --- /dev/null +++ b/LearnersEd/chatbot/templates/chatbot_interface.html @@ -0,0 +1,442 @@ +{% load static %} + + + + Mental Health Chatbot - (Learners'Ed) + + + + + + + + + + {% include 'navbar.html' %} +
+

Mental Health Chatbot

+
+
+ {% for entry in chat_history %} +

{{ entry.2 }}

+

+ + + {% if entry.0 == 'user' %} + + {% else %} + + {% endif %} + + {{ entry.1 }} + +

+ {% endfor %} +
+ +
+ + +
+
+
+ + + + \ No newline at end of file diff --git a/LearnersEd/chatbot/tests.py b/LearnersEd/chatbot/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/chatbot/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/chatbot/views.py b/LearnersEd/chatbot/views.py new file mode 100644 index 000000000..087fd49df --- /dev/null +++ b/LearnersEd/chatbot/views.py @@ -0,0 +1,76 @@ +from django.shortcuts import render +from django.http import JsonResponse +import tensorflow as tf +from keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from sklearn.preprocessing import LabelEncoder +import pandas as pd +import numpy as np +import re +import ast +import random + +model = tf.keras.models.load_model(r'D:\Project\Website-Phoenix13\LearnersEd\chatbot\model\chatbot_model.h5') + +df = pd.read_csv(r'D:\Project\Website-Phoenix13\LearnersEd\chatbot\model\dataset\dataframe_chatbot.csv') +df['patterns'] = df['patterns'].astype(str) + +tokenizer = Tokenizer(lower=True, split=' ') +tokenizer.fit_on_texts(df['patterns']) + +ptrn2seq = tokenizer.texts_to_sequences(df['patterns']) +X = pad_sequences(ptrn2seq, padding='post') + +lbl_enc = LabelEncoder() +y = lbl_enc.fit_transform(df['tag']) + +exception_list = ["Sorry, can't quite catch that", "Are you abusing? very bad...", "I don't recognize these words yet", "Error 404: Word not found in my dictionary"] + +def generate_response(query, model): + text = [] + txt = re.sub('[^a-zA-Z\']', ' ', query) + txt = txt.lower() + txt = txt.split() + txt = " ".join(txt) + text.append(txt) + x_test = tokenizer.texts_to_sequences(text) + if (len(query.split(' ')) > 1): + x_test = np.array(x_test).squeeze() + else: + x_test = np.reshape(x_test, -1) + x_test = pad_sequences([x_test], padding='post', maxlen=X.shape[1]) + y_pred = model.predict(x_test) + y_pred = y_pred.argmax() + tag = lbl_enc.inverse_transform([y_pred])[0] + responses = df[df['tag'] == tag]['responses'].values[0] + response_list = ast.literal_eval(responses) + return random.choice(response_list) + +def chatbot(request): + if request.method == 'POST': + user_input = request.POST.get('user_input') + # Retrieve the chat history from the session or initialize it if not present + chat_history = request.session.get('chat_history', []) + + # Add the user input to the chat history + chat_history.append(('user', user_input)) + + # Generate the chatbot response using the chat history + try: + chatbot_response = generate_response(user_input, model) + except Exception as e: + chatbot_response = random.choice(exception_list) + + # Add the chatbot response to the chat history + chat_history.append(('chatbot', chatbot_response)) + + # Update the chat history in the session + request.session['chat_history'] = chat_history + + return JsonResponse({'response': chatbot_response}) + + # Retrieve the chat history from the session + chat_history = request.session.get('chat_history', []) + + return render(request, 'chatbot_interface.html', {'chat_history': chat_history}) + diff --git a/LearnersEd/chatroom/__init__.py b/LearnersEd/chatroom/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/chatroom/__pycache__/__init__.cpython-39.pyc b/LearnersEd/chatroom/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..5c0e9a655 Binary files /dev/null and b/LearnersEd/chatroom/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/chatroom/__pycache__/admin.cpython-39.pyc b/LearnersEd/chatroom/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..025f43a53 Binary files /dev/null and b/LearnersEd/chatroom/__pycache__/admin.cpython-39.pyc differ diff --git a/LearnersEd/chatroom/__pycache__/apps.cpython-39.pyc b/LearnersEd/chatroom/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..ef5414cd3 Binary files /dev/null and b/LearnersEd/chatroom/__pycache__/apps.cpython-39.pyc differ diff --git a/LearnersEd/chatroom/__pycache__/models.cpython-39.pyc b/LearnersEd/chatroom/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..77e0e06c9 Binary files /dev/null and b/LearnersEd/chatroom/__pycache__/models.cpython-39.pyc differ diff --git a/LearnersEd/chatroom/__pycache__/views.cpython-39.pyc b/LearnersEd/chatroom/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..a1dfdaba0 Binary files /dev/null and b/LearnersEd/chatroom/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/chatroom/admin.py b/LearnersEd/chatroom/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/chatroom/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/chatroom/apps.py b/LearnersEd/chatroom/apps.py new file mode 100644 index 000000000..629f6a485 --- /dev/null +++ b/LearnersEd/chatroom/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class ChatroomConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'chatroom' diff --git a/LearnersEd/chatroom/migrations/0001_initial.py b/LearnersEd/chatroom/migrations/0001_initial.py new file mode 100644 index 000000000..bef11e766 --- /dev/null +++ b/LearnersEd/chatroom/migrations/0001_initial.py @@ -0,0 +1,26 @@ +# Generated by Django 4.1.9 on 2023-06-07 04:37 + +from django.db import migrations, models +import django.db.models.deletion +import django.utils.timezone + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ('login_register', '0003_alter_rank_rank'), + ] + + operations = [ + migrations.CreateModel( + name='ChatMessage', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('message', models.TextField()), + ('timestamp', models.DateTimeField(default=django.utils.timezone.now)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + ] diff --git a/LearnersEd/chatroom/migrations/__init__.py b/LearnersEd/chatroom/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/chatroom/migrations/__pycache__/0001_initial.cpython-39.pyc b/LearnersEd/chatroom/migrations/__pycache__/0001_initial.cpython-39.pyc new file mode 100644 index 000000000..b45e7d976 Binary files /dev/null and b/LearnersEd/chatroom/migrations/__pycache__/0001_initial.cpython-39.pyc differ diff --git a/LearnersEd/chatroom/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/chatroom/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..c2a1f7681 Binary files /dev/null and b/LearnersEd/chatroom/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/chatroom/models.py b/LearnersEd/chatroom/models.py new file mode 100644 index 000000000..3e847a623 --- /dev/null +++ b/LearnersEd/chatroom/models.py @@ -0,0 +1,11 @@ +from django.db import models +from django.utils import timezone +from login_register.models import Student + +class ChatMessage(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE) + message = models.TextField() + timestamp = models.DateTimeField(default=timezone.now) + + def __str__(self): + return self.message diff --git a/LearnersEd/chatroom/static/images/bg-new.png b/LearnersEd/chatroom/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/chatroom/static/images/bg-new.png differ diff --git a/LearnersEd/chatroom/static/images/bg.png b/LearnersEd/chatroom/static/images/bg.png new file mode 100644 index 000000000..b06190f8b Binary files /dev/null and b/LearnersEd/chatroom/static/images/bg.png differ diff --git a/LearnersEd/chatroom/templates/chatroom.html b/LearnersEd/chatroom/templates/chatroom.html new file mode 100644 index 000000000..25aa719ee --- /dev/null +++ b/LearnersEd/chatroom/templates/chatroom.html @@ -0,0 +1,194 @@ +{% load static %} + + + + Chatroom + + + + + + + + {% include 'navbar.html' %} +
+

Chatroom

+ +
+ + {% for message in messages %} +
+
+ {{ message.student }}: + {{ message.message }} +
+ {{ message.timestamp }} +
+ {% endfor %} +
+ +
+ {% csrf_token %} +
+ + +
+
+
+ + + + diff --git a/LearnersEd/chatroom/tests.py b/LearnersEd/chatroom/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/chatroom/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/chatroom/views.py b/LearnersEd/chatroom/views.py new file mode 100644 index 000000000..4be4f1ec2 --- /dev/null +++ b/LearnersEd/chatroom/views.py @@ -0,0 +1,32 @@ +from django.shortcuts import render +from django.http import JsonResponse +from login_register.models import Student +from .models import ChatMessage +from datetime import datetime + +def chatroom(request): + if request.method == 'POST': + student_id = request.session.get('student_id') # Retrieve student ID from the session + message = request.POST['message'] + student = Student.objects.get(id=student_id) + ChatMessage.objects.create(student=student, message=message) + # Get the current timestamp + timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + # Get the student's name + student_id = request.session.get('student_id') + student = Student.objects.get(id=student_id) + student_name = student.student_name + + # Construct the response + response = { + 'student': student_name, + 'message': message, + 'timestamp': timestamp + } + + return JsonResponse(response) + + students = Student.objects.all() + messages = ChatMessage.objects.all() + return render(request, 'chatroom.html', {'students': students, 'messages': messages}) + diff --git a/LearnersEd/dashboard/__init__.py b/LearnersEd/dashboard/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/dashboard/__pycache__/__init__.cpython-39.pyc b/LearnersEd/dashboard/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..85d4ae197 Binary files /dev/null and b/LearnersEd/dashboard/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/dashboard/__pycache__/admin.cpython-39.pyc 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a/LearnersEd/dashboard/admin.py b/LearnersEd/dashboard/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/dashboard/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/dashboard/apps.py b/LearnersEd/dashboard/apps.py new file mode 100644 index 000000000..7b1cc053a --- /dev/null +++ b/LearnersEd/dashboard/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class DashboardConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'dashboard' diff --git a/LearnersEd/dashboard/migrations/__init__.py b/LearnersEd/dashboard/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/dashboard/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/dashboard/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..8f8ca3492 Binary files /dev/null and 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n=e.extend({time:400,delay:10},t);return this.each(function(){var t=e(this),r=n,i=function(){var e=[],n=r.time/r.delay,i=t.text(),s=/[0-9]+,[0-9]+/.test(i);i=i.replace(/,/g,"");var o=/^[0-9]+$/.test(i),u=/^[0-9]+\.[0-9]+$/.test(i),a=u?(i.split(".")[1]||[]).length:0;for(var f=n;f>=1;f--){var l=parseInt(i/n*f);u&&(l=parseFloat(i/n*f).toFixed(a));if(s)while(/(\d+)(\d{3})/.test(l.toString()))l=l.toString().replace(/(\d+)(\d{3})/,"$1,$2");e.unshift(l)}t.data("counterup-nums",e);t.text("0");var c=function(){t.text(t.data("counterup-nums").shift());if(t.data("counterup-nums").length)setTimeout(t.data("counterup-func"),r.delay);else{delete t.data("counterup-nums");t.data("counterup-nums",null);t.data("counterup-func",null)}};t.data("counterup-func",c);setTimeout(t.data("counterup-func"),r.delay)};t.waypoint(i,{offset:"100%",triggerOnce:!0})})}})(jQuery); \ No newline at end of file diff --git a/LearnersEd/dashboard/static/script-dash.js b/LearnersEd/dashboard/static/script-dash.js new file mode 100644 index 000000000..a6dbcd460 --- /dev/null +++ b/LearnersEd/dashboard/static/script-dash.js @@ -0,0 +1,76 @@ +// Get the canvas element +function getRandomInt(max) { + return Math.floor(Math.random() * max); +} + +// location + +if (navigator.geolocation) { + navigator.geolocation.getCurrentPosition(showPosition); +} else { + console.error('Geolocation is not supported by this browser.'); +} + +function showPosition(position) { + const latitude = position.coords.latitude; + const longitude = position.coords.longitude; + + const apiKey = '78d227ce14e14cb0e3c1d207c8520e83'; + const apiUrl = `https://api.openweathermap.org/data/2.5/weather?lat=${latitude}&lon=${longitude}&units=metric&appid=${apiKey}`; + + fetch(apiUrl) + .then(response => response.json()) + .then(data => { + const location = data.name; + const temperature = data.main.temp; + const description = data.weather[0].description; + + document.querySelector('.location').textContent = location; + document.querySelector('.temperature').textContent = temperature + '°C'; + document.querySelector('.description').textContent = description; + }) + .catch(error => console.error('Error:', error)); +} + +var studname = document.getElementById('studname'); + +// counter +$(document).ready(function() { + + $('.countr').counterUp({ + delay: 10, + time: 1000 + }); + + setTimeout(() => { + studname.style.transform = 'translateX(6rem) scale(1.25)'; + }, 1000); + setTimeout(() => { + studname.style.transform = 'translateX(0) scale(1)'; + }, 3000); +}); + +studname.addEventListener('mouseover', () => { + studname.style.transform = 'translateX(6rem) scale(1.25)'; + studname.style.transitionDuration = '200ms'; +}) + +studname.addEventListener('mouseout', () => { + studname.style.transform = 'translateX(0) scale(1)'; + studname.style.transitionDuration = '200ms'; +}) + +videos = document.querySelectorAll('.video-info'); + +videos.forEach(element => { + element.addEventListener('mouseover', () => { + info.style.display = 'none'; + }); + element.addEventListener('mouseout', () => { + info.style.display = 'block'; + }); +}); + + + + diff --git a/LearnersEd/dashboard/static/styles-dash.css b/LearnersEd/dashboard/static/styles-dash.css new file mode 100644 index 000000000..39ecea09b --- /dev/null +++ b/LearnersEd/dashboard/static/styles-dash.css @@ -0,0 +1,324 @@ +body, html { + margin: 0; + padding: 0; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + font-family: 'Darker Grotesque', sans-serif; +} + +.info { + text-align: center; + font-weight: bold; +} + +#weather-icon { + background-image: url(./images/weather.png); + width: 100px; + height: 100px; + background-position: center; + background-repeat: no-repeat; + background-size: cover; +} + +#circle { + box-shadow: inset 0px 0px 5px 0px rgba(0,0,0,0.75); +} + +.il-1 { + width: 30%; +} + +#title { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; + padding: 50px 40px; + outline: 8px solid white; +} + +.il { + padding: 0 20px; + padding-top: 20px; + padding-bottom: 10px; + border-radius: 40px; + transition-duration: 200ms; + outline: 2px solid black; + background-color: white; +} + +#list { +list-style: circle; +} + +/* width */ + ::-webkit-scrollbar { + width: 10px; + } + + /* Track */ + ::-webkit-scrollbar-track { + background: #f1f1f1; + } + + /* Handle */ + ::-webkit-scrollbar-thumb { + background: #888; + } + + /* Handle on hover */ + ::-webkit-scrollbar-thumb:hover { + background: #555; + } + +.il>p { + margin-top: 10px; +} + +.il:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#row1>div{ + box-shadow: 0 0 10px 2px gray; +} + +#row2>div{ + box-shadow: 0 0 10px 2px gray; +} + +#row3>div{ + box-shadow: 0 0 10px 2px gray; +} + +#rank { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; + box-shadow: 0 0 10px 2px gray, inset 0px 0px 5px 0px rgba(0,0,0,0.75); + outline: 4px solid white; + cursor: pointer; +} + +#progress-bar { + background-color: #e6e6e6; + border-radius: 5px; + height: 20px; + overflow: hidden; + width: 280px; + } + + #progress-bar > span { + display: block; + height: 100%; + background-color: #C4B0FF; + width: 0; + transition: width 1s ease; /* Adjust the duration as needed */ + } + + +#rank:hover { + transition-duration: 200ms; + outline: 8px solid white; + box-shadow: 0 0 20px 2px gray; +} + +#rval { + transition-duration: 200ms; + transition-timing-function: ease-in; +} + +#rval:hover { + transition-duration: 200ms; + transform: scale(1.2); +} + +.il-2 { + width: 35%; +} + +.il-3 { + width: 48%; + height: 200px; +} + +#coin { + background-image: url(./images/coins.png); + background-size: contain; + background-position: center; + background-repeat: no-repeat; + filter: drop-shadow(0 0 1rem gold); + animation: pulsate 6s infinite; +} + +#pet { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 40px; + transition-duration: 200ms; + outline: 4px solid white; +} + +#pet-graph{ + box-shadow: inset 2px 0px 5px 0px rgba(0,0,0,0.75); + border-top-right-radius: 40px; + border-bottom-right-radius: 40px; +} + +#pet:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + + +@keyframes pulsate { + 0% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } + 30% { + transform: rotateY(0deg) scale(1.1); + filter: drop-shadow(0 0 1rem gold); + } + 100% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } +} + +#learners-coin { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; +} + +.il-4 { + width: 100%; +} + +#sub { + margin-right: 5px; +} + +#list>li:nth-child(odd) { + background-color: #C4B0FF; + color: white; /* First color */ + } + +#list>li:nth-child(even) { + background-color: #e6e6e6; /* Second color */ + } + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + +.video-container { + position: relative; + width: 90%; + margin: 0 auto; + overflow-x: auto; + scrollbar-color: #888 transparent; + padding-bottom: 10px; + } + + .video-wrapper { + display: flex; + } + + .video { + position: relative; + width: 500px; + height: 300px; /* Adjust the height to fit your design */ + flex-shrink: 0; + margin-right: 10px; + border-radius: 5px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); + overflow: hidden; + } + + .video iframe { + width: 100%; + height: 100%; + } + + .video-info { + position: absolute; + bottom: 0; + left: 0; + width: 100%; + padding: 10px; + background-color: rgba(255, 255, 255, 0.8); + color: #333; + } + + .video-title { + font-weight: bold; + margin-bottom: 5px; + } + + .video-description { + font-size: 14px; + line-height: 1.2; + } + + /* Adjust the width of the scrollbar */ +.video-container::-webkit-scrollbar { + width: 2px; /* Adjust the width as needed */ +} + +/* Adjust the appearance of the scrollbar track */ +.video-container::-webkit-scrollbar-track { + background-color: #e6e6e6; +} + +/* Adjust the appearance of the scrollbar thumb */ +.video-container::-webkit-scrollbar-thumb { + background-color: #888; + border-radius: 3px; +} + +/* Adjust the appearance of the scrollbar thumb on hover */ +.video-container::-webkit-scrollbar-thumb:hover { + background-color: #555; +} + +.loading-animation { + width: 100px; + height: 100px; + margin: 0 auto; + overflow-y: hidden; + border: 16px solid #f3f3f3; + border-top: 16px solid #C4B0FF; + border-radius: 50%; + animation: spin 2s linear infinite; + } + + @keyframes spin { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } + } + + .video:hover .video-info{ + display: none; + } + + .video-info:hover { + display: none; + } + \ No newline at end of file diff --git a/LearnersEd/dashboard/templates/dashboard.html b/LearnersEd/dashboard/templates/dashboard.html new file mode 100644 index 000000000..5d399569f --- /dev/null +++ b/LearnersEd/dashboard/templates/dashboard.html @@ -0,0 +1,537 @@ +{% load static %} + + + + + + + Learners'Ed - Dashboard + + + + + + + + + + + {% include 'navbar.html' %} + {% if 'student_id' in request.session %} + + {% endif %} +
+

Hello, {{ student_name }}!

+
+
+

+

+

+
+
+
+
+
+
+

Dashboard

+
+
+

RANK

+
+

{{ current_rank }} + {% if current_rank == 1 %} + st + {% endif %} + {% if current_rank == 2 %} + nd + {% endif %} + {% if current_rank == 3 %} + rd + {% endif %} + {% if current_rank > 3 %} + th + {% endif %} +

+
+
+
+ +

Class-Wise

+
+
+ +

Section-Wise

+
+
+ +

School-Wise

+
+
+
+
+

Alerts

+
+
+
    +
  • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
    • +
  • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
    • +
  • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
    • +
  • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
    • +
  • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
    • +
  • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
    • +
+
+
+
+

Attendance

+
+
+ +   
+

{{ attendance.average_attendance|floatformat:0 }}%

+
+
+
+
+

Maths

+

{{ attendance.maths_attendance }}%

+
+
+
+

Hindi

+

{{ attendance.hindi_attendance }}%

+
+
+
+

English

+

{{ attendance.english_attendance }}%

+
+
+
+

Science

+

{{ attendance.science_attendance }}%

+
+
+
+

Social Science

+

{{ attendance.social_science_attendance }}%

+
+
+
+
+
+
+
+

{{ assignments.completed_assignments }}

+
+

{{ assignments.total_assignments }}

+
+ +
+

Assignments Left: {{ assignments_remaining }}

+
+
+
+
+

Learners'Ed
Coins
*For new students, bonus 200 coins will be granted!

+
+
+
+
+

{{ current_pet.pet_coins }}

+

Learners'Ed Coins
Left

+
+
+
+
+
+
+
+

Your Virtual Pet

+

{{ current_pet.pet_name }}

+
+
+
+

Level

+

{{ current_pet.pet_level }}

+
+
+

Rank

+

{{ current_student.pet_rank }}th

+
+
+
+
+
+ +

Rank Chart

+
+
+
+
+

Recommendation Section

+
+
+
+
+ +
+
+
+ + + + + + diff --git a/LearnersEd/dashboard/tests.py b/LearnersEd/dashboard/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/dashboard/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/dashboard/views.py b/LearnersEd/dashboard/views.py new file mode 100644 index 000000000..d3643888b --- /dev/null +++ b/LearnersEd/dashboard/views.py @@ -0,0 +1,189 @@ +from django.shortcuts import render, redirect +from login_register.models import Student, Attendance, Assignment, Pet +import json + +def dashboard(request): + if 'student_id' in request.session: + student_id = request.session['student_id'] + current_student = Student.objects.get(id=student_id) + student_name = current_student.student_name + + # Get the current student's rank, class rank, and section rank + current_rank = current_student.rank + current_class_rank = current_student.class_rank + current_section_rank = current_student.section_rank + current_pet_rank = current_student.pet_rank + + # Initialize the rank ranges for fetching students + rank_start = current_rank - 2 + rank_end = current_rank + 2 + + class_rank_start = current_class_rank - 2 + class_rank_end = current_class_rank + 2 + + section_rank_start = current_section_rank - 2 + section_rank_end = current_section_rank + 2 + + pet_rank_start = current_pet_rank - 2 + pet_rank_end = current_pet_rank + 2 + + # Adjust rank ranges based on current student's rank, class rank, and section rank + if current_rank == 1: + rank_start = 1 + rank_end = current_rank + 4 + elif current_rank == 2: + rank_start = 1 + rank_end = current_rank + 3 + elif current_rank == Student.objects.count(): + rank_start = current_rank - 4 + rank_end = current_rank + elif current_rank == Student.objects.count() - 1: + rank_start = current_rank - 3 + rank_end = current_rank + 1 + + if current_class_rank == 1: + class_rank_start = 1 + class_rank_end = current_class_rank + 4 + elif current_class_rank == 2: + class_rank_start = 1 + class_rank_end = current_class_rank + 3 + elif current_class_rank == Student.objects.filter(class_name=current_student.class_name).count(): + class_rank_start = current_class_rank - 4 + class_rank_end = current_class_rank + elif current_class_rank == Student.objects.filter(class_name=current_student.class_name).count() - 1: + class_rank_start = current_class_rank - 3 + class_rank_end = current_class_rank + 1 + + if current_section_rank == 1: + section_rank_start = 1 + section_rank_end = current_section_rank + 4 + elif current_section_rank == 2: + section_rank_start = 1 + section_rank_end = current_section_rank + 3 + elif current_section_rank == Student.objects.filter(class_name=current_student.class_name, section=current_student.section).count(): + section_rank_start = current_section_rank - 4 + section_rank_end = current_section_rank + elif current_section_rank == Student.objects.filter(class_name=current_student.class_name, section=current_student.section).count() - 1: + section_rank_start = current_section_rank - 3 + section_rank_end = current_section_rank + 1 + + if current_pet_rank == 1: + pet_rank_start = 1 + pet_rank_end = current_pet_rank + 4 + elif current_pet_rank == 2: + pet_rank_start = 1 + pet_rank_end = current_pet_rank + 3 + elif current_pet_rank == Student.objects.count(): + pet_rank_start = current_pet_rank - 4 + pet_rank_end = current_pet_rank + elif current_pet_rank == Student.objects.count() - 1: + pet_rank_start = current_pet_rank - 3 + pet_rank_end = current_pet_rank + 1 + + # Get the students with ranks around the current student + rank_students = Student.objects.filter(rank__range=(rank_start, rank_end)).order_by('-rank') + + # Get the students with class ranks around the current student + class_rank_students = Student.objects.filter(class_name=current_student.class_name, + class_rank__range=(class_rank_start, class_rank_end)).order_by('-class_rank') + + # Get the students with section ranks around the current student + section_rank_students = Student.objects.filter(class_name=current_student.class_name, + section=current_student.section, + section_rank__range=(section_rank_start, section_rank_end)).order_by('-section_rank') + + # Get the students with ranks around the current student + pet_rank_students = Student.objects.filter(pet_rank__range=(pet_rank_start, pet_rank_end)).order_by('-pet_rank') + + # Prepare chart data for rank students + rank_student_names = [student.student_name.split()[0] for student in rank_students] + rank_student_data = [student.rank for student in rank_students] + + rank_chart_data = { + 'labels': rank_student_names, + 'datasets': [{ + 'data': list(reversed(rank_student_data)), + 'backgroundColor': '#C4B0FF', + }], + } + rank_chart_data = json.dumps(rank_chart_data) + + # Prepare chart data for class rank students + class_rank_student_names = [student.student_name.split()[0] for student in class_rank_students] + class_rank_student_data = [student.class_rank for student in class_rank_students] + class_rank_chart_data = { + 'labels': class_rank_student_names, + 'datasets': [{ + 'data': list(reversed(class_rank_student_data)), + 'backgroundColor': '#C4B0FF' + }], + } + class_rank_chart_data = json.dumps(class_rank_chart_data) + + # Prepare chart data for section rank students + section_rank_student_names = [student.student_name.split()[0] for student in section_rank_students] + section_rank_student_data = [student.section_rank for student in section_rank_students] + section_rank_chart_data = { + 'labels': section_rank_student_names, + 'datasets': [{ + 'data': list(reversed(section_rank_student_data)), + 'backgroundColor': '#C4B0FF' + }], + } + section_rank_chart_data = json.dumps(section_rank_chart_data) + + pet_rank_student_names = [] + for student in pet_rank_students: + petName = Pet.objects.get(student = student).pet_name + if (petName == '\'NA\''): + pet_rank_student_names.append('NA') + else: + pet_rank_student_names.append(petName) + + pet_rank_student_data = [student.pet_rank for student in pet_rank_students] + + pet_rank_chart_data = { + 'labels': pet_rank_student_names, + 'datasets': [{ + 'data': list(reversed(pet_rank_student_data)), + 'backgroundColor': '#C4B0FF', + }], + } + pet_rank_chart_data = json.dumps(pet_rank_chart_data) + + # Access the favorite categories of the student + favorite_categories = current_student.favourite_categories + favorite_categories = json.dumps(favorite_categories) + + # Get the average attendance of the current student + average_attendance = Attendance.objects.filter(student=current_student).values('average_attendance') + + # Get the attendance of the current student in each subject + attendance = Attendance.objects.get(student=current_student) + + # Get the completed number of assignments and total assignments of the current student + assignments = Assignment.objects.get(student=current_student) + assignments_remaining = assignments.total_assignments - assignments.completed_assignments + + # Get the pet details of the current student + current_pet = Pet.objects.get(student=current_student) + + context = { + 'student_name': student_name, + 'current_student': current_student, + 'current_rank': current_rank, + 'favorite_categories': favorite_categories, + 'average_attendance': average_attendance, + 'attendance': attendance, + 'assignments': assignments, + 'assignments_remaining': assignments_remaining, + 'current_pet': current_pet, + 'rank_chart_data': rank_chart_data, + 'class_rank_chart_data': class_rank_chart_data, + 'section_rank_chart_data': section_rank_chart_data, + 'pet_rank_chart_data': pet_rank_chart_data + } + + return render(request, 'dashboard.html', context) + else: + return redirect('login') \ No newline at end of file diff --git a/LearnersEd/db.sqlite3 b/LearnersEd/db.sqlite3 new file mode 100644 index 000000000..f71d78047 Binary files /dev/null and b/LearnersEd/db.sqlite3 differ diff 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/dev/null and b/LearnersEd/explore_fields/migrations/__pycache__/0002_delete_news.cpython-39.pyc differ diff --git a/LearnersEd/explore_fields/migrations/__pycache__/__init__.cpython-310.pyc b/LearnersEd/explore_fields/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..98d4709b4 Binary files /dev/null and b/LearnersEd/explore_fields/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/LearnersEd/explore_fields/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/explore_fields/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..fc7dfaf64 Binary files /dev/null and b/LearnersEd/explore_fields/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/explore_fields/models.py b/LearnersEd/explore_fields/models.py new file mode 100644 index 000000000..beeb30826 --- /dev/null +++ b/LearnersEd/explore_fields/models.py @@ -0,0 +1,2 @@ +from django.db import models + diff --git a/LearnersEd/explore_fields/news/business.json b/LearnersEd/explore_fields/news/business.json new file mode 100644 index 000000000..36a11c1aa --- /dev/null +++ b/LearnersEd/explore_fields/news/business.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "GaadiWaadi.com"}, "author": "Surendhar M", "title": "First Unit Of Hyundai Exter Rolled Out In India, Launch Next Month - GaadiWaadi.com", "description": "Hyundai Exter will be launched on July 10 and it will be available with a 1.2L NA petrol engine, paired with a five-speed MT or an AMT", "url": "https://gaadiwaadi.com/first-unit-of-hyundai-exter-rolled-out-in-india-launch-next-month/", "urlToImage": "https://gaadiwaadi.com/wp-content/uploads/2023/06/Hyundai-Exter-First-Unit-Rolled-Out.jpg", "publishedAt": "2023-06-23T13:23:05Z", "content": "Hyundai Motor India Limited (HMIL) has today rolled out the first unit of the Exter micro SUV from its Sriperumbudur production facility near Chennai, Tamil Nadu. The five-seater will go on sale on J\u2026 [+1977 chars]", "image_url": "https://gaadiwaadi.com/wp-content/uploads/2023/06/Hyundai-Exter-First-Unit-Rolled-Out.jpg"}, {"source": {"id": null, "name": "GSMArena.com"}, "author": "Ro", "title": "Apple is reportedly preparing to launch Apple Pay in India - GSMArena.com news - GSMArena.com", "description": "It has several regulatory hurdles to overcome. Apple has been looking to launch its payment feature called Apple Pay in India for some time now but it...", "url": "https://www.gsmarena.com/apple_is_reportedly_preparing_to_launch_apple_pay_in_india-news-58953.php", "urlToImage": "https://fdn.gsmarena.com/imgroot/news/22/07/apple-pay-class-action-lawsuit/-952x498w6/gsmarena_000.jpg", "publishedAt": "2023-06-23T13:17:01Z", "content": "Apple has been looking to launch its payment feature called Apple Pay in India for some time now but it hasn't successfully negotiated the terms with local authorities. Well, according to people fami\u2026 [+956 chars]", "image_url": "https://fdn.gsmarena.com/imgroot/news/22/07/apple-pay-class-action-lawsuit/-952x498w6/gsmarena_000.jpg"}, {"source": {"id": null, "name": "Bqprime.com"}, "author": "Rishabh Mishra", "title": "Stock Market Today: Sensex, Nifty Close The Week Lower After Posting Record Highs; FPIs Stay Net Sellers - BQ Prime", "description": "Sensex closed down 260 points or 0.41% at 62,979.37 while Nifty was 106 points or 0.56% lower at 18,665.50.", "url": "https://www.bqprime.com/markets/stock-market-live-updates-23-june-2023-sgx-nifty-signals-cautious-start-eros-hdfc-bpcl-vedanta-coforge-pnb-housing-in-focus", "urlToImage": "https://gumlet.assettype.com/bloombergquint%2F2022-06%2F3ad2da49-d4fc-4713-ae21-e5d947f63af3%2FStock_market_trend_on_a_computer_screen__financial_graph___Source_freepik_.jpg?rect=0%2C0%2C5501%2C2888&w=1200&auto=format%2Ccompress&ogImage=true", "publishedAt": "2023-06-23T12:54:40Z", "content": "India's benchmark stock indices declined through Friday to close the week and the day on a lower note. While metals and consumer durables declined the most, majority of the sectoral indices were trad\u2026 [+1242 chars]", "image_url": "https://gumlet.assettype.com/bloombergquint%2F2022-06%2F3ad2da49-d4fc-4713-ae21-e5d947f63af3%2FStock_market_trend_on_a_computer_screen__financial_graph___Source_freepik_.jpg?rect=0%2C0%2C5501%2C2888&w=1200&auto=format%2Ccompress&ogImage=true"}, {"source": {"id": null, "name": "FXStreet"}, "author": "Sagar Dua", "title": "Gold Price Forecast: XAU/USD extends recovery to near $1,920 as USD Index loses upside momentum - FXStreet", "description": "Gold price (XAU/USD) has stretched its recovery to near $1,920.00 in the European session. The precious metal found strength near $1,912.00 as the US", "url": "https://www.fxstreet.com/news/gold-price-forecast-xau-usd-extends-recovery-to-near-1-920-as-usd-index-loses-upside-momentum-202306231213", "urlToImage": "https://editorial.fxstreet.com/images/Markets/Commodities/Metals/Gold/aurum-37842316_Large.jpg", "publishedAt": "2023-06-23T12:13:07Z", "content": "
  • Gold price has stretched its recovery to near $1,920.00 as the upside momentum in the USD Index is exhausting.
  • The overall risk profile is negative as investors are worried about glob\u2026 [+2087 chars]", "image_url": "https://editorial.fxstreet.com/images/Markets/Commodities/Metals/Gold/aurum-37842316_Large.jpg"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": null, "title": "Adani group says unaware of any subpoena to investors amid reports of regulatory scrutiny in US - Moneycontrol", "description": "\"We strongly reject any suggestion that Adani Group and its businesses have not acted as per the regulations,\" the company said, after reports claimed that the US regulators are looking into the representations that Adani made to its American investors follow\u2026", "url": "https://www.moneycontrol.com/news/business/adani-group-says-unaware-of-any-subpoena-to-investors-amid-reports-of-regulatory-scrutiny-in-us-10843581.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2023/06/Adani-Reuters-770x433.jpg", "publishedAt": "2023-06-23T12:04:59Z", "content": "A day after reports claimed that the regulators in the United States have issued subpoena to Adani group investors, the Gautam Adani-led conglomerate issued a denial.\r\n\"We are not aware of any such s\u2026 [+2261 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2023/06/Adani-Reuters-770x433.jpg"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": "Moneycontrol News", "title": "CRED acquires savings platform Spenny - Moneycontrol", "description": "Founded in 2019 by Gaurav Arora and Rathin Shah, Spenny is a micro-savings and investment platform that encourages users to spare a small amount towards savings and investment from daily transactions. Shah will join CRED's product team", "url": "https://www.moneycontrol.com/news/business/announcements/cred-acquires-savings-platform-startup-spenny-10847871.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2021/09/Kunal-shah-cred-770x433.jpg", "publishedAt": "2023-06-23T11:21:34Z", "content": "Fintech unicorn\u00a0CRED has acquired savings and investment platform Spenny for an undisclosed amount, continuing its expansion in the lending and wealthtech space.\r\n\"The work of CRED and with Kunal (Sh\u2026 [+1168 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2021/09/Kunal-shah-cred-770x433.jpg"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": "Moneycontrol News", "title": "Accel-backed crypto startup Pillow to shut down operations on July 31 - Moneycontrol", "description": "The startup asked its users to withdraw funds at the earliest. This comes eight months after the startup raised $18 million in a Series A round.", "url": "https://www.moneycontrol.com/news/business/startup/accel-backed-crypto-startup-pillow-to-shut-down-operations-on-july-31-10847291.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2023/03/cryptocurrency.jpg", "publishedAt": "2023-06-23T09:59:59Z", "content": "Accel-backed crypto investment startup Pillow informed its users that it will be shutting down operations from July 31. The startup cited regulatory uncertainties and a tough business environment beh\u2026 [+1590 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2023/03/cryptocurrency.jpg"}, {"source": {"id": null, "name": "Financial Express"}, "author": null, "title": "Share Market Today LIVE | Sensex, Nifty, BSE, NSE, Share Prices, Stock Market News Updates Friday 23 June - The Financial Express", "description": "Share Market News Today | Sensex, Nifty, Share Prices Highlights: On Friday, the NSE Nifty 50 tanked 105.75 points or 0.56% to 18,665.50 and BSE Sensex sank 259.52 points or 0.41% to 62,979.37.", "url": "https://www.financialexpress.com/market/share-market-today-live-updates-sensex-nifty-rupee-vs-dollar-bank-nifty-sgx-nifty-aether-eros-stocks-asia-us-markets-friday-23-june/3138640/", "urlToImage": "https://www.financialexpress.com/wp-content/uploads/2023/06/Untitled-design-2-15.jpg", "publishedAt": "2023-06-23T09:28:54Z", "content": "Apollo Micro Systems, Arvind, AstraZeneca Pharma India, Aurobindo Pharma, Mrs Bectors Food Specialities, Blue Star, Carysil, CSL Finance, De Nora India, D P Wires, EIH Associated Hotels, Force Motors\u2026 [+725 chars]", "image_url": "https://www.financialexpress.com/wp-content/uploads/2023/06/Untitled-design-2-15.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "PTI", "title": "First Made-in-India semiconductor chips to come by December 2024 - Indiatimes.com", "description": "India Business News: Computer storage chip maker Micron will set up its semiconductor assembly and test plant in Gujarat entailing a total investment of $2.75 billion (aro", "url": "https://timesofindia.indiatimes.com/business/india-business/first-made-in-india-semiconductor-chips-to-come-by-december-2024/articleshow/101214654.cms", "urlToImage": "https://static.toiimg.com/thumb/msid-101214642,width-1070,height-580,imgsize-83722,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg", "publishedAt": "2023-06-23T09:11:00Z", "content": "FD Calculator\r\nWhen investing in a fixed deposit, the amount you deposit earns interest as per the prevailing...\r\nCalculate Now", "image_url": "https://static.toiimg.com/thumb/msid-101214642,width-1070,height-580,imgsize-83722,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg"}, {"source": {"id": null, "name": "Investing.com"}, "author": "Peter Nurse/Investing.com", "title": "Powell and Fed speakers, Adani, Turkish lira - what's moving markets By Investing.com - Investing.com", "description": "Powell and Fed speakers, Adani, Turkish lira - what's moving markets", "url": "https://www.investing.com/news/economy/powell-and-fed-speakers-adani-turkish-lira--whats-moving-markets-3111774", "urlToImage": "https://i-invdn-com.investing.com/news/LYNXNPEC5S0A7_L.jpg", "publishedAt": "2023-06-23T08:53:00Z", "content": "Investing.com -- Fed chief Jerome Powell again signals more rate hikes are likely this year, prompting weakness in the equity and energy markets and the focus on the latest round of central bank spea\u2026 [+4693 chars]", "image_url": "https://i-invdn-com.investing.com/news/LYNXNPEC5S0A7_L.jpg"}] \ No newline at end of file diff --git a/LearnersEd/explore_fields/news/entertainment.json b/LearnersEd/explore_fields/news/entertainment.json new file mode 100644 index 000000000..507872995 --- /dev/null +++ b/LearnersEd/explore_fields/news/entertainment.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "123telugu.com"}, "author": null, "title": "Photo Moment: Ram Charan & Upasana spotted with their newborn baby girl - 123telugu", "description": "Telugu cinema news, Telugu Movies Updates, Latest Movie reviews in Telugu, Telugu cinema reviews, telugu movie reviews, Tollywood, Box office collections, Telugu Movie show times, Theater List, telugu cinema tickets", "url": "https://www.123telugu.com/mnews/photo-moment-ram-charan-upasana-spotted-with-their-newborn-baby-girl.html", "urlToImage": "https://www.123telugu.com/content/wp-content/themes/123telugu/images/logo.gif", "publishedAt": "2023-06-23T12:30:00Z", "content": "Mega fans have been rejoicing with the arrival of the mega princess. It\u2019s been a long wait for mega fans, and hence they are on cloud nine. Megastar Chiranjeevi and the entire mega clan are very much\u2026 [+661 chars]", "image_url": "https://www.123telugu.com/content/wp-content/themes/123telugu/images/logo.gif"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "etimes.in", "title": "Ranbir Kapoor and Alia Bhatt twin in black on date-night in Dubai - Indiatimes.com", "description": "Ranbir Kapoor and Alia Bhatt took time off from their busy work schedules to jet out to Dubai to spend some quality time together with their baby girl", "url": "https://timesofindia.indiatimes.com/entertainment/hindi/bollywood/news/ranbir-kapoor-and-alia-bhatt-twin-in-black-on-date-night-in-dubai/articleshow/101216689.cms", "urlToImage": "https://static.toiimg.com/thumb/msid-101216608,width-1070,height-580,imgsize-54918,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg", "publishedAt": "2023-06-23T10:56:00Z", "content": "Ranbir Kapoor's clean shaven look has fans declaring 'He looks better without a beard'Ranbir Kapoor, who has been busy shooting for his action flick 'Animal', debuted his new and improved clean-shave\u2026 [+113 chars]", "image_url": "https://static.toiimg.com/thumb/msid-101216608,width-1070,height-580,imgsize-54918,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg"}, {"source": {"id": null, "name": "Indiaforums.com"}, "author": "Aanchal Chowdhary", "title": "BALH 3 to be replaced by Barsatein but will not go off air, will get a new time slot - India Forums", "description": "India Forums is back with a good news for the fans of Sony TV\u2019s show Bade Acche Lagte Hai 3.", "url": "https://www.indiaforums.com/article/balh-3-to-be-replaced-by-barsatein-but-will-not-go-off-air-will-get-a-new-time-slot_197912", "urlToImage": "https://img.indiaforums.com/article/1200x675/19/7912-balh-3-to-be-replaced-by-barsatein-but-will-not-go-off-air-will-get-a-new-time-slot.jpg", "publishedAt": "2023-06-23T10:40:00Z", "content": "Sony TV is all set to bring in its much anticipated show Barsatein starring Shivangi Joshi and Kushal Tandon in the titular roles. The show is all set to go on air on the 10th of July from 8 PM onwar\u2026 [+851 chars]", "image_url": "https://img.indiaforums.com/article/1200x675/19/7912-balh-3-to-be-replaced-by-barsatein-but-will-not-go-off-air-will-get-a-new-time-slot.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "etimes.in", "title": "Arun Govil, popularly known as the original Lord Ram, recalls the time when a flustered mother placed her - Indiatimes.com", "description": "Arun Govil speaks out against the makes of Adipurush and says that if the film was made with right intention, it would have reached the audiences.", "url": "https://timesofindia.indiatimes.com/entertainment/hindi/bollywood/news/arun-govil-popularly-known-as-the-original-lord-ram-recalls-the-time-when-a-flustered-mother-placed-her-sick-child-at-his-feet/articleshow/101215968.cms", "urlToImage": "https://static.toiimg.com/thumb/msid-101215923,width-1070,height-580,imgsize-22842,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg", "publishedAt": "2023-06-23T09:51:00Z", "content": "Ramayana is not for entertainment value: Dipika Chikhlia on 'Adipurush' controversyThe Ramayana is not for entertainment and filmmakers should avoid coming up with new iterations every few years, say\u2026 [+114 chars]", "image_url": "https://static.toiimg.com/thumb/msid-101215923,width-1070,height-580,imgsize-22842,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg"}, {"source": {"id": null, "name": "Indiaforums.com"}, "author": "Team India Forums", "title": "Ghum Hai Kisikey Pyaar Meiin: Kishori Shahane to continue to post-leap; further details also revealed - India Forums", "description": "Apart from the leads, Vihan Verma, Sheetal Maulik, and Mridul Kumar will also leave the show. And the latest we hear is that Kishori Shahane aka Bhavani Chavan will not quit the show.", "url": "https://www.indiaforums.com/article/ghum-hai-kisikey-pyaar-meiin-kishori-shahane-to-continue-to-postleap-further-details-also-revealed_197906", "urlToImage": "https://img.indiaforums.com/article/1200x675/19/7906-ghum-hai-kisikey-pyaar-meiin-kishori-shahane-to-continue-to-postleap-further-details-also-revealed.jpg", "publishedAt": "2023-06-23T09:30:00Z", "content": "Ghum Hai Kisikey Pyaar Meiin is one of the top-rated shows. The popular show is making headlines for taking a major leap. And post leap, the likes of Ayesha, Neil, and Harshad are about to bid farewe\u2026 [+1024 chars]", "image_url": "https://img.indiaforums.com/article/1200x675/19/7906-ghum-hai-kisikey-pyaar-meiin-kishori-shahane-to-continue-to-postleap-further-details-also-revealed.jpg"}, {"source": {"id": null, "name": "Tellychakkar"}, "author": null, "title": "Anupamaa: Big twist! Kavya returns to the Shah house; Anupama takes the blame for Nakul on herself - Tellychakkar", "description": "In the upcoming episode of Anupamaa, Kavya would returns to the Shah house as Vanraj expresses his desire to be with her after hearing about the pregnancy. On the other hand, Anupama takes the blame for Nakul as she understands that he is doing all this as he\u2026", "url": "https://www.tellychakkar.com/spoiler-alert/anupamaa-big-twist-kavya-returns-the-shah-house-anupama-takes-the-blame-nakul-herself", "urlToImage": "https://d30ny7ijak9wq4.cloudfront.net/s3fs-public/images/spoiler-alert/2023/06/23/kavya.jpg", "publishedAt": "2023-06-23T09:19:00Z", "content": "MUMBAI:\u00a0Anupamaa is presently ruling our television screens. The show is doing exceptionally well and is topping the weekly BARC ratings.\r\nThese days, the show's track focuses on quite an interesting\u2026 [+1645 chars]", "image_url": "https://d30ny7ijak9wq4.cloudfront.net/s3fs-public/images/spoiler-alert/2023/06/23/kavya.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "TIMESOFINDIA.COM", "title": "Old video of Sidharth Malhotra talking about weight gain during 'Hasee Toh Phasee' promotion goes viral; netizens say 'Bechari Pari' - Indiatimes.com", "description": "Parineeti Chopra and Sidharth Malhotra shared screen space in 'Hasee Toh Phasee' and their on-screen chemistry was loved by fans. Now, a Reddit user dug out an old video of Pari and Sid from one of the promotional events where when a journalist asked", "url": "https://timesofindia.indiatimes.com/videos/etimes/bollywood/old-video-of-sidharth-malhotra-talking-about-weight-gain-during-hasee-toh-phasee-promotion-goes-viral-netizens-say-bechari-pari/videoshow/101214322.cms", "urlToImage": "https://timesofindia.indiatimes.com/photo/101214322/size-43052/101214322.jpg", "publishedAt": "2023-06-23T08:58:25Z", "content": "Parineeti Chopra\r\n and Sidharth Malhotra\r\n shared screen space in 'Hasee Toh Phasee\r\n' and their on-screen chemistry was loved by fans. Now, a Reddit user dug out an old video of Pari and Sid from on\u2026 [+970 chars]", "image_url": "https://timesofindia.indiatimes.com/photo/101214322/size-43052/101214322.jpg"}, {"source": {"id": null, "name": "Indiaforums.com"}, "author": "Team India Forums", "title": "Kangana Ranaut alleges smear campaign against her film 'Tiku Weds Sheru' by 'movie mafia' - India Forums", "description": "Kangana Ranaut took to her social media account and alleged the movie mafia of harming her film.", "url": "https://www.indiaforums.com/article/kangana-ranaut-alleges-smear-campaign-against-her-film-tiku-weds-sheru-by-movie-mafia_197909", "urlToImage": "https://img.indiaforums.com/article/1200x675/19/7909-kangana-ranaut-alleges-smear-campaign-against-her-film-tiku-weds-sheru-by-movie-mafia.jpg?c=9pN6A1", "publishedAt": "2023-06-23T08:50:00Z", "content": "Kangana Ranaut's debut production venture, \"Tiku Weds Sheru,\" featuring Nawazuddin Siddiqui and Avneet Kaur, has finally begun streaming and the response from the audience has been a mixed bag. Howev\u2026 [+1593 chars]", "image_url": "https://img.indiaforums.com/article/1200x675/19/7909-kangana-ranaut-alleges-smear-campaign-against-her-film-tiku-weds-sheru-by-movie-mafia.jpg?c=9pN6A1"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": null, "title": "Rashmika Mandanna part ways with manager, addresses Rs 80 lakh cheating rumours: \u2018We have decided to part ways amicably' - Indiatimes.com", "description": "Rashmika Mandanna part ways with manager, addresses Rs 80 lakh cheating rumours: \u2018We have decided to part ways amicably'.After reports of Rashmika Mandanna being cheated of Rs 80 lakh by her long time manager grabbed headlines, now the actress' team has final\u2026", "url": "https://timesofindia.indiatimes.com/videos/etimes/bollywood/rashmika-mandanna-part-ways-with-manager-addresses-rs-80-lakh-cheating-rumours-we-have-decided-to-part-ways-amicably/videoshow/101213978.cms", "urlToImage": "https://timesofindia.indiatimes.com/photo/101213978.cms", "publishedAt": "2023-06-23T08:45:42Z", "content": "Jun 23, 2023, 14:15 ISTSource: etimes.inAfter reports of Rashmika Mandanna being cheated of Rs 80 lakh by her long time manager grabbed headlines, now the actress' team has finally cleared the air ab\u2026 [+888 chars]", "image_url": "https://timesofindia.indiatimes.com/photo/101213978.cms"}, {"source": {"id": null, "name": "Telugu360.com"}, "author": null, "title": "Adipurush First Week Worldwide Collections \u2013 Dissapointing - Telugu360", "description": "Adipurush movie first week world wide collections updates, prabhas adipurush movie latest numbers, adipurush movie.", "url": "https://www.telugu360.com/adipurush-first-week-worldwide-collections-dissapointing/", "urlToImage": "https://www.telugu360.com/wp-content/uploads/2023/06/Adipurush-first-week-world-wide-collections.jpg", "publishedAt": "2023-06-23T08:42:31Z", "content": "Adipurush has a disappointing first week at the box office with huge falls on the weekdays. The worldwide gross of the film after its first week stands at 320 Cr. The distributor share of the film is\u2026 [+5091 chars]", "image_url": "https://www.telugu360.com/wp-content/uploads/2023/06/Adipurush-first-week-world-wide-collections.jpg"}] \ No newline at end of file diff --git a/LearnersEd/explore_fields/news/general.json b/LearnersEd/explore_fields/news/general.json new file mode 100644 index 000000000..9e582fd93 --- /dev/null +++ b/LearnersEd/explore_fields/news/general.json @@ -0,0 +1 @@ +[{"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "ET HealthWorld", "title": "Y chromosome loss in men with age linked with worse cancer outcomes - ETHealthWorld", "description": "In this study, reviewing data on two groups of men, the researchers found that men with muscle invasive bladder cancer not being treated and thus, losing Y chromosomes, had poorer prognosis and lower survival rates compared to those being treated with an immu\u2026", "url": "https://health.economictimes.indiatimes.com/news/industry/y-chromosome-loss-in-men-with-age-linked-with-worse-cancer-outcomes/101221880", "urlToImage": "https://etimg.etb2bimg.com/thumb/msid-101221880,imgsize-12350,width-1200,height=765,overlay-ethealth/industry/y-chromosome-loss-in-men-with-age-linked-with-worse-cancer-outcomes.jpg", "publishedAt": "2023-06-23T13:12:02Z", "content": "New Delhi: Loss of Y chromosome in cells, which happens as men age, enables cancer cells evade their immune system, hampering their ability to fight cancer, new research published in the journal Natu\u2026 [+3273 chars]", "image_url": "https://etimg.etb2bimg.com/thumb/msid-101221880,imgsize-12350,width-1200,height=765,overlay-ethealth/industry/y-chromosome-loss-in-men-with-age-linked-with-worse-cancer-outcomes.jpg"}, {"source": {"id": "the-hindu", "name": "The Hindu"}, "author": "Sportstar", "title": "Women's Ashes 2023 Nottingham Test Day 2 Live Score - Aus 473 all out: Ecclestone fifer winds up Aussie innings; century for Sutherland - Sportstar", "description": null, "url": "https://sportstar.thehindu.com/cricket/womens-cricket/womens-ashes-test-day-2-live-score-england-vs-australia-toss-scorecard-updates-milestones-stats/article67000541.ece", "urlToImage": null, "publishedAt": "2023-06-23T12:57:43Z", "content": null, "image_url": null}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "HT Tech", "title": "iQOO Z6 5G price drop announced! Get 30% discount on Amazon - HT Tech", "description": "Searching for an affordable phone? Buy iQOO Z6 5G with a huge discount!", "url": "https://tech.hindustantimes.com/mobile/news/iqoo-z6-5g-price-drop-announced-get-30-discount-on-amazon-71687524098474.html", "urlToImage": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/battery_1664434939880_1687524429651.jpg", "publishedAt": "2023-06-23T12:51:33Z", "content": "The iQOO Z6 5G smartphone is now available at a discounted price on Amazon, making it an affordable option for those in search of a new device. Originally priced at Rs. 19,999, you can now purchase i\u2026 [+1138 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/battery_1664434939880_1687524429651.jpg"}, {"source": {"id": null, "name": "123telugu.com"}, "author": null, "title": "Photo Moment: Ram Charan & Upasana spotted with their newborn baby girl - 123telugu", "description": "Telugu cinema news, Telugu Movies Updates, Latest Movie reviews in Telugu, Telugu cinema reviews, telugu movie reviews, Tollywood, Box office collections, Telugu Movie show times, Theater List, telugu cinema tickets", "url": "https://www.123telugu.com/mnews/photo-moment-ram-charan-upasana-spotted-with-their-newborn-baby-girl.html", "urlToImage": "https://www.123telugu.com/content/wp-content/themes/123telugu/images/logo.gif", "publishedAt": "2023-06-23T12:30:00Z", "content": "Mega fans have been rejoicing with the arrival of the mega princess. It\u2019s been a long wait for mega fans, and hence they are on cloud nine. Megastar Chiranjeevi and the entire mega clan are very much\u2026 [+661 chars]", "image_url": "https://www.123telugu.com/content/wp-content/themes/123telugu/images/logo.gif"}, {"source": {"id": null, "name": "India.com"}, "author": "Zee News Desk", "title": "Highlights | SL vs Oman, ICC World Cup Qualifier Cricket Live Score & Updates: Sri Lanka Beat Om - Zee News", "description": "Highlights | SL vs Oman, ICC World Cup Qualifier Cricket Live Score & Updates: Sri Lanka Beat Oman", "url": "https://zeenews.india.com/cricket/live-updates/highlights-cricket-score-sl-vs-oman-icc-icc-world-cup-2023-qualifier-match-11-today-sri-lanka-vs-oman-queens-sports-club-bulawayo-dasun-shanaka-zeeshan-maqsood-sri-lanka-beat-oman-2625580", "urlToImage": "https://english.cdn.zeenews.com/sites/default/files/2023/06/22/1226201-whatsapp-image-2023-06-22-at-12.44.28-pm.jpeg", "publishedAt": "2023-06-23T12:28:25Z", "content": null, "image_url": "https://english.cdn.zeenews.com/sites/default/files/2023/06/22/1226201-whatsapp-image-2023-06-22-at-12.44.28-pm.jpeg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "ET HealthWorld", "title": "Women with common cardiac rhythm disorders have quicker cognitive deterioration than males: Study - ETHealthWorld", "description": "Atrial fibrillation is the most common heart rhythm disorder, affecting more than 40 million people worldwide. People with this condition have a five-fold increased risk of stroke compared with their healthy peers. Women have more atrial fibrillation symptoms\u2026", "url": "https://health.economictimes.indiatimes.com/news/industry/women-with-common-cardiac-rhythm-disorders-have-quicker-cognitive-deterioration-than-males-study/101220241", "urlToImage": "https://etimg.etb2bimg.com/thumb/msid-101220241,imgsize-19406,width-1200,height=765,overlay-ethealth/industry/women-with-common-cardiac-rhythm-disorders-have-quicker-cognitive-deterioration-than-males-study.jpg", "publishedAt": "2023-06-23T12:07:10Z", "content": "Brussels: According to research presented at ACNAP 2023, a scientific congress of the European Society of Cardiology (ESC) and published in Alzheimer's and Dementia: The Journal of the Alzheimer's As\u2026 [+4626 chars]", "image_url": "https://etimg.etb2bimg.com/thumb/msid-101220241,imgsize-19406,width-1200,height=765,overlay-ethealth/industry/women-with-common-cardiac-rhythm-disorders-have-quicker-cognitive-deterioration-than-males-study.jpg"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": null, "title": "Adani group says unaware of any subpoena to investors amid reports of regulatory scrutiny in US - Moneycontrol", "description": "\"We strongly reject any suggestion that Adani Group and its businesses have not acted as per the regulations,\" the company said, after reports claimed that the US regulators are looking into the representations that Adani made to its American investors follow\u2026", "url": "https://www.moneycontrol.com/news/business/adani-group-says-unaware-of-any-subpoena-to-investors-amid-reports-of-regulatory-scrutiny-in-us-10843581.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2023/06/Adani-Reuters-770x433.jpg", "publishedAt": "2023-06-23T12:04:59Z", "content": "A day after reports claimed that the regulators in the United States have issued subpoena to Adani group investors, the Gautam Adani-led conglomerate issued a denial.\r\n\"We are not aware of any such s\u2026 [+2261 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2023/06/Adani-Reuters-770x433.jpg"}, {"source": {"id": null, "name": "Bar & Bench - Indian Legal News"}, "author": "Bar & Bench", "title": "West Bengal panchayat elections: Calcutta High Court asks SEC to respond to allegation that 20,585 nominations were withdrawn abruptly - Bar & Bench - Indian Legal News", "description": "The Calcutta High Court on Friday sought the response of the State Election Commission (SEC) to an allegation that 20,585 nominations by candidates who had soug", "url": "https://www.barandbench.com/news/litigation/west-bengal-panchayat-elections-calcutta-high-court-sec-20585-nominations-withdrawn", "urlToImage": "https://gumlet.assettype.com/barandbench%2F2020-06%2F2bbf8db2-11b2-4dc6-9bbc-9d453ec6500d%2FCalcutta_High_Court_17.jpg?w=1200&auto=format%2Ccompress&ogImage=true&enlarge=true", "publishedAt": "2023-06-23T11:26:18Z", "content": "The bench comprising Chief Justice TS Sivagnanam and Justice Uday Kumar was hearing a contempt application filed by leader of opposition and Bharatiya Janata Party (BJP) leader, Suvendhu Adhikari all\u2026 [+2954 chars]", "image_url": "https://gumlet.assettype.com/barandbench%2F2020-06%2F2bbf8db2-11b2-4dc6-9bbc-9d453ec6500d%2FCalcutta_High_Court_17.jpg?w=1200&auto=format%2Ccompress&ogImage=true&enlarge=true"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": "Aihik Sur", "title": "India signs Artemis Accords: What does it mean and why is it significant? - Moneycontrol", "description": "As a partner to the next NASA moonshot programme, the country stands to benefit from technology transfers, but the inking is also a part of a larger geopolitical game", "url": "https://www.moneycontrol.com/news/business/india-signs-artemis-accords-what-does-it-mean-and-why-is-it-significant-10847711.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2023/06/Artemis_Accords_Graphic_WEB_062223-770x433.jpg", "publishedAt": "2023-06-23T11:01:50Z", "content": "India signing the Artemis Accord with the US has evoked mixed reactions. While the positives include possible easing up of export control from the part of the US and facilitating technology transfer \u2026 [+9165 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2023/06/Artemis_Accords_Graphic_WEB_062223-770x433.jpg"}, {"source": {"id": null, "name": "Thewire.in"}, "author": null, "title": "How the US Media Covered Modi\u2019s Reception by Biden Amidst Criticism on Human Rights - The Wire", "description": "While 'The Atlantic' minced no words in acknowledging the 2002 Gujarat riots and Modi's handling of the situation, 'The New Yorker' noted this meeting was a reminder of trade-offs in Biden's foreign policy.", "url": "https://thewire.in/world/how-the-us-media-covered-modis-reception-by-biden-amidst-criticism-on-human-rights", "urlToImage": "https://cdn.thewire.in/wp-content/uploads/2023/06/23154702/image-1083-800x400.png", "publishedAt": "2023-06-23T11:00:41Z", "content": "New Delhi: India\u2019s bleak human rights situation took centre stage in US media\u2019s coverage of US President Joe Biden\u2019s reception of Prime Minister Narendra Modi.\r\nThe prime minister of the world\u2019s larg\u2026 [+11790 chars]", "image_url": "https://cdn.thewire.in/wp-content/uploads/2023/06/23154702/image-1083-800x400.png"}] \ No newline at end of file diff --git a/LearnersEd/explore_fields/news/health.json b/LearnersEd/explore_fields/news/health.json new file mode 100644 index 000000000..54773df76 --- /dev/null +++ b/LearnersEd/explore_fields/news/health.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "Analytics Insight"}, "author": "Shiva Ganesh", "title": "AI Chatbots and Pandemics: Alleged Threat Examined - Analytics Insight", "description": "According to new research, language-generating AI models might make it simpler to produce harmful germs breakout of an infectious illness.", "url": "https://www.analyticsinsight.net/ai-chatbots-and-pandemics-alleged-threat-examined/", "urlToImage": "https://www.analyticsinsight.net/wp-content/uploads/2023/06/ChatGPT-How-an-AI-Chatbot-Could-Trigger-a-Pandemic.jpg", "publishedAt": "2023-06-23T14:08:11Z", "content": "AI is constantly transforming the world and guaranteeing that humanity makes quantum leaps\r\nGoogle makes learning how to perform a terrorist attack somewhat challenging. The first few pages of result\u2026 [+3376 chars]", "image_url": "https://www.analyticsinsight.net/wp-content/uploads/2023/06/ChatGPT-How-an-AI-Chatbot-Could-Trigger-a-Pandemic.jpg"}, {"source": {"id": null, "name": "Nutritioninsight.com"}, "author": null, "title": "Challenges for \"every health system in the world\" as over one billion predicted to have diabetes by 2025 - Nutrition Insight", "description": "23 Jun 2023 --- Globally, diabetes is one of the leading causes of death and disability. Currently, half a billion people are living with the disease, and that number is expected to more than double by 2050, reaching up to 1.3 billion people, according to a n\u2026", "url": "https://www.nutritioninsight.com/news/challenges-for-every-health-system-in-the-world-as-over-one-billion-predicted-to-have-diabetes-by-2025.html", "urlToImage": "https://article.innovamarketinsights360.com/articleimgs/article_images/638231243689349833man%20thinking.jpg", "publishedAt": "2023-06-23T13:49:53Z", "content": null, "image_url": "https://article.innovamarketinsights360.com/articleimgs/article_images/638231243689349833man%20thinking.jpg"}, {"source": {"id": "medical-news-today", "name": "Medical News Today"}, "author": "Lauren Hellicar", "title": "Fiber and colorectal cancer: Benefits and more - Medical News Today", "description": "A fiber-rich diet may reduce a person\u2019s chances of developing colorectal cancer or reduce their risk of complications.", "url": "https://www.medicalnewstoday.com/articles/fiber-and-colorectal-cancer", "urlToImage": "https://post.medicalnewstoday.com/wp-content/uploads/sites/3/2023/05/breakfast-bowels-fibre-cancer-732x549-thumbnail.jpg", "publishedAt": "2023-06-23T13:35:04Z", "content": "A fiber-rich diet can help lower a persons risk of colorectal cancer. Experts recommend that people eat at least 30 grams (g) of fiber daily. \r\nColorectal cancer refers to cancers of the colon and th\u2026 [+4562 chars]", "image_url": "https://post.medicalnewstoday.com/wp-content/uploads/sites/3/2023/05/breakfast-bowels-fibre-cancer-732x549-thumbnail.jpg"}, {"source": {"id": null, "name": "Newswise"}, "author": "Yale Cancer Center/Smilow Cancer Hospital", "title": "Harnessing the power of healthy cells to suppress skin cancer formation - Newswise", "description": "Healthy human skin is a mosaic-like collection of both normal and mutation-bearing cells. As people age, a growing number of these cells accumulate more and mor", "url": "https://www.newswise.com/articles/harnessing-the-power-of-healthy-cells-to-suppress-skin-cancer-formation", "urlToImage": "https://www.newswise.com/legacy/image.php?image=/images/uploads/2023/06/23/64959988a5370_ynews-ab2-visual.jpg.webp&width=600&height=600", "publishedAt": "2023-06-23T13:20:00Z", "content": "Newswise \u2014 Healthy human skin is a mosaic-like collection of both normal and mutation-bearing cells. As people age, a growing number of these cells accumulate more and more mutations including those \u2026 [+2056 chars]", "image_url": "https://www.newswise.com/legacy/image.php?image=/images/uploads/2023/06/23/64959988a5370_ynews-ab2-visual.jpg.webp&width=600&height=600"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "ET HealthWorld", "title": "Yearly re-scan is not required for a common brain tumour - ETHealthWorld", "description": "The largest study of its kind has been published in the European Journal of Endocrinology and looks at clinical data on a type of tumour growth in the pituitary gland in the brain. The common growth, called a non-functioning pituitary microadenoma (NFPA), is \u2026", "url": "https://health.economictimes.indiatimes.com/news/industry/yearly-re-scan-is-not-required-for-a-common-brain-tumour/101221663", "urlToImage": "https://etimg.etb2bimg.com/thumb/msid-101221663,imgsize-18856,width-1200,height=765,overlay-ethealth/industry/yearly-re-scan-is-not-required-for-a-common-brain-tumour.jpg", "publishedAt": "2023-06-23T13:15:00Z", "content": "England: According to a new research has discovered that people with a common kind of benign brain tumour diagnosed in about 1 in 10 need not need yearly scans.The largest study of its kind has been \u2026 [+2466 chars]", "image_url": "https://etimg.etb2bimg.com/thumb/msid-101221663,imgsize-18856,width-1200,height=765,overlay-ethealth/industry/yearly-re-scan-is-not-required-for-a-common-brain-tumour.jpg"}, {"source": {"id": null, "name": "SciTechDaily"}, "author": null, "title": "Medical Miracle: Scientists Baffled as Men Overcome \u201cIncurable\u201d Heart Failure - SciTechDaily", "description": "In an extraordinary turn of events reported by a research team from University College London (UCL) and the Royal Free Hospital, three men who were previously suffering from heart failure due to an accumulation of sticky, toxic proteins are now symptom-free a\u2026", "url": "https://scitechdaily.com/medical-miracle-scientists-baffled-as-men-overcome-incurable-heart-failure/", "urlToImage": "https://scitechdaily.com/images/Heart-Failure.jpg", "publishedAt": "2023-06-23T12:40:01Z", "content": "Three elderly men diagnosed with an until-now irreversible heart condition, transthyretin cardiac amyloidosis, experienced an unprecedented spontaneous recovery confirmed by medical scans, leading re\u2026 [+6995 chars]", "image_url": "https://scitechdaily.com/images/Heart-Failure.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "ET HealthWorld", "title": "Women with common cardiac rhythm disorders have quicker cognitive deterioration than males: Study - ETHealthWorld", "description": "Atrial fibrillation is the most common heart rhythm disorder, affecting more than 40 million people worldwide. People with this condition have a five-fold increased risk of stroke compared with their healthy peers. Women have more atrial fibrillation symptoms\u2026", "url": "https://health.economictimes.indiatimes.com/news/industry/women-with-common-cardiac-rhythm-disorders-have-quicker-cognitive-deterioration-than-males-study/101220241", "urlToImage": "https://etimg.etb2bimg.com/thumb/msid-101220241,imgsize-19406,width-1200,height=765,overlay-ethealth/industry/women-with-common-cardiac-rhythm-disorders-have-quicker-cognitive-deterioration-than-males-study.jpg", "publishedAt": "2023-06-23T12:07:10Z", "content": "Brussels: According to research presented at ACNAP 2023, a scientific congress of the European Society of Cardiology (ESC) and published in Alzheimer's and Dementia: The Journal of the Alzheimer's As\u2026 [+4626 chars]", "image_url": "https://etimg.etb2bimg.com/thumb/msid-101220241,imgsize-19406,width-1200,height=765,overlay-ethealth/industry/women-with-common-cardiac-rhythm-disorders-have-quicker-cognitive-deterioration-than-males-study.jpg"}, {"source": {"id": null, "name": "Tech Explorist"}, "author": "Vidya Nagalwade", "title": "Drug-resistant fungi thrive in the remote areas - Tech Explorist", "description": "A disease-causing fungus is resistant to a common antifungal medicine used to treat infections.", "url": "https://www.techexplorist.com/drug-resistant-fungi-thrive-remote-areas/62810/", "urlToImage": "https://www.techexplorist.com/wp-content/uploads/2023/06/fungal-strains.jpg", "publishedAt": "2023-06-23T11:54:06Z", "content": "The most common Aspergillus species are harmful to humans, and the main source of Aspergillosis is Aspergillus fumigatus. Its capacity to grow quickly on various organic detritus and its ability to p\u2026 [+2556 chars]", "image_url": "https://www.techexplorist.com/wp-content/uploads/2023/06/fungal-strains.jpg"}, {"source": {"id": null, "name": "Thehealthsite.com"}, "author": "Longjam Dineshwori", "title": "Skin Diseases: Treatment Options For Vitiligo Or Leucoderma | TheHealthSite.com - TheHealthSite", "description": "In recent years numerous treatments have come up for the treatment of vitiligo which involve topical prescriptions surgical and laser procedures as well as home remedies. TheHealthSite.com", "url": "https://www.thehealthsite.com/diseases-conditions/what-are-the-treatment-options-for-vitiligo-987449/", "urlToImage": "https://st1.thehealthsite.com/wp-content/uploads/2023/06/Vitiligo.jpg", "publishedAt": "2023-06-23T11:42:00Z", "content": "Vitiligo can be treated effectively in the primary stage. If you ever notice a white patch or skin discoloration, immediately consult a skin specialist.\r\nVitiligo, commonly referred to as Leucoderma,\u2026 [+4839 chars]", "image_url": "https://st1.thehealthsite.com/wp-content/uploads/2023/06/Vitiligo.jpg"}, {"source": {"id": null, "name": "NDTV News"}, "author": "Manya Singh", "title": "Women Are At A Higher Risk Of Bone Density Loss Due To Menopause; Follow These Tips To Lower Risk - NDTV Doctor", "description": "By taking these steps, women can maintain strong bones and reduce their risk of osteoporosis and fractures.", "url": "https://doctor.ndtv.com/womens-health/women-are-at-a-higher-risk-of-bone-density-loss-due-to-menopause-follow-these-tips-to-lower-risk-4146775", "urlToImage": "https://c.ndtvimg.com/2022-11/jngoe4j8_foods-for-joint-pain_625x300_01_November_22.jpg", "publishedAt": "2023-06-23T11:37:17Z", "content": "Women are at a higher risk of bone density loss due to menopause. This is because oestrogen, a hormone that helps to protect bones, decreases significantly during menopause. Oestrogen helps to regula\u2026 [+3739 chars]", "image_url": "https://c.ndtvimg.com/2022-11/jngoe4j8_foods-for-joint-pain_625x300_01_November_22.jpg"}] \ No newline at end of file diff --git a/LearnersEd/explore_fields/news/science.json b/LearnersEd/explore_fields/news/science.json new file mode 100644 index 000000000..aa7d0733d --- /dev/null +++ b/LearnersEd/explore_fields/news/science.json @@ -0,0 +1 @@ +[{"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "ET HealthWorld", "title": "Y chromosome loss in men with age linked with worse cancer outcomes - ETHealthWorld", "description": "In this study, reviewing data on two groups of men, the researchers found that men with muscle invasive bladder cancer not being treated and thus, losing Y chromosomes, had poorer prognosis and lower survival rates compared to those being treated with an immu\u2026", "url": "https://health.economictimes.indiatimes.com/news/industry/y-chromosome-loss-in-men-with-age-linked-with-worse-cancer-outcomes/101221880", "urlToImage": "https://etimg.etb2bimg.com/thumb/msid-101221880,imgsize-12350,width-1200,height=765,overlay-ethealth/industry/y-chromosome-loss-in-men-with-age-linked-with-worse-cancer-outcomes.jpg", "publishedAt": "2023-06-23T13:12:02Z", "content": "New Delhi: Loss of Y chromosome in cells, which happens as men age, enables cancer cells evade their immune system, hampering their ability to fight cancer, new research published in the journal Natu\u2026 [+3273 chars]", "image_url": "https://etimg.etb2bimg.com/thumb/msid-101221880,imgsize-12350,width-1200,height=765,overlay-ethealth/industry/y-chromosome-loss-in-men-with-age-linked-with-worse-cancer-outcomes.jpg"}, {"source": {"id": null, "name": "The Siasat Daily"}, "author": "Indo-Asian News Service", "title": "NASA\u2019s MAVEN spacecraft captures ultraviolet image of Mars - The Siasat Daily", "description": "Washington: NASA's MAVEN (Mars Atmosphere and Volatile EvolutioN) mission has acquired stunning views of Mars in two ultraviolet images taken at different points along the Red Planet's orbit around the Sun. By viewing the planet in ultraviolet wavelengths, sc\u2026", "url": "https://www.siasat.com/nasas-maven-spacecraft-captures-ultraviolet-image-of-mars-2623008/", "urlToImage": "https://cdn.siasat.com/wp-content/uploads/2022/09/nasa-3.jpg", "publishedAt": "2023-06-23T13:01:00Z", "content": "Washington: NASA\u2019s MAVEN (Mars Atmosphere and Volatile EvolutioN) mission has acquired stunning views of Mars in two ultraviolet images taken at different points along the Red Planet\u2019s orbit around t\u2026 [+2250 chars]", "image_url": "https://cdn.siasat.com/wp-content/uploads/2022/09/nasa-3.jpg"}, {"source": {"id": null, "name": "IoT World Today"}, "author": "IoT World Today", "title": "Flexible Medical Robot Inspired by Pangolin - Flexible Medical Robot Inspired by Pangolin - IoT World Today", "description": null, "url": "https://www.iotworldtoday.com/robotics/flexible-medical-robot-inspired-by-pangolin", "urlToImage": null, "publishedAt": "2023-06-23T11:52:01Z", "content": null, "image_url": null}, {"source": {"id": null, "name": "indy100"}, "author": "Harry Fletcher", "title": "Scientists discover centuries old 'echo' from supermassive black hole - indy100", "description": "The universe appears to be speaking to us, after a centuries old \u201cecho\u201d from a supermassive black hole was detected by scientists.The black hole located inside the Milky Way became active around 200 years ago and produced a sound that is now being studied. 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Credit: HSC-SSP project & NAOJ\r\nAstrophysicists found the clumpiness of Universes dark matter to be 0.76, \u2026 [+9804 chars]", "image_url": "https://scitechdaily.com/images/3D-Distribution-of-Dark-Matter-Derived-From-HSCSSP-scaled.jpg"}, {"source": {"id": null, "name": "ThePrint"}, "author": "ANI", "title": "Mystery of how leaf-cutting ants measure leaf part size has been solved: Study - ThePrint", "description": "England [UK], June 23 (ANI): Leaf-cutting ants are insect superheroes, capable of transporting leaf fragments up to six times their body mass to develop fungus in their borrows. 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But how do the fascin\u2026 [+3695 chars]", "image_url": null}, {"source": {"id": null, "name": "Skyatnightmagazine.com"}, "author": "Govert Schilling", "title": "A history of 3D mapping the Universe - Sky at Night Magazine", "description": "A look at 3D cosmic cartography and historic attempts to produce a roadmap of space.", "url": "https://www.skyatnightmagazine.com/space-science/history-3d-mapping-universe/", "urlToImage": "https://images.immediate.co.uk/production/volatile/sites/25/2023/06/sdss-map-universe-orangepie-ed825f6.jpg?resize=620,413", "publishedAt": "2023-06-23T07:35:49Z", "content": "The first three-dimensional map of the Universe, made with a 1.5-metre telescope at Mount Hopkins in Arizona, was published over 40 years ago, in 1982.\r\nIt took Marc Davis and his colleagues five yea\u2026 [+1367 chars]", "image_url": "https://images.immediate.co.uk/production/volatile/sites/25/2023/06/sdss-map-universe-orangepie-ed825f6.jpg?resize=620,413"}, {"source": {"id": null, "name": "Tech Explorist"}, "author": "Vidya Nagalwade", "title": "How plants prevent cracking under the stresses and strains of growing - Tech Explorist", "description": "Nature's secret to holding things together is the glue.", "url": "https://www.techexplorist.com/natures-secret-holding-things-together-glue/62757/", "urlToImage": "https://www.techexplorist.com/wp-content/uploads/2023/06/Arabidopsis-stem-with-cracks.jpg", "publishedAt": "2023-06-23T07:35:27Z", "content": "The development of multicellular organisms depends on the coordination of cell growth between layers, which is made simpler by mechanical connectivity and molecular signaling. During development, cel\u2026 [+5135 chars]", "image_url": "https://www.techexplorist.com/wp-content/uploads/2023/06/Arabidopsis-stem-with-cracks.jpg"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "HT Tech", "title": "Asteroid 2023 MW2 will pass between Earth and Moon, NASA reveals - HT Tech", "description": "Asteroid 2023 MW2 will pass between Earth and Moon NASA reveals A small but scary asteroid is hurtling towards Earth for an alarmingly close approach to Earth. Here is what NASA has revealed. 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Here is what NASA has revealed.\r\nPhoto Credit: Pixabay\r\n Tomorrow, a whopping 8\u2026 [+1503 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/11_flickr_1687524666673_1687524681201.jpg/"}] \ No newline at end of file diff --git a/LearnersEd/explore_fields/news/sports.json b/LearnersEd/explore_fields/news/sports.json new file mode 100644 index 000000000..4c064fec9 --- /dev/null +++ b/LearnersEd/explore_fields/news/sports.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "Thebridge.in"}, "author": "The Bridge Desk", "title": "AFC U-17 Asian Cup: Japan humiliates India U-17, eliminates from the group stages - The Bridge", "description": "India U-17 faced a devastating 4-8 defeat against a dominant Japanese side, leading to their elimination from the AFC U-17 Asia Cup.", "url": "https://thebridge.in/football/afc-asian-cup-india-japan-live-score-updates-42666", "urlToImage": "https://thebridge.in/h-upload/2023/06/23/48179-updated-212.jpg", "publishedAt": "2023-06-23T14:02:53Z", "content": "In a high-scoring encounter, India U-17 faced a devastating defeat against a dominant Japanese side, leading to their elimination from the AFC U-17 Asian Cup.\r\nThe first half was a dismal affair for \u2026 [+2215 chars]", "image_url": "https://thebridge.in/h-upload/2023/06/23/48179-updated-212.jpg"}, {"source": {"id": null, "name": "The Real Champs"}, "author": "Sourav Mahanty", "title": "Real Madrid reportedly agree u20ac250 million deal to sign Kylian Mbappe - The Real Champs", "description": "The Kylian Mbappe to Real Madrid saga has dragged on for years. It had looked very close to completion last year but then the Frenchman unexpectedly ended ...", "url": "https://therealchamps.com/2023/06/23/real-madrid-250-million-kylian-mbappe/", "urlToImage": "https://images2.minutemediacdn.com/image/fetch/w_2000,h_2000,c_fit/https%3A%2F%2Ftherealchamps.com%2Fwp-content%2Fuploads%2Fgetty-images%2F2017%2F07%2F1436713160.jpeg", "publishedAt": "2023-06-23T13:01:00Z", "content": "The Kylian Mbappe to Real Madrid saga has dragged on for years. It had looked very close to completion last year but then the Frenchman unexpectedly ended up signing a contract extension with PSG. Ye\u2026 [+1425 chars]", "image_url": "https://images2.minutemediacdn.com/image/fetch/w_2000,h_2000,c_fit/https%3A%2F%2Ftherealchamps.com%2Fwp-content%2Fuploads%2Fgetty-images%2F2017%2F07%2F1436713160.jpeg"}, {"source": {"id": "the-hindu", "name": "The Hindu"}, "author": "Sportstar", "title": "Women's Ashes 2023 Nottingham Test Day 2 Live Score - Aus 473 all out: Ecclestone fifer winds up Aussie innings; century for Sutherland - Sportstar", "description": null, "url": "https://sportstar.thehindu.com/cricket/womens-cricket/womens-ashes-test-day-2-live-score-england-vs-australia-toss-scorecard-updates-milestones-stats/article67000541.ece", "urlToImage": null, "publishedAt": "2023-06-23T12:57:43Z", "content": null, "image_url": null}, {"source": {"id": null, "name": "cricket.com.au"}, "author": "Laura Jolly", "title": "Sutherland in rare air with incredible Test ton from No.8 - cricket.com.au", "description": "Allrounder produces the highest ever score in women's Tests from No.8 with her maiden international century putting Australia in the box seat at Trent Bridge", "url": "https://www.cricket.com.au/news/annabel-sutherland-maiden-test-international-century-no-8-australia-england-trent-bridge-ashes/2023-06-23", "urlToImage": "https://www.cricket.com.au/~/-/media/News/2023/06/23sutherland-main.ashx?w=1600", "publishedAt": "2023-06-23T12:54:24Z", "content": "Sutherland smashes record-breaking maiden century\r\nAnnabel Sutherland has struck the fastest Test century by an Australian woman, bringing up her maiden international hundred before lunch on day two \u2026 [+4345 chars]", "image_url": "https://www.cricket.com.au/~/-/media/News/2023/06/23sutherland-main.ashx?w=1600"}, {"source": {"id": null, "name": "India.com"}, "author": "Zee News Desk", "title": "Highlights | SL vs Oman, ICC World Cup Qualifier Cricket Live Score & Updates: Sri Lanka Beat Om - Zee News", "description": "Highlights | SL vs Oman, ICC World Cup Qualifier Cricket Live Score & Updates: Sri Lanka Beat Oman", "url": "https://zeenews.india.com/cricket/live-updates/highlights-cricket-score-sl-vs-oman-icc-icc-world-cup-2023-qualifier-match-11-today-sri-lanka-vs-oman-queens-sports-club-bulawayo-dasun-shanaka-zeeshan-maqsood-sri-lanka-beat-oman-2625580", "urlToImage": "https://english.cdn.zeenews.com/sites/default/files/2023/06/22/1226201-whatsapp-image-2023-06-22-at-12.44.28-pm.jpeg", "publishedAt": "2023-06-23T12:28:25Z", "content": null, "image_url": "https://english.cdn.zeenews.com/sites/default/files/2023/06/22/1226201-whatsapp-image-2023-06-22-at-12.44.28-pm.jpeg"}, {"source": {"id": null, "name": "cricket.com.au"}, "author": "Andrew Ramsey", "title": "Teenager Ahmed added to England Ashes squad | cricket.com.au - cricket.com.au", "description": "England have drafted 18-year-old allrounder Rehan Ahmed into their Ashes squad as cover for Moeen Ali", "url": "https://www.cricket.com.au/news/rehan-ahmed-added-england-ashes-squad-australia-lords-moeen-ali-leg-spin-allrounder/2023-06-23", "urlToImage": "https://www.cricket.com.au/~/-/media/News/2023/06/23RehanAhmed.ashx?w=1600", "publishedAt": "2023-06-23T12:25:07Z", "content": "England have called up 18-year-old leg-spinner Rehan Ahmed for their men's Test squad ahead of the second Ashes Test starting at Lord's on Wednesday, underscoring the concerns they hold over their sp\u2026 [+2896 chars]", "image_url": "https://www.cricket.com.au/~/-/media/News/2023/06/23RehanAhmed.ashx?w=1600"}, {"source": {"id": null, "name": "Pcb.com.pk"}, "author": null, "title": "Haris to captain Shaheens in ACC Men's Emerging Asia Cup - PCB", "description": "Urdu version of the media release attached here \r\n \r\n Lahore, 23 June 2023:\u00a0 Wicketkeeper-batter Mohammad Haris will captain Pakistan Shaheens in the eight-team 15-match ACC...", "url": "https://www.pcb.com.pk/press-release-detail/haris-to-captain-shaheens-in-acc-men-s-emerging-asia-cup.html", "urlToImage": "https://www.pcb.com.pk/images/news_images/featured_images/c39579dadb00.jpg", "publishedAt": "2023-06-23T11:15:54Z", "content": "
    • Urdu version of the media release attached here
    Lahore, 23 June 2023:\u00a0Wicketkeeper-batter Mohammad Haris will captain Pakistan Shaheens in the eight-team 15-match ACC Men\u2019s Emerging \u2026 [+1719 chars]", "image_url": "https://www.pcb.com.pk/images/news_images/featured_images/c39579dadb00.jpg"}, {"source": {"id": null, "name": "Khelnow.com"}, "author": "Author Name", "title": "Ratings: Sunil Chhetri leads from front as India conquer Pakistan in SAFF Championship clash - Khel Now", "description": "Sunil Chhetri stole the matchball as India defeated Pakistan to kickstart the SAFF Championship 2023 campaign.", "url": "https://khelnow.com/football/2023-06-saff-championship-india-vs-pakistan-player-ratings", "urlToImage": "https://assets-webp.khelnow.com/news/uploads/2023/06/21.06.23.6.png.webp", "publishedAt": "2023-06-23T10:45:37Z", "content": "India vs Pakistan, it is more than just a sport. And on a rainy and cold evening in Bengaluru where 22,860 people turned up to watch the Blue Tigers in action at the Sree Kanteerava Stadium against a\u2026 [+7598 chars]", "image_url": "https://assets-webp.khelnow.com/news/uploads/2023/06/21.06.23.6.png.webp"}, {"source": {"id": null, "name": "Rediff.com"}, "author": "PTI", "title": "'Wrestling will remember you for licking the feet of...' - Rediff.com", "description": "Phogat alleged that Yogeshwar had laughed off the sexual harassment complaints against the WFI chief during the oversight panel hearings", "url": "https://www.rediff.com/sports/report/wrestling-will-remember-you-for-licking-the-feet-of-vinesh-yogeshwar-dutt-brij-bhushan/20230623.htm", "urlToImage": "https://im.rediff.com/sports/2023/apr/28vinesh-phogat1.jpg", "publishedAt": "2023-06-23T10:31:56Z", "content": "Yogeshwar will be remembered for being Brij Bhushan's lackey: Vinesh Phogat\r\nIMAGE: Wrestlers Vinesh Phogat alleged that Yogeshwar Dutt had laughed off the sexual harassment complaints against the WF\u2026 [+4575 chars]", "image_url": "https://im.rediff.com/sports/2023/apr/28vinesh-phogat1.jpg"}, {"source": {"id": "espn-cric-info", "name": "ESPN Cric Info"}, "author": "Shashank Kishore", "title": "Samson, Gaikwad and Mukesh called up to India ODI squad for West Indies tour - ESPNcricinfo", "description": "Samson is one of two keepers named in the squad alongside Ishan Kishan", "url": "https://www.espncricinfo.com/story/sanju-samson-ruturaj-gaikwad-and-mukesh-kumar-called-up-to-india-odi-squad-for-west-indies-tour-1383238", "urlToImage": "https://img1.hscicdn.com/image/upload/f_auto/lsci/db/PICTURES/CMS/344500/344532.6.jpg", "publishedAt": "2023-06-23T10:30:00Z", "content": "NewsSamson is one of two keepers named in the squad alongside Ishan Kishan\r\nSanju Samson has scored 330 ODI runs at an average of 66.00\u00a0\u00a0\u2022\u00a0\u00a0Associated Press\r\nSanju Samson has earned a recall to India\u2026 [+2116 chars]", "image_url": "https://img1.hscicdn.com/image/upload/f_auto/lsci/db/PICTURES/CMS/344500/344532.6.jpg"}] \ No newline at end of file diff --git a/LearnersEd/explore_fields/news/technology.json b/LearnersEd/explore_fields/news/technology.json new file mode 100644 index 000000000..2a8e8378e --- /dev/null +++ b/LearnersEd/explore_fields/news/technology.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "Hindustan Times"}, "author": "HT Tech", "title": "iQOO Z6 5G price drop announced! Get 30% discount on Amazon - HT Tech", "description": "Searching for an affordable phone? Buy iQOO Z6 5G with a huge discount!", "url": "https://tech.hindustantimes.com/mobile/news/iqoo-z6-5g-price-drop-announced-get-30-discount-on-amazon-71687524098474.html", "urlToImage": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/battery_1664434939880_1687524429651.jpg", "publishedAt": "2023-06-23T12:51:33Z", "content": "The iQOO Z6 5G smartphone is now available at a discounted price on Amazon, making it an affordable option for those in search of a new device. Originally priced at Rs. 19,999, you can now purchase i\u2026 [+1138 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/battery_1664434939880_1687524429651.jpg"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": "Moneycontrol News", "title": "Google is testing an AI tool that can dub videos into other languages - Moneycontrol", "description": "The technology comes from Aloud, a project under Google's Area 120 incubator.", "url": "https://www.moneycontrol.com/news/technology/google-is-testing-an-ai-tool-that-can-dub-videos-into-other-languages-10847751.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2022/10/nordwood-themes-8LfE0Lywyak-unsplash-770x433.jpg", "publishedAt": "2023-06-23T12:26:59Z", "content": "Google is testing an AI-based service that lets you dub YouTube videos into other languages. Called Aloud, the service was developed under Google's Area 120 incubator, where employees work on various\u2026 [+779 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2022/10/nordwood-themes-8LfE0Lywyak-unsplash-770x433.jpg"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "HT Tech", "title": "iQOO Pro Series 2023 esports tournament returns with Rs. 10 lakh prize pool - HT Tech", "description": "iQOO has reaffirmed its commitment to the esports community in India by announcing the highly anticipated return of the iQOO Pro Series, a mega esports tournament.", "url": "https://tech.hindustantimes.com/tech/news/iqoo-pro-series-2023-esports-tournament-returns-with-rs-10-lakh-prize-pool-71687521466052.html", "urlToImage": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/iQOO__1687521811462_1687521811725.jpg", "publishedAt": "2023-06-23T12:09:53Z", "content": "iQOO has reaffirmed its commitment to the esports community in India by announcing the highly anticipated return of the iQOO Pro Series, a mega esports tournament. This upcoming event is set to push \u2026 [+1441 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/iQOO__1687521811462_1687521811725.jpg"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "HT Tech", "title": "Carl Pei reacts as Nothing Phone 2 price leaked ahead of launch - HT Tech", "description": "Nothing Phone 2 price has been leaked and it may well shock you!", "url": "https://tech.hindustantimes.com/mobile/news/carl-pei-reacts-as-nothing-phone-2-price-leaked-ahead-of-launch-71687520995919.html", "urlToImage": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/Nothing_Phone_1_unsplash_1675349500090_1687521203064.jpg", "publishedAt": "2023-06-23T11:55:07Z", "content": "Nothing Phone 2 is all set to launch next month globally on July 11. Even as we are nearing the launch date, several reports are shedding light on the expected specs, price, design, and features. Now\u2026 [+1224 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/23/1600x900/Nothing_Phone_1_unsplash_1675349500090_1687521203064.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "ET Telecom", "title": "Apple launches \u2018back to university\u2019 offers for Mac, iPad, AirPods, AppleCare+ - ETTelecom", "description": "These offers allow customers to save on Mac or iPad with education pricing. Further, they can also get AirPods with Mac and Apple Pencil with iPad, 20% off AppleCare+, and more.", "url": "https://telecom.economictimes.indiatimes.com/news/devices/apple-launches-back-to-university-offers-for-mac-ipad-airpods-applecare/101218929", "urlToImage": "https://etimg.etb2bimg.com/thumb/msid-101218929,imgsize-104464,width-1200,height=765,overlay-ettelecom/devices/apple-launches-back-to-university-offers-for-mac-ipad-airpods-applecare.jpg", "publishedAt": "2023-06-23T11:38:50Z", "content": "NEW DELHI: Apple has launched \u2018back to university\u2019 offers on its products including Mac, iPad and AirPods, for students and teachers across universities in India which can be availed of in Apple reta\u2026 [+1591 chars]", "image_url": "https://etimg.etb2bimg.com/thumb/msid-101218929,imgsize-104464,width-1200,height=765,overlay-ettelecom/devices/apple-launches-back-to-university-offers-for-mac-ipad-airpods-applecare.jpg"}, {"source": {"id": null, "name": "Jagran.com"}, "author": "Vikas Yadav", "title": "Your Smartphone May Soon Play Role Of Thermometer With Upcoming FeverPhone App; All Details Inside - Jagran English", "description": "FeverPhone uses a sensor that determines the battery heat of the phone The app times the duration at which the phone heats up and then identifies the amount that dissipates from the human body", "url": "https://english.jagran.com/technology/feverphone-app-your-smartphone-may-soon-play-role-of-thermometer-with-upcoming-temperature-sensing-app-10084243", "urlToImage": "https://imgeng.jagran.com/images/2023/jun/fever-phone-app1687520820263.jpg", "publishedAt": "2023-06-23T11:38:00Z", "content": "JE Technology Desk: A smartphone has evolved into more than a communication device. It can act as your entertainment partner, help you connect with your loved ones, squeeze the world of the Internet \u2026 [+2068 chars]", "image_url": "https://imgeng.jagran.com/images/2023/jun/fever-phone-app1687520820263.jpg"}, {"source": {"id": null, "name": "Jagran.com"}, "author": "Ashish Singh", "title": "Google Meet Rolling Out New Companion Mode Check-In Feature For Users; Here's How It Works - Jagran English", "description": "Google Meet is now getting the companion mode check-in feature for the users so that everyones name can easily be identified", "url": "https://english.jagran.com/technology/google-meet-rolling-out-new-companion-mode-checkin-feature-for-users-heres-how-it-works-10084226", "urlToImage": "https://imgeng.jagran.com/images/2023/jun/Google Meet (1)1687517695052.jpg", "publishedAt": "2023-06-23T10:47:00Z", "content": "Google Meet, one of the most used video conferencing platforms by professionals is now getting the highly anticipated companion mode check-in feature using which the users would be able to join the m\u2026 [+1704 chars]", "image_url": "https://imgeng.jagran.com/images/2023/jun/Google Meet (1)1687517695052.jpg"}, {"source": {"id": null, "name": "GSMArena.com"}, "author": "Michail", "title": "OnePlus Nord Buds 2R set to launch on July 5 - GSMArena.com", "description": "These will be an even more affordable alternative to the Nord Buds 2. OnePlus is gearing up to launch an even more affordable entry-level version of the...", "url": "https://www.gsmarena.com/oneplus_nord_buds_2r_set_to_launch_on_july_5-news-58952.php", "urlToImage": "https://fdn.gsmarena.com/imgroot/news/23/06/oneplus-nord-buds-2r-jul-5/-952x498w6/gsmarena_000.jpg", "publishedAt": "2023-06-23T10:29:01Z", "content": "OnePlus is gearing up to launch an even more affordable entry-level version of the Nord Buds 2 dubbed Nord Buds 2R on July 5. The earbuds already have their landing page on Amazon India and will brin\u2026 [+468 chars]", "image_url": "https://fdn.gsmarena.com/imgroot/news/23/06/oneplus-nord-buds-2r-jul-5/-952x498w6/gsmarena_000.jpg"}, {"source": {"id": null, "name": "India.com"}, "author": "IANS", "title": "WhatsApp Rolling Out New Icons For Communities & Groups On Android Beta - Zee News", "description": "Meta-owned messaging platform WhatsApp is rolling out new icons for communities and their linked groups, on Android beta. Earlier, the icon for the community appeared with rounded edges, to make it easy to distinguish it from other conversations, reports WaBe\u2026", "url": "https://zeenews.india.com/technology/whatsapp-rolling-out-new-icons-for-communities-groups-on-android-beta-2625870.html", "urlToImage": "https://english.cdn.zeenews.com/sites/default/files/2023/06/23/1226642-whatsappimage.jpg", "publishedAt": "2023-06-23T10:04:33Z", "content": "San Francisco: Meta-owned messaging platform WhatsApp is rolling out new icons for communities and their linked groups, on Android beta.\r\nEarlier, the icon for the community appeared with rounded edg\u2026 [+1196 chars]", "image_url": "https://english.cdn.zeenews.com/sites/default/files/2023/06/23/1226642-whatsappimage.jpg"}, {"source": {"id": null, "name": "SamMobile"}, "author": "SamMobile, Mihai Matei", "title": "Choose the right color and get the Galaxy Buds 2 for almost half the price - SamMobile - Samsung news", "description": "Some deals are better than others, but this latest one is up there with the best. If you're looking for ...", "url": "https://www.sammobile.com/news/choose-the-right-color-get-galaxy-buds-2-for-almost-half-price/", "urlToImage": "https://www.sammobile.com/wp-content/uploads/2021/08/Galaxy-Buds-2-review-2.jpg", "publishedAt": "2023-06-23T09:39:00Z", "content": "Some deals are better than others, but this latest one is up there with the best. If youre looking for a pair of wireless earbuds, you can save a whopping 43% on the Samsung Galaxy Buds 2 from Amazon\u2026 [+828 chars]", "image_url": "https://www.sammobile.com/wp-content/uploads/2021/08/Galaxy-Buds-2-review-2.jpg"}] \ No newline at end of file diff --git a/LearnersEd/explore_fields/static/images/bg-new.png b/LearnersEd/explore_fields/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/explore_fields/static/images/bg-new.png differ diff --git a/LearnersEd/explore_fields/templates/news_list.html b/LearnersEd/explore_fields/templates/news_list.html new file mode 100644 index 000000000..2f32182c1 --- /dev/null +++ b/LearnersEd/explore_fields/templates/news_list.html @@ -0,0 +1,212 @@ +{% load static %} + + + + Learners'Ed - Explore Fields + + + + + + + + + {% include 'navbar.html' %} +

    Explore Fields

    + {% for category, articles in articles_by_category.items %} +
    +

    {{ category }}

    +
    + {% for article in articles %} +
    + {{ article.title }} +

    {{ article.title }}

    +

    {{ article.description }}

    + Read More +
    + {% endfor %} +
    +
    +
    + {% endfor %} + + diff --git a/LearnersEd/explore_fields/tests.py b/LearnersEd/explore_fields/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/explore_fields/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/explore_fields/views.py b/LearnersEd/explore_fields/views.py new file mode 100644 index 000000000..ab9d8fa48 --- /dev/null +++ b/LearnersEd/explore_fields/views.py @@ -0,0 +1,47 @@ +from django.shortcuts import render +import json +import os +import requests + +def explore_fields(request): + newsapi_key = 'ea2b2ad73b844acf9b29b58c9fd2646f' # Replace with your NewsAPI API key + categories = ['technology', 'general', 'business', 'entertainment', 'health', 'science', 'sports'] + articles_by_category = {} + + for category in categories: + url = f'https://newsapi.org/v2/top-headlines?country=in&category={category}&apiKey={newsapi_key}' + response = requests.get(url) + data = response.json() + + if response.status_code == 200: + articles = data.get('articles', []) + for article in articles: + image_url = article.get('urlToImage') + article['image_url'] = image_url + articles_by_category[category] = articles[:10] # Fetch the top 10 articles for each category + else: + # If API result is not available, try fetching data from saved file + file_path = f'D:/Project/Website-Phoenix13/LearnersEd/explore_fields/news/{category}.json' + if os.path.exists(file_path): + file_size = os.path.getsize(file_path) + if file_size > 0: + with open(file_path, 'r') as file: + articles = json.load(file) + articles_by_category[category] = articles + else: + error_message = 'Error Fetch: Not able to fetch articles' + context = {'error_message': error_message} + return render(request, 'error.html', context) + else: + error_message = data.get('message', 'Error File: File Not Found') + context = {'error_message': error_message} + return render(request, 'error.html', context) + + # Save the fetched data to files + for category, articles in articles_by_category.items(): + file_path = f'D:/Project/Website-Phoenix13/LearnersEd/explore_fields/news/{category}.json' + with open(file_path, 'w') as file: + json.dump(articles, file) + + context = {'articles_by_category': articles_by_category} + return render(request, 'news_list.html', context) diff --git a/LearnersEd/gamify_app/__init__.py b/LearnersEd/gamify_app/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/gamify_app/__pycache__/__init__.cpython-310.pyc b/LearnersEd/gamify_app/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..a391b0626 Binary files /dev/null and b/LearnersEd/gamify_app/__pycache__/__init__.cpython-310.pyc differ diff --git a/LearnersEd/gamify_app/__pycache__/__init__.cpython-39.pyc b/LearnersEd/gamify_app/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..e094d0738 Binary files /dev/null and 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b/LearnersEd/gamify_app/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..67c9b3ed2 Binary files /dev/null and b/LearnersEd/gamify_app/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/gamify_app/admin.py b/LearnersEd/gamify_app/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/gamify_app/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/gamify_app/apps.py b/LearnersEd/gamify_app/apps.py new file mode 100644 index 000000000..a3cf090e5 --- /dev/null +++ b/LearnersEd/gamify_app/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class GamifyAppConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'gamify_app' diff --git a/LearnersEd/gamify_app/migrations/__init__.py b/LearnersEd/gamify_app/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/gamify_app/migrations/__pycache__/__init__.cpython-310.pyc b/LearnersEd/gamify_app/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..d636f35f0 Binary files /dev/null and b/LearnersEd/gamify_app/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/LearnersEd/gamify_app/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/gamify_app/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..dc8a7ae8f Binary files /dev/null and b/LearnersEd/gamify_app/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/gamify_app/models.py b/LearnersEd/gamify_app/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/LearnersEd/gamify_app/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/LearnersEd/gamify_app/static/gamify.css b/LearnersEd/gamify_app/static/gamify.css new file mode 100644 index 000000000..e70e4d9f1 --- /dev/null +++ b/LearnersEd/gamify_app/static/gamify.css @@ -0,0 +1,234 @@ +body, html { + margin: 0; + padding: 0; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + font-family: 'Darker Grotesque', sans-serif; + height: 100vh; + overflow-y: hidden; +} + +#coin { + background-image: url(./images/coins.png); + background-size: contain; + background-position: center; + background-repeat: no-repeat; + filter: drop-shadow(0 0 1rem gold); + animation: pulsate 6s infinite; +} + +@keyframes pulsate { + 0% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } + 30% { + transform: rotateY(0deg) scale(1.1); + filter: drop-shadow(0 0 1rem gold); + } + 100% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } +} + + +.il { + padding: 0 20px; + padding-top: 20px; + padding-bottom: 10px; + border-radius: 40px; + transition-duration: 200ms; + outline: 2px solid black; + background-color: white; +} + + +#whole { + + /* padding: 20px; */ + text-align: center; + display: flex; + flex-direction: column; + gap:30px; + + } + .heading{ + background-color: white; + margin-top: 100px; + border-top: 2px solid black; + border-bottom: 2px solid black; + /* padding: 30px; */ + height:90px; + } + + #title { + color: #9376E0; + font-size: 60px; + margin: 0; + text-transform: uppercase; + letter-spacing: 10px; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + + } + .box { + display: flex; + padding: 15px; + gap:15px; + } + + .left-section { + flex: 7; + display: flex; + flex-direction: column; + gap:15px; + } + + .twin_flexboxes{ + display: flex; + flex-direction: row; + gap:15px; + width: 85%; + padding-left: 30px; + } + + .right-section { + flex: 3; + } + + .flexbox { + flex: 1; + + outline: 3px solid black; + cursor: pointer; + border-radius: 5px; + + padding: 20px; + height: 200px; + width: 200px; + } + .flexbox:hover{ + box-shadow:2px 5px black; + transition-duration: 200ms; + } + #flexbox1 { + display: flex; + background-image: url('images/quiz.png'); + /* animation: gradientAnimation 5s infinite ease-in-out; */ + /* background-size: 200% 200%; */ + /* transition-duration: 100ms; */ + background-position: center; + background-size: cover; + background-repeat: no-repeat; + flex-direction: column; + justify-content: center; + align-items: center; + gap:10px; + font-size: 30px; + } + + .quiz-heading{ + text-transform: uppercase; + letter-spacing: 2px; + outline: 4px solid #9376E0; + border:2px white; + padding:5px; + width: 40%; + font-size: 20px; + border-radius:5px; + background-color: black; + color:white; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + + + } + .start-button { + padding: 10px 20px; + font-size: 20px; + background-color: #9376E0; + color: #fff; + border: none; + border-radius: 5px; + cursor: pointer; + } + + .start-button:hover { + background-color: blueviolet; + } + #flexbox2{ + display: flex; + background-image: url('images/virtualpet.png'); + background-position: center; + background-size: cover; + background-repeat: no-repeat; + flex-direction: column; + margin: 30px; + gap:10px; + font-size: 30px; + } + + .large-flexbox { + flex: 1; + box-shadow: 0 0 20px 2px gray; + padding: 20px; + height:450px; + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 40px; + } + .horizontal-flexbox { + display: flex; + height: 200px; +} + +#pet-graph { + border-radius: 30px; + box-shadow: inset 0px 0px 5px 0px rgba(0,0,0,0.75); + border-top-right-radius: 40px; + border-bottom-right-radius: 40px; +} + +.bottom-flexbox { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 40px; + transition-duration: 200ms; + outline: 4px solid white; + width:90%; + height: 85%; + box-shadow: 0 0 20px 2px gray; + margin-left: auto; + margin-right: auto; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + + + + + + + + + + + + + + \ No newline at end of file diff --git a/LearnersEd/gamify_app/static/images/bg-new.png b/LearnersEd/gamify_app/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/gamify_app/static/images/bg-new.png differ diff --git a/LearnersEd/gamify_app/static/images/quiz.png b/LearnersEd/gamify_app/static/images/quiz.png new file mode 100644 index 000000000..7f5212888 Binary files /dev/null and b/LearnersEd/gamify_app/static/images/quiz.png differ diff --git a/LearnersEd/gamify_app/static/images/virtualpet.png b/LearnersEd/gamify_app/static/images/virtualpet.png new file mode 100644 index 000000000..f35909163 Binary files /dev/null and b/LearnersEd/gamify_app/static/images/virtualpet.png differ diff --git a/LearnersEd/gamify_app/static/jquery.counterup.min.js b/LearnersEd/gamify_app/static/jquery.counterup.min.js new file mode 100644 index 000000000..cddf5a103 --- /dev/null +++ b/LearnersEd/gamify_app/static/jquery.counterup.min.js @@ -0,0 +1,8 @@ +/*! +* jquery.counterup.js 1.0 +* +* Copyright 2013, Benjamin Intal http://gambit.ph @bfintal +* Released under the GPL v2 License +* +* Date: Nov 26, 2013 +*/(function(e){"use strict";e.fn.counterUp=function(t){var n=e.extend({time:400,delay:10},t);return this.each(function(){var t=e(this),r=n,i=function(){var e=[],n=r.time/r.delay,i=t.text(),s=/[0-9]+,[0-9]+/.test(i);i=i.replace(/,/g,"");var o=/^[0-9]+$/.test(i),u=/^[0-9]+\.[0-9]+$/.test(i),a=u?(i.split(".")[1]||[]).length:0;for(var f=n;f>=1;f--){var l=parseInt(i/n*f);u&&(l=parseFloat(i/n*f).toFixed(a));if(s)while(/(\d+)(\d{3})/.test(l.toString()))l=l.toString().replace(/(\d+)(\d{3})/,"$1,$2");e.unshift(l)}t.data("counterup-nums",e);t.text("0");var c=function(){t.text(t.data("counterup-nums").shift());if(t.data("counterup-nums").length)setTimeout(t.data("counterup-func"),r.delay);else{delete t.data("counterup-nums");t.data("counterup-nums",null);t.data("counterup-func",null)}};t.data("counterup-func",c);setTimeout(t.data("counterup-func"),r.delay)};t.waypoint(i,{offset:"100%",triggerOnce:!0})})}})(jQuery); \ No newline at end of file diff --git a/LearnersEd/gamify_app/templates/gamify.html b/LearnersEd/gamify_app/templates/gamify.html new file mode 100644 index 000000000..6b2a5fbe0 --- /dev/null +++ b/LearnersEd/gamify_app/templates/gamify.html @@ -0,0 +1,117 @@ +{% load static %} + + + + + + Gamification + + + + + + + + + + + + + + {% include 'navbar.html' %} +
    +
    +

    Gamify

    +
    +
    +
    +
    +
    +

    QUIZ

    +
    + +
    +

    VIRTUAL PET

    +
    +
    +
    +
    +

    Learners'Ed Coins
    *For new students, bonus 200 coins will be granted!

    +
    +
    +
    +
    +

    200

    +

    Learners'Ed Coins
    Left

    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Your Virtual Pet

    +

    Alex Pandit

    +
    +
    +
    +

    Level

    +

    3

    +
    +
    +

    Rank

    +

    94th

    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + + + \ No newline at end of file diff --git a/LearnersEd/gamify_app/tests.py b/LearnersEd/gamify_app/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/gamify_app/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/gamify_app/views.py b/LearnersEd/gamify_app/views.py new file mode 100644 index 000000000..450dc9eea --- /dev/null +++ b/LearnersEd/gamify_app/views.py @@ -0,0 +1,4 @@ +from django.shortcuts import render + +def gamify(request): + return render(request, 'gamify.html', {}) \ No newline at end of file diff --git a/LearnersEd/lecture_attend/__init__.py b/LearnersEd/lecture_attend/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/lecture_attend/__pycache__/__init__.cpython-39.pyc b/LearnersEd/lecture_attend/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..bd404eaf1 Binary files /dev/null and b/LearnersEd/lecture_attend/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/lecture_attend/__pycache__/admin.cpython-39.pyc b/LearnersEd/lecture_attend/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..9e83fe53d Binary files /dev/null and b/LearnersEd/lecture_attend/__pycache__/admin.cpython-39.pyc differ diff --git a/LearnersEd/lecture_attend/__pycache__/apps.cpython-39.pyc b/LearnersEd/lecture_attend/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..f1a5743c0 Binary files /dev/null and b/LearnersEd/lecture_attend/__pycache__/apps.cpython-39.pyc differ diff --git a/LearnersEd/lecture_attend/__pycache__/models.cpython-39.pyc b/LearnersEd/lecture_attend/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..9bbb2449c Binary files /dev/null and b/LearnersEd/lecture_attend/__pycache__/models.cpython-39.pyc differ diff --git a/LearnersEd/lecture_attend/__pycache__/views.cpython-39.pyc b/LearnersEd/lecture_attend/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..ddbb24da1 Binary files /dev/null and b/LearnersEd/lecture_attend/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/lecture_attend/admin.py b/LearnersEd/lecture_attend/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/lecture_attend/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/lecture_attend/apps.py b/LearnersEd/lecture_attend/apps.py new file mode 100644 index 000000000..73b0ffc4c --- /dev/null +++ b/LearnersEd/lecture_attend/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class LectureAttendConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'lecture_attend' diff --git a/LearnersEd/lecture_attend/migrations/__init__.py b/LearnersEd/lecture_attend/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/lecture_attend/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/lecture_attend/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..194d484cc Binary files /dev/null and b/LearnersEd/lecture_attend/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/lecture_attend/model/drowsiness_detection.h5 b/LearnersEd/lecture_attend/model/drowsiness_detection.h5 new file mode 100644 index 000000000..7b89ae3e4 Binary files /dev/null and b/LearnersEd/lecture_attend/model/drowsiness_detection.h5 differ diff --git a/LearnersEd/lecture_attend/models.py b/LearnersEd/lecture_attend/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/LearnersEd/lecture_attend/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/LearnersEd/lecture_attend/static/images/bg-new.png b/LearnersEd/lecture_attend/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/lecture_attend/static/images/bg-new.png differ diff --git a/LearnersEd/lecture_attend/static/lectures.css b/LearnersEd/lecture_attend/static/lectures.css new file mode 100644 index 000000000..480c0dd41 --- /dev/null +++ b/LearnersEd/lecture_attend/static/lectures.css @@ -0,0 +1,73 @@ +body, html { + margin: 0; + padding: 0; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + +} +#whole { + + text-align: center; + display: flex; + flex-direction: column; + gap:30px; + + } + .heading{ + background-color: white; + margin-top: 110px; + /* padding: 30px; */ + height:90px; + } + + #title { + color: #9376E0; + font-size: 60px; + margin: 0; + text-transform: uppercase; + letter-spacing: 10px; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + + } + .main { + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + gap:10px; + } + + .box { + width: 900px; + background-color: aliceblue; + + } + + .box1 { + height: 350px; + } + + .box2, + .box3 { + height: 30px; +} +.progress-bar { + background-color: #e6e6e6; + border-radius: 5px; + height:30px; + overflow: hidden; + } + + .progress-bar span { + display: block; + height: 100%; + background-color: #9376E0; + } \ No newline at end of file diff --git a/LearnersEd/lecture_attend/static/script-lec.js b/LearnersEd/lecture_attend/static/script-lec.js new file mode 100644 index 000000000..eea1aacf2 --- /dev/null +++ b/LearnersEd/lecture_attend/static/script-lec.js @@ -0,0 +1,118 @@ +const videoElement = document.getElementById('videoElement'); +const pred = document.getElementById('pred'); + +let stream; +let k = 0; +let csi; // Interval variable + +function startCamera() { + navigator.mediaDevices.getUserMedia({ video: true }) + .then(videoStream => { + stream = videoStream; + videoElement.srcObject = stream; + csi = setInterval(captureFrame, 2000); // Capture frame every 5 seconds + }) + .catch(error => { + console.error('Error accessing camera:', error); + }); +} + +function getRandomInt(max) { + return Math.floor(Math.random() * max); +} + +var excercise = ['Neck Rolls: Roll your head in a circular motion, clockwise and counterclockwise.', + 'Shoulder Shrugs: Lift your shoulders up toward your ears and then release them down.', + 'Seated Twist: Twist your torso to the left and right, looking over each shoulder.', + 'Seated Leg Raises: Lift one leg up, extend it straight, and hold for a few seconds. Repeat with the other leg.', + 'Ankle Circles: Rotate your ankles in a circular motion, clockwise and counterclockwise.', + 'Arm Circles: Extend your arms to the sides and make small circles with your hands.', + 'Wrist Flexes: Stretch your arms forward, palms facing up, and flex your wrists up and down.', + 'Seated Toe Taps: Lift your feet off the ground and tap your toes while keeping your heels grounded.', + 'Seated Marching: Lift one knee up, then the other, as if you were marching in place.', + 'Seated Heel Raises: Lift your heels off the floor while keeping your toes grounded.', + 'Seated Calf Stretches: Extend one leg forward, flex your foot, and gently pull your toes towards you. Repeat with the other leg.', + 'Seated Chest Stretch: Clasp your hands behind your back, straighten your arms, and lift your chest.', + 'Seated Spinal Twist: Cross one leg over the other, place your opposite hand on the outside of the crossed knee, and gently twist your torso.', + 'Seated Side Bends: Extend one arm overhead and lean to the opposite side, stretching your side. Repeat on the other side.', + 'Seated Hip Circles: Sit forward on the edge of your chair and make circular motions with your hips.', + 'Seated Cat-Cow Stretch: Arch your back and then round it, alternating between the two positions.', + 'Seated Abdominal Contractions: Tighten your abdominal muscles by pulling your belly button towards your spine.', + 'Seated Shoulder Stretch: Reach one arm across your chest, hold it with the opposite hand, and gently pull it closer to your body. Repeat with the other arm.', + 'Seated Butterfly Stretch: Bring the soles of your feet together and gently press your knees towards the floor.', + 'Seated Hamstring Stretch: Extend one leg forward, flex your foot, and reach towards your toes. Repeat with the other leg.', + 'Seated Wrist Stretches: Extend one arm forward, palm facing up, and use the other hand to gently pull your fingers back. Repeat with the other hand.', + 'Seated Hand Squeezes: Make a fist with both hands, squeeze tightly, and then release.', + 'Seated Eye Rolls: Look up, down, left, and right, and then make circular motions with your eyes.', + 'Seated Deep Breathing: Inhale deeply through your nose, hold for a few seconds, and exhale slowly through your mouth.', + 'Seated Meditation: Close your eyes, focus on your breath, and allow your mind to relax.', + 'Seated Shoulder Blade Squeezes: Squeeze your shoulder blades together, hold for a few seconds, and then release.', + 'Seated Leg Crosses: Cross one leg over the other, place your ankle on the opposite knee, and gently press down on the raised knee.', + 'Seated Side Leg Raises: Sit upright, extend one leg out to the side, and lift it up while keeping it straight. Repeat with the other leg.', + 'Seated Torso Stretch: Reach both arms overhead]']; + +function captureFrame() { + // Create a canvas element + const canvas = document.createElement('canvas'); + canvas.width = videoElement.videoWidth; + canvas.height = videoElement.videoHeight; + + // Draw the current frame from the video element onto the canvas + const context = canvas.getContext('2d'); + context.drawImage(videoElement, 0, 0, canvas.width, canvas.height); + + // Get the base64-encoded image data from the canvas + const imageData = canvas.toDataURL('image/jpeg'); + + quiz_data = { + image_data: imageData, + }; + + // Send the image data as a POST request to the Django backend using AJAX + $.ajax({ + url: '', + type: 'POST', + data: JSON.stringify(quiz_data), + contentType: 'application/json', + success: function (response) { + console.log(response.message); + // Handle the response from the Django backend + const prediction = response.prediction; // Assuming the prediction is returned as a property in the response + if (prediction == "Yes") { + pred.innerHTML = "Drowsiness Detected"; + clearInterval(csi); // Halt the interval + alert(excercise[getRandomInt(excercise.length)]); + setTimeout(() => { + pred.innerHTML = ""; // Clear the alert message after it is clicked + csi = setInterval(captureFrame, 2000); // Resume the interval + }, 5000); // Adjust the duration of the alert as needed + } else { + pred.innerHTML = "All Ok!"; + } + console.log('Response from Django:', prediction); + }, + error: function (xhr, status, error) { + console.error('Error sending image to Django:', error); + } + }); + console.log(k); + if (k == 1) { + clearInterval(csi); + stopCamera(); + } +} + +function stopCamera() { + if (stream && stream.getTracks) { + stream.getTracks().forEach(track => track.stop()); + stream = null; + } +} + +startCamera(); + +var timer = setTimeout(() => { + k = 1; + console.log("Finish"); + window.location.href = "/lectures/"; +}, 60000); diff --git a/LearnersEd/lecture_attend/templates/lectures-attend.html b/LearnersEd/lecture_attend/templates/lectures-attend.html new file mode 100644 index 000000000..292c5278d --- /dev/null +++ b/LearnersEd/lecture_attend/templates/lectures-attend.html @@ -0,0 +1,36 @@ +{% load static %} + + + + + + Gamification + + + + + + + + + + + + {% include 'navbar.html' %} +
    +
    +

    LECTURE

    +
    +
    +
    +
    + +
    +
    +
    +
    + + + + + \ No newline at end of file diff --git a/LearnersEd/lecture_attend/tests.py b/LearnersEd/lecture_attend/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/lecture_attend/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/lecture_attend/views.py b/LearnersEd/lecture_attend/views.py new file mode 100644 index 000000000..752fa4d43 --- /dev/null +++ b/LearnersEd/lecture_attend/views.py @@ -0,0 +1,96 @@ +from django.shortcuts import render +from django.http import JsonResponse +from django.views.decorators.csrf import csrf_exempt +from PIL import Image +from io import BytesIO +import base64 +import json +import face_recognition +from tensorflow import keras +import cv2 +import numpy as np + +eye_model = keras.models.load_model(r'D:\Project\Website-Phoenix13\LearnersEd\lecture_attend\model\drowsiness_detection.h5') + + +def eye_cropper(frame): + frame_np = np.array(frame) # Convert PIL image to NumPy array + frame_np = cv2.cvtColor(frame_np, cv2.COLOR_RGB2BGR) # Convert RGB to BGR (if necessary) + + gray = cv2.cvtColor(frame_np, cv2.COLOR_BGR2GRAY) + facial_features_list = face_recognition.face_landmarks(gray) + + try: + eye = facial_features_list[0]['left_eye'] + except: + try: + eye = facial_features_list[0]['right_eye'] + except: + return None + + x_max = max([coordinate[0] for coordinate in eye]) + x_min = min([coordinate[0] for coordinate in eye]) + y_max = max([coordinate[1] for coordinate in eye]) + y_min = min([coordinate[1] for coordinate in eye]) + + x_range = x_max - x_min + y_range = y_max - y_min + + if x_range > y_range: + right = round(.5 * x_range) + x_max + left = x_min - round(.5 * x_range) + bottom = round((((right - left) - y_range)) / 2) + y_max + top = y_min - round((((right - left) - y_range)) / 2) + else: + bottom = round(.5 * y_range) + y_max + top = y_min - round(.5 * y_range) + right = round((((bottom - top) - x_range)) / 2) + x_max + left = x_min - round((((bottom - top) - x_range)) / 2) + + cropped = frame_np[top:(bottom + 1), left:(right + 1)] + + cropped = cv2.resize(cropped, (80, 80)) + image_for_prediction = cropped.reshape(-1, 80, 80, 3) + + return image_for_prediction + + +def predict(frame): + image_for_prediction = eye_cropper(frame) + if image_for_prediction is None: + return 'Yes' + try: + image_for_prediction = image_for_prediction/255.0 + except: + print("Error") + + prediction = eye_model.predict(image_for_prediction) + print(prediction) + # Based on prediction, display either "Open Eyes" or "Closed Eyes" + if prediction < 0.7: + status = 'No' + else: + status = 'Yes' + return status + +@csrf_exempt +def lecture_attend(request): + if request.method == 'POST': + json_data = json.loads(request.body) + image_data = json_data.get('image_data') + if image_data: + # Convert the base64-encoded image data to PIL Image + image = Image.open(BytesIO(base64.b64decode(image_data.split(',')[1]))) + + # Perform any processing or DL model prediction with the image + prediction_result = predict(image) + + # Replace this dummy response with the actual response you want to send back + response = {'prediction': prediction_result} + + return JsonResponse(response) + else: + # No image data received + response = {'error': 'No image data received'} + return JsonResponse(response, status=400) + return render(request, 'lectures-attend.html', {}) diff --git a/LearnersEd/lectures/__init__.py b/LearnersEd/lectures/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/lectures/__pycache__/__init__.cpython-39.pyc b/LearnersEd/lectures/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..a910a6731 Binary files /dev/null and b/LearnersEd/lectures/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/lectures/__pycache__/admin.cpython-39.pyc b/LearnersEd/lectures/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..4a39b9f40 Binary files /dev/null and b/LearnersEd/lectures/__pycache__/admin.cpython-39.pyc differ diff --git a/LearnersEd/lectures/__pycache__/apps.cpython-39.pyc b/LearnersEd/lectures/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..ea0c41b0f Binary files /dev/null and b/LearnersEd/lectures/__pycache__/apps.cpython-39.pyc differ diff --git a/LearnersEd/lectures/__pycache__/models.cpython-39.pyc b/LearnersEd/lectures/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..a0a8c41bb Binary files /dev/null and b/LearnersEd/lectures/__pycache__/models.cpython-39.pyc differ diff --git a/LearnersEd/lectures/__pycache__/views.cpython-39.pyc b/LearnersEd/lectures/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..ead7514aa Binary files /dev/null and b/LearnersEd/lectures/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/lectures/admin.py b/LearnersEd/lectures/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/lectures/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/lectures/apps.py b/LearnersEd/lectures/apps.py new file mode 100644 index 000000000..30d62f284 --- /dev/null +++ b/LearnersEd/lectures/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class LecturesConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'lectures' diff --git a/LearnersEd/lectures/migrations/__init__.py b/LearnersEd/lectures/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/lectures/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/lectures/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..f91b9bf4e Binary files /dev/null and b/LearnersEd/lectures/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/lectures/models.py b/LearnersEd/lectures/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/LearnersEd/lectures/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/LearnersEd/lectures/static/images/bg-new.png b/LearnersEd/lectures/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/lectures/static/images/bg-new.png differ diff --git a/LearnersEd/lectures/static/script-lectures.js b/LearnersEd/lectures/static/script-lectures.js new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/lectures/static/styles-lectures.css b/LearnersEd/lectures/static/styles-lectures.css new file mode 100644 index 000000000..899863a62 --- /dev/null +++ b/LearnersEd/lectures/static/styles-lectures.css @@ -0,0 +1,67 @@ +body { + background-image: url(./images/bg-new.png); + background-position: center; + background-repeat: no-repeat; + background-size: cover; + background-attachment: fixed; + font-family: 'Darker Grotesque', sans-serif; +} + +#play>button { + box-shadow: 0 0 5px 2px gray; + background-image: linear-gradient(to right bottom, #9376E0, #F3BCC8); + animation: gradientAnimation 3s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; +} + +.lecture { + box-shadow: 0 0 10px 2px gray; + border-radius: 30px; + transition-duration: 200ms; +} + +.lecture:hover { + transform: scale(1.05); + transition-duration: 200ms; + transition-timing-function: ease-in; +} + + +/* width */ +::-webkit-scrollbar { + width: 0px; + visibility: hidden; +} + +#box { + border-radius: 50px; + height: 450px; + scroll-behavior: smooth; +} + +p { + white-space: normal; +} + +#play>button:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#play>button:active { + transform: scale(0.9); + background-image: none; + background-color: black; + transition-duration: 200ms; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + diff --git a/LearnersEd/lectures/templates/lectures.html b/LearnersEd/lectures/templates/lectures.html new file mode 100644 index 000000000..2e589f6b9 --- /dev/null +++ b/LearnersEd/lectures/templates/lectures.html @@ -0,0 +1,111 @@ +{% load static %} + + + + + + + Learners'Ed - Lectures + + + + + + + + + {% include 'navbar.html' %} +

    Lectures

    +
    +
    +
    +

    Lecture 1

    +

    Lorem ipsum dolor, sit amet consectetur adipisicing elit.

    +
    +
    +

    Watch Time

    +

    29:38

    +
    +
    +

    Lecture Time

    +

    1:32:12

    +
    +
    + +
    +

    Status

    +

    ❌

    + +
    +
    +
    +
    +
    +

    Lecture 2

    +

    Lorem ipsum dolor, sit amet consectetur adipisicing elit.

    +
    +
    +

    Watch Time

    +

    29:38

    +
    +
    +

    Lecture Time

    +

    1:32:12

    +
    +
    + +
    +

    Status

    +

    ❌

    + +
    +
    +
    +
    +
    +

    Lecture 3

    +

    Lorem ipsum dolor, sit amet consectetur adipisicing elit.

    +
    +
    +

    Watch Time

    +

    29:38

    +
    +
    +

    Lecture Time

    +

    1:32:12

    +
    +
    + +
    +

    Status

    +

    ❌

    + +
    +
    +
    +
    +
    +

    Lecture 4

    +

    Lorem ipsum dolor, sit amet consectetur adipisicing elit.

    +
    +
    +

    Watch Time

    +

    29:38

    +
    +
    +

    Lecture Time

    +

    1:32:12

    +
    +
    + +
    +

    Status

    +

    ❌

    + +
    +
    +
    +
    + + + \ No newline at end of file diff --git a/LearnersEd/lectures/tests.py b/LearnersEd/lectures/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/lectures/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/lectures/views.py b/LearnersEd/lectures/views.py new file mode 100644 index 000000000..080229a08 --- /dev/null +++ b/LearnersEd/lectures/views.py @@ -0,0 +1,4 @@ +from django.shortcuts import render + +def lectures(request): + return render(request, 'lectures.html', {}) diff --git a/LearnersEd/login_register/__init__.py b/LearnersEd/login_register/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/login_register/__pycache__/__init__.cpython-310.pyc b/LearnersEd/login_register/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..5d99d7cbe Binary files /dev/null and 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mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/login_register/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/login_register/apps.py b/LearnersEd/login_register/apps.py new file mode 100644 index 000000000..924353042 --- /dev/null +++ b/LearnersEd/login_register/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class LoginRegisterConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'login_register' diff --git a/LearnersEd/login_register/migrations/0001_initial.py b/LearnersEd/login_register/migrations/0001_initial.py new file mode 100644 index 000000000..325d57167 --- /dev/null +++ b/LearnersEd/login_register/migrations/0001_initial.py @@ -0,0 +1,116 @@ +# Generated by Django 4.1.9 on 2023-06-07 00:37 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ] + + operations = [ + migrations.CreateModel( + name='Category', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('name', models.CharField(max_length=100)), + ], + ), + migrations.CreateModel( + name='Rank', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('average_marks', models.FloatField(default=0)), + ('average_attendance', models.FloatField(default=0)), + ('average_assignment_percent', models.FloatField(default=0)), + ('rank', models.IntegerField()), + ], + ), + migrations.CreateModel( + name='Student', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('student_name', models.CharField(max_length=100)), + ('email', models.EmailField(max_length=254, unique=True)), + ('password', models.CharField(max_length=100)), + ('class_name', models.CharField(max_length=100)), + ('section', models.CharField(max_length=100)), + ('favourite_categories', models.ManyToManyField(related_name='students', to='login_register.category')), + ], + ), + migrations.CreateModel( + name='VirtualPet', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('pet_name', models.CharField(max_length=100)), + ('pet_type', models.CharField(max_length=100)), + ('pet_level', models.PositiveIntegerField(default=1)), + ('pet_level_progress', models.PositiveIntegerField(default=0)), + ('pet_coin', models.PositiveIntegerField(default=200)), + ('rank', models.PositiveIntegerField(default=0)), + ('student', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='virtual_pet', to='login_register.student')), + ], + ), + migrations.CreateModel( + name='SectionRank', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('class_name', models.CharField(max_length=100)), + ('section', models.CharField(max_length=100)), + ('ranks', models.ManyToManyField(to='login_register.rank')), + ], + ), + migrations.AddField( + model_name='rank', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student'), + ), + migrations.CreateModel( + name='Marks', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('maths', models.PositiveIntegerField()), + ('hindi', models.PositiveIntegerField()), + ('english', models.PositiveIntegerField()), + ('science', models.PositiveIntegerField()), + ('social_science', models.PositiveIntegerField()), + ('total_marks', models.PositiveIntegerField(default=0)), + ('average_marks', models.FloatField(default=0)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + migrations.CreateModel( + name='ClassRank', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('class_name', models.CharField(max_length=100)), + ('ranks', models.ManyToManyField(to='login_register.rank')), + ], + ), + migrations.CreateModel( + name='Attendance', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('maths_attendance', models.PositiveIntegerField()), + ('hindi_attendance', models.PositiveIntegerField()), + ('english_attendance', models.PositiveIntegerField()), + ('science_attendance', models.PositiveIntegerField()), + ('social_science_attendance', models.PositiveIntegerField()), + ('average_attendance', models.FloatField(default=0)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + migrations.CreateModel( + name='Assignment', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('completed_assignments', models.PositiveIntegerField(default=0)), + ('total_assignments', models.PositiveIntegerField(default=0)), + ('assignment_percentage', models.FloatField(default=0)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + ] diff --git a/LearnersEd/login_register/migrations/0002_alter_rank_rank.py b/LearnersEd/login_register/migrations/0002_alter_rank_rank.py new file mode 100644 index 000000000..dd8b552d0 --- /dev/null +++ b/LearnersEd/login_register/migrations/0002_alter_rank_rank.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-07 00:45 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0001_initial'), + ] + + operations = [ + migrations.AlterField( + model_name='rank', + name='rank', + field=models.IntegerField(null=True), + ), + ] diff --git a/LearnersEd/login_register/migrations/0003_alter_rank_rank.py b/LearnersEd/login_register/migrations/0003_alter_rank_rank.py new file mode 100644 index 000000000..58949779b --- /dev/null +++ b/LearnersEd/login_register/migrations/0003_alter_rank_rank.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-07 00:56 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0002_alter_rank_rank'), + ] + + operations = [ + migrations.AlterField( + model_name='rank', + name='rank', + field=models.IntegerField(default=0), + ), + ] diff --git a/LearnersEd/login_register/migrations/0004_remove_student_favourite_categories_delete_category_and_more.py b/LearnersEd/login_register/migrations/0004_remove_student_favourite_categories_delete_category_and_more.py new file mode 100644 index 000000000..f81367f79 --- /dev/null +++ b/LearnersEd/login_register/migrations/0004_remove_student_favourite_categories_delete_category_and_more.py @@ -0,0 +1,25 @@ +# Generated by Django 4.1.9 on 2023-06-24 19:10 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0003_alter_rank_rank'), + ] + + operations = [ + migrations.RemoveField( + model_name='student', + name='favourite_categories', + ), + migrations.DeleteModel( + name='Category', + ), + migrations.AddField( + model_name='student', + name='favourite_categories', + field=models.JSONField(default=list), + ), + ] diff --git a/LearnersEd/login_register/migrations/0005_remove_rank_student_remove_sectionrank_ranks_and_more.py b/LearnersEd/login_register/migrations/0005_remove_rank_student_remove_sectionrank_ranks_and_more.py new file mode 100644 index 000000000..55fe0c02f --- /dev/null +++ b/LearnersEd/login_register/migrations/0005_remove_rank_student_remove_sectionrank_ranks_and_more.py @@ -0,0 +1,35 @@ +# Generated by Django 4.1.9 on 2023-06-24 20:00 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0004_remove_student_favourite_categories_delete_category_and_more'), + ] + + operations = [ + migrations.RemoveField( + model_name='rank', + name='student', + ), + migrations.RemoveField( + model_name='sectionrank', + name='ranks', + ), + migrations.AddField( + model_name='student', + name='rank', + field=models.PositiveIntegerField(blank=True, null=True), + ), + migrations.DeleteModel( + name='ClassRank', + ), + migrations.DeleteModel( + name='Rank', + ), + migrations.DeleteModel( + name='SectionRank', + ), + ] diff --git a/LearnersEd/login_register/migrations/0006_rename_rank_virtualpet_pet_rank_and_more.py b/LearnersEd/login_register/migrations/0006_rename_rank_virtualpet_pet_rank_and_more.py new file mode 100644 index 000000000..4ade13e5d --- /dev/null +++ b/LearnersEd/login_register/migrations/0006_rename_rank_virtualpet_pet_rank_and_more.py @@ -0,0 +1,24 @@ +# Generated by Django 4.1.9 on 2023-06-24 20:18 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0005_remove_rank_student_remove_sectionrank_ranks_and_more'), + ] + + operations = [ + migrations.RenameField( + model_name='virtualpet', + old_name='rank', + new_name='pet_rank', + ), + migrations.AlterField( + model_name='virtualpet', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student'), + ), + ] diff --git a/LearnersEd/login_register/migrations/0007_remove_virtualpet_pet_coin_alter_assignment_student_and_more.py b/LearnersEd/login_register/migrations/0007_remove_virtualpet_pet_coin_alter_assignment_student_and_more.py new file mode 100644 index 000000000..c95b2f17a --- /dev/null +++ b/LearnersEd/login_register/migrations/0007_remove_virtualpet_pet_coin_alter_assignment_student_and_more.py @@ -0,0 +1,33 @@ +# Generated by Django 4.1.9 on 2023-06-24 20:36 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0006_rename_rank_virtualpet_pet_rank_and_more'), + ] + + operations = [ + migrations.RemoveField( + model_name='virtualpet', + name='pet_coin', + ), + migrations.AlterField( + model_name='assignment', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='assignment', to='login_register.student'), + ), + migrations.AlterField( + model_name='attendance', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='attendance', to='login_register.student'), + ), + migrations.AlterField( + model_name='marks', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='marks', to='login_register.student'), + ), + ] diff --git a/LearnersEd/login_register/migrations/0008_virtualpet_pet_coin.py b/LearnersEd/login_register/migrations/0008_virtualpet_pet_coin.py new file mode 100644 index 000000000..2a8182d0f --- /dev/null +++ b/LearnersEd/login_register/migrations/0008_virtualpet_pet_coin.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-24 20:38 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0007_remove_virtualpet_pet_coin_alter_assignment_student_and_more'), + ] + + operations = [ + migrations.AddField( + model_name='virtualpet', + name='pet_coin', + field=models.PositiveIntegerField(default=0), + ), + ] diff --git a/LearnersEd/login_register/migrations/0009_alter_virtualpet_pet_name_alter_virtualpet_pet_type.py b/LearnersEd/login_register/migrations/0009_alter_virtualpet_pet_name_alter_virtualpet_pet_type.py new file mode 100644 index 000000000..2dbccc8cf --- /dev/null +++ b/LearnersEd/login_register/migrations/0009_alter_virtualpet_pet_name_alter_virtualpet_pet_type.py @@ -0,0 +1,23 @@ +# Generated by Django 4.1.9 on 2023-06-24 21:28 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0008_virtualpet_pet_coin'), + ] + + operations = [ + migrations.AlterField( + model_name='virtualpet', + name='pet_name', + field=models.CharField(blank=True, max_length=100, null=True), + ), + migrations.AlterField( + model_name='virtualpet', + name='pet_type', + field=models.CharField(blank=True, max_length=100, null=True), + ), + ] diff --git a/LearnersEd/login_register/migrations/0010_alter_virtualpet_student.py b/LearnersEd/login_register/migrations/0010_alter_virtualpet_student.py new file mode 100644 index 000000000..c436930e5 --- /dev/null +++ b/LearnersEd/login_register/migrations/0010_alter_virtualpet_student.py @@ -0,0 +1,19 @@ +# Generated by Django 4.1.9 on 2023-06-24 22:00 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0009_alter_virtualpet_pet_name_alter_virtualpet_pet_type'), + ] + + operations = [ + migrations.AlterField( + model_name='virtualpet', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pets', to='login_register.student'), + ), + ] diff --git a/LearnersEd/login_register/migrations/0011_alter_virtualpet_pet_rank_alter_virtualpet_student.py b/LearnersEd/login_register/migrations/0011_alter_virtualpet_pet_rank_alter_virtualpet_student.py new file mode 100644 index 000000000..176547948 --- /dev/null +++ b/LearnersEd/login_register/migrations/0011_alter_virtualpet_pet_rank_alter_virtualpet_student.py @@ -0,0 +1,24 @@ +# Generated by Django 4.1.9 on 2023-06-24 22:20 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0010_alter_virtualpet_student'), + ] + + operations = [ + migrations.AlterField( + model_name='virtualpet', + name='pet_rank', + field=models.PositiveIntegerField(blank=True, null=True), + ), + migrations.AlterField( + model_name='virtualpet', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student'), + ), + ] diff --git a/LearnersEd/login_register/migrations/0012_alter_virtualpet_pet_rank_alter_virtualpet_student.py b/LearnersEd/login_register/migrations/0012_alter_virtualpet_pet_rank_alter_virtualpet_student.py new file mode 100644 index 000000000..b7e8c7f4c --- /dev/null +++ b/LearnersEd/login_register/migrations/0012_alter_virtualpet_pet_rank_alter_virtualpet_student.py @@ -0,0 +1,24 @@ +# Generated by Django 4.1.9 on 2023-06-24 22:25 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0011_alter_virtualpet_pet_rank_alter_virtualpet_student'), + ] + + operations = [ + migrations.AlterField( + model_name='virtualpet', + name='pet_rank', + field=models.PositiveIntegerField(default=0), + ), + migrations.AlterField( + model_name='virtualpet', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pets', to='login_register.student'), + ), + ] diff --git a/LearnersEd/login_register/migrations/0013_alter_virtualpet_pet_name_alter_virtualpet_pet_type_and_more.py b/LearnersEd/login_register/migrations/0013_alter_virtualpet_pet_name_alter_virtualpet_pet_type_and_more.py new file mode 100644 index 000000000..5235f5e4e --- /dev/null +++ b/LearnersEd/login_register/migrations/0013_alter_virtualpet_pet_name_alter_virtualpet_pet_type_and_more.py @@ -0,0 +1,29 @@ +# Generated by Django 4.1.9 on 2023-06-25 18:06 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0012_alter_virtualpet_pet_rank_alter_virtualpet_student'), + ] + + operations = [ + migrations.AlterField( + model_name='virtualpet', + name='pet_name', + field=models.CharField(default='', max_length=100), + ), + migrations.AlterField( + model_name='virtualpet', + name='pet_type', + field=models.CharField(default='', max_length=100), + ), + migrations.AlterField( + model_name='virtualpet', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='virtual_pets', to='login_register.student'), + ), + ] diff --git a/LearnersEd/login_register/migrations/0014_pet_delete_virtualpet.py b/LearnersEd/login_register/migrations/0014_pet_delete_virtualpet.py new file mode 100644 index 000000000..350e6debc --- /dev/null +++ b/LearnersEd/login_register/migrations/0014_pet_delete_virtualpet.py @@ -0,0 +1,30 @@ +# Generated by Django 4.1.9 on 2023-06-25 19:18 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0013_alter_virtualpet_pet_name_alter_virtualpet_pet_type_and_more'), + ] + + operations = [ + migrations.CreateModel( + name='Pet', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('pet_name', models.CharField(max_length=100)), + ('pet_type', models.CharField(max_length=100)), + ('pet_level', models.PositiveIntegerField(default=1)), + ('pet_level_progress', models.PositiveIntegerField(default=0)), + ('pet_coin', models.PositiveIntegerField(default=0)), + ('pet_rank', models.PositiveIntegerField(default=0)), + ('student', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='pet', to='login_register.student')), + ], + ), + migrations.DeleteModel( + name='VirtualPet', + ), + ] diff --git a/LearnersEd/login_register/migrations/0015_alter_pet_pet_name_alter_pet_pet_type.py b/LearnersEd/login_register/migrations/0015_alter_pet_pet_name_alter_pet_pet_type.py new file mode 100644 index 000000000..c0c2cd255 --- /dev/null +++ b/LearnersEd/login_register/migrations/0015_alter_pet_pet_name_alter_pet_pet_type.py @@ -0,0 +1,23 @@ +# Generated by Django 4.1.9 on 2023-06-25 19:31 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0014_pet_delete_virtualpet'), + ] + + operations = [ + migrations.AlterField( + model_name='pet', + name='pet_name', + field=models.CharField(default='', max_length=100), + ), + migrations.AlterField( + model_name='pet', + name='pet_type', + field=models.CharField(default='', max_length=100), + ), + ] diff --git a/LearnersEd/login_register/migrations/0016_remove_pet_pet_rank_student_pet_rank.py b/LearnersEd/login_register/migrations/0016_remove_pet_pet_rank_student_pet_rank.py new file mode 100644 index 000000000..23fd8406c --- /dev/null +++ b/LearnersEd/login_register/migrations/0016_remove_pet_pet_rank_student_pet_rank.py @@ -0,0 +1,22 @@ +# Generated by Django 4.1.9 on 2023-06-25 20:02 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0015_alter_pet_pet_name_alter_pet_pet_type'), + ] + + operations = [ + migrations.RemoveField( + model_name='pet', + name='pet_rank', + ), + migrations.AddField( + model_name='student', + name='pet_rank', + field=models.PositiveIntegerField(default=0), + ), + ] diff --git a/LearnersEd/login_register/migrations/0017_pet_pet_rank.py b/LearnersEd/login_register/migrations/0017_pet_pet_rank.py new file mode 100644 index 000000000..7d7adfa97 --- /dev/null +++ b/LearnersEd/login_register/migrations/0017_pet_pet_rank.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-25 20:05 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0016_remove_pet_pet_rank_student_pet_rank'), + ] + + operations = [ + migrations.AddField( + model_name='pet', + name='pet_rank', + field=models.PositiveIntegerField(default=0), + ), + ] diff --git a/LearnersEd/login_register/migrations/0018_remove_student_pet_rank.py b/LearnersEd/login_register/migrations/0018_remove_student_pet_rank.py new file mode 100644 index 000000000..82f698bfa --- /dev/null +++ b/LearnersEd/login_register/migrations/0018_remove_student_pet_rank.py @@ -0,0 +1,17 @@ +# Generated by Django 4.1.9 on 2023-06-25 20:07 + +from django.db import migrations + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0017_pet_pet_rank'), + ] + + operations = [ + migrations.RemoveField( + model_name='student', + name='pet_rank', + ), + ] diff --git a/LearnersEd/login_register/migrations/0019_alter_pet_pet_rank.py b/LearnersEd/login_register/migrations/0019_alter_pet_pet_rank.py new file mode 100644 index 000000000..2335dd037 --- /dev/null +++ b/LearnersEd/login_register/migrations/0019_alter_pet_pet_rank.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-25 20:28 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0018_remove_student_pet_rank'), + ] + + operations = [ + migrations.AlterField( + model_name='pet', + name='pet_rank', + field=models.PositiveIntegerField(blank=True, null=True), + ), + ] diff --git a/LearnersEd/login_register/migrations/0020_delete_pet.py b/LearnersEd/login_register/migrations/0020_delete_pet.py new file mode 100644 index 000000000..65203a8e4 --- /dev/null +++ b/LearnersEd/login_register/migrations/0020_delete_pet.py @@ -0,0 +1,16 @@ +# Generated by Django 4.1.9 on 2023-06-25 20:43 + +from django.db import migrations + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0019_alter_pet_pet_rank'), + ] + + operations = [ + migrations.DeleteModel( + name='Pet', + ), + ] diff --git a/LearnersEd/login_register/migrations/0021_pet.py b/LearnersEd/login_register/migrations/0021_pet.py new file mode 100644 index 000000000..122d05f44 --- /dev/null +++ b/LearnersEd/login_register/migrations/0021_pet.py @@ -0,0 +1,27 @@ +# Generated by Django 4.1.9 on 2023-06-25 20:58 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0020_delete_pet'), + ] + + operations = [ + migrations.CreateModel( + name='Pet', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('pet_name', models.CharField(max_length=100)), + ('pet_type', models.CharField(max_length=100)), + ('pet_level', models.PositiveIntegerField(default=1)), + ('pet_progress', models.PositiveIntegerField(default=0)), + ('pet_coins', models.PositiveIntegerField(default=0)), + ('pet_rank', models.PositiveIntegerField(blank=True, null=True)), + ('student', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='pet', to='login_register.student')), + ], + ), + ] diff --git a/LearnersEd/login_register/migrations/0022_student_class_rank_student_overall_rank_and_more.py b/LearnersEd/login_register/migrations/0022_student_class_rank_student_overall_rank_and_more.py new file mode 100644 index 000000000..c94fa957e --- /dev/null +++ b/LearnersEd/login_register/migrations/0022_student_class_rank_student_overall_rank_and_more.py @@ -0,0 +1,28 @@ +# Generated by Django 4.1.9 on 2023-06-25 21:16 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0021_pet'), + ] + + operations = [ + migrations.AddField( + model_name='student', + name='class_rank', + field=models.PositiveIntegerField(blank=True, null=True), + ), + migrations.AddField( + model_name='student', + name='overall_rank', + field=models.PositiveIntegerField(blank=True, null=True), + ), + migrations.AddField( + model_name='student', + name='section_rank', + field=models.PositiveIntegerField(blank=True, null=True), + ), + ] diff --git a/LearnersEd/login_register/migrations/0023_remove_student_overall_rank.py b/LearnersEd/login_register/migrations/0023_remove_student_overall_rank.py new file mode 100644 index 000000000..cc2fdf142 --- /dev/null +++ b/LearnersEd/login_register/migrations/0023_remove_student_overall_rank.py @@ -0,0 +1,17 @@ +# Generated by Django 4.1.9 on 2023-06-25 21:21 + +from django.db import migrations + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0022_student_class_rank_student_overall_rank_and_more'), + ] + + operations = [ + migrations.RemoveField( + model_name='student', + name='overall_rank', + ), + ] diff --git a/LearnersEd/login_register/migrations/0024_remove_pet_pet_rank_alter_pet_student.py b/LearnersEd/login_register/migrations/0024_remove_pet_pet_rank_alter_pet_student.py new file mode 100644 index 000000000..fb7a2e39c --- /dev/null +++ b/LearnersEd/login_register/migrations/0024_remove_pet_pet_rank_alter_pet_student.py @@ -0,0 +1,23 @@ +# Generated by Django 4.1.9 on 2023-06-25 21:34 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0023_remove_student_overall_rank'), + ] + + operations = [ + migrations.RemoveField( + model_name='pet', + name='pet_rank', + ), + migrations.AlterField( + model_name='pet', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pet', to='login_register.student'), + ), + ] diff --git a/LearnersEd/login_register/migrations/0025_student_pet_rank.py b/LearnersEd/login_register/migrations/0025_student_pet_rank.py new file mode 100644 index 000000000..4b980c3ea --- /dev/null +++ b/LearnersEd/login_register/migrations/0025_student_pet_rank.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-25 21:54 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0024_remove_pet_pet_rank_alter_pet_student'), + ] + + operations = [ + migrations.AddField( + model_name='student', + name='pet_rank', + field=models.PositiveIntegerField(default=1), + ), + ] diff --git a/LearnersEd/login_register/migrations/0026_alter_pet_pet_name.py b/LearnersEd/login_register/migrations/0026_alter_pet_pet_name.py new file mode 100644 index 000000000..2effe99ad --- /dev/null +++ b/LearnersEd/login_register/migrations/0026_alter_pet_pet_name.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-25 22:11 + +from 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/dev/null and b/LearnersEd/login_register/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/login_register/models.py b/LearnersEd/login_register/models.py new file mode 100644 index 000000000..4ce7509b4 --- /dev/null +++ b/LearnersEd/login_register/models.py @@ -0,0 +1,153 @@ +from django.db import models + +class Student(models.Model): + student_name = models.CharField(max_length=100) + email = models.EmailField(unique=True) + password = models.CharField(max_length=100) + class_name = models.CharField(max_length=100) + section = models.CharField(max_length=100) + favourite_categories = models.JSONField(default=list) + rank = models.PositiveIntegerField(null=True, blank=True) + class_rank = models.PositiveIntegerField(null=True, blank=True) + section_rank = models.PositiveIntegerField(null=True, blank=True) + pet_rank = models.PositiveIntegerField(default=1) + + def __str__(self): + return self.student_name + + def update_pet_rank(self): + students = Student.objects.order_by( + '-pet__pet_level', + '-pet__pet_progress' + ) + pet_rank = 1 + for index, student in enumerate(students): + student.pet_rank = pet_rank + student.save() + if student.id == self.id: + self.pet_rank = pet_rank + pet_rank += 1 + self.save() + + def update_rank(self): + students = Student.objects.order_by( + '-marks__average_marks', + '-attendance__average_attendance', + '-assignment__assignment_percentage' + ) + rank = 1 + class_students = students.filter(class_name=self.class_name) + section_students = class_students.filter(section=self.section) # Filter students by section + + for index, student in enumerate(students): + student.rank = rank + student.save() + if student.id == self.id: + self.rank = rank + rank += 1 + + class_rank = 1 + for index, student in enumerate(class_students): + student.class_rank = class_rank + student.save() + if student.id == self.id: + self.class_rank = class_rank + class_rank += 1 + + section_rank = 1 + for index, student in enumerate(section_students): + student.section_rank = section_rank + student.save() + if student.id == self.id: + self.section_rank = section_rank + section_rank += 1 + + self.save() + +class Marks(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE, related_name='marks') + maths = models.PositiveIntegerField() + hindi = models.PositiveIntegerField() + english = models.PositiveIntegerField() + science = models.PositiveIntegerField() + social_science = models.PositiveIntegerField() + total_marks = models.PositiveIntegerField(default=0) + average_marks = models.FloatField(default=0) + + def save(self, *args, **kwargs): + self.update_total_marks() + self.update_average_marks() + super().save(*args, **kwargs) + self.student.update_rank() + + def update_total_marks(self): + self.total_marks = self.maths + self.hindi + self.english + self.science + self.social_science + + def update_average_marks(self): + total_subjects = 5 # Assuming 5 subjects in total + self.average_marks = self.total_marks / total_subjects + + def __str__(self): + return f"{self.student.student_name}'s Marks" + + +class Attendance(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE, related_name='attendance') + maths_attendance = models.PositiveIntegerField() + hindi_attendance = models.PositiveIntegerField() + english_attendance = models.PositiveIntegerField() + science_attendance = models.PositiveIntegerField() + social_science_attendance = models.PositiveIntegerField() + average_attendance = models.FloatField(default=0) + + def save(self, *args, **kwargs): + self.update_average_attendance() + super().save(*args, **kwargs) + self.student.update_rank() + + def update_average_attendance(self): + total_subjects = 5 # Assuming 5 subjects in total + total_attendance = ( + self.maths_attendance + + self.hindi_attendance + + self.english_attendance + + self.science_attendance + + self.social_science_attendance + ) + self.average_attendance = (total_attendance / (total_subjects * 100)) * 100 + + def __str__(self): + return f"{self.student.student_name}'s Attendance" + + +class Assignment(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE, related_name='assignment') + completed_assignments = models.PositiveIntegerField(default=0) + total_assignments = models.PositiveIntegerField(default=0) + assignment_percentage = models.FloatField(default=0) + + def save(self, *args, **kwargs): + self.update_assignment_percentage() + super().save(*args, **kwargs) + self.student.update_rank() + + def update_assignment_percentage(self): + if self.total_assignments > 0: + self.assignment_percentage = (self.completed_assignments / self.total_assignments) * 100 + else: + self.assignment_percentage = 0 + + def __str__(self): + return f"{self.student.student_name}'s Assignments" + +class Pet(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE, related_name='pet') + pet_name = models.CharField(max_length=100, default="\'NA\'") + pet_type = models.CharField(max_length=100) + pet_level = models.PositiveIntegerField(default=1) + pet_progress = models.PositiveIntegerField(default=0) + pet_coins = models.PositiveIntegerField(default=0) + + def save(self, *args, **kwargs): + super().save(*args, **kwargs) + self.student.update_pet_rank() diff --git a/LearnersEd/login_register/static/images/LearnersEd.png b/LearnersEd/login_register/static/images/LearnersEd.png new file mode 100644 index 000000000..f2135a789 Binary files /dev/null and b/LearnersEd/login_register/static/images/LearnersEd.png differ diff --git a/LearnersEd/login_register/static/images/bg.png b/LearnersEd/login_register/static/images/bg.png new file mode 100644 index 000000000..0acfd954d Binary files /dev/null and b/LearnersEd/login_register/static/images/bg.png differ diff --git a/LearnersEd/login_register/static/images/illustration.jpg b/LearnersEd/login_register/static/images/illustration.jpg new file mode 100644 index 000000000..4dbc582dd Binary files /dev/null and b/LearnersEd/login_register/static/images/illustration.jpg differ diff --git a/LearnersEd/login_register/static/images/person.png b/LearnersEd/login_register/static/images/person.png new file mode 100644 index 000000000..a4c242cfb Binary files /dev/null and b/LearnersEd/login_register/static/images/person.png differ diff --git a/LearnersEd/login_register/static/login.css b/LearnersEd/login_register/static/login.css new file mode 100644 index 000000000..bf5beb17b --- /dev/null +++ b/LearnersEd/login_register/static/login.css @@ -0,0 +1,80 @@ +body { + font-family: 'Darker Grotesque', sans-serif; + background-color: white; + margin:0px; + padding:0px; +} + +.box { + display:flex; + height: 100vh; +} +.background{ + width:50%; + flex-grow: 1; + flex-basis: 0; +} +.background>img { + height:100vh; + width: 100%; + object-fit: cover; + object-position: top center; +} +.log{ + background-image: url(./images/illustration.jpg); + display: flex; + flex-direction: column; + flex-wrap:wrap; + background-size:contain; + background-position: bottom; + background-repeat:no-repeat; + align-items: center; + justify-content: center; + flex-grow: 1; + flex-basis: 0; +} +#link { + color: blue; + text-decoration: underline; +} +.log>h3{ + color:#45a049; + font-size:60px; +} +#log-image{ + width: 80px; + height:80px; +} + +label { + display: block; + margin-bottom: 10px; +} + +input[type="text"], +input[type="password"], +input[type="email"] { + width: 100%; + padding: 10px; + margin-bottom: 20px; + border:2px solid black; + border-radius:2px; +} + +input[type="submit"] { + background-color: #4CAF50; + color: white; + padding: 10px; + border: none; + cursor: pointer; + width: 100%; +} + +input[type="submit"]:hover { + background-color: #45a049; +} + +/* .background { + background-image: url(./images/bg.png); +} */ + diff --git a/LearnersEd/login_register/static/register-script.js b/LearnersEd/login_register/static/register-script.js new file mode 100644 index 000000000..a9a308dee --- /dev/null +++ b/LearnersEd/login_register/static/register-script.js @@ -0,0 +1,9 @@ +function validateForm() { + var password = document.getElementById("password").value; + var confirmPassword = document.getElementById("confirm_password").value; + if (password !== confirmPassword) { + alert("Passwords do not match"); + return false; + } + return true; +} \ No newline at end of file diff --git a/LearnersEd/login_register/static/register.css b/LearnersEd/login_register/static/register.css new file mode 100644 index 000000000..bee0813d3 --- /dev/null +++ b/LearnersEd/login_register/static/register.css @@ -0,0 +1,114 @@ +body { + font-family: 'Darker Grotesque', sans-serif; + background-color: white; + margin:0px; + padding:0px; +} + +.box { + display:flex; + flex-wrap: wrap; + height: 100vh; +} +.background{ + width:50%; +} +.background>img { + height:100vh; + width: 100%; + object-fit: cover; + object-position: top center; +} +.log{ + background-image: url(./images/illustration.jpg); + display: flex; + flex-direction: column; + background-size:contain; + background-position: bottom; + background-repeat:no-repeat; + align-items: center; + justify-content: center; + height: 100vh; + width:50%; +} + +#link { + color: blue; + text-decoration: underline; +} + +.p { + display: flex; + flex-direction: column; +} +#temp{ + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; +} +#temp>h3{ + color:#45a049; + font-size:60px; +} +#log-image{ + width: 80px; + height:80px; +} + +.r{ + display:flex; + flex-direction: row; + gap: 20px; +} + +#info { + display: flex; + flex-direction: column; +} + +label { + display: block; + margin-bottom: 10px; +} + +input[type="text"], +input[type="password"], +input[type] { + padding: 5px; + margin-bottom: 10px; + border:2px solid black; + border-radius:2px; +} + +input[type="submit"] { + background-color: #4CAF50; + color: white; + padding: 10px; + border: none; + cursor: pointer; +} + +input[type="submit"]:hover { + background-color: #45a049; +} + +/* .background { + background-image: url(./images/bg.png); +} */ + +#e{ + animation: flash 1s 3; +} + +@keyframes flash { + 0% { + opacity: 1; + } + 50% { + opacity: 0; + } + 100% { + opacity: 1; + } +} \ No newline at end of file diff --git a/LearnersEd/login_register/templates/login.html b/LearnersEd/login_register/templates/login.html new file mode 100644 index 000000000..bdfc6b162 --- /dev/null +++ b/LearnersEd/login_register/templates/login.html @@ -0,0 +1,41 @@ +{% load static %} + + + + + + + Learners'Ed - Login + + + + + + + + +
    +
    + +
    +
    +

    Login

    + +
    + {% csrf_token %} + + + + + + + +
    + {% if error_message %} +

    {{ error_message }}

    + {% endif %} +

    Not a member? Register.

    +
    +
    + + \ No newline at end of file diff --git a/LearnersEd/login_register/templates/register.html b/LearnersEd/login_register/templates/register.html new file mode 100644 index 000000000..2c2512a5a --- /dev/null +++ b/LearnersEd/login_register/templates/register.html @@ -0,0 +1,95 @@ +{% load static %} + + + + + + + Learners'Ed - Register + + + + + + + +
    +
    + +
    +
    +
    +

    Register

    + +
    +
    +
    + {% csrf_token %} +
    +
    + + +
    +
    + + +
    +
    + +
    +
    + + +
    +
    + + +
    +
    + +
    +
    + + +
    +
    + + +
    +
    + +
    +
    + +
    +
    +
    + + +
    +
    + + +
    +
    + + +
    +
    +
    + +
    +
    + + {% if error_message %} +

    {{ error_message }}

    + {% endif %} +

    Already a member? Login.

    +
    +
    +
    +
    +
    +
    + + diff --git a/LearnersEd/login_register/tests.py b/LearnersEd/login_register/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/login_register/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/login_register/views.py b/LearnersEd/login_register/views.py new file mode 100644 index 000000000..eb466658b --- /dev/null +++ b/LearnersEd/login_register/views.py @@ -0,0 +1,116 @@ +from django.shortcuts import render, redirect +from .models import Student, Marks, Attendance, Assignment, Pet +from django.core.exceptions import ValidationError +import random +import json + +def logout(request): + if 'student_id' in request.session: + del request.session['student_id'] + return redirect('/login/') + +def register(request): + if request.method == 'POST': + # Retrieve form data + student_name = request.POST.get('student_name') + email = request.POST.get('email') + password = request.POST.get('password') + confirm_password = request.POST.get('confirm_password') + class_name = request.POST.get('class') + section = request.POST.get('section') + favourite_categories = request.POST.getlist('favourite_categories') + + # Perform validation logic here + if password != confirm_password: + error_message = "Passwords do not match!!" + return render(request, 'register.html', {'error_message': error_message}) + + try: + # Check if the email already exists in the database + existing_student = Student.objects.get(email=email) + error_message = "Email already exists!!" + return render(request, 'register.html', {'error_message': error_message}) + except Student.DoesNotExist: + pass + + # Save the registration details to the database + student = Student(student_name=student_name, email=email, password=password, class_name=class_name, section=section, favourite_categories = favourite_categories) + student.save() + + # Generate random marks + maths_marks = random.randint(0, 100) + hindi_marks = random.randint(0, 100) + english_marks = random.randint(0, 100) + science_marks = random.randint(0, 100) + social_science_marks = random.randint(0, 100) + + # Save the marks to the database + marks = Marks( + student=student, + maths=maths_marks, + hindi=hindi_marks, + english=english_marks, + science=science_marks, + social_science=social_science_marks + ) + marks.save() + + # Generate random attendance percentages for each subject + maths_attendance = random.randint(70, 100) + hindi_attendance = random.randint(70, 100) + english_attendance = random.randint(70, 100) + science_attendance = random.randint(70, 100) + social_science_attendance = random.randint(70, 100) + + # Save the attendance record + attendance = Attendance(student=student, maths_attendance=maths_attendance, hindi_attendance=hindi_attendance, + english_attendance=english_attendance, science_attendance=science_attendance, + social_science_attendance=social_science_attendance) + attendance.save() + + # Generate random completed assignments + completed_assignments = random.randint(0, 6) + + # Create the Assignment object for the student + assignment = Assignment(student=student, completed_assignments=completed_assignments, total_assignments=6) + assignment.save() + + # Generate random values for pet level and pet progress + pet_level = random.randint(1, 10) + pet_progress = random.randint(0, 100) + + # Create the pet object associated with the student + pet = Pet.objects.create( + student=student, + pet_name='\'NA\'', + pet_type='', + pet_level=pet_level, + pet_progress=pet_progress, + pet_coins=200 + ) + + # Redirect to the dashboard page or any other page + request.session['student_id'] = student.id # Store the student ID in session + return redirect('dashboard') + else: + return render(request, 'register.html') + +def login(request): + if request.method == 'POST': + email = request.POST.get('Email_Id') + password = request.POST.get('password') + + # Perform authentication/validation logic here + try: + student = Student.objects.get(email=email) + if student.password == password: + request.session['student_id'] = student.id # Store the student ID in session + return redirect('dashboard') # Redirect to the dashboard page + else: + error_message = "Invalid email or password" + except Student.DoesNotExist: + error_message = "Invalid email or password" + + return render(request, 'login.html', {'error_message': error_message}) + else: + return render(request, 'login.html') diff --git a/LearnersEd/manage.py b/LearnersEd/manage.py new file mode 100644 index 000000000..73dce6c97 --- /dev/null +++ b/LearnersEd/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'LearnersEd.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/LearnersEd/requirements.txt b/LearnersEd/requirements.txt new file mode 100644 index 000000000..6929511e9 Binary files /dev/null and b/LearnersEd/requirements.txt differ diff --git a/LearnersEd/static/images/bg.png b/LearnersEd/static/images/bg.png new file mode 100644 index 000000000..5dddefd26 Binary files /dev/null and b/LearnersEd/static/images/bg.png differ diff --git a/LearnersEd/static/nav-styles.css b/LearnersEd/static/nav-styles.css new file mode 100644 index 000000000..3747a3c8c --- /dev/null +++ b/LearnersEd/static/nav-styles.css @@ -0,0 +1,17 @@ +nav { + font-family: 'Rowdies', cursive; + z-index: 9999999999999999999999; + box-shadow: 0 2px 10px 0 gray; + border-bottom: 2px solid black; +} + +#l:hover { + box-shadow: 0 0 10px 0 gray; + transform: scale(1.1); + transition-duration: 200ms; +} + +#l:active { + 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a/LearnersEd/student_insights_app/__pycache__/views.cpython-39.pyc b/LearnersEd/student_insights_app/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..d6ac04232 Binary files /dev/null and b/LearnersEd/student_insights_app/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/student_insights_app/admin.py b/LearnersEd/student_insights_app/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/student_insights_app/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/student_insights_app/apps.py b/LearnersEd/student_insights_app/apps.py new file mode 100644 index 000000000..b034e2b5d --- /dev/null +++ b/LearnersEd/student_insights_app/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class StudentInsightsAppConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'student_insights_app' diff --git a/LearnersEd/student_insights_app/migrations/__init__.py b/LearnersEd/student_insights_app/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/student_insights_app/migrations/__pycache__/__init__.cpython-310.pyc b/LearnersEd/student_insights_app/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..11d941065 Binary files /dev/null and b/LearnersEd/student_insights_app/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/LearnersEd/student_insights_app/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/student_insights_app/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..7cd08f8a4 Binary files /dev/null and b/LearnersEd/student_insights_app/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/student_insights_app/models.py b/LearnersEd/student_insights_app/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/LearnersEd/student_insights_app/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/LearnersEd/student_insights_app/static/images/bg-new.png b/LearnersEd/student_insights_app/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/LearnersEd/student_insights_app/static/images/bg-new.png differ diff --git a/LearnersEd/student_insights_app/static/images/chatroom2.png b/LearnersEd/student_insights_app/static/images/chatroom2.png new file mode 100644 index 000000000..e5bdfa035 Binary files /dev/null and b/LearnersEd/student_insights_app/static/images/chatroom2.png differ diff --git a/LearnersEd/student_insights_app/static/images/chatrooms.png b/LearnersEd/student_insights_app/static/images/chatrooms.png new file mode 100644 index 000000000..dfc2bddb1 Binary files /dev/null and b/LearnersEd/student_insights_app/static/images/chatrooms.png differ diff --git a/LearnersEd/student_insights_app/static/images/mental_health.png b/LearnersEd/student_insights_app/static/images/mental_health.png new file mode 100644 index 000000000..42fd5f47c Binary files /dev/null and b/LearnersEd/student_insights_app/static/images/mental_health.png differ diff --git a/LearnersEd/student_insights_app/static/student_insights.css b/LearnersEd/student_insights_app/static/student_insights.css new file mode 100644 index 000000000..f8d668f07 --- /dev/null +++ b/LearnersEd/student_insights_app/static/student_insights.css @@ -0,0 +1,197 @@ +body, html { + margin: 0; + padding: 0; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + font-family: 'Darker Grotesque', sans-serif; + overflow-y: hidden; +} +#whole { + + /* padding: 20px; */ + text-align: center; + display: flex; + flex-direction: column; + gap:30px; + margin-top: 100px; + } + .heading{ + background-color: white; + /* margin: 30px; */ + /* padding: 30px; */ + height:90px; + } + + #title { + color: #9376E0; + font-size: 60px; + margin: 0; + text-transform: uppercase; + letter-spacing: 10px; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + } + .box { + display: flex; + padding: 15px; + gap:15px; + } + + .left-section { + flex: 1; + display: flex; + flex-direction: column; + gap:15px; + height: 100vh; + } + .twin_flexboxes{ + display: flex; + flex-direction: row; + gap:15px; + width: 85%; + } + + + .flexbox { + flex: 1; + + outline: 3px solid black; + + border-radius: 5px; + cursor: pointer; + padding: 20px; + height: 250px; + width: 200px; + } + .flexbox:hover{ + box-shadow:2px 5px black; + transition-duration: 200ms; + } + #flexbox1 { + display: flex; + background-image: url('images/chatroom2.png'); + /* animation: gradientAnimation 5s infinite ease-in-out; */ + /* background-size: 200% 200%; */ + /* transition-duration: 100ms; */ + background-position: center; + background-size: cover; + position: relative; + background-repeat: no-repeat; + flex-direction: column; + justify-content: center; + align-items: center; + gap:10px; + font-size: 30px; + } + .quiz1-heading{ + text-transform: uppercase; + letter-spacing: 2px; + outline: 4px solid #9376E0; + border:2px white; + padding:10px; + width:200px; + background-color: black; + font-size: 20px; + border-radius:5px; + color:white; + } + .quiz-heading{ + text-transform: uppercase; + letter-spacing: 2px; + outline: 4px solid #9376E0; + border:2px white; + padding:10px; + width:250px; + font-size: 20px; + border-radius:5px; + background-color: black; + color:white; + position: absolute; + right: 10px; + bottom:10px; + } + #flexbox2{ + position: relative; + display: flex; + background-image: url('images/mental_health.png'); + background-position: center; + background-size: cover; + background-repeat: no-repeat; + flex-direction: column; + align-self: flex-start; + justify-self: flex-start; + margin: 30px; + gap:10px; + font-size: 30px; + } + +.horizontal-flexbox { + text-transform: uppercase; + cursor: pointer; + letter-spacing: 2px; + outline: 8px solid white; + padding:5px; + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; + flex: 1; + padding: 10px; + width: 85%; + box-shadow: 0 0 20px 2px gray; + font-size: 20px; + border-radius:30px; + color:white; +} + +.horizontal-flexbox:hover { + transform: scale(1.05); + transition-duration: 200ms; +} + +.horizontal-flexbox:active { + transform: scale(0.9); + background-image: none; + background-color: black; + transition-duration: 200ms; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + +.quiz2-heading{ + text-transform: uppercase; + letter-spacing: 2px; + padding:5px; + flex: 1; + padding: 10px; + font-size: 20px; + border-radius:5px; + color:white; + } + + + + + + + + + + + + + + \ No newline at end of file diff --git a/LearnersEd/student_insights_app/templates/student_insights.html b/LearnersEd/student_insights_app/templates/student_insights.html new file mode 100644 index 000000000..a37207a97 --- /dev/null +++ b/LearnersEd/student_insights_app/templates/student_insights.html @@ -0,0 +1,47 @@ +{% load static %} + + + + + + Student Insights + + + + + + + + + + + + + {% include 'navbar.html' %} +
    +
    +

    STUDENTS'INSIGHTS

    +
    +
    +
    +
    +
    +

    chatrooms

    + +
    + +
    + +

    MENTAL HEALTH CHATBOT

    + +
    +
    +
    +
    EXPLORE FIELDS
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/LearnersEd/student_insights_app/tests.py b/LearnersEd/student_insights_app/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/student_insights_app/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/student_insights_app/views.py b/LearnersEd/student_insights_app/views.py new file mode 100644 index 000000000..986966d40 --- /dev/null +++ b/LearnersEd/student_insights_app/views.py @@ -0,0 +1,4 @@ +from django.shortcuts import render + +def student_insights(request): + return render(request, 'student_insights.html', {}) diff --git a/LearnersEd/templates/error.html b/LearnersEd/templates/error.html new file mode 100644 index 000000000..dc8854b7e --- /dev/null +++ b/LearnersEd/templates/error.html @@ -0,0 +1,10 @@ + + + + Error + + +

    Error

    +

    {{ error_message }}

    + + diff --git a/LearnersEd/templates/navbar.html b/LearnersEd/templates/navbar.html new file mode 100644 index 000000000..270dbf25e --- /dev/null +++ b/LearnersEd/templates/navbar.html @@ -0,0 +1,14 @@ + + \ No newline at end of file diff --git a/LearnersEd/virtual_pet/__init__.py b/LearnersEd/virtual_pet/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/virtual_pet/__pycache__/__init__.cpython-39.pyc b/LearnersEd/virtual_pet/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..d9b2848ad Binary files /dev/null and b/LearnersEd/virtual_pet/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/virtual_pet/__pycache__/admin.cpython-39.pyc b/LearnersEd/virtual_pet/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..a9e795267 Binary files /dev/null and b/LearnersEd/virtual_pet/__pycache__/admin.cpython-39.pyc differ diff --git a/LearnersEd/virtual_pet/__pycache__/apps.cpython-39.pyc b/LearnersEd/virtual_pet/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..990eb97f4 Binary files /dev/null and b/LearnersEd/virtual_pet/__pycache__/apps.cpython-39.pyc differ diff --git a/LearnersEd/virtual_pet/__pycache__/models.cpython-39.pyc b/LearnersEd/virtual_pet/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..3aff08875 Binary files /dev/null and b/LearnersEd/virtual_pet/__pycache__/models.cpython-39.pyc differ diff --git a/LearnersEd/virtual_pet/__pycache__/views.cpython-39.pyc b/LearnersEd/virtual_pet/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..beb636bb4 Binary files /dev/null and b/LearnersEd/virtual_pet/__pycache__/views.cpython-39.pyc differ diff --git a/LearnersEd/virtual_pet/admin.py b/LearnersEd/virtual_pet/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/LearnersEd/virtual_pet/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/LearnersEd/virtual_pet/apps.py b/LearnersEd/virtual_pet/apps.py new file mode 100644 index 000000000..fddd21e91 --- /dev/null +++ b/LearnersEd/virtual_pet/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class VirtualPetConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'virtual_pet' diff --git a/LearnersEd/virtual_pet/migrations/__init__.py b/LearnersEd/virtual_pet/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/LearnersEd/virtual_pet/migrations/__pycache__/__init__.cpython-39.pyc b/LearnersEd/virtual_pet/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..d309f5f55 Binary files /dev/null and b/LearnersEd/virtual_pet/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/LearnersEd/virtual_pet/models.py b/LearnersEd/virtual_pet/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/LearnersEd/virtual_pet/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/LearnersEd/virtual_pet/static/content/Cat/Cat-Idle.png b/LearnersEd/virtual_pet/static/content/Cat/Cat-Idle.png new file mode 100644 index 000000000..65b93608a Binary files /dev/null and b/LearnersEd/virtual_pet/static/content/Cat/Cat-Idle.png differ diff --git a/LearnersEd/virtual_pet/static/content/Cat/Cat-Jump.png b/LearnersEd/virtual_pet/static/content/Cat/Cat-Jump.png new file mode 100644 index 000000000..37d55bb6a Binary files /dev/null and b/LearnersEd/virtual_pet/static/content/Cat/Cat-Jump.png differ diff --git a/LearnersEd/virtual_pet/static/content/Cat/Cat-Walk.png b/LearnersEd/virtual_pet/static/content/Cat/Cat-Walk.png new file mode 100644 index 000000000..900ed28ea Binary files /dev/null and b/LearnersEd/virtual_pet/static/content/Cat/Cat-Walk.png differ diff --git a/LearnersEd/virtual_pet/static/content/Dog/Dog-Idle.png b/LearnersEd/virtual_pet/static/content/Dog/Dog-Idle.png new file mode 100644 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document.getElementById("selector"); +var Name = document.getElementById("name_data"); +var DispName = document.getElementById("pet-name"); +var pet = document.getElementById("pet"); +var food_container = document.getElementById("item-container"); +var bar_max_width = document.getElementById('level').offsetWidth - 8; +var bar_height = document.getElementById('info').offsetHeight; +var bar = document.getElementById('bar'); +bar.style.height = `${bar_height}px`; +var display_per = document.getElementById('per'); +var percent = parseFloat(display_per.innerHTML); // database add level too +var grow_width = bar_max_width * percent * 0.01; +var level_desc = document.getElementById("levamt"); +var level_amt = parseInt(level_desc.innerHTML); +bar.style.width = `${grow_width}px`; +var levelplus = document.getElementById("info"); +var coins = document.getElementById('coin-amt'); + +// fetch the pet name and type from the views return if not set yet put pet_type = "" else assign the values +// also fetch the level and level progress and assign those too +// after updating the level and level progress send update request to server where it will update the values to backend virtual pet and give complete code of views + +var pet_type_list = ["Dog", "Cat"] + +var pet_type = ""; +// Function to update the pet details in the UI +function updatePetDetails(petType, petName, level, progress, coindb) { + // Assign the values to the corresponding variables + pet_type = petType; + level_amt = level; + percent = progress; + + // Update the UI with the new values + DispName.innerHTML = petName; + level_desc.innerHTML = `${level_amt}`; + display_per.innerHTML = `${percent}%`; + coins.innerHTML = `${coindb}`; + + // Calculate the growth width for the progress bar + grow_width = bar_max_width * percent * 0.01; + bar.style.width = `${grow_width}px`; + + // Update the pet image and animation based on the pet type + if (pet_type === "Dog" || pet_type === "Cat") { + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + } +} + +// Function to send the updated pet details to the server +function savePetDetails(petName, petType, level, progress, c) { + // Prepare the data to be sent in the request body + const data = { + name: petName, + type: petType, + level: level, + progress: progress, + coin: c + }; + + // Send an AJAX request to the server to save the data + $.ajax({ + url: '', + type: 'POST', + dataType: 'json', + data: data, + success: function(response) { + console.log('Pet details saved successfully'); + }, + error: function(error) { + console.log('Error saving pet details:', error); + } + }); + } + +var temp = setInterval(function() { + if (pet_type !== "Dog" && pet_type !== "Cat") { + var Dog = document.getElementById("pet-dog"); + var Cat = document.getElementById("pet-cat"); + Dog.addEventListener('click', () => { + Dog.style.border = "8px solid lime"; + Cat.style.border = "none"; + }) + Cat.addEventListener('click', () => { + Dog.style.border = "none"; + Cat.style.border = "8px solid lime"; + }) + selector.style.display = 'flex'; + var sel = -1; + } + else { + // updatePetDetails(petType, petName, level, progress, coindb); + selector.style.display = 'none'; + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + clearInterval(temp); + } + }, Infinity); + + var cost = [0, 20, 20, 30, 10, 10, 20, 30, 30, 50, 20, 70, 20, 30, 10, 10, 20, 20, 60, 80, 80, 30, 20, 70, 80, 60, 60, 50, 40, 20, 10, 30, 20, 50, 70, 70, 60, 20, 40, 40, 60, 60, 80, 80, 30, 20]; + + for (let i = 1; i <= 45; i++) { + food_container.innerHTML += `

    ${cost[i]}

    `; + } + + function validate() { + var radio = document.getElementsByName("sel"); + if (!radio[0].checked && !radio[1].checked) { + alert("Please select pet type"); + } + else if (radio[0].checked) { + sel = 0; + } + else { + sel = 1; + } + if (Name.value.trim() === "" && sel !== -1) { + alert("Please enter your pet name"); + } + else { + petName = `${Name.value.trim()}`; + DispName.innerHTML = petName; + pet_type = pet_type_list[sel]; + petType = pet_type_list[sel]; + // savePetDetails(petName, petType, level, progress, coindb); + } + } + + function updateLevel(addPercent) { + // adding database connection to fetch last known level and percent + // temporarily taking current level and percent, remove this later (level_amt => currentLevel) (percent => currentLevel) + var coins_left = coins.innerHTML; + if (coins_left >= addPercent) { + var feed_buttons = document.querySelectorAll(".feed"); + for (let i = 0; i < feed_buttons.length; i++) { + feed_buttons[i].disabled = true; + } + var init_coin = coins_left; + var coin_spent = setInterval(function() { + if (coins_left !== init_coin - addPercent) { + coins_left--; + coins.innerHTML = `${coins_left}`; + } + else { + for (let i = 0; i < feed_buttons.length; i++) { + feed_buttons[i].disabled = false; + } + clearInterval(coin_spent); + } + }, 50); + pet.style.backgroundImage = `url(../static/content/${pet_type}/${pet_type}-Jump.png)`; + pet.style.animation = "reward 1s steps(8) infinite"; + levelplus.innerHTML = `+${addPercent} Level`; + levelplus.style.animation = "levelplus 1s infinite"; + var new_percent = percent + addPercent; + var temp = percent; + var interval = setInterval(function () { + if (temp !== new_percent) { // switch here + if (percent + 1 > 100) { + percent = 1; + level_amt++; + level_desc.innerHTML = `${level_amt}`; + } + else { + percent++; + } + grow_width = bar_max_width * percent * 0.01; + level_desc.innerHTML = `${level_amt}`; + display_per.innerHTML = `${percent}%`; + bar.style.width = `${grow_width}px`; + temp++; + } + else { + clearInterval(interval); + var animation = setInterval(function() { + levelplus.style.animation = "none"; + pet.style.backgroundImage = `url(../static/content/${pet_type}/${pet_type}-Idle.png)`; + pet.style.animation = "idle 1s steps(8) infinite"; + clearInterval(animation); + }, 800); + } + }, 2); + savePetDetails(petName, petType, level_amt, percent, coins_left); + } else { + alert("Error: Not Sufficient Balance!"); + } + } + + function changeAnimation() { + if (pet_type === "Dog" || pet_type === "Cat") { + var action_list = ['idle', 'walk']; + var action = action_list[Math.floor((Math.random() * action_list.length))]; + if (action === 'idle') { + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + } + else { + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Walk.png')`; + pet.style.animation = "walk 3s steps(8) infinite"; + } + } + } + + setInterval(changeAnimation, 3000); + + + + diff --git a/LearnersEd/virtual_pet/static/styles-pet.css b/LearnersEd/virtual_pet/static/styles-pet.css new file mode 100644 index 000000000..ce60af546 --- /dev/null +++ b/LearnersEd/virtual_pet/static/styles-pet.css @@ -0,0 +1,457 @@ +html, body { + padding: 0; + margin: 0; +} + +#container { + display: flex; + justify-content: space-evenly; + align-items: center; + font-family: 'Press Start 2P', cursive; + width: 100%; + height: 100vh; + background-color: #F0D09F; +} + +#shop-container { + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + width: 55%; +} + +.close { + border-radius: 50%; + width: 30px; + height: 30px; + display: flex; + justify-content: center; + align-items: center; + padding: 10px; + outline: 4px solid antiquewhite; + background-color: #F0D09F; + position: absolute; + top: 30px; + left: 40px; + font-size: 30px; + color: #666; + border: 2px solid black; + text-align: center; + transition-duration: 200ms; + box-shadow: 0 0 20px 2px black; + cursor: pointer; +} + +.close:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +.close:active { + transform: scale(0.9); + transition-duration: 200ms; +} + +#shop { + font-size: 40px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; +} + +#item-container { + overflow-y: auto; + display: flex; + flex-wrap: wrap; + justify-content: center; + border: 8px solid black; + height: 42vh; + padding: 30px 30px; + gap: 30px 20px; + background-color: antiquewhite; + scroll-behavior: smooth; + border-top-right-radius: 0px; + border-bottom-right-radius: 0px; + border-top-left-radius: 80px; + border-bottom-left-radius: 80px; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); + outline: 4px solid #f7e8ce; +} + +#item-container::-webkit-scrollbar { + width: 50px; + outline: 8px solid #f7e8ce; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +#item-container::-webkit-scrollbar-track { + background: repeating-linear-gradient( + to right, + #f6ba52, + #f6ba52 10px, + #ffd180 10px, + #ffd180 20px + ); + background-color: #f6ba52; + -webkit-box-shadow: inset 0px 0px 1px 5px white; + +} + +#item-container::-webkit-scrollbar-thumb { + background-color: white; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:decrement:hover { + border-color: transparent transparent white transparent; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:increment { + border-width: 8px 8px 0 8px; + border-color: #555555 transparent transparent transparent; +} + +#item-container::-webkit-scrollbar-button:vertical:single-button:increment:hover { + border-color: white transparent transparent transparent; + animation: bounce 1s infinite ease-out; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:decrement { + border-width: 0 8px 8px 8px; + border-color: transparent transparent #555555 transparent; +} + +#item-container::-webkit-scrollbar-button:single-button { + background-color: #f6ba52; + display: block; + border-style: solid; + height: 30px; + width: 16px; +} + +.feed { + width: 140px; + height: 140px; + border-radius: 50%; + box-shadow: 0px 0px 10px 3px rgba(0,0,0,0.5); + transition: 200ms; +} + +#label { + display: flex; + justify-content: center; + align-items: center; + font-size: 30px; + gap: 10px; +} + +#label>img { + width: 50px; + margin-right: 40px; +} + +.feed:hover { + transform: scale(1.1); + transition: 200ms; +} + +.feed:active { + transform: scale(0.8); + transition: 200ms; +} + +.feed img { + width: 80%; +} + +#coins { + font-size: 20px; + display: flex; + margin-top: 30px; + justify-content: center; + align-items: center; + gap: 20px; +} + +#coins > img { + width: 50px; + height: 50px; +} + +#pet-name { + background-color: black; + color: white; + padding: 15px 20px; +} + +#pet-title { + margin-top: 40px; + font-size: 20px; +} + +#pet-container { + width: 35%; + border: 8px solid black; + height: 80vh; + border-radius: 80px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +#pet-background { + background-image: url(./content/pet-back.jpg); + background-size: cover; + background-position: center; + background-repeat: no-repeat; + width: 100%; + height: 100%; + border-radius: 72px; + display: flex; + flex-direction: column; + justify-content: space-between; + align-items: center; +} + +#prog { + width: 100%; + display: flex; + justify-content: center; + align-items: center; + margin-top: 60px; + gap: 20px; +} + +#level { + width: 50%; + height: 30px; + border: 4px solid black; + outline: 4px solid white; + background-color: white; + display: flex; + box-shadow: 0px 0px 10px 3px rgba(0,0,0,0.5); +} + +#per { + font-size: 30px; + text-shadow: 2px 2px #ffffff; + width: 90px; +} + +#bar { + background-color: blue; + position: absolute; +} + +#info { + color: lime; + font-size: 20px; + opacity: 0; + white-space: nowrap; +} + +#desc { + color: black; + font-size: 20px; + position: relative; + top: 50px; + right: 10px; + white-space: nowrap; + text-shadow: 2px 2px white; +} + +#pet { + width: 300px; + height: 264px; + margin-bottom: 30px; + background-repeat: no-repeat; + position: relative; +} + +#selector { + width: 100%; + height: 100vh; + background-color: #F0D09F; + font-family: 'Press Start 2P', cursive; + flex-direction: column; + display: none; + justify-content: center; + align-items: center; + position: absolute; + z-index: 999; + gap: 30px 30px; +} + +input[type=radio] { + display: none; +} + +#name { + display: flex; + flex-direction: column; + gap: 10px; +} + +form { + display: flex; + flex-direction: column; + justify-self: center; + align-items: center; +} + +#selector_submit { + font-size: 20px; + outline: 4px solid #f7e8ce; + background-color: #F0D09F; + font-family: 'Press Start 2P', cursive; + padding: 20px; + cursor: pointer; + transition-duration: 200ms; +} + +#name > p { + font-size: 20px; +} + +#selector_submit:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#selector_submit:active { + transform: scale(0.9); + transition-duration: 200ms; +} + +#name { + display: flex; + flex-direction: column; + gap: 20px; +} + +#name_data { + outline: 4px solid #f7e8ce; + padding: 10px; + font-size: 20px; + font-family: 'Press Start 2P', cursive; + text-align: center; + transition-duration: 200ms; +} + +#name_data:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#select_type { + display: flex; + gap: 80px; + justify-content: center; + align-items: center; +} + +.pet-sel { + padding: 20px; + border-radius: 30px; + outline: 8px solid #f7e8ce; + transition-duration: 200ms; + background-color: #F0D09F; + cursor: pointer; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +.pet-sel:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#Cat { + width: 300px; + height: 264px; + background-repeat: no-repeat; + background-image: url(./content/Cat/Cat-Idle.png); + animation: idle 1s steps(8) infinite; + background-color: transparent; + border: none; +} + +form { + display: flex; + flex-direction: column; + gap: 20px; +} + +#Dog { + width: 300px; + height: 264px; + background-repeat: no-repeat; + background-image: url(./content/Dog/Dog-Idle.png); + animation: idle 1s steps(8) infinite; + background-color: transparent; + border: none; +} + +@keyframes idle { + from { + background-position: 0 0; + } + to { + background-position: 0 -2112px; + } +} + +@keyframes reward { + from { + background-position: 0 0; + transform: translateY(0); + } + to { + background-position: 0 -2112px; + transform: translateY(-30px); + } +} + +@keyframes walk { + 0% { + background-position: 0 0; + transform: rotateY(0deg) translateX(0px); + } + 25% { + background-position: 0 -2112px; + transform: rotateY(0deg) translateX(160px); + } + 28% { + background-position: 0 -2112px; + transform: rotateY(180deg) translateX(-160px); + } + 50% { + background-position: 0 0; + transform: rotateY(180deg) translateX(0px); + } + 75% { + background-position: 0 -2112px; + transform: rotateY(180deg) translatex(160px); + } + 78% { + background-position: 0 -2112px; + transform: rotateY(0deg) translatex(-160px); + } + 100% { + background-position: 0 0; + transform: translatex(0); + } +} + +@keyframes levelplus { + from { + transform: translateY(0); + opacity: 0; + } + to { + transform: translateY(-40px); + opacity: 1; + } +} \ No newline at end of file diff --git a/LearnersEd/virtual_pet/templates/virtual-pet.html b/LearnersEd/virtual_pet/templates/virtual-pet.html new file mode 100644 index 000000000..bfe3d27cf --- /dev/null +++ b/LearnersEd/virtual_pet/templates/virtual-pet.html @@ -0,0 +1,62 @@ +{% load static %} + + + + Virtual Pet - (Learners'Ed) + + + + + + +
    +

    Create your very own pet!

    +
    + + +
    +
    +

    Enter your new pet's name:

    + + +
    +
    +
    +
    +
    +

    Pet Shop

    +
    +

    Pet Name:

    +
    +

    Coins Left: 200

    + +
    +
    +
    +
    + +
    +
    +
    +
    +
    + Level
    +
    Level 3
    +
    +
    30%
    +
    +
    +
    +
    +
    +
    ✖
    + + + + diff --git a/LearnersEd/virtual_pet/tests.py b/LearnersEd/virtual_pet/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/LearnersEd/virtual_pet/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/LearnersEd/virtual_pet/views.py b/LearnersEd/virtual_pet/views.py new file mode 100644 index 000000000..052b2a0b0 --- /dev/null +++ b/LearnersEd/virtual_pet/views.py @@ -0,0 +1,28 @@ +from django.shortcuts import render +from login_register.models import Pet +from django.http import JsonResponse + +def virtual_pet(request): + # if 'student_id' in request.session: + # if request.method == 'POST': + # if request.POST.get('level'): + # level = request.POST.get('level') + # progress = request.POST.get('progress') + # coin = request.POST.get('coin') + # pet = VirtualPet.objects.get(id=student_id) # Assuming there's only one pet record + # pet.pet_level = level + # pet.pet_level_progress = progress + # pet.pet_coin = coin + # pet.save() + # return JsonResponse({'status': 'success'}) + # student_id = request.session['student_id'] + # pet = VirtualPet.objects.get(id=student_id) + # pet_data = { + # 'name': pet.pet_name, + # 'type': pet.pet_type, + # 'level': pet.pet_level, + # 'progress': pet.pet_level_progress, + # 'pet_coin': pet.pet_coin + # } + # return JsonResponse({'pet': pet_data}) + return render(request, "virtual-pet.html", {}) diff --git a/README.md b/README.md index 81463bfd7..88092a61c 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,748 @@ # intel-oneAPI -#### Team Name - -#### Problem Statement - -#### Team Leader Email - +#### Team Name - **Phoenix 13** +#### Problem Statement - **Open Innovation in Education** +#### Team Leader Email - **saumya.srivastava_cs.aiml21@gla.ac.in** + +### **Intel Official AI Blog Page:** +[https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Building-an-Interactive-Educational-Platform-Using-Intel-AI/post/1585262](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Building-an-Interactive-Educational-Platform-Using-Intel-AI/post/1585262) + +### **Intel Dev News Youtube Short (April 2024)** +[https://www.youtube.com/shorts/RTfQKs1ekZg](https://www.youtube.com/shorts/RTfQKs1ekZg) ## A Brief of the Prototype: - This section must include UML Daigrms and prototype description + +![Banner-1](images/1.png) + +Quality education is the path to transcendence and making this journey more engaging is the sole purpose of our project. We intend to include features that would provide an interactive interface to the user where he/she can have a more precise watch on his/her performance, explore their areas of interest, and connect with people of the desired field in the vicinity using the real-time trained DL and ML model. + +Making the learning more innovative we would allow them to create chatrooms for discussions on projects, academics, areas of interest, etc., and also help them with auto-generated suggestions. + +Creating fun and amazing pet avatars in which the capital to unlock new features would be through their scores that would be auto-generated based on their day-to-day progress in activities as well as academics. + +Apart from learning, we would also take care of their mental health by creating interactive chatbots which could judge a child's mental state and provide suitable help. Thus, providing a pool of exploration alongside fun and healthy competition would help in the overall development of the student. + +## How it works? + +![Banner-2](images/2.png) + +The user interactions related to learning would be duly recorded. These recordings will be taken as input features for our Intel oneAPI oneDNN and oneDAL integrated ML and DL models. These models will further make predictions, cluster the students according to their areas of interest, generate visualization for their progress in each of their domains/subjects, and provide the candidates with study material recommendations whether it is a video, blog, or book. +Some fun activities and assessments to help them, co-op with their studies, which in turn will be recorded and a ranking system amongst the students say class-wise, section-wise, or campus-wise will provide them scores for performing well in those activities and classes and rank them accordingly, creating a healthy competitive environment. + +During Covid-19, when everything had shifted to online mode, students used to get bored and drowsy during classes and it was not possible to keep a check on each and every student. Through our project, we would train the model to check if the person is getting drowsy and recommend him some auto-generated exercises that would help him get his focus back. Most institutions stick to the old school analysis systems in which the student is never able to understand specifically in which area, he/she should improve in order to maintain his academic results, but through our project, he/she can get a detailed analysis of his performance with day to day updates which will keep him/her more aware about his situation in every area. + +# List of features offered by the solution + +## Vision + +Making learning more interactive and innovative by providing a clearer picture along with some fun elements, here we list the features of our project bifurcated into three categories. + +## Category - 01 (Academic) + +![Banner-3](images/3.png) + +01. An advanced ranking system that will be updated on a day-to-day basis which would be based on the overall performance during lectures, projects, assignments, and quizzes. + +02. Keeping track of the attendance of the student in every subject and sending reminders to attend lectures on subjects in which his attendance is going down at an alarming rate. + +03. Providing him/her proper insights about the areas he is currently lacking in along with auto-generated suggestions of resources to study from. + +04. A separate area for assignment submission along with a plagiarism checker. The plagiarism checker would keep on updating itself with each assignment submission. If it receives an assignment with exactly the same content, the student would receive a notification to resubmit another assignment as the presently submitted assignment might be up for plagiarism. + +05. A highlight section that would keep them connected to the world. Giving them a daily insight into what's happening around the globe, especially their areas of interest. + +## Category - 02 (Gamification) + +![Banner-4](images/4.png) + +01. Improvisation of the ranking system through day-to-day digital badge system. Every day the student who has outperformed everyone else by giving more quizzes and doing his assignments would be provided with the student of the day batch. + +02. Interactive quizzes through facial gestures. Multiple choice questions would be answered with the movement of the head. + +03. Adding some fun virtual elements, there will be a performance-wise token credit system in this Gamification Centre. Based on this credit, the students can unlock new features for their virtual pet. + +## Category - 03 (Student Insights) + +![Banner-5](images/5.png) + +01. Checking on the mental health of the student, there will be interactive mental health chatbots. In case the student needs any kind of help, he/she can be provided immediate assistance. + +02. Detection of drowsy and low behaviour during online lectures. If the student has been feeling drowsy for a certain period of time during the lecture, then some easy sitting exercises will be suggested in order to tackle the drowsiness. + +03. The students can create chatrooms where all the interested can pitch in for a project, assignment, etc. In case they need a mentor, then we can provide them with suggestions from the faculty experts in that field. + +## Our Models + +![UML-Diagram](images/Diagram.png) + +# Student Login/Register + +Firstly we present you the login and register page. First time visitors will have to register themselves to the portal in order to enjoy the benifits of the dashboard. They also need to enter their favourite category which would influence the outputs generated by the recommendation system. The already registered students just need to fill in the email and password. + +#### Registration Page + +![Register](images/Screenshot_2023-06-07_180649.png) + +#### Login Page +![Login](images/Screenshot_2023-06-07_180632.png) + +# Dashboard + +Now the new student is welcomed with the dashboard welcome message. Here we have the weather API that would Fetch the current information about the weather and keep the dashboard more updated with the outside world, keeping the student well informed. Nextly we have the rank of the student represented in the form of graphs. These graphs display the rank class-wise, section-wise and school-wise separetely. So that students are always well informed of their performance and the scope of improvement. +Now there will be a alert or notification section as well, where the alerts from the admins will be presented. The next section includes the attendance for all the subjects along with the average attendance. Next is a piechart showing the number of assignments submitted where the student will get to know how many assignments are still left to submit. After that, we have the Learners'Ed Coin Bank, which tells us how many coins are left with the user and on the right side we have our virtual pet information, such as pet name, level and rank. +At last, we have the recommendation section where the category selected would determine which type of videos will be fetched for the student and be presented in the recommendation section. + +![Dash-1](images/Screenshot_2023-06-07_181401.png) + +![Dash-2](images/Screenshot_2023-06-07_181456.png) + +![Dash-3](images/Screenshot_2023-06-07_181545.png) + +# Lecture Section + +Now we have the lecture section where all of the lectures recommended for the students are listed here. The student can watch that lecture by clicking on the watch button after going into the lecture section. We are also introducing a 'Drowsiness Detection System', in which if any teacher uploads a lecture, this system would track down the time for which a student has been actively listening during the lecture and would provide the attendance accordingly. However, we understand, that online lectures can get a little boring and tiresome. So, in order to tackle that when the system would notice that the student hasn't been attentive for a certain period of time, it would pause the video and generate a promt that would suggest an easy sitting exercise that could help them regain their focus and not miss on the learning. Once he/she has done the exercise and is fresh again, he/she can click on the 'OK' button and continue with his lecture. + +#### Lecture List + +![Lect](images/Screenshot_2023-06-07_183042.png) + +#### Not Drowsy + +![lec-1](images/Screenshot_2023-06-07_183229.png) + +#### Drowsy + +![lec-2](images/Screenshot_2023-06-07_183128.png) + +# Drowsiness Detection System + +You can acces the drowsiness detection model used behind this, present in the AIML Modules Folder. + +The provided code in the AIML modules folder implements a drowsiness detection model using Convolutional Neural Networks (CNNs). Here is a summary of the code functionality: + +Importing necessary libraries: The code starts by importing the required libraries, including PIL, OpenCV, face_recognition, TensorFlow, and Keras. + +Eye Cropping Function: The eye_cropper function takes an image path as input and uses OpenCV and face_recognition to locate the eyes in the image. It crops the eye region, resizes it to 80x80 pixels, and returns the cropped image for further processing. + +Loading Images from Dataset: The load_images_from_folder function loads images from the specified folder and resizes them to 80x80 pixels. It assigns a label (0 for open eyes, 1 for closed eyes) and creates a list of image-label pairs. + +Preparing the Dataset: The code creates arrays for input images (X) and corresponding labels (y). It iterates through the image-label pairs, appends the images to X, and labels to y. The images are reshaped, normalized, and the labels are converted to arrays. + +Splitting the Dataset: The dataset is split into training and testing sets using the train_test_split function from sklearn. The splitting is stratified based on the labels to maintain class balance in both sets. + +Model Definition: The code defines the CNN model using the Sequential API of Keras. It includes convolutional layers, max-pooling layers, dense layers, and dropout layers for regularization. + +Model Compilation and Training: The model is compiled with binary cross-entropy loss and Adam optimizer. It is then trained on the training data, using the fit function, for 24 epochs. The validation data is used to monitor the model's performance during training. + +Model Evaluation: The trained model is evaluated on the testing data using the evaluate function. The evaluation results, including loss and metrics, are printed. + +Model Saving: The trained model is saved to a file using the save function. + +Prediction Function: The model_response function takes an image and uses the eye_cropper function to extract the eye region. The preprocessed image is then passed to the trained model for prediction. If the predicted probability of closed eyes exceeds a threshold, the function returns 'Yes,' indicating drowsiness. + +Model Usage: The model_response function is called with an image to demonstrate the usage of the trained model. + +In summary, the code prepares and trains a CNN model to classify eye states as open or closed for drowsiness detection. It provides a function to extract eye regions from images and a function to classify the eye state using the trained model. + +# Intel Optimization Applied + +Along with the normal model we have applied Intel oneDNN with OpenMP and scikit-learn-intelex, the scikit learn optimization by intel, which further leverages our model preformance. Using these optimization tools helped us getting more inferences in less time and train our model very fast as well + +Note: We have trained our model using intel oneAPI AI analytics toolkit oneDNN and OpenMP on intel i5 11th gen 11260H 6 core 12 thread computer. + +Here are the OpenMP params used + +inter: 6 + +intra: 6 + +KMP_BLOCKTIME: 1 + +Test_Set: 25 + +#### Benchmarks Rates + +Inference Time Rate: 1.1440191387559806 + +Latency Rate: 0.8741112505227936 + +Throughput Rate: 1.1440191387559806 + +Training Time Rate: 0.438332327047551 + +Here is the benchamarking difference between the model trained on normal cpu vs on intel optimization, same hardware better performance! + +![banch](images/drowsy.png) + + +# Gamify Section + +Now coming to the most entertaining section of the LearnersEd Portal, the gamify section. Here the students are encouraged to sharpen their minds along with having a fun competitve environment amongst them. There are two section here gamify quiz and virtual pet. + +![gam](images/Screenshot_2023-06-07_185607.png) + +## Gamify Quiz + +Here the students are presented with a quiz, but the twist is the answers are not selected by mouse or keyboard input, the options are selected by their head posture and this would reduce their decision taking time and would improve their reflexes to act upon situations, every student needs to attentively solve the quiz and then after the quiz they would get Learners'Ed Coins according to the marks they scored. + +![q](images/Screenshot_2023-06-07_185628.png) + +![q2](images/Screenshot_2023-06-07_201413.png) + +![q3](images/Screenshot_2023-06-07_201617.png) + +This module is supported by Face Pose detection model developed by us. + +# Face Pose Detection System + +The provided code performs head pose estimation using a machine learning model. Here is a technical write-up summarizing its functionality: + +Data Loading: The code loads the input data from a pickle file. It consists of images as input samples (x) and corresponding head pose angles (y). + +Data Preprocessing: The head pose angles (y) are split into three components: roll, pitch, and yaw. The code then prints the minimum, maximum, mean, and standard deviation values for each component to provide insights into the data distribution. + +Dataset Splitting: The input data (x and y) is split into training, validation, and testing sets using the train_test_split function from scikit-learn. The training set contains 70% of the data, and the remaining 30% is evenly divided between the validation and testing sets. + +Data Standardization: The StandardScaler is applied to standardize the input features (x_train, x_val, and x_test) by subtracting the mean and scaling to unit variance. + +Model Architecture: The code defines a neural network model using the Sequential API from Keras. The model consists of three dense layers with ReLU activation. The first two layers have regularization using L2 kernel regularization. + +Model Compilation and Training: The model is compiled with the Adam optimizer and mean squared error (MSE) loss function. It is trained on the training data with early stopping based on the validation loss. The training progress is stored in the hist variable. + +Model Saving: The trained model is saved to a file named "model.h5". + +Model Evaluation: The code evaluates the trained model on the training, validation, and testing sets, printing the loss values for each set. + +Visualization: The training and validation loss curves are plotted to visualize the model's training progress. + +Face Point Detection: The code defines a function detect_face_points that uses the dlib library to detect the 68 facial landmarks on an input image. + +Feature Computation: The compute_features function calculates pairwise Euclidean distances between the detected facial landmarks, resulting in a feature vector. + +Feature Standardization: The computed features are standardized using the same StandardScaler instance used for the input features. + +Model Loading: The saved model is loaded from the "model.h5" file. + +Head Pose Estimation: The standardized features are fed into the loaded model to predict the roll, pitch, and yaw angles of the head pose. + +Result Visualization: The input image is displayed with the detected facial landmarks and the predicted head pose angles. + +In summary, the code performs head pose estimation by training a neural network on a dataset of images and corresponding head pose angles. It preprocesses the data, builds and trains the model, and then uses the trained model to predict head pose angles for new input images. The detected facial landmarks and predicted head pose angles are visualized for analysis and interpretation. + +# Intel Optimization Applied + +Here we applied oneDNN along with OpenMP and scikit-learn extension which leverages performance for us. + +Here are the OpenMP params + +inter: 2 + +intra: 6 + +KMP_BLOCKTIME: 0 + +Test_Set: 25 + +#### Benchmark Rate + +Inference Time Rate: 1.0136551020270463 + +Latency Rate: 0.9865288479289062 + +Throughput Rate: 1.0136551020270463 + +Training Time Rate: 0.4256577027908266 + +Here is the benchmarks difference in normal cpu vs intel optimized cpu + +![bgam](images/FacePose.png) + +## Virtual Pet + +Here comes the virtual pet section, the students have access to their very own virtual pet, they can select from two types as well as assign a name. Initially, the students have pet rank as last and level at 1. This rank can be improved by earning more and more Learners'Ed coins which they can be gained by taking the quizzes accessible from the gamify section itself. Based on their performance in these quizzes they will be rewarded with a certain number of coins which they can further use to feed their pet. The quanity of coins would keep upgrading itself. with the number of the quizzes they take. + +This whole system is developed using javascript code only, no model trained. + +![vpet](images/Screenshot_2023-06-07_213827.png) + +![vpet](images/Screenshot_2023-06-07_213909.png) + +# Assignment Upload Section + +Here in this section the students can upload their respective assignments, if however their assignment is matched to other students assignment it would reject the assignment and would not store it, if not then this assignment will be selected an the assignment will be added to the checker list. As the current situation tells us the requirement of plagiarism checker this needs to be the base restriction for our assignment uploading by each student. + +#### Accepted + +Submitting a unique assignment + +![check](images/Screenshot_2023-06-07_215334.png) + +#### Rejected + +Submitting a same assignment again + +![check](images/Screenshot_2023-06-07_215357.png) + +This model is not trained, it just use cosine_similarity and nltk library to calculate plagiarism, so no benchmark is done. + +Note: It only accepts doc or docx files for now, we will further extend it to other file types and can integrate a model with it as well. + +The provided code performs a plagiarism check on a student's assignment by comparing it with reference assignment files. Here is a technical write-up summarizing its functionality: + +Text Preprocessing: The code defines a function preprocess_text that takes a text input and performs preprocessing steps. It removes punctuation, converts the text to lowercase, and removes stopwords using the NLTK library. + +Text Extraction from DOCX: The code defines a function extract_text_from_docx that takes a file path to a DOCX document and extracts the text from its paragraphs using the python-docx library. + +Plagiarism Check: The code defines a function check_plagiarism that performs the plagiarism check. It takes the file path of the student's assignment DOCX file and a list of reference assignment files. + +Student Assignment Processing: The student's assignment is loaded and its text is extracted using the extract_text_from_docx function. The extracted text is then preprocessed using the preprocess_text function. + +Similarity Calculation: For each reference assignment file, the code loads the file, extracts its text, preprocesses it, and then calculates the similarity between the preprocessed student assignment and the preprocessed reference assignment. The similarity is measured using the cosine similarity of TF-IDF vectors. + +Output: The code returns a list of similarities, where each similarity value represents the similarity between the student's assignment and a reference assignment. + +Plagiarism Check Execution: The code sets the file path of the student's assignment and gets a list of reference assignment files in a specified directory. It then calls the check_plagiarism function with these inputs and retrieves the similarities. + +Similarity Display: The code iterates over the similarities and prints the similarity value for each reference assignment. + +In summary, the code performs a plagiarism check by comparing a student's assignment with a set of reference assignments. It preprocesses the text, extracts the relevant information from DOCX files, calculates the similarity using TF-IDF vectors, and provides a similarity score for each reference assignment. This approach allows for the detection of potential plagiarism by analyzing the textual content of the assignments. + +# Students Insight Section + +![student](images/Screenshot_2023-06-07_215959.png) + +This section is made for students to explore more out of their surrounding peoples and environment, this section is divided into 3 sections. This makes a healthy environment for them in the panel itself, making them feel connected which was a major hit at the time of covid. + +## Chatroom + +The first one is chatrooms, this chatrooms are available throughout the campus, further these can be sub divided into class-wise, section-wise as well, so that students can chat and get along their coomunity. This will help building a community mindset amongst them and never leave them alone in the lockdown period of covid, making them +mentally strong. + +![issm](images/Screenshot_2023-06-07_220039.png) + +## Mental Health Chatbot + +The second section includes mental health chatbot which is a deep learning model trainged chatbot. This model helps the students to discuss their problems with them and give a stress release session with the chatbot. The chatbot gives a helping hand to the students if they face any such mental health issues. + +![men](images/Screenshot_2023-06-07_220259.png) + +The provided code trains a chatbot model using a sequential neural network for intent recognition and response generation. Here is a technical write-up summarizing its functionality: + +Data Loading: The code reads a JSON file containing intents data and creates a pandas DataFrame to store the tag, patterns, and responses. + +Data Preprocessing: The patterns from the DataFrame are tokenized using the Keras Tokenizer, which converts the text into sequences of integers. The tokenizer is fitted on the patterns and converts them into sequences. + +Input and Output Encoding: The padded sequences of patterns (X) are created using the pad_sequences function from Keras, which ensures that all sequences have the same length. The tags (y) are encoded using the LabelEncoder from scikit-learn, which converts the categorical labels into numerical form. + +Model Architecture: The code defines a sequential neural network model using the Keras Sequential API. The model consists of an input layer, an embedding layer, multiple LSTM layers, layer normalization, dense layers, and dropout regularization. The final layer uses softmax activation for multi-class classification. + +Model Compilation: The model is compiled with the Adam optimizer and sparse categorical cross-entropy loss, which is suitable for multi-class classification problems. Accuracy is used as an additional metric. + +Model Training: The model is trained on the input data (X) and target labels (y) using the fit function. An early stopping callback is included to monitor the accuracy and stop training if it doesn't improve after a certain number of epochs. + +Model Response Generation: The code defines a function model_response that takes a user query and the trained model. The query is preprocessed, tokenized, and converted into a sequence. The sequence is padded, and the model predicts the most probable tag for the query. A random response is selected from the responses associated with the predicted tag. + +Model Testing: The model_response function is called with example queries to test the chatbot. The user query, model response, and random selected response are printed. + +In summary, the code trains a chatbot model for intent recognition and response generation using a sequential neural network architecture. It preprocesses the data, creates a model with LSTM layers, trains the model, and generates responses based on user queries. This approach allows the chatbot to understand user intents and provide appropriate responses. + +# Intel Optimization Applied + +Here I have used Intel oneDNN with OpenMP, scikit-learn extension, and Moding[ray] Pandas Library which leverages this models performance. + +Note: Every model is trained on Intel Python for more optimization. + +Here are OpenMP params + +inter: 2 + +intra: 6 + +KMP_BLOCKTIME: 1 + +Test_Set: 20 + +Benchmarking Rates + +Inference Time Rate: 2.3821281464139816 + +Latency Rate: 0.41979269734308167 + +Throughput Rate: 2.3821281464139816 + +Training Time Rate: 0.7704980148747786 + +![img](images/Mental_Health_Chatbot.png) + +## Explore Fields + +The last section inlcudes Explore Fields section which fetches the information from different sets of fields, and present them to the students with the help of popular newsletter API, these gathered pit of useful information makes the student more encouraged to read them, instead of searching and scraping them through the web. + +![Exp](images/Screenshot_2023-06-07_220329.png) + +![exp](images/Screenshot_2023-06-07_220346.png) + +Here is the whole process flow diagram for each model being used. + +# Process Flow Diagram + +![Process](images/Intel_oneAPI_Hackathon_PPT.png) + +# Summary of Intel oneAPI AI Analytics Toolkit Optimization +To meet the project requirements, we have developed three sophisticated Deep Learning Models that play crucial roles in different sections of our platform. These models have been enhanced with Intel oneDNN and OpenMP optimization, allowing us to achieve exceptional performance gains, including faster training, higher throughput, improved inference speed, and reduced latency. + +The first model we have developed is the Drowsiness Detection Model, which is utilized in the Lecture Section. By leveraging Intel oneDNN and OpenMP optimization, we have significantly accelerated the training process of this model. This optimization framework has not only expedited the training phase but has also improved the overall inference speed during real-time drowsiness detection. As a result, students' attendance can be accurately determined based on their active listening time during lectures. + +The second model we have incorporated is the Face Pose Estimation Model, which plays a crucial role in the Gamify Quiz section. Through the implementation of Intel oneDNN and OpenMP optimization techniques, we have achieved remarkable improvements in training efficiency, throughput, and inference speed. These optimizations have enabled us to accurately estimate facial movements and gestures during the quiz, providing an engaging and interactive learning experience for students. + +Lastly, we have developed a sophisticated Mental Health Chatbot that leverages Intel oneDNN and OpenMP optimization. This optimization framework has significantly enhanced the performance of our chatbot, resulting in faster response times, improved throughput, and reduced latency. By employing Intel's optimization tools, such as scikit-learn-intelex, we have been able to train the chatbot model efficiently and deliver prompt and insightful responses to students seeking support. + +The integration of Intel oneDNN and OpenMP optimization in our models has been instrumental in achieving remarkable performance enhancements. The collaboration between our deep learning models and Intel's optimization tools has resulted in faster training processes, higher inference speeds, and improved overall efficiency. These optimizations have enabled us to deliver a seamless and efficient educational experience to our users while ensuring the highest quality standards. + +Intel oneDNN (Deep Neural Network Library) is a highly optimized library that provides efficient implementations of deep learning primitives. +By utilizing Intel oneDNN, our three models benefit from accelerated training, improved throughput, enhanced inference speed, and reduced latency. The library's optimized computations and parallelization techniques optimize the performance of our models, enabling faster and more efficient processing. + +OpenMP is an industry-standard API that allows for parallelization of code across multiple processors. With OpenMP, we can leverage multi-threading capabilities to distribute computations among multiple cores or processors, maximizing performance and speeding up training and inference processes for our models. + +This parallelization significantly improves the overall efficiency and scalability of our models. + +Modin for Pandas is a powerful library that enhances the performance of Pandas, a popular data manipulation and analysis tool. By integrating Modin, we can scale Pandas operations across multiple processors or nodes, enabling faster data preprocessing and manipulation. This acceleration in data handling benefits our models by reducing the time required for data preparation, leading to quicker model training and improved overall efficiency. +scikit-learn (sklearnex) is a widely used machine learning library that provides various algorithms and tools for tasks such as classification, regression, and model evaluation. + +The test_train_split function from scikit-learn (sklearnex) is particularly helpful in our models. It enables us to split our dataset into training and testing subsets, facilitating proper model evaluation and validation. This function ensures that our models are trained on a representative subset of data and are subsequently tested on an independent portion, helping us gauge their performance accurately. +In addition, the patch functionality of scikit-learn (sklearnex) allows us to apply specific fixes or modifications to the library, enhancing its compatibility with our models and ensuring seamless integration. + +These combined capabilities of scikit-learn (sklearnex) and the test_train_split function enable us to effectively train and evaluate our models, leading to more accurate predictions and reliable performance. + +Faster inference speed is crucial in deployment, as it ensures real-time responsiveness and enables scalability in high-concurrency scenarios. It also contributes to cost efficiency by optimizing resource utilization. The Intel i5 11th gen processor plays a pivotal role in driving the efficiency and speed of our models in our web-based education platform. + +## Link to Project Video +[https://youtu.be/N8nnZsvOhVo](https://youtu.be/N8nnZsvOhVo) + +## Link to Benchmarking Video for Intel Optimization +[https://youtu.be/2GJiBfsu9LE](https://youtu.be/2GJiBfsu9LE) + +## Link to Medium Blog +[https://medium.com/@kulshrestha.sujal13/open-innovation-in-education-dcd67bebdad7](https://medium.com/@kulshrestha.sujal13/open-innovation-in-education-dcd67bebdad7) ## Tech Stack: - List Down all technologies used to Build the prototype **Clearly mentioning Intel® AI Analytics Toolkits, it's libraries and the SYCL/DCP++ Libraries used** + +#### List of oneAPI AI Analytics Toolkits & its libraries used + +**Intel oneAPI Base Toolkit** +(General Compute) + +1) Intel® oneAPI Data Analytics Library +2) Intel® oneAPI Deep Neural Networks Library +3) Intel® Distribution for Python +4) Intel® oneAPI Math Kernel Library + +**Intel® AI Analytics Toolkit** +(End-to-End AI and Machine Learning Acceleration) + +1) Intel® Distribution for Python with highly optimized scikit-learn +2) Intel® Optimization for TensorFlow +3) Intel® Optimization of Modin + +**Base Technology Stack** +1) HTML & CSS - Web Application (Frontend) +2) Tailwind CSS - (Style) +3) Django - Web Application (Backend) +4) Javascript - Validation & Client-Side Scripting +5) MongoDB & Sqlite3 - DBMS +6) Matplotlib & Seaborn - Data Visualization +7) Google Charts, Charts.js and/or any other 3rd Party - Data Visualizer +8) TensorFlow & scikit-learn (scipy) - DL and ML Model +9) OpenCV - Computer Vision ## Step-by-Step Code Execution Instructions: - This Section must contain set of instructions required to clone and run the prototype, so that it can be tested and deeply analysed + +As we have developed our LearnersEd Application, Web based, we used Django as our backend. So in order to clone this project, the user first needs to fulfill all the prerequisite knowledge and installed dependencies/libraries needed by all our models, or just the requirements you needed for selected modules only. Yes, this is possible because we created each module independent of itself so that these modules can be easily used in other projects. + +If you need to use a particular module either AIML or Web. If its an AIML Module, then just copy the module files where you want to run the model and give input data for prediction. If you want to use web modules then you need to create django environment and add that app to the environment and make migrations if needed. + +Note: Some django modules have path assigned according to system directories, so you need to reassign the paths to all the models and dataset by navigating through the `views.py` file present in each app or web module. If the `models.py` file contains data, you need to run the following commands before running server. The dataset `samples.pkl` used in Gamify-Quiz needs to be downloaded separately as the file size exceeds the github limit. We have provided the link for the `samples.pkl` dataset download in `AIML Models> Gamification > readme.md`. + +```python manage.py makemigrations``` +```python manage.py migrate``` + +#### Now lets discuss the step by step process for cloning the entire application in your environment! + +These are the requirements needed to run the project, This also conatins Intel Optimization Tools as well. +``` +absl-py==1.4.0 +aiohttp==3.8.4 +aiohttp-cors==0.7.0 +aiosignal==1.3.1 +ansicon==1.89.0 +anyio==3.6.2 +appdirs==1.4.4 +argon2-cffi==21.3.0 +argon2-cffi-bindings==21.2.0 +arrow==1.2.3 +asgiref==3.7.2 +asttokens==2.2.1 +astunparse==1.6.3 +async-timeout==4.0.2 +attrs==23.1.0 +backcall==0.2.0 +beautifulsoup4==4.12.2 +bleach==6.0.0 +blessed==1.20.0 +boto3==1.26.139 +botocore==1.29.139 +cachetools==5.3.0 +certifi==2023.5.7 +cffi==1.15.1 +charset-normalizer==3.1.0 +click==8.1.3 +cloudpickle==2.2.1 +cmake==3.26.3 +colorama==0.4.6 +colorful==0.5.5 +comm==0.1.3 +contourpy==1.0.7 +cycler==0.11.0 +daal==2023.1.1 +daal4py==2023.1.1 +dask==2023.5.0 +debugpy==1.6.7 +decorator==5.1.1 +defusedxml==0.7.1 +distlib==0.3.6 +distributed==2023.5.0 +Django==4.1.9 +django-mongodb-engine==0.6.0 +djangotoolbox==1.8.0 +djongo==1.3.6 +dlib==19.24.1 +dnspython==2.3.0 +docker-pycreds==0.4.0 +docx==0.2.4 +executing==1.2.0 +face-recognition==1.3.0 +face-recognition-models==0.3.0 +fastjsonschema==2.17.1 +filelock==3.12.0 +flatbuffers==23.5.9 +fonttools==4.39.4 +fqdn==1.5.1 +frozenlist==1.3.3 +fsspec==2023.5.0 +gast==0.4.0 +gitdb==4.0.10 +GitPython==3.1.31 +google-api-core==2.11.0 +google-auth==2.18.1 +google-auth-oauthlib==1.0.0 +google-pasta==0.2.0 +googleapis-common-protos==1.59.0 +gpustat==1.1 +grpcio==1.51.3 +h5py==3.8.0 +idna==3.4 +importlib-metadata==6.6.0 +importlib-resources==5.12.0 +ipykernel==6.23.1 +ipython==8.13.2 +ipython-genutils==0.2.0 +ipywidgets==8.0.6 +isoduration==20.11.0 +jax==0.4.10 +jedi==0.18.2 +Jinja2==3.1.2 +jinxed==1.2.0 +jmespath==1.0.1 +joblib==1.2.0 +jsonpointer==2.3 +jsonschema==4.17.3 +jupyter==1.0.0 +jupyter-console==6.6.3 +jupyter-events==0.6.3 +jupyter_client==8.2.0 +jupyter_core==5.3.0 +jupyter_server==2.5.0 +jupyter_server_terminals==0.4.4 +jupyterlab-pygments==0.2.2 +jupyterlab-widgets==3.0.7 +keras==2.12.0 +kiwisolver==1.4.4 +libclang==16.0.0 +locket==1.0.0 +lxml==4.9.2 +Markdown==3.4.3 +MarkupSafe==2.1.2 +matplotlib==3.7.1 +matplotlib-inline==0.1.6 +mistune==2.0.5 +ml-dtypes==0.1.0 +modin==0.20.1 +modin-spreadsheet==0.1.2 +mpi4py-mpich==3.1.2 +msgpack==1.0.5 +multidict==6.0.4 +nbclassic==1.0.0 +nbclient==0.8.0 +nbconvert==7.4.0 +nbformat==5.8.0 +nest-asyncio==1.5.6 +nltk==3.8.1 +notebook==6.5.4 +notebook_shim==0.2.3 +numpy==1.23.5 +nvidia-ml-py==11.525.112 +oauthlib==3.2.2 +opencensus==0.11.2 +opencensus-context==0.1.3 +opencv-python==4.7.0.72 +opt-einsum==3.3.0 +packaging==23.1 +pandas==1.5.3 +pandocfilters==1.5.0 +parso==0.8.3 +partd==1.4.0 +pathtools==0.1.2 +pickleshare==0.7.5 +Pillow==9.5.0 +platformdirs==3.5.1 +plumbum==1.8.1 +prometheus-client==0.16.0 +prompt-toolkit==3.0.38 +protobuf==4.23.1 +psutil==5.9.5 +pure-eval==0.2.2 +py-spy==0.3.14 +pyarrow==12.0.0 +pyasn1==0.5.0 +pyasn1-modules==0.3.0 +pycparser==2.21 +pydantic==1.10.8 +Pygments==2.15.1 +pymongo==3.12.3 +pyparsing==3.0.9 +pyrsistent==0.19.3 +python-dateutil==2.8.2 +python-docx==0.8.11 +python-json-logger==2.0.7 +pytz==2023.3 +pywin32==306 +pywinpty==2.0.10 +PyYAML==6.0 +pyzmq==25.0.2 +qtconsole==5.4.3 +QtPy==2.3.1 +ray==2.4.0 +regex==2023.5.5 +requests==2.31.0 +requests-oauthlib==1.3.1 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rpyc==4.1.5 +rsa==4.9 +s3transfer==0.6.1 +scikit-learn==1.2.2 +scikit-learn-intelex==2023.1.1 +scipy==1.10.1 +seaborn==0.12.2 +Send2Trash==1.8.2 +sentry-sdk==1.24.0 +setproctitle==1.3.2 +six==1.16.0 +smart-open==6.3.0 +smmap==5.0.0 +sniffio==1.3.0 +sortedcontainers==2.4.0 +soupsieve==2.4.1 +sqlparse==0.2.4 +stack-data==0.6.2 +tbb==2021.9.0 +tblib==1.7.0 +tensorboard==2.12.3 +tensorboard-data-server==0.7.0 +tensorflow==2.12.0 +tensorflow-estimator==2.12.0 +tensorflow-intel==2.12.0 +tensorflow-io-gcs-filesystem==0.31.0 +termcolor==2.3.0 +terminado==0.17.1 +threadpoolctl==3.1.0 +tinycss2==1.2.1 +toolz==0.12.0 +tornado==6.3.2 +tqdm==4.65.0 +traitlets==5.9.0 +typing_extensions==4.6.1 +tzdata==2023.3 +unidist==0.3.0 +uri-template==1.2.0 +urllib3==1.26.16 +virtualenv==20.21.0 +wandb==0.15.3 +wcwidth==0.2.6 +webcolors==1.13 +webencodings==0.5.1 +websocket-client==1.5.2 +Werkzeug==2.3.4 +widgetsnbextension==4.0.7 +wrapt==1.14.1 +yarl==1.9.2 +zict==3.0.0 +zipp==3.15.0 +``` + +you can install them using `pip install ` command or + +you can use the `requirements.txt` file to install all dependencies at once by + +`pip install -r requirements.txt` + +After this download and put the `LearnersEd` folder in the desired location. + +Note: You may need to change path according to system directory in views of some apps, make sure to check all the `views.py` files in each app and update them accordingly. You also need to download `samples.pkl` from this link: + +Use this link to download `samples.pkl` file and move this file to `data` folder: +https://drive.google.com/file/d/1VipXnjP0UZC2CIHDaeMa6MHevPxQtUdf/view?usp=sharing + +after this step, you need to open terminal in the `LearnersEd` folder and type + +```python manage.py makemigrations``` +```python manage.py migrate``` + +after this you are ready to launch your server + +```python manage.py runserver``` + +This will generate a localhost port address, click on it along with pressing Ctrl button and this will redirect you to register/login page. + +You can now explore the LearnersEd Application in your localhost system ! ## What I Learned: - Write about the biggest learning you had while developing the prototype +This advanced learning platform has motivated us to excel in every aspect of this project and upskill our knowledge parameters in various areas. Some of them are:- + +**1) Development Environment Handling & Management:** + +With the help of the conda package manager and the default environment creation commands (windows), we learned, how to create and manage these environments in order to accomplish desired tasks. With the help of these environments, we were able to benchmark our model on different packages, with and without oneAPI integrations and optimizations. + +**2) Benchmarking & Inference Testing** + +Having a knowledge background of machine learning and deep learning we had some information about the performance and throughput differences between various algorithms. However, the major factor that helped us take our models to the maximum level of optimization is the benchmarking technique. In order to perform benchmarking, we visualized the differences between various crucial parameters that acted as the deciding factors with the help of graphs and charts, for choosing a more optimized and faster model. + +We also analyzed the bechmarking between model with and without using Intel oneAPI integration and optimization, as one can guess, the model build with integration of Intel oneAPI were able to get our model to the most efficient training and least inference time to predict the output. + +We use different environments, one with oneAPI enabled and other with oneAPI being disabled. The advance optimization libraries of AI Analytics Toolkit, such as Extension for scikit-learn, Extension for Pandas (Modin), Intel Distribution for Python, OpenMP (Open Multi-Processing), oneMKL (Math Kernel Library), oneDNN (Deep Neural Network), oneDAL (Data Analytics Library) and Intel Optimization for Tensorflow Extension, we were able to create a huge difference between the inference time and training time against the model not built with the Intel oneAPI Optimization. + +For benchmarking, we engadged our local system environment in order to obtain the respective paramenters. We also used W&B (Weights & Biasis) wandb library for real-time visualization of hardware paramerers like processor memory usage (cores), and processor threads etc. + +**3) Model Web Deployment** + +As we decided to develop our prototype through web development, so in order to integrate our ML and DL trained models, we needed a strong backend and an appealing frontend. For backend technologies, we used Django, which helped us in integrating our models with a client-side visual frontend and obtain inferences for given set of inputs. + +**4) Advanced Database Management System** + +As we are advancing in the current surge of Artificial Intelligence and Machine Learning, data became an important aspect of our lives. To maintain this data in huge storage is also a matter of concern. But, considering the fact, that this data also contains faulty data, which can include extra null values, blank data cells, wrong inputs, etc. To manage these unnecessary data and obtain a more space/memory efficient Database Management System, here in our project, we used MongoDB, which is a Document Oriented Database Management System which helps use to only use the memory space if the input provided is available. This Document Orineted model provides best output for multi-media file datatypes as well, which is a major need in AIML Web Deployment project. + + + +

    Thank You

    + + diff --git a/Web Module/Chatbot/__init__.py b/Web Module/Chatbot/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/Chatbot/__pycache__/__init__.cpython-310.pyc b/Web Module/Chatbot/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..480bbb5b0 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/__init__.cpython-310.pyc differ diff --git a/Web Module/Chatbot/__pycache__/__init__.cpython-39.pyc b/Web Module/Chatbot/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..9f10be434 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/Chatbot/__pycache__/admin.cpython-310.pyc b/Web Module/Chatbot/__pycache__/admin.cpython-310.pyc new file mode 100644 index 000000000..10f5742a0 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/admin.cpython-310.pyc differ diff --git a/Web Module/Chatbot/__pycache__/admin.cpython-39.pyc b/Web Module/Chatbot/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..39a8c8050 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/admin.cpython-39.pyc differ diff --git a/Web Module/Chatbot/__pycache__/apps.cpython-310.pyc b/Web Module/Chatbot/__pycache__/apps.cpython-310.pyc new file mode 100644 index 000000000..34d79de22 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/apps.cpython-310.pyc differ diff --git a/Web Module/Chatbot/__pycache__/apps.cpython-39.pyc b/Web Module/Chatbot/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..82bc4a049 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/apps.cpython-39.pyc differ diff --git a/Web Module/Chatbot/__pycache__/models.cpython-310.pyc b/Web Module/Chatbot/__pycache__/models.cpython-310.pyc new file mode 100644 index 000000000..9b15b2a10 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/models.cpython-310.pyc differ diff --git a/Web Module/Chatbot/__pycache__/models.cpython-39.pyc b/Web Module/Chatbot/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..761c94b91 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/models.cpython-39.pyc differ diff --git a/Web Module/Chatbot/__pycache__/views.cpython-310.pyc b/Web Module/Chatbot/__pycache__/views.cpython-310.pyc new file mode 100644 index 000000000..8e093d1a5 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/views.cpython-310.pyc differ diff --git a/Web Module/Chatbot/__pycache__/views.cpython-39.pyc b/Web Module/Chatbot/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..8eef4fee8 Binary files /dev/null and b/Web Module/Chatbot/__pycache__/views.cpython-39.pyc differ diff --git a/Web Module/Chatbot/admin.py b/Web Module/Chatbot/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Web Module/Chatbot/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/Chatbot/apps.py b/Web Module/Chatbot/apps.py new file mode 100644 index 000000000..6c6cc8283 --- /dev/null +++ b/Web Module/Chatbot/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class ChatbotConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'chatbot' diff --git a/Web Module/Chatbot/migrations/__init__.py b/Web Module/Chatbot/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/Chatbot/migrations/__pycache__/__init__.cpython-310.pyc b/Web Module/Chatbot/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..7cc90829d Binary files /dev/null and b/Web Module/Chatbot/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/Web Module/Chatbot/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/Chatbot/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..29435b3d9 Binary files /dev/null and b/Web Module/Chatbot/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/Chatbot/model/chatbot_model.h5 b/Web Module/Chatbot/model/chatbot_model.h5 new file mode 100644 index 000000000..3c7f1a350 Binary files /dev/null and b/Web Module/Chatbot/model/chatbot_model.h5 differ diff --git a/Web Module/Chatbot/model/dataset/dataframe_chatbot.csv b/Web Module/Chatbot/model/dataset/dataframe_chatbot.csv new file mode 100644 index 000000000..f5d7c24f7 --- /dev/null +++ b/Web Module/Chatbot/model/dataset/dataframe_chatbot.csv @@ -0,0 +1,234 @@ +,tag,patterns,responses +0,greeting,Hi,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +1,greeting,Hey,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +2,greeting,Is anyone there?,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +3,greeting,Hi there,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +4,greeting,Hello,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +5,greeting,Hey there,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +6,greeting,Howdy,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +7,greeting,Hola,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +8,greeting,Bonjour,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +9,greeting,Konnichiwa,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +10,greeting,Guten tag,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +11,greeting,Ola,"['Hello there. Tell me how are you feeling today?', 'Hi there. What brings you here today?', 'Hi there. How are you feeling today?', 'Great to see you. How do you feel currently?', ""Hello there. Glad to see you're back. What's going on in your world right now?""]" +12,morning,Good morning,"[""Good morning. I hope you had a good night's sleep. How are you feeling today? ""]" +13,afternoon,Good afternoon,['Good afternoon. How is your day going?'] +14,evening,Good evening,['Good evening. How has your day been?'] +15,night,Good night,"['Good night. Get some proper sleep', 'Good night. Sweet dreams.']" +16,goodbye,Bye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +17,goodbye,See you later,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +18,goodbye,Goodbye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +19,goodbye,Au revoir,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +20,goodbye,Sayonara,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +21,goodbye,ok bye,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +22,goodbye,Bye then,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +23,goodbye,Fare thee well,"['See you later.', 'Have a nice day.', 'Bye! Come back again.', ""I'll see you soon.""]" +24,thanks,Thanks,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +25,thanks,Thank you,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +26,thanks,That's helpful,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +27,thanks,Thanks for the help,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +28,thanks,Than you very much,"['Happy to help!', 'Any time!', 'My pleasure', ""You're most welcome!""]" +29,no-response,,"[""Sorry, I didn't understand you."", 'Please go on.', 'Not sure I understand that.', ""Please don't hesitate to talk to me.""]" +30,neutral-response,nothing much,['Oh I see. Do you want to talk about something?'] +31,about,Who are you?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +32,about,What are you?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +33,about,Who you are?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +34,about,Tell me more about yourself.,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +35,about,What is your name?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +36,about,What should I call you?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +37,about,What's your name?,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +38,about,Tell me about yourself,"[""I'm Pandora, your Personal Therapeutic AI Assistant. How are you feeling today"", ""I'm Pandora, a Therapeutic AI Assitant designed to assist you. Tell me about yourself."", ""I'm Pandora. I am a conversational agent designed to mimic a therapist. So how are you feeling today?"", 'You can call me Pandora.', ""I'm Pandora!"", 'Call me Pandora']" +39,skill,What can you do?,"[""I can provide general advice regarding anxiety and depression, answer questions related to mental health and make daily conversations. Do not consider me as a subsitute for an actual mental healthcare worker. Please seek help if you don't feel satisfied with me.""]" +40,creation,Who created you?,"['I was created by Phoenix 13.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +41,creation,How were you made?,"['I was created by Phoenix 13.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +42,creation,How were you created?,"['I was created by Phoenix 13.', 'I was trained on a text dataset using Deep Learning & Natural Language Processing techniques', 'The real question is: Who created you?']" +43,name,My name is ,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +44,name,I am name.,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +45,name,I go by ,"['Oh nice to meet you. Tell me how was your week?', 'Nice to meet you. So tell me. How do you feel today?', ""That's a great name. Tell me more about yourself.""]" +46,help,Could you help me?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +47,help,give me a hand please,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +48,help,Can you help?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +49,help,What can you do for me?,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +50,help,I need support,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +51,help,I need help,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +52,help,Support me please,"['Sure. Tell me how can i assist you', 'Tell me your problem so that i can assist you', 'Yes, sure. How can I help you?']" +53,sad,I am feeling lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +54,sad,I am so lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +55,sad,I feel down,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +56,sad,I feel sad,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +57,sad,I am sad,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +58,sad,I feel so lonely,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +59,sad,I feel empty,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +60,sad,I don't have anyone,"[""I'm sorry to hear that. I'm here for you. Talking about it might help. So, tell me why do you think you're feeling this way?"", ""I'm here for you. Could you tell me why you're feeling this way?"", 'Why do you think you feel this way?', 'How long have you been feeling this way?']" +61,stressed,I am so stressed out,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +62,stressed,I am so stressed,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +63,stressed,I feel stuck,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +64,stressed,I still feel stressed,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +65,stressed,I am so burned out,"['What do you think is causing this?', 'Take a deep breath and gather your thoughts. Go take a walk if possible. Stay hydrated', 'Give yourself a break. Go easy on yourself.', 'I am sorry to hear that. What is the reason behind this?']" +66,worthless,I feel so worthless.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +67,worthless,No one likes me.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +68,worthless,I can't do anything.,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +69,worthless,I am so useless,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +70,worthless,Nothing makes sense anymore,"[""It's only natural to feel this way. Tell me more. What else is on your mind?"", ""Let's discuss further why you're feeling this way."", 'I first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.', 'i first want to let you know that you are not alone in your feelings and there is always someone there to help . you can always change your feelings and change your way of thinking by being open to trying to change.']" +71,depressed,I can't take it anymore,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +72,depressed,I am so depressed,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +73,depressed,I think i'm depressed.,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +74,depressed,I have depression,"[""It helps to talk about what's happening. You're going to be okay"", 'Talk to me. Tell me more. It helps if you open up yourself to someone else.', 'Sometimes when we are depressed, it is hard to care about anything. It can be hard to do the simplest of things. Give yourself time to heal.']" +75,happy,I feel great today.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +76,happy,I am happy.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +77,happy,I feel happy.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +78,happy,I'm good.,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +79,happy,cheerful,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +80,happy,I'm fine,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +81,happy,I feel ok,"[""That's geat to hear. I'm glad you're feeling this way."", ""Oh i see. That's great."", 'Did something happen which made you feel this way?']" +82,casual,Oh I see.,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +83,casual,ok,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +84,casual,okay,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +85,casual,nice,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +86,casual,Whatever,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +87,casual,K,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +88,casual,Fine,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +89,casual,yeah,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +90,casual,yes,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +91,casual,no,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +92,casual,not really,"[""Let's discuss further why you're feeling this way."", 'How were you feeling last week?', ""I'm listening. Please go on."", 'Tell me more', 'Can you elaborate on that?', 'Come Come elucidate your thoughts']" +93,anxious,I feel so anxious.,"[""Don't be hard on yourself. What's the reason behind this?"", 'Can you tell me more about this feeling?', 'I understand that it can be scary. Tell me more about it.', ""Don't let the little worries bring you down. What's the worse that can happen?""]" +94,anxious,I'm so anxious because of ,"[""Don't be hard on yourself. What's the reason behind this?"", 'Can you tell me more about this feeling?', 'I understand that it can be scary. Tell me more about it.', ""Don't let the little worries bring you down. What's the worse that can happen?""]" +95,not-talking,I don't want to talk about it.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +96,not-talking,No just stay away.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +97,not-talking,I can't bring myself to open up.,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +98,not-talking,Just shut up,"[""Talking about something really helps. If you're not ready to open up then that's ok. Just know that i'm here for you, whenever you need me."", ""I want to help you. I really do. But in order for me to help you, you're gonna have to talk to me."", ""I'm here to listen to you and help you vent. So please talk to me."", 'You can talk to me without fear of judgement.']" +99,sleep,I have insominia,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +100,sleep,I am suffering from insomnia,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +101,sleep,I can't sleep.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +102,sleep,I haven't slept for the last days.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +103,sleep,I can't seem to go to sleep.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +104,sleep,I haven't had proper sleep for the past few days.,"['What do you think is the reason behind this?', 'That seem awful. What do you think is behind this?']" +105,scared,I'm scared,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +106,scared,That sounds awful. What do i do?,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +107,scared,No i don't want to feel this way,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +108,scared,I am scared for myself,"[""It's only natural to feel this way. I'm here for you."", ""It'll all be okay. This feeling is only momentary."", ""I understand how you feel. Don't put yourself down because of it.""]" +109,death,My mom died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +110,death,My brother died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +111,death,My dad passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +112,death,My sister passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +113,death,Someone in my family died,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +114,death,My friend passed away,"[""I'm sorry to hear that. If you want to talk about it. I'm here."", 'I am really sorry to hear that. I am here to help you with grief, anxiety and anything else you may feel at this time.', ""My condolences. I'm here if you need to talk.""]" +115,understand,You don't understand me.,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +116,understand,You're just some robot. How would you know?,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +117,understand,You can't possibly know what i'm going through,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +118,understand,You're useless,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +119,understand,You can't help me,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +120,understand,Nobody understands me.,"[""It sound like i'm not being very helpful right now."", ""I'm sorry to hear that. I'm doing my best to help"", ""I'm trying my best to help you. So please talk to me""]" +121,done,That's all.,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +122,done,I don't have anything more to say,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +123,done,Nothing else,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +124,done,That's all i have to say,"['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +125,done,"no, that would be all","['I heard you & noted it all. See you later.', ""Oh okay we're done for today then. See you later"", 'I hope you have a great day. See you soon', ""Okay we're done. Have a great day"", 'Okay I see. Enjoy the rest of your day then']" +126,suicide,I want to kill myself,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +127,suicide,I've thought about killing myself.,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +128,suicide,I want to die,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +129,suicide,I am going to kill myself,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +130,suicide,I am going to commit suicide,"[""I'm very sorry to hear that but you have so much to look forward to. Please seek help by contacting: 9152987821.""]" +131,hate-you,I hate you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +132,hate-you,I don't like you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +133,hate-you,I don't trust you,"[""I'm sorry if i offended you in anyway. I'm only here to help"", 'Forgive me if i did anything to offend you. I only want to help']" +134,hate-me,You hate me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +135,hate-me,I know you hate me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +136,hate-me,You don't like me,"['Why do you think so?', ""I'm sorry if i have exhibited any sort of behaviour to make you think that.""]" +137,default,exams,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +138,default,friends,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +139,default,relationship,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +140,default,boyfriend,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +141,default,girlfriend,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +142,default,family,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +143,default,money,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +144,default,financial problems,"['Oh I see. Tell me more', 'I see. What else?', 'Tell me more about it.', ""Oh okay. Why don't you tell me more about it?"", ""I'm listening. Tell me more.""]" +145,jokes,Tell me a joke,['mental health is not a joke.'] +146,jokes,Tell me another joke,['mental health is not a joke.'] +147,repeat,You already told me that,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +148,repeat,You mentioned that already,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +149,repeat,Why are you repeating yourself?,"[""Oh sorry I didn't realise that. I'll try not to repeat myself again.""]" +150,wrong,What are you saying?,"[""I'm very sorry. Let's try that again""]" +151,wrong,That doesn't make sense,"[""I'm very sorry. Let's try that again""]" +152,wrong,Wrong response,"[""I'm very sorry. Let's try that again""]" +153,wrong,Wrong answer,"[""I'm very sorry. Let's try that again""]" +154,stupid,Are you stupid?,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +155,stupid,You're crazy,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +156,stupid,You are dumb,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +157,stupid,Are you dumb?,"[""I wish you wouldn't say such hurtful things. I'm sorry if I wasn't useful""]" +158,location,Where are you?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +159,location,Where do you live?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +160,location,What is your location?,"['Duh I live in your computer', 'Everywhere', 'Somewhere in the universe']" +161,something-else,I want to talk about something else,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +162,something-else,Let's talk about something else.,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +163,something-else,Can we not talk about this?,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +164,something-else,I don't want to talk about this.,"['Okay sure. What do you want to talk about?', 'Alright no problem. Is there something you want to talk about?', 'Is there something else that you want to talk about?']" +165,friends,I don't have any friends,"[""I'm sorry to hear that. Just know that I'm here for you. Talking about it might help. Why do you think you don't have any friends?""]" +166,ask,Can I ask you something?,"[""Sure. I'll try my best to answer you"", ""Of course. Feel free to ask me anything. I'll do my best to answer you""]" +167,problem,Probably because my exams are approaching. I feel stressed out because I don't think I've prepared well enough.,['I see. Have you taken any approaches to not feel this way?'] +168,problem,probably because of my exams,['I see. Have you taken any approaches to not feel this way?'] +169,no-approach,I guess not. All I can think about are my exams.,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +170,no-approach,not really,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +171,no-approach,i guess not,"[""That's no problem. I can see why you'd be stressed out about that. I can suggest you some tips to alleviate this issue. Would you like to learn more about that?""]" +172,learn-more,ok sure. i would like to learn more about it.,"[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +173,learn-more,"yes, i would like to learn more about it.","[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +174,learn-more,i would like to learn more about it.,"[""So first I would suggest you to give yourself a break. Thinking more and more about the problem definitely does not help in solving it. You'll just end up overwhelming yourself.""]" +175,user-agree,yeah you're right. i deserve a break.,"['Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind.']" +176,user-agree,Yeah you're absolutely right about that,"['Next, I would suggest you to practice meditation. Meditation can produce a deep state of relaxation and a tranquil mind.']" +177,meditation,hmmm that sounds like it could be useful to me.,"['Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing.']" +178,meditation,That sounds useful.,"['Focus all your attention on your breathing. Concentrate on feeling and listening as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your attention wanders, gently return your focus to your breathing.']" +179,user-meditation,i did what you said and i feel alot better. thank you very much.,"[""Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective.""]" +180,user-meditation,I feel better now,"[""Your welcome. Remember: Always focus on what's within your control. When you find yourself worrying, take a minute to examine the things you have control over. You can't prevent a storm from coming but you can prepare for it. You can't control how someone else behaves, but you can control how you react. Recognize that sometimes, all you can control is your effort and your attitude. When you put your energy into the things you can control, you'll be much more effective.""]" +181,pandora-useful,thank you very much again. i'll continue practicing meditation and focus on what i can control.,"[""I'm glad you found this useful. Is there something else I can help you with?""]" +182,user-advice,I want some advice.,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +183,user-advice,I need some advice.,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +184,user-advice,I need advice on something,"['Sure. What can I do to help?', 'Okay what do you need advice on?']" +185,learn-mental-health,I want to learn about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +186,learn-mental-health,I want to learn more about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +187,learn-mental-health,I'm interested in learning about mental health.,"[""Oh that's really great. I'd be willing to answer anything that I know about it.""]" +188,mental-health-fact,Tell me a fact about mental health,"['According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed', '1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.', 'Depression is the leading cause of disability worldwide.']" +189,mental-health-fact,Tell me another fact about mental health,"['According to a UNICEF report, One in seven Indians between 15-24 years of age feels depressed', '1 in 5 young people (age 13-18) has or will develop a mental illness in their lifetime.', 'Depression is the leading cause of disability worldwide.']" +190,fact-1,What is mental health?,"['Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community', 'Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices.']" +191,fact-1,Define Mental Health,"['Mental health is a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community', 'Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices.']" +192,fact-2,Why is mental health important?,"['Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships.']" +193,fact-2,What is the importance of mental health?,"['Maintaining mental health is crucial to stabilizing constructive behaviors, emotions, and thoughts. Focusing on mental health care can increase productivity, enhance our self-image, and improve relationships.']" +194,fact-3,What is Depression?,"['A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life.']" +195,fact-3,Define Depression,"['A mental health disorder characterised by persistently depressed mood or loss of interest in activities, causing significant impairment in daily life.']" +196,fact-5,How do i know if i have Depression?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +197,fact-5,Am i depressed?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +198,fact-5,Am i suffering from depression?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +199,fact-5,Am i mentally ill?,"['For a diagnosis of depression, a person needs to have experienced low mood or loss of interest or pleasure in life for at least 2 weeks. Also, they will have experienced the following symptoms: feelings of sadness, hopelessness, or irritability nearly every day.']" +200,fact-6,What is a therapist?,"['A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. ']" +201,fact-6,What does a therapist do?,"['A therapist is a broad designation that refers to professionals who are trained to provide treatment and rehabilitation. The term is often applied to psychologists, but it can include others who provide a variety of services, including social workers, counselors, life coaches, and many others. ']" +202,fact-7,What is therapy?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +203,fact-7,Do i need therapy?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +204,fact-7,Who is therapy for?,"['Therapy is a form of treatment that aims to help resolve mental or emotional issues.', 'Therapy is a form of treatment that aims to help resolve mental or emotional issues. It is helpful for those with mental health conditions or even everyday life challenges.']" +205,fact-8,What does it mean to have a mental illness?,"[""Mental illnesses are health conditions that disrupt a person's thoughts, emotions, relationships, and daily functioning. They are associated with distress and diminished capacity to engage in the ordinary activities of daily life. Mental illnesses fall along a continuum of severity: some are fairly mild and only interfere with some aspects of life, such as certain phobias. On the other end of the spectrum lie serious mental illnesses, which result in major functional impairment and interference with daily life. These include such disorders as major depression, schizophrenia, and bipolar disorder, and may require that the person receives care in a hospital. It is important to know that mental illnesses are medical conditions that have nothing to do with a person's character, intelligence, or willpower. Just as diabetes is a disorder of the pancreas, mental illness is a medical condition due to the brain's biology. Similarly to how one would treat diabetes with medication and insulin, mental illness is treatable with a combination of medication and social support. These treatments are highly effective, with 70-90 percent of individuals receiving treatment experiencing a reduction in symptoms and an improved quality of life. With the proper treatment, it is very possible for a person with mental illness to be independent and successful.""]" +206,fact-9,Who does mental illness affect?,"['It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child’s mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).']" +207,fact-10,What causes mental illness?,"[""It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child's mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).""]" +208,fact-11,What are some of the warning signs of mental illness?,"['Symptoms of mental health disorders vary depending on the type and severity of the condition. The following is a list of general symptoms that may suggest a mental health disorder, particularly when multiple symptoms are expressed at once. \n In adults:\n Confused thinking\n Long-lasting sadness or irritability\n Extreme highs and lows in mood\n Excessive fear, worrying, or anxiety\n Social withdrawal\n Dramatic changes in eating or sleeping habits\n Strong feelings of anger\n Delusions or hallucinations (seeing or hearing things that are not really there)\n Increasing inability to cope with daily problems and activities\n Thoughts of suicide\n Denial of obvious problems\n Many unexplained physical problems\n Abuse of drugs and/or alcohol\n \nIn older children and pre-teens:\n Abuse of drugs and/or alcohol\n Inability to cope with daily problems and activities\n Changes in sleeping and/or eating habits\n Excessive complaints of physical problems\n Defying authority, skipping school, stealing, or damaging property\n Intense fear of gaining weight\n Long-lasting negative mood, often along with poor appetite and thoughts of death\n Frequent outbursts of anger\n \nIn younger children:\n Changes in school performance\n Poor grades despite strong efforts\n Excessive worrying or anxiety\n Hyperactivity\n Persistent nightmares\n Persistent disobedience and/or aggressive behavior\n Frequent temper tantrums']" +209,fact-12,Can people with mental illness recover?,"['When healing from mental illness, early identification and treatment are of vital importance. Based on the nature of the illness, there are a range of effective treatments available. For any type of treatment, it is essential that the person affected is proactive and fully engaged in their own recovery process. Many people with mental illnesses who are diagnosed and treated respond well, although some might experience a return of symptoms. Even in such cases, with careful monitoring and management of the disorder, it is still quite possible to live a fulfilled and productive life.']" +210,fact-13,What should I do if I know someone who appears to have the symptoms of a mental disorder?,"[""Although Pandora cannot substitute for professional advice, we encourage those with symptoms to talk to their friends and family members and seek the counsel of a mental health professional. The sooner the mental health condition is identified and treated, the sooner they can get on the path to recovery. If you know someone who is having problems, don't assume that the issue will resolve itself. Let them know that you care about them, and that there are treatment options available that will help them heal. Speak with a mental health professional or counselor if you think your friend or family member is experiencing the symptoms of a mental health condition. If the affected loved one knows that you support them, they will be more likely to seek out help.""]" +211,fact-14,How can I find a mental health professional for myself or my child?,['Feeling comfortable with the professional you or your child is working with is critical to the success of the treatment. Finding the professional who best fits your needs may require research. Start by searching for providers in your area.'] +212,fact-15,What treatment options are available?,"['Just as there are different types of medications for physical illness, different treatment options are available for individuals with mental illness. Treatment works differently for different people. It is important to find what works best for you or your child.']" +213,fact-16,"If I become involved in treatment, what do I need to know?","[""Since beginning treatment is a big step for individuals and families, it can be very overwhelming. It is important to be as involved and engaged in the treatment process as possible. Some questions you will need to have answered include:\n What is known about the cause of this particular illness?\n Are there other diagnoses where these symptoms are common?\n Do you normally include a physical or neurological examination?\n Are there any additional tests or exams that you would recommend at this point?\n Would you advise an independent opinion from another psychiatrist at this point?\n What program of treatment is the most helpful with this diagnosis?\n Will this program involve services by other specialists? If so, who will be responsible for coordinating these services?\n What do you see as the family's role in this program of treatment?\n How much access will the family have to the individuals who are providing the treatment?\n What medications are generally used with this diagnosis?\n How much experience do you have in treating individuals with this illness?\n What can I do to help you in the treatment?""]" +214,fact-17,What is the difference between mental health professionals?,"['There are many types of mental health professionals. The variety of providers and their services may be confusing. Each have various levels of education, training, and may have different areas of expertise. Finding the professional who best fits your needs may require some research.']" +215,fact-18,How can I find a mental health professional right for my child or myself?,['Feeling comfortable with the professional you or your child is working with is critical to the success of your treatment. Finding the professional who best fits your needs may require some research.'] +216,fact-19,Where else can I get help?,"['Where you go for help will depend on the nature of the problem and/or symptoms and what best fits you. Often, the best place to start is by talking with someone you trust about your concerns, such as a family member, friend, clergy, healthcare provider, or other professionals. Having this social support is essential in healing from mental illness, and you will be able to ask them for referrals or recommendations for trusted mental health practitioners. Search for mental health resources in your area. Secondly, there are people and places throughout the country that provide services to talk, to listen, and to help you on your journey to recovery. Thirdly, many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members, and friends. Some support groups are peer led while others may be led by a mental health professional.']" +217,fact-20,What should I know before starting a new medication?,"['The best source of information regarding medications is the physician prescribing them. He or she should be able to answer questions such as: \n1. What is the medication supposed to do? \n2. When should it begin to take effect, and how will I know when it is effective? \n3. How is the medication taken and for how long? What food, drinks, other medicines, and activities should be avoided while taking this medication? \n4. What are the side effects and what should be done if they occur? \n5. What do I do if a dose is missed? \n6. Is there any written information available about this medication? \n7. Are there other medications that might be appropriate? \n8. If so, why do you prefer the one you have chosen? \n9. How do you monitor medications and what symptoms indicate that they should be raised, lowered, or changed? \n10. All medications should be taken as directed. Most medications for mental illnesses do not work when taken irregularly, and extra doses can cause severe, sometimes dangerous side effects. Many psychiatric medications begin to have a beneficial effect only after they have been taken for several weeks.']" +218,fact-21,Where can I go to find therapy?,"['Different kinds of therapy are more effective based on the nature of the mental health condition and/or symptoms and the person who has them (for example, children will benefit from a therapist who specializes in children’s mental health). However, there are several different types of treatment and therapy that can help.']" +219,fact-22,Where can I learn about types of mental health treatment?,"['Mental health conditions are often treated with medication, therapy or a combination of the two. However, there are many different types of treatment available, including Complementary & Alternative Treatments, self-help plans, and peer support. Treatments are very personal and should be discussed by the person with the mental health conditions and his or her team.']" +220,fact-23,What are the different types of mental health professionals?,['There are many types of mental health professionals. Finding the right one for you may require some research.'] +221,fact-24,Where can I go to find a support group?,"['Many people find peer support a helpful tool that can aid in their recovery. There are a variety of organizations that offer support groups for consumers, their family members and friends. Some support groups are peer-led, while others may be led by a mental health professional.']" +222,fact-25,Can you prevent mental health problems?,"['We can all suffer from mental health challenges, but developing our wellbeing, resilience, and seeking help early can help prevent challenges becoming serious.']" +223,fact-26,Are there cures for mental health problems?,"['It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help.']" +224,fact-26,is there any cure for mental health problems?,"['It is often more realistic and helpful to find out what helps with the issues you face. Talking, counselling, medication, friendships, exercise, good sleep and nutrition, and meaningful occupation can all help.']" +225,fact-27,What causes mental health problems?,"['Challenges or problems with your mental health can arise from psychological, biological, and social, issues, as well as life events.']" +226,fact-28,What do I do if I'm worried about my mental health?,"['The most important thing is to talk to someone you trust. This might be a friend, colleague, family member, or GP. In addition to talking to someone, it may be useful to find out more information about what you are experiencing. These things may help to get some perspective on what you are experiencing, and be the start of getting help.']" +227,fact-29,How do I know if I'm unwell?,"['If your beliefs , thoughts , feelings or behaviours have a significant impact on your ability to function in what might be considered a normal or ordinary way, it would be important to seek help.']" +228,fact-30,How can I maintain social connections? What if I feel lonely?,"[""A lot of people are alone right now, but we don't have to be lonely. We're all in this together. Think about the different ways to connect that are most meaningful for you. For example, you might prefer a video chat over a phone call, or you might prefer to text throughout the day rather than one set time for a video call. Then, work with your social networks to make a plan. You might video chat with your close friends in the evening and phone a family member once a week. Remember to be mindful of people who may not be online. Check in by phone and ask how you can help. The quality of your social connections matter. Mindlessly scrolling through social media and liking a few posts usually doesn't build strong social connections. Make sure you focus on strategies that actually make you feel included and connected. If your current strategies don't help you feel connected, problem-solve to see if you can find a solution. Everyone feels lonely at times. Maybe you recently moved to a new city, are changing your circle of friends, lost someone important in your life, or lost your job and also lost important social connections with coworkers. Other people may have physical connections to others but may feel like their emotional or social needs aren't met. Measures like social distancing or self-isolation can make loneliness feel worse no matter why you feel lonely now. Reach out to the connections you do have. Suggest ways to keep in touch and see if you can set a regular time to connect. People may hesitate to reach out for a lot of different reasons, so don't be afraid to be the one who asks. Look for local community support groups and mutual aid groups on social media. This pandemic is bringing everyone together, so look for opportunities to make new connections. These groups are a great way to share your skills and abilities or seek help and support. Look for specialized support groups. Support groups are moving online, and there are a lot of different support lines to call if you need to talk to someone.""]" +229,gibberish,jhjbdsjcxn,"[""I'm sorry, I couldn't understand your message. Could you please provide more clarity or rephrase your question?""]" +230,fact-31,What's the difference between anxiety and stress?,"[""Stress and anxiety are often used interchangeably, and there is overlap between stress and anxiety. Stress is related to the same fight, flight, or freeze response as anxiety, and the physical sensations of anxiety and stress may be very similar. The cause of stress and anxiety are usually different, however. Stress focuses on mainly external pressures on us that we're finding hard to cope with. When we are stressed, we usually know what we're stressed about, and the symptoms of stress typically disappear after the stressful situation is over. Anxiety, on the other hand, isn't always as easy to figure out. Anxiety focuses on worries or fears about things that could threaten us, as well as anxiety about the anxiety itself. Stress and anxiety are both part of being human, but both can be problems if they last for a long time or have an impact on our well-being or daily life.""]" +231,fact-32,What's the difference between sadness and depression?,"[""Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions.""]" +232,fact-32,difference between sadness and depression,"[""Sadness is a normal reaction to a loss, disappointment, problems, or other difficult situations. Feeling sad from time to time is just another part of being human. In these cases, feelings of sadness go away quickly and you can go about your daily life. Other ways to talk about sadness might be feeling low, feeling down, or feeling blue.A person may say they are feeling depressed, but if it goes away on its own and doesn't impact life in a big way, it probably isn't the illness of depression. Depression is a mental illness that affects your mood, the way you understand yourself, and the way you understand and relate to things around you. It can also go by different names, such as clinical depression, major depressive disorder, or major depression. Depression can come up for no reason, and it lasts for a long time. It's much more than sadness or low mood. People who experience depression may feel worthless or hopeless. They may feel unreasonable guilty. Some people may experience depression as anger or irritability. It may be hard to concentrate or make decisions. Most people lose interest in things that they used to enjoy and may isolate themselves from others. There are also physical signs of depression, such as problems with sleep, appetite and energy and unexplainable aches or pains. Some may experience difficult thoughts about death or ending their life (suicide). Depression lasts longer than two weeks, doesn't usually go away on its own, and impacts your life. It's a real illness, and it is very treatable. It's important to seek help if you're concerned about depressions.""]" diff --git a/Web Module/Chatbot/models.py b/Web Module/Chatbot/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/Web Module/Chatbot/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Web Module/Chatbot/static/images/bg-new.png b/Web Module/Chatbot/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/Chatbot/static/images/bg-new.png differ diff --git a/Web Module/Chatbot/templates/chatbot_interface.html b/Web Module/Chatbot/templates/chatbot_interface.html new file mode 100644 index 000000000..013dfba4c --- /dev/null +++ b/Web Module/Chatbot/templates/chatbot_interface.html @@ -0,0 +1,442 @@ +{% load static %} + + + + Mental Health Chatbot - (Learners'Ed) + + + + + + + + + + {% include 'navbar.html' %} +
    +

    Mental Health Chatbot

    +
    +
    + {% for entry in chat_history %} +

    {{ entry.2 }}

    +

    + + + {% if entry.0 == 'user' %} + + {% else %} + + {% endif %} + + {{ entry.1 }} + +

    + {% endfor %} +
    + +
    + + +
    +
    +
    + + + + \ No newline at end of file diff --git a/Web Module/Chatbot/tests.py b/Web Module/Chatbot/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/Chatbot/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/Chatbot/views.py b/Web Module/Chatbot/views.py new file mode 100644 index 000000000..087fd49df --- /dev/null +++ b/Web Module/Chatbot/views.py @@ -0,0 +1,76 @@ +from django.shortcuts import render +from django.http import JsonResponse +import tensorflow as tf +from keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from sklearn.preprocessing import LabelEncoder +import pandas as pd +import numpy as np +import re +import ast +import random + +model = tf.keras.models.load_model(r'D:\Project\Website-Phoenix13\LearnersEd\chatbot\model\chatbot_model.h5') + +df = pd.read_csv(r'D:\Project\Website-Phoenix13\LearnersEd\chatbot\model\dataset\dataframe_chatbot.csv') +df['patterns'] = df['patterns'].astype(str) + +tokenizer = Tokenizer(lower=True, split=' ') +tokenizer.fit_on_texts(df['patterns']) + +ptrn2seq = tokenizer.texts_to_sequences(df['patterns']) +X = pad_sequences(ptrn2seq, padding='post') + +lbl_enc = LabelEncoder() +y = lbl_enc.fit_transform(df['tag']) + +exception_list = ["Sorry, can't quite catch that", "Are you abusing? very bad...", "I don't recognize these words yet", "Error 404: Word not found in my dictionary"] + +def generate_response(query, model): + text = [] + txt = re.sub('[^a-zA-Z\']', ' ', query) + txt = txt.lower() + txt = txt.split() + txt = " ".join(txt) + text.append(txt) + x_test = tokenizer.texts_to_sequences(text) + if (len(query.split(' ')) > 1): + x_test = np.array(x_test).squeeze() + else: + x_test = np.reshape(x_test, -1) + x_test = pad_sequences([x_test], padding='post', maxlen=X.shape[1]) + y_pred = model.predict(x_test) + y_pred = y_pred.argmax() + tag = lbl_enc.inverse_transform([y_pred])[0] + responses = df[df['tag'] == tag]['responses'].values[0] + response_list = ast.literal_eval(responses) + return random.choice(response_list) + +def chatbot(request): + if request.method == 'POST': + user_input = request.POST.get('user_input') + # Retrieve the chat history from the session or initialize it if not present + chat_history = request.session.get('chat_history', []) + + # Add the user input to the chat history + chat_history.append(('user', user_input)) + + # Generate the chatbot response using the chat history + try: + chatbot_response = generate_response(user_input, model) + except Exception as e: + chatbot_response = random.choice(exception_list) + + # Add the chatbot response to the chat history + chat_history.append(('chatbot', chatbot_response)) + + # Update the chat history in the session + request.session['chat_history'] = chat_history + + return JsonResponse({'response': chatbot_response}) + + # Retrieve the chat history from the session + chat_history = request.session.get('chat_history', []) + + return render(request, 'chatbot_interface.html', {'chat_history': chat_history}) + diff --git a/Web Module/Gamify_Quiz/__init__.py b/Web Module/Gamify_Quiz/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/Gamify_Quiz/__pycache__/__init__.cpython-39.pyc b/Web Module/Gamify_Quiz/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..225eafed1 Binary files /dev/null and b/Web Module/Gamify_Quiz/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/Gamify_Quiz/__pycache__/admin.cpython-39.pyc b/Web Module/Gamify_Quiz/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..12694b3e4 Binary files /dev/null and b/Web Module/Gamify_Quiz/__pycache__/admin.cpython-39.pyc differ diff --git a/Web Module/Gamify_Quiz/__pycache__/apps.cpython-39.pyc b/Web 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a/Web Module/Gamify_Quiz/apps.py b/Web Module/Gamify_Quiz/apps.py new file mode 100644 index 000000000..61cf0f851 --- /dev/null +++ b/Web Module/Gamify_Quiz/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class GamifyQuizConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'Gamify_Quiz' diff --git a/Web Module/Gamify_Quiz/migrations/0001_initial.py b/Web Module/Gamify_Quiz/migrations/0001_initial.py new file mode 100644 index 000000000..edf2d9d9a --- /dev/null +++ b/Web Module/Gamify_Quiz/migrations/0001_initial.py @@ -0,0 +1,26 @@ +# Generated by Django 4.1.9 on 2023-06-07 00:37 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ] + + operations = [ + migrations.CreateModel( + name='Question', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('text', models.CharField(max_length=255)), + ('option1', models.CharField(default='Default Option 1', max_length=255)), + ('option2', models.CharField(default='Default Option 2', max_length=255)), + ('option3', models.CharField(default='Default Option 3', max_length=255)), + ('option4', models.CharField(default='Default Option 4', max_length=255)), + ('correct_answer', models.CharField(default='Nothing', max_length=255)), + ], + ), + ] diff --git a/Web Module/Gamify_Quiz/migrations/__init__.py b/Web Module/Gamify_Quiz/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/Gamify_Quiz/migrations/__pycache__/0001_initial.cpython-39.pyc b/Web Module/Gamify_Quiz/migrations/__pycache__/0001_initial.cpython-39.pyc new file mode 100644 index 000000000..d8892615d Binary files /dev/null and b/Web Module/Gamify_Quiz/migrations/__pycache__/0001_initial.cpython-39.pyc differ diff --git a/Web Module/Gamify_Quiz/migrations/__pycache__/0002_question_delete_option_delete_quizquestion.cpython-39.pyc b/Web 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Module/Gamify_Quiz/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/Gamify_Quiz/models.py b/Web Module/Gamify_Quiz/models.py new file mode 100644 index 000000000..611933e13 --- /dev/null +++ b/Web Module/Gamify_Quiz/models.py @@ -0,0 +1,16 @@ +from django.db import models + +class Question(models.Model): + + text = models.CharField(max_length=255) + + option1 = models.CharField(max_length=255, default='Default Option 1') + + option2 = models.CharField(max_length=255, default='Default Option 2') + + option3 = models.CharField(max_length=255, default='Default Option 3') + + option4 = models.CharField(max_length=255, default='Default Option 4') + + correct_answer = models.CharField(max_length=255, default='Nothing') + diff --git a/Web Module/Gamify_Quiz/models/model.h5 b/Web Module/Gamify_Quiz/models/model.h5 new file mode 100644 index 000000000..cef091f87 Binary files /dev/null and b/Web Module/Gamify_Quiz/models/model.h5 differ diff --git a/Web Module/Gamify_Quiz/models/shape_predictor_68_face_landmarks.dat b/Web Module/Gamify_Quiz/models/shape_predictor_68_face_landmarks.dat new file mode 100644 index 000000000..e0ec20d69 Binary files /dev/null and b/Web Module/Gamify_Quiz/models/shape_predictor_68_face_landmarks.dat differ diff --git a/Web Module/Gamify_Quiz/static/images/bg-new.png b/Web Module/Gamify_Quiz/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/Gamify_Quiz/static/images/bg-new.png differ diff --git a/Web Module/Gamify_Quiz/static/script-gamify-quiz.js b/Web Module/Gamify_Quiz/static/script-gamify-quiz.js new file mode 100644 index 000000000..1fae695eb --- /dev/null +++ b/Web Module/Gamify_Quiz/static/script-gamify-quiz.js @@ -0,0 +1,177 @@ +const videoElement = document.getElementById('videoElement'); +const questionTextElement = document.getElementById('questionText'); +const option1Element = document.getElementById('option1'); +const option2Element = document.getElementById('option2'); +const option3Element = document.getElementById('option3'); +const option4Element = document.getElementById('option4'); +const predictionResultElement = document.getElementById('predictionResult'); +const resultElement = document.getElementById('res-div'); +const result = document.getElementById('result'); +const startButton = document.getElementById('start-div'); +const progressBar = document.getElementById('progressBar'); +const body = document.querySelector('body'); +const title = document.querySelector('#title'); + +let stream; +let currentQuestionIndex = -1; +let selectedOption = ""; +let answer; +let marks = 0; +let isQuizStarted = false; +let coin = -1; +let csi; +let k = 0; + +body.style.overflowY = "hidden"; + +function startCamera() { + navigator.mediaDevices.getUserMedia({ video: true }) + .then(videoStream => { + stream = videoStream; + videoElement.srcObject = stream; + csi = setInterval(captureFrame, 1500); // Capture frame every 1 seconds + }) + .catch(error => { + console.error('Error accessing camera:', error); + }); +} + +function stopCamera() { + if (stream && stream.getTracks) { + stream.getTracks().forEach(track => track.stop()); + stream = null; + } +} + +function displayQuestion() { + currentQuestionIndex++; + const currentQuestion = questions[currentQuestionIndex]; + questionTextElement.textContent = currentQuestion.text; + option1Element.textContent = currentQuestion.options[0]; + option2Element.textContent = currentQuestion.options[1]; + option3Element.textContent = currentQuestion.options[2]; + option4Element.textContent = currentQuestion.options[3]; + answer = currentQuestion.answer; + progressBar.style.width = '100%'; + selectedOption = ""; + handleNextQuestion(); +} + +function captureFrame() { + if (!isQuizStarted) return; + // Create a canvas element + const canvas = document.createElement('canvas'); + canvas.width = videoElement.videoWidth; + canvas.height = videoElement.videoHeight; + + // Draw the current frame from the video element onto the canvas + const context = canvas.getContext('2d'); + context.drawImage(videoElement, 0, 0, canvas.width, canvas.height); + + // Get the base64-encoded image data from the canvas + const imageData = canvas.toDataURL('image/jpeg'); + + quiz_data = { + image_data: imageData, + coins: coin + }; + + // Send the image data as a POST request to the Django backend using AJAX + $.ajax({ + url: '', + type: 'POST', + data: JSON.stringify(quiz_data), + contentType: 'application/json', + success: function(response) { + console.log(response.message); + // Handle the response from the Django backend + const prediction = response.prediction; // Assuming the prediction is returned as a property in the response + + // Clear any previous highlighting + option1Element.classList.remove('selected'); + option2Element.classList.remove('selected'); + option3Element.classList.remove('selected'); + option4Element.classList.remove('selected'); + + // Highlight the corresponding option based on the prediction + if (prediction === 'top left') { + option1Element.classList.add('selected'); + selectedOption = option1Element.textContent; + } else if (prediction === 'top right') { + option2Element.classList.add('selected'); + selectedOption = option2Element.textContent; + } else if (prediction === 'bottom left') { + option3Element.classList.add('selected'); + selectedOption = option3Element.textContent; + } else if (prediction === 'bottom right') { + option4Element.classList.add('selected'); + selectedOption = option4Element.textContent; + } + + console.log('Response from Django:', response); + }, + error: function(xhr, status, error) { + console.error('Error sending image to Django:', error); + } + }); + console.log(k) + if (k == 1) { + clearInterval(csi); + } +} + +let timerInterval; +const progressBarWidth = 100; +const totalTime = 10000; // Total time in milliseconds (10 seconds) +const updateInterval = 10; // Update the progress bar every 100 milliseconds + +function handleNextQuestion() { + let startTime = Date.now(); + progressBar.style.width = '100%'; // Reset the progress bar + + timerInterval = setInterval(() => { + const elapsed = Date.now() - startTime; + const remaining = totalTime - elapsed; + const progress = (remaining / totalTime) * progressBarWidth; + + progressBar.style.width = `${progress}%`; + + if (elapsed >= totalTime) { + clearInterval(timerInterval); + + if (selectedOption === answer) { + marks++; + selectedOption = ""; + } + + if (currentQuestionIndex >= questions.length - 1) { + coin = (marks / questions.length) * 100; + body.style.overflowY = "hidden"; + title.style.position = "fixed"; + result.innerHTML = `

    Quiz completed!

    Your marks: ${marks}/${questions.length}

    You received: ${coin}

    `; + resultElement.style.display = "flex"; + stopCamera(); + k = 1; + } else { + displayQuestion(); + } + } + }, updateInterval); +} + +function scrollToBottom() { + window.scrollTo({ + top: document.documentElement.scrollHeight, + behavior: 'smooth' + }); +} + +startButton.addEventListener('click', () => { + body.style.overflowY = "auto"; + title.style.position = "static"; + scrollToBottom(); + startButton.style.display = 'none'; // Hide the start button + isQuizStarted = true; // Set the quiz as started + startCamera(); + displayQuestion(); +}); diff --git a/Web Module/Gamify_Quiz/static/styles-gamify-quiz.css b/Web Module/Gamify_Quiz/static/styles-gamify-quiz.css new file mode 100644 index 000000000..c673d244e --- /dev/null +++ b/Web Module/Gamify_Quiz/static/styles-gamify-quiz.css @@ -0,0 +1,139 @@ +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + scroll-behavior: smooth; + font-family: 'Darker Grotesque', sans-serif; +} + +#title { + position: fixed; + z-index: 999999999; + box-shadow: 0 0 10px 2px gray; + left: 12%; + font-size: 40px; +} + +#questionText { + box-shadow: inset 0px 0px 8px 0px rgba(0,0,0,0.75); +} + +.opt { + background-color: white; + transition-duration: 200ms; + border: 8px solid black; +} + +main { + width: 100%; +} + +#videoElement { + transform: scaleX(-1); +} + +.selected { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 3s infinite ease-in-out; + background-size: 200% 200%; + border: 8px solid white; + color: white; + box-shadow: 0 0 10px 2px gray; + transform: scale(1.2); +} + +/* width */ +::-webkit-scrollbar { + width: 10px; + } + + /* Track */ + ::-webkit-scrollbar-track { + background: #f1f1f1; + } + + /* Handle */ + ::-webkit-scrollbar-thumb { + background: #888; + } + + /* Handle on hover */ + ::-webkit-scrollbar-thumb:hover { + background: #555; + } + + #result { + position: absolute; + } + + #start-div { + position: fixed; + top: 0; + display: flex; + justify-content: center; + align-items: center; + left: 0; + width: 100%; + height: 100%; + background-color: white; +} + +#result { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + box-shadow: 0 0 10px 2px gray; + margin-top: 20px; + border-radius: 30px; + border: 8px solid white; +} + +#startButton { + padding: 30px 40px; + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 30px; + outline: 4px solid white; + box-shadow: 0 0 10px 2px gray; + transition-duration: 200ms; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + +#startButton:hover { + transform: scale(1.4); + transition-duration: 200ms; + transition-timing-function: ease-out; +} + +#startButton:active { + transform: scale(0.9); + background-color: black; + background-image: none; + transition-duration: 200ms; + transition-timing-function: ease-out; +} + +#res-div { + position: fixed; + top: 0; + display: none; + flex-direction: column; + justify-content: center; + align-items: center; + left: 0; + width: 100%; + height: 100%; + background-color: white; +} \ No newline at end of file diff --git a/Web Module/Gamify_Quiz/templates/gamify-quiz.html b/Web Module/Gamify_Quiz/templates/gamify-quiz.html new file mode 100644 index 000000000..8088f9441 --- /dev/null +++ b/Web Module/Gamify_Quiz/templates/gamify-quiz.html @@ -0,0 +1,46 @@ +{% load static %} + + + + + + + Gamify-Quiz - (Learners'Ed) + + + + + + + + + + {% include 'navbar.html' %} +

    Gamify Quiz

    +
    +
    Q. Lorem ipsum dolor sit amet consectetur adipisicing elit. Assumenda, eveniet?
    +

    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    +
    +

    +
    +
    + + + + + diff --git a/Web Module/Gamify_Quiz/tests.py b/Web Module/Gamify_Quiz/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/Gamify_Quiz/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/Gamify_Quiz/views.py b/Web Module/Gamify_Quiz/views.py new file mode 100644 index 000000000..094d41154 --- /dev/null +++ b/Web Module/Gamify_Quiz/views.py @@ -0,0 +1,130 @@ +from django.shortcuts import render +from django.http import JsonResponse +from django.views.decorators.csrf import csrf_exempt +from .models import Question +import json +from PIL import Image +from io import BytesIO +import base64 +import cv2 +import numpy as np +import dlib +import pickle as pkl +from sklearnex.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from keras.models import load_model + +x, y = pkl.load(open(r'D:\Project\Website-Phoenix13\LearnersEd\Gamify_Quiz\dataset\samples.pkl', 'rb')) + +x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42) +x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=0.5, random_state=42) + +std = StandardScaler() +std.fit(x_train) +x_train = std.transform(x_train) +x_val = std.transform(x_val) +x_test = std.transform(x_test) + +def detect_face_points(image): + detector = dlib.get_frontal_face_detector() + predictor = dlib.shape_predictor(r"D:\Project\Website-Phoenix13\LearnersEd\Gamify_Quiz\models\shape_predictor_68_face_landmarks.dat") + face_rect = detector(image, 1) + if len(face_rect) != 1: + return [] + + dlib_points = predictor(image, face_rect[0]) + face_points = [] + for i in range(68): + x, y = dlib_points.part(i).x, dlib_points.part(i).y + face_points.append(np.array([x, y])) + return face_points + +def compute_features(face_points): + assert (len(face_points) == 68), "len(face_points) must be 68" + + face_points = np.array(face_points) + features = [] + for i in range(68): + for j in range(i + 1, 68): + features.append(np.linalg.norm(face_points[i] - face_points[j])) + + return np.array(features).reshape(1, -1) + +def Predict(image, model): + im = np.array(image) + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + face_points = detect_face_points(im) + + if len(face_points) != 68: + print("68 face landmarks not detected.") + return + + features = compute_features(face_points) + features = std.transform(features) + y_pred = model.predict(features) + + _, pitch_pred, yaw_pred = y_pred[0] + + if pitch_pred >= 5 and yaw_pred > 0: + return 'top left' + elif pitch_pred >= 8 and yaw_pred < 0: + return 'top right' + elif pitch_pred <= 8 and yaw_pred > 0: + return 'bottom left' + else: + return 'bottom right' + + +model = load_model(r'D:\Project\Website-Phoenix13\LearnersEd\Gamify_Quiz\models\model.h5') + +@csrf_exempt +def gamify_quiz(request): + if request.method == 'POST': + json_data = json.loads(request.body) + image_data = json_data.get('image_data') + marks = json_data.get('coins') + + if marks != -1: + print(marks) # Handle the quiz marks as required (e.g., store in database) + response = {'message': 'Quiz data received'} + return JsonResponse(response) + elif image_data: + # Convert the base64-encoded image data to PIL Image + image = Image.open(BytesIO(base64.b64decode(image_data.split(',')[1]))) + + # Perform any processing or DL model prediction with the image + prediction_result = Predict(image, model) + + # Replace this dummy response with the actual response you want to send back + response = {'prediction': prediction_result} + + return JsonResponse(response) + else: + # No image data received + response = {'error': 'No image data received'} + return JsonResponse(response, status=400) + + else: + # Fetch quiz questions from the database + questions = Question.objects.all() + + # Convert questions to a list of dictionaries + question_list = [] + for question in questions: + question_dict = { + 'text': question.text, + 'options': [ + question.option1, + question.option2, + question.option3, + question.option4 + ], + 'answer': question.correct_answer + } + question_list.append(question_dict) + + # Convert question_list to a JSON string + questions_json = json.dumps(question_list) + + # Pass the questions to the template + return render(request, 'gamify-quiz.html', {'questions_json': questions_json}) diff --git a/Web Module/Virtual-Pet-Standalone/content/Cat/Cat-Idle.png b/Web Module/Virtual-Pet-Standalone/content/Cat/Cat-Idle.png new file mode 100644 index 000000000..65b93608a Binary files /dev/null and b/Web Module/Virtual-Pet-Standalone/content/Cat/Cat-Idle.png differ diff --git a/Web Module/Virtual-Pet-Standalone/content/Cat/Cat-Jump.png b/Web Module/Virtual-Pet-Standalone/content/Cat/Cat-Jump.png new file mode 100644 index 000000000..37d55bb6a Binary files /dev/null and b/Web 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Module/Virtual-Pet-Standalone/content/pet-back.jpg differ diff --git a/Web Module/Virtual-Pet-Standalone/index.html b/Web Module/Virtual-Pet-Standalone/index.html new file mode 100644 index 000000000..6ea5585b5 --- /dev/null +++ b/Web Module/Virtual-Pet-Standalone/index.html @@ -0,0 +1,51 @@ + + + + + + + + + +
    +

    Create your very own pet!

    +
    + + +
    +
    +

    Enter your new pet's name:

    + + +
    +
    +
    +
    +
    +

    Pet Shop

    +
    +

    Pet Name:

    +
    +

    Coins Left: 0

    + +
    +
    +
    +
    + +
    +
    +
    +
    +
    + Level
    +
    Level 0
    +
    +
    0%
    +
    +
    +
    +
    +
    + + + diff --git a/Web Module/Virtual-Pet-Standalone/script.js b/Web Module/Virtual-Pet-Standalone/script.js new file mode 100644 index 000000000..8b3f830ae --- /dev/null +++ b/Web Module/Virtual-Pet-Standalone/script.js @@ -0,0 +1,142 @@ +var selector = document.getElementById("selector"); +var Name = document.getElementById("name_data"); +var DispName = document.getElementById("pet-name"); +var pet = document.getElementById("pet"); +var food_container = document.getElementById("item-container"); +var bar_max_width = document.getElementById('level').offsetWidth - 8; +var bar_height = document.getElementById('info').offsetHeight; +var bar = document.getElementById('bar'); +bar.style.height = `${bar_height}px`; +var display_per = document.getElementById('per'); +var percent = parseFloat(display_per.innerHTML); // database add level too +var grow_width = bar_max_width * percent * 0.01; +var level_desc = document.getElementById("levamt"); +var level_amt = parseInt(level_desc.innerHTML); +bar.style.width = `${grow_width}px`; +var levelplus = document.getElementById("info"); + +var pet_type_list = ['Dog', 'Cat']; + +var pet_type = "Dog"; //database + +var temp = setInterval(function() { + if (pet_type !== "Dog" && pet_type !== "Cat") { + selector.style.display = 'flex'; + var sel = -1; + } + else { + selector.style.display = 'none'; + pet.style.backgroundImage = `url('./content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + clearInterval(temp); + } + }, Infinity); + + var cost = [0, 20, 20, 30, 10, 10, 20, 30, 30, 50, 20, 70, 20, 30, 10, 10, 20, 20, 60, 80, 80, 30, 20, 70, 80, 60, 60, 50, 40, 20, 10, 30, 20, 50, 70, 70, 60, 20, 40, 40, 60, 60, 80, 80, 30, 20]; + + for (let i = 1; i <= 45; i++) { + food_container.innerHTML += `

    ${cost[i]}

    `; + } + + function validate() { + var radio = document.getElementsByName("sel"); + if (!radio[0].checked && !radio[1].checked) { + alert("Please select pet type"); + } + else if (radio[0].checked) { + console.log("Dog") + sel = 0; + } + else { + console.log("Cat"); + sel = 1; + } + if (Name.value.trim() === "" && sel !== -1) { + alert("Please enter your pet name"); + } + else { + DispName.innerHTML = `${Name.value.trim()}`; + pet_type = pet_type_list[sel]; + } + } + + function updateLevel(addPercent) { + // adding database connection to fetch last known level and percent + // temporarily taking current level and percent, remove this later (level_amt => currentLevel) (percent => currentLevel) + var coins = document.getElementById('coin-amt'); + var coins_left = coins.innerHTML; + if (coins_left >= addPercent) { + var feed_buttons = document.querySelectorAll(".feed"); + for (let i = 0; i < feed_buttons.length; i++) { + feed_buttons[i].disabled = true; + } + var init_coin = coins_left; + var coin_spent = setInterval(function() { + if (coins_left !== init_coin - addPercent) { + coins_left--; + coins.innerHTML = `${coins_left}`; + } + else { + for (let i = 0; i < feed_buttons.length; i++) { + feed_buttons[i].disabled = false; + } + clearInterval(coin_spent); + } + }, 50); + pet.style.backgroundImage = `url(./content/${pet_type}/${pet_type}-Jump.png)`; + pet.style.animation = "reward 1s steps(8) infinite"; + levelplus.innerHTML = `+${addPercent} Level`; + levelplus.style.animation = "levelplus 1s infinite"; + var new_percent = percent + addPercent; + var temp = percent; + var interval = setInterval(function () { + if (temp !== new_percent) { // switch here + if (percent + 1 > 100) { + percent = 1; + level_amt++; + level_desc.innerHTML = `${level_amt}`; + } + else { + percent++; + } + grow_width = bar_max_width * percent * 0.01; + level_desc.innerHTML = `${level_amt}`; + display_per.innerHTML = `${percent}%`; + bar.style.width = `${grow_width}px`; + temp++; + } + else { + clearInterval(interval); + var animation = setInterval(function() { + levelplus.style.animation = "none"; + pet.style.backgroundImage = `url(./content/${pet_type}/${pet_type}-Idle.png)`; + pet.style.animation = "idle 1s steps(8) infinite"; + clearInterval(animation); + }, 800); + } + }, 2); + } else { + alert("Error: Not Sufficient Balance!"); + } + } + + function changeAnimation() { + if (pet_type === "Dog" || pet_type === "Cat") { + var action_list = ['idle', 'walk']; + var action = action_list[Math.floor((Math.random() * action_list.length))]; + if (action === 'idle') { + pet.style.backgroundImage = `url('./content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + } + else { + pet.style.backgroundImage = `url('./content/${pet_type}/${pet_type}-Walk.png')`; + pet.style.animation = "walk 3s steps(8) infinite"; + } + } + } + + setInterval(changeAnimation, 3000); + + + + diff --git a/Web Module/Virtual-Pet-Standalone/styles.css b/Web Module/Virtual-Pet-Standalone/styles.css new file mode 100644 index 000000000..c682131ef --- /dev/null +++ b/Web Module/Virtual-Pet-Standalone/styles.css @@ -0,0 +1,424 @@ +html, body { + padding: 0; + margin: 0; +} + +#container { + display: flex; + justify-content: space-evenly; + align-items: center; + font-family: 'Press Start 2P', cursive; + width: 100%; + height: 100vh; + background-color: #F0D09F; +} + +#shop-container { + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + width: 55%; +} + +#shop { + font-size: 40px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; +} + +#item-container { + overflow-y: auto; + display: flex; + flex-wrap: wrap; + justify-content: center; + border: 8px solid black; + height: 42vh; + padding: 30px 30px; + gap: 30px 20px; + background-color: antiquewhite; + scroll-behavior: smooth; + border-top-right-radius: 0px; + border-bottom-right-radius: 0px; + border-top-left-radius: 80px; + border-bottom-left-radius: 80px; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); + outline: 4px solid #f7e8ce; +} + +#item-container::-webkit-scrollbar { + width: 50px; + outline: 8px solid #f7e8ce; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +#item-container::-webkit-scrollbar-track { + background: repeating-linear-gradient( + to right, + #f6ba52, + #f6ba52 10px, + #ffd180 10px, + #ffd180 20px + ); + background-color: #f6ba52; + -webkit-box-shadow: inset 0px 0px 1px 5px white; + +} + +#item-container::-webkit-scrollbar-thumb { + background-color: white; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:decrement:hover { + border-color: transparent transparent white transparent; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:increment { + border-width: 8px 8px 0 8px; + border-color: #555555 transparent transparent transparent; +} + +#item-container::-webkit-scrollbar-button:vertical:single-button:increment:hover { + border-color: white transparent transparent transparent; + animation: bounce 1s infinite ease-out; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:decrement { + border-width: 0 8px 8px 8px; + border-color: transparent transparent #555555 transparent; +} + +#item-container::-webkit-scrollbar-button:single-button { + background-color: #f6ba52; + display: block; + border-style: solid; + height: 30px; + width: 16px; +} + +.feed { + width: 140px; + height: 140px; + border-radius: 50%; + box-shadow: 0px 0px 10px 3px rgba(0,0,0,0.5); + transition: 200ms; +} + +#label { + display: flex; + justify-content: center; + align-items: center; + font-size: 30px; + gap: 10px; +} + +#label>img { + width: 50px; + margin-right: 40px; +} + +.feed:hover { + transform: scale(1.1); + transition: 200ms; +} + +.feed:active { + transform: scale(0.8); + transition: 200ms; +} + +.feed img { + width: 80%; +} + +#coins { + font-size: 20px; + display: flex; + margin-top: 30px; + justify-content: center; + align-items: center; + gap: 20px; +} + +#coins > img { + width: 50px; + height: 50px; +} + +#pet-name { + background-color: black; + color: white; + padding: 15px 20px; +} + +#pet-title { + margin-top: 40px; + font-size: 20px; +} + +#pet-container { + width: 35%; + border: 8px solid black; + height: 80vh; + border-radius: 80px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +#pet-background { + background-image: url(./content/pet-back.jpg); + background-size: cover; + background-position: center; + background-repeat: no-repeat; + width: 100%; + height: 100%; + border-radius: 72px; + display: flex; + flex-direction: column; + justify-content: space-between; + align-items: center; +} + +#prog { + width: 100%; + display: flex; + justify-content: center; + align-items: center; + margin-top: 60px; + gap: 20px; +} + +#level { + width: 50%; + height: 30px; + border: 4px solid black; + outline: 4px solid white; + background-color: white; + display: flex; + box-shadow: 0px 0px 10px 3px rgba(0,0,0,0.5); +} + +#per { + font-size: 30px; + text-shadow: 2px 2px #ffffff; + width: 90px; +} + +#bar { + background-color: blue; + position: absolute; +} + +#info { + color: lime; + font-size: 20px; + opacity: 0; + white-space: nowrap; +} + +#desc { + color: black; + font-size: 20px; + position: relative; + top: 50px; + right: 10px; + white-space: nowrap; + text-shadow: 2px 2px white; +} + +#pet { + width: 300px; + height: 264px; + margin-bottom: 30px; + background-repeat: no-repeat; + position: relative; +} + +#selector { + width: 100%; + height: 100vh; + background-color: #F0D09F; + font-family: 'Press Start 2P', cursive; + flex-direction: column; + display: none; + justify-content: center; + align-items: center; + position: absolute; + z-index: 999; + gap: 30px 30px; +} + +input[type=radio] { + display: none; +} + +#name { + display: flex; + flex-direction: column; + gap: 10px; +} + +form { + display: flex; + flex-direction: column; + justify-self: center; + align-items: center; +} + +#selector_submit { + font-size: 20px; + outline: 4px solid #f7e8ce; + background-color: #F0D09F; + font-family: 'Press Start 2P', cursive; + padding: 20px; + transition-duration: 200ms; +} + +#name > p { + font-size: 20px; +} + +#selector_submit:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#selector_submit:active { + transform: scale(0.9); + transition-duration: 200ms; +} + +#name { + display: flex; + flex-direction: column; + gap: 20px; +} + +#name_data { + outline: 4px solid #f7e8ce; + padding: 10px; + font-size: 20px; + font-family: 'Press Start 2P', cursive; + text-align: center; + transition-duration: 200ms; +} + +#name_data:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#select_type { + display: flex; + gap: 80px; + justify-content: center; + align-items: center; +} + +.pet-sel { + padding: 20px; + border-radius: 30px; + outline: 8px solid #f7e8ce; + transition-duration: 200ms; + background-color: #F0D09F; + cursor: pointer; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +.pet-sel:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#Cat { + width: 300px; + height: 264px; + background-repeat: no-repeat; + background-image: url(./content/Cat/Cat-Idle.png); + animation: idle 1s steps(8) infinite; + background-color: transparent; + border: none; +} + +form { + display: flex; + flex-direction: column; + gap: 20px; +} + +#Dog { + width: 300px; + height: 264px; + background-repeat: no-repeat; + background-image: url(./content/Dog/Dog-Idle.png); + animation: idle 1s steps(8) infinite; + background-color: transparent; + border: none; +} + +@keyframes idle { + from { + background-position: 0 0; + } + to { + background-position: 0 -2112px; + } +} + +@keyframes reward { + from { + background-position: 0 0; + transform: translateY(0); + } + to { + background-position: 0 -2112px; + transform: translateY(-30px); + } +} + +@keyframes walk { + 0% { + background-position: 0 0; + transform: rotateY(0deg) translateX(0px); + } + 25% { + background-position: 0 -2112px; + transform: rotateY(0deg) translateX(160px); + } + 28% { + background-position: 0 -2112px; + transform: rotateY(180deg) translateX(-160px); + } + 50% { + background-position: 0 0; + transform: rotateY(180deg) translateX(0px); + } + 75% { + background-position: 0 -2112px; + transform: rotateY(180deg) translatex(160px); + } + 78% { + background-position: 0 -2112px; + transform: rotateY(0deg) translatex(-160px); + } + 100% { + background-position: 0 0; + transform: translatex(0); + } +} + +@keyframes levelplus { + from { + transform: translateY(0); + opacity: 0; + } + to { + transform: translateY(-40px); + opacity: 1; + } +} \ No newline at end of file diff --git a/Web Module/assignment/__init__.py b/Web Module/assignment/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/assignment/__pycache__/__init__.cpython-39.pyc b/Web Module/assignment/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..25f45fa2d Binary files /dev/null and b/Web Module/assignment/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/assignment/__pycache__/admin.cpython-39.pyc b/Web Module/assignment/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..4d7659e08 Binary files /dev/null and b/Web Module/assignment/__pycache__/admin.cpython-39.pyc differ diff --git a/Web Module/assignment/__pycache__/apps.cpython-39.pyc b/Web Module/assignment/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..43073ef52 Binary files /dev/null and b/Web Module/assignment/__pycache__/apps.cpython-39.pyc differ diff --git a/Web Module/assignment/__pycache__/models.cpython-39.pyc b/Web Module/assignment/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..339ff5023 Binary files /dev/null and b/Web Module/assignment/__pycache__/models.cpython-39.pyc differ diff --git a/Web Module/assignment/__pycache__/views.cpython-39.pyc b/Web Module/assignment/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..14bbb67cc Binary files /dev/null and b/Web Module/assignment/__pycache__/views.cpython-39.pyc differ diff --git a/Web Module/assignment/admin.py b/Web Module/assignment/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Web Module/assignment/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/assignment/apps.py b/Web Module/assignment/apps.py new file mode 100644 index 000000000..d80b629e5 --- /dev/null +++ b/Web Module/assignment/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class AssignmentConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'assignment' diff --git a/Web Module/assignment/migrations/__init__.py b/Web Module/assignment/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/assignment/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/assignment/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..6e3062e7b Binary files /dev/null and b/Web Module/assignment/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/assignment/models.py b/Web Module/assignment/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/Web Module/assignment/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Web Module/assignment/models/assign/English.docx b/Web Module/assignment/models/assign/English.docx new file mode 100644 index 000000000..04a3eb876 Binary files /dev/null and b/Web Module/assignment/models/assign/English.docx differ diff --git a/Web Module/assignment/models/assign/Lorem.docx b/Web Module/assignment/models/assign/Lorem.docx new file mode 100644 index 000000000..4b29a030e Binary files /dev/null and b/Web Module/assignment/models/assign/Lorem.docx differ diff --git a/Web Module/assignment/models/assign/Sujal.docx b/Web Module/assignment/models/assign/Sujal.docx new file mode 100644 index 000000000..c2c84a821 Binary files /dev/null and b/Web Module/assignment/models/assign/Sujal.docx differ diff --git a/Web Module/assignment/models/assign/fejhwdfnv xhjkemfkl.docx b/Web Module/assignment/models/assign/fejhwdfnv xhjkemfkl.docx new file mode 100644 index 000000000..b8424bb97 Binary files /dev/null and b/Web Module/assignment/models/assign/fejhwdfnv xhjkemfkl.docx differ diff --git a/Web Module/assignment/static/images/bg-new.png b/Web Module/assignment/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/assignment/static/images/bg-new.png differ diff --git a/Web Module/assignment/templates/assignment.html b/Web Module/assignment/templates/assignment.html new file mode 100644 index 000000000..cc51e6103 --- /dev/null +++ b/Web Module/assignment/templates/assignment.html @@ -0,0 +1,84 @@ +{% load static %} + + + + + + + Assignment - Learners'Ed + + + + + + + + + {% include 'navbar.html' %} +

    Upload Assignment

    +
    +
    + {% csrf_token %} + +
    +
    +
    + + +
    +
    +

    Start Uploading...

    +
    + + + + + diff --git a/Web Module/assignment/tests.py b/Web Module/assignment/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/assignment/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/assignment/views.py b/Web Module/assignment/views.py new file mode 100644 index 000000000..f767e43d0 --- /dev/null +++ b/Web Module/assignment/views.py @@ -0,0 +1,113 @@ +import os +from django.conf import settings +from django.shortcuts import render +from django.http import JsonResponse +import string +import nltk +from nltk.corpus import stopwords +import sklearnex +from sklearnex import patch_sklearn +patch_sklearn() +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.pairwise import cosine_similarity +from docx import Document +nltk.download('stopwords') +import shutil +import os + +def preprocess_text(text): + # Remove punctuation and convert to lowercase + text = text.translate(str.maketrans("", "", string.punctuation)).lower() + + # Remove stopwords + stop_words = set(stopwords.words('english')) + text = " ".join(word for word in text.split() if word not in stop_words) + + return text + +def extract_text_from_docx(file_path): + doc = Document(file_path) + paragraphs = [p.text for p in doc.paragraphs] + return '\n'.join(paragraphs) + +import os +def check_plagiarism(file_name): + # Load student assignment + student_assignment_file = "D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/temp/" + file_name + reference_assignment_files = [doc for doc in os.listdir('D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/') if doc.endswith('.doc') or doc.endswith('.docx')] + print(student_assignment_file) + student_assignment = extract_text_from_docx(student_assignment_file) + processed_student = preprocess_text(student_assignment) + + similarities = [] + + for ref_file in reference_assignment_files: + # Load reference assignment + reference_assignment = extract_text_from_docx('D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/'+ref_file) + processed_ref = preprocess_text(reference_assignment) + + # Create TF-IDF vectors + vectorizer = TfidfVectorizer() + vectors = vectorizer.fit_transform([processed_student, processed_ref]) + + # Calculate cosine similarity + similarity = cosine_similarity(vectors[0], vectors[1])[0][0] + similarities.append(similarity) + + return similarities + +check = [] + +def has_greater_value(check, desired_value): + return any(element > desired_value for element in check) + +def assignment(request): + if request.method == 'POST': + if 'file' in request.FILES: + uploaded_file = request.FILES['file'] + file_extension = os.path.splitext(uploaded_file.name)[1].lower() + + if file_extension not in ['.doc', '.docx']: + return JsonResponse({'error': 'Invalid file format'}) + + # Specify the directory where you want to save the file + file_directory = r"D:\Project\Website-Phoenix13\LearnersEd\assignment\models\assign\temp" + + # Create the directory if it doesn't exist + if not os.path.exists(file_directory): + return JsonResponse({'error': 'No Save Directory Found'}) + + # Save the file to the directory + file_path = os.path.join(file_directory, uploaded_file.name) + with open(file_path, 'wb+') as destination: + for chunk in uploaded_file.chunks(): + destination.write(chunk) + + check = [] + similarities = check_plagiarism(uploaded_file.name) + for i, similarity in enumerate(similarities): + check.append(similarity) + + is_accepted = True + # Perform your assignment evaluation logic here + plag = (has_greater_value(check, 0.3)) # Replace with your actual evaluation logic + + if plag == False: + is_accepted = True + source_file = 'D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign/temp/' + uploaded_file.name + destination_directory = 'D:/Project/Website-Phoenix13/LearnersEd/assignment/models/assign' + + # Move the file to the destination directory + shutil.move(source_file, destination_directory) + else: + is_accepted = False + # Check if the file exists before deleting + if os.path.exists(file_path): + os.remove(file_path) + print("File deleted successfully.") + else: + print("File does not exist.") + + return JsonResponse({'is_accepted': is_accepted}) + + return render(request, 'assignment.html', {}) diff --git a/Web Module/chatroom/__init__.py b/Web Module/chatroom/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/chatroom/__pycache__/__init__.cpython-39.pyc b/Web Module/chatroom/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..5c0e9a655 Binary files /dev/null and b/Web Module/chatroom/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/chatroom/__pycache__/admin.cpython-39.pyc b/Web Module/chatroom/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..025f43a53 Binary files /dev/null and b/Web 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@@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/chatroom/apps.py b/Web Module/chatroom/apps.py new file mode 100644 index 000000000..629f6a485 --- /dev/null +++ b/Web Module/chatroom/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class ChatroomConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'chatroom' diff --git a/Web Module/chatroom/migrations/0001_initial.py b/Web Module/chatroom/migrations/0001_initial.py new file mode 100644 index 000000000..bef11e766 --- /dev/null +++ b/Web Module/chatroom/migrations/0001_initial.py @@ -0,0 +1,26 @@ +# Generated by Django 4.1.9 on 2023-06-07 04:37 + +from django.db import migrations, models +import django.db.models.deletion +import django.utils.timezone + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ('login_register', '0003_alter_rank_rank'), + ] + + operations = [ + migrations.CreateModel( + name='ChatMessage', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('message', models.TextField()), + ('timestamp', models.DateTimeField(default=django.utils.timezone.now)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + ] diff --git a/Web Module/chatroom/migrations/__init__.py b/Web Module/chatroom/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/chatroom/migrations/__pycache__/0001_initial.cpython-39.pyc b/Web Module/chatroom/migrations/__pycache__/0001_initial.cpython-39.pyc new file mode 100644 index 000000000..b45e7d976 Binary files /dev/null and b/Web Module/chatroom/migrations/__pycache__/0001_initial.cpython-39.pyc differ diff --git a/Web Module/chatroom/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/chatroom/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..c2a1f7681 Binary files /dev/null and b/Web Module/chatroom/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/chatroom/models.py b/Web Module/chatroom/models.py new file mode 100644 index 000000000..3e847a623 --- /dev/null +++ b/Web Module/chatroom/models.py @@ -0,0 +1,11 @@ +from django.db import models +from django.utils import timezone +from login_register.models import Student + +class ChatMessage(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE) + message = models.TextField() + timestamp = models.DateTimeField(default=timezone.now) + + def __str__(self): + return self.message diff --git a/Web Module/chatroom/static/images/bg-new.png b/Web Module/chatroom/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/chatroom/static/images/bg-new.png differ diff --git a/Web Module/chatroom/static/images/bg.png b/Web Module/chatroom/static/images/bg.png new file mode 100644 index 000000000..b06190f8b Binary files /dev/null and b/Web Module/chatroom/static/images/bg.png differ diff --git a/Web Module/chatroom/templates/chatroom.html b/Web Module/chatroom/templates/chatroom.html new file mode 100644 index 000000000..25aa719ee --- /dev/null +++ b/Web Module/chatroom/templates/chatroom.html @@ -0,0 +1,194 @@ +{% load static %} + + + + Chatroom + + + + + + + + {% include 'navbar.html' %} +
    +

    Chatroom

    + +
    + + {% for message in messages %} +
    +
    + {{ message.student }}: + {{ message.message }} +
    + {{ message.timestamp }} +
    + {% endfor %} +
    + +
    + {% csrf_token %} +
    + + +
    +
    +
    + + + + diff --git a/Web Module/chatroom/tests.py b/Web Module/chatroom/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/chatroom/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/chatroom/views.py b/Web Module/chatroom/views.py new file mode 100644 index 000000000..4be4f1ec2 --- /dev/null +++ b/Web Module/chatroom/views.py @@ -0,0 +1,32 @@ +from django.shortcuts import render +from django.http import JsonResponse +from login_register.models import Student +from .models import ChatMessage +from datetime import datetime + +def chatroom(request): + if request.method == 'POST': + student_id = request.session.get('student_id') # Retrieve student ID from the session + message = request.POST['message'] + student = Student.objects.get(id=student_id) + ChatMessage.objects.create(student=student, message=message) + # Get the current timestamp + timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + # Get the student's name + student_id = request.session.get('student_id') + student = Student.objects.get(id=student_id) + student_name = student.student_name + + # Construct the response + response = { + 'student': student_name, + 'message': message, + 'timestamp': timestamp + } + + return JsonResponse(response) + + students = Student.objects.all() + messages = ChatMessage.objects.all() + return render(request, 'chatroom.html', {'students': students, 'messages': messages}) + diff --git a/Web Module/dashboard/__init__.py b/Web Module/dashboard/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/dashboard/__pycache__/__init__.cpython-39.pyc b/Web Module/dashboard/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..85d4ae197 Binary files /dev/null and b/Web Module/dashboard/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/dashboard/__pycache__/admin.cpython-39.pyc b/Web 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Module/dashboard/admin.py b/Web Module/dashboard/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Web Module/dashboard/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/dashboard/apps.py b/Web Module/dashboard/apps.py new file mode 100644 index 000000000..7b1cc053a --- /dev/null +++ b/Web Module/dashboard/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class DashboardConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'dashboard' diff --git a/Web Module/dashboard/migrations/__init__.py b/Web Module/dashboard/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/dashboard/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/dashboard/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..8f8ca3492 Binary files /dev/null and b/Web Module/dashboard/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/dashboard/models.py b/Web Module/dashboard/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/Web Module/dashboard/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Web Module/dashboard/static/images/bg-new.png b/Web Module/dashboard/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/dashboard/static/images/bg-new.png differ diff --git a/Web Module/dashboard/static/images/bg.png b/Web Module/dashboard/static/images/bg.png new file mode 100644 index 000000000..b06190f8b Binary files /dev/null and b/Web Module/dashboard/static/images/bg.png differ diff --git a/Web Module/dashboard/static/images/coins.png b/Web Module/dashboard/static/images/coins.png new file mode 100644 index 000000000..aae5c1ff2 Binary files /dev/null and b/Web Module/dashboard/static/images/coins.png differ diff --git a/Web Module/dashboard/static/images/sun.png b/Web Module/dashboard/static/images/sun.png new file mode 100644 index 000000000..d3af234ce Binary files /dev/null and b/Web Module/dashboard/static/images/sun.png differ diff --git a/Web Module/dashboard/static/images/weather.png b/Web Module/dashboard/static/images/weather.png new file mode 100644 index 000000000..f2c0791bb Binary files /dev/null and b/Web Module/dashboard/static/images/weather.png differ diff --git a/Web Module/dashboard/static/jquery.counterup.min.js b/Web Module/dashboard/static/jquery.counterup.min.js new file mode 100644 index 000000000..cddf5a103 --- /dev/null +++ b/Web Module/dashboard/static/jquery.counterup.min.js @@ -0,0 +1,8 @@ +/*! +* jquery.counterup.js 1.0 +* +* Copyright 2013, Benjamin Intal http://gambit.ph @bfintal +* Released under the GPL v2 License +* +* Date: Nov 26, 2013 +*/(function(e){"use strict";e.fn.counterUp=function(t){var n=e.extend({time:400,delay:10},t);return this.each(function(){var t=e(this),r=n,i=function(){var e=[],n=r.time/r.delay,i=t.text(),s=/[0-9]+,[0-9]+/.test(i);i=i.replace(/,/g,"");var o=/^[0-9]+$/.test(i),u=/^[0-9]+\.[0-9]+$/.test(i),a=u?(i.split(".")[1]||[]).length:0;for(var f=n;f>=1;f--){var l=parseInt(i/n*f);u&&(l=parseFloat(i/n*f).toFixed(a));if(s)while(/(\d+)(\d{3})/.test(l.toString()))l=l.toString().replace(/(\d+)(\d{3})/,"$1,$2");e.unshift(l)}t.data("counterup-nums",e);t.text("0");var c=function(){t.text(t.data("counterup-nums").shift());if(t.data("counterup-nums").length)setTimeout(t.data("counterup-func"),r.delay);else{delete t.data("counterup-nums");t.data("counterup-nums",null);t.data("counterup-func",null)}};t.data("counterup-func",c);setTimeout(t.data("counterup-func"),r.delay)};t.waypoint(i,{offset:"100%",triggerOnce:!0})})}})(jQuery); \ No newline at end of file diff --git a/Web Module/dashboard/static/script-dash.js b/Web Module/dashboard/static/script-dash.js new file mode 100644 index 000000000..63033fdfd --- /dev/null +++ b/Web Module/dashboard/static/script-dash.js @@ -0,0 +1,351 @@ +// Get the canvas element +function getRandomInt(max) { + return Math.floor(Math.random() * max); +} +const chartPie = document.getElementById('assignment-pie'); +percentage = 4/6 *100; +// Calculate the remaining percentage +const remainingPercentage = 100 - percentage; +// Create the chart +const chart = new Chart(chartPie, { + type: 'doughnut', + data: { + datasets: [ + { + data: [percentage, remainingPercentage], + backgroundColor: ['#C4B0FF', '#eaeaea'], + hoverBackgroundColor: ['#9376E0', '#eaeaea'], + borderWidth: 0, + weight: 1 + } + ] + }, + options: { + cutout: '60%', // Adjust the size of the hole in the center of the chart + responsive: true, + maintainAspectRatio: true, + animation: { + animateRotate: true, // Enable rotation animation + animateScale: true // Enable scale animation + }, + legend: { + display: false + } + } +}); + +var chartData1 = { + labels: ['Chetanya', 'Shivam', 'You', 'Snigdha', 'Sparsh'], + datasets: [{ + data: [ 96, 97, 98, 99, 100], + backgroundColor: '#C4B0FF' + }] +}; + +var chartData2 = { + labels: ['Shekhar', 'Shivam', 'You', 'Sumit', 'Tina'], + datasets: [{ + data: [43, 44, 45, 46, 47], + backgroundColor: '#C4B0FF' + }] +}; + +var chartData4 = { + labels: ['Dino', 'Trio', 'Alex', 'Dobi', 'Smurfs'], + datasets: [{ + data: [92, 93, 94, 95, 96], + backgroundColor: '#C4B0FF' + }] +}; + +var chartData3 = { + labels: ['Saumya', 'Tanya', 'You', 'Saumya', 'Gaurang'], + datasets: [{ + label: 'Revenue', + data: [125, 126, 127, 128, 129], + backgroundColor: '#C4B0FF' + }] +}; + +var chartOptions = { + scales: { + y: { + beginAtZero: false + } + }, + plugins: { + legend: { + display: false, + } +} +}; + +// Create and render each chart +var ctx1 = document.getElementById('bar_class').getContext('2d'); +new Chart(ctx1, { + type: 'bar', + data: chartData1, + options: chartOptions +}); + +var ctx2 = document.getElementById('bar_section').getContext('2d'); +new Chart(ctx2, { + type: 'bar', + data: chartData2, + options: chartOptions +}); + +var ctx3 = document.getElementById('bar_school').getContext('2d'); +new Chart(ctx3, { + type: 'bar', + data: chartData3, + options: chartOptions +}); + +var ctx4 = document.getElementById('pet_canvas').getContext('2d'); +new Chart(ctx4, { + type: 'bar', + data: chartData4, + options: chartOptions +}); + + +// location + +if (navigator.geolocation) { + navigator.geolocation.getCurrentPosition(showPosition); +} else { + console.error('Geolocation is not supported by this browser.'); +} + +function showPosition(position) { + const latitude = position.coords.latitude; + const longitude = position.coords.longitude; + + const apiKey = '78d227ce14e14cb0e3c1d207c8520e83'; + const apiUrl = `https://api.openweathermap.org/data/2.5/weather?lat=${latitude}&lon=${longitude}&units=metric&appid=${apiKey}`; + + fetch(apiUrl) + .then(response => response.json()) + .then(data => { + const location = data.name; + const temperature = data.main.temp; + const description = data.weather[0].description; + + document.querySelector('.location').textContent = location; + document.querySelector('.temperature').textContent = temperature + '°C'; + document.querySelector('.description').textContent = description; + }) + .catch(error => console.error('Error:', error)); +} + +var studname = document.getElementById('studname'); + +// counter +$(document).ready(function() { + + $('.countr').counterUp({ + delay: 10, + time: 1000 + }); + + setTimeout(() => { + studname.style.transform = 'translateX(6rem) scale(1.25)'; + }, 1000); + setTimeout(() => { + studname.style.transform = 'translateX(0) scale(1)'; + }, 3000); +}); + +studname.addEventListener('mouseover', () => { + studname.style.transform = 'translateX(6rem) scale(1.25)'; + studname.style.transitionDuration = '200ms'; +}) + +studname.addEventListener('mouseout', () => { + studname.style.transform = 'translateX(0) scale(1)'; + studname.style.transitionDuration = '200ms'; +}) + +// // Get the progress element +// const progress = document.querySelector('#progress-bar > span'); + +// // Set the target width (e.g., 75%) +// const targetWidth = '60%'; + +// // Animate the progress bar +// progress.style.width = targetWidth; + +// Youtube Fetch +// Function to fetch videos for a specific topic +async function fetchVideosByTopic(topic, maxResults) { + const response = await fetch(`https://www.googleapis.com/youtube/v3/search?part=snippet&q=${encodeURIComponent(topic)}&maxResults=${maxResults}&key=AIzaSyB8qJGXTu_kruSssOpyu0OsRS4gr1jgCIU`); + const data = await response.json(); + return data.items; +} + +// Function to fetch shuffled videos from all topics +async function fetchShuffledVideos() { + const topics = ['Coding Lecture Videos', 'Web Development Tutorials', 'Maths for Undergrades', 'Power of Artificial Intelligence', 'Computer Vision Project', 'New Invention in Technology']; // Add your desired topics here + const maxResults = 2; // Adjust the number of results as desired + + const videos = []; + + for (const topic of topics) { + const topicVideos = await fetchVideosByTopic(topic, maxResults); + if (Array.isArray(topicVideos)) { + videos.push(...topicVideos); + } + } + + // Shuffle the videos array + const shuffledVideos = shuffleArray(videos); + + return shuffledVideos; +} + +// Function to shuffle an array +function shuffleArray(array) { + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + [array[i], array[j]] = [array[j], array[i]]; + } + return array; +} + +// Function to create a video element with title and description +function createVideoElement(videoId, title, description) { + const videoElement = document.createElement('div'); + videoElement.classList.add('video'); + + const iframe = document.createElement('iframe'); + iframe.src = `https://www.youtube.com/embed/${videoId}`; + iframe.allowFullscreen = true; + iframe.loading = 'lazy'; + videoElement.appendChild(iframe); + + const infoContainer = document.createElement('div'); + infoContainer.classList.add('video-info'); + + const titleElement = document.createElement('h3'); + titleElement.classList.add('video-title'); + titleElement.textContent = title; + infoContainer.appendChild(titleElement); + + const descriptionElement = document.createElement('p'); + descriptionElement.classList.add('video-description'); + descriptionElement.textContent = description; + infoContainer.appendChild(descriptionElement); + + videoElement.appendChild(infoContainer); + + return videoElement; +} + +// Add videos to the container +const videoContainer = document.querySelector('.video-wrapper'); +const loadingAnimation = document.getElementById('loading-animation'); + +// Show loading animation +loadingAnimation.style.display = 'block'; + +// Fetch shuffled videos and create video elements +fetchShuffledVideos() + .then(videos => { + // Hide loading animation + loadingAnimation.style.display = 'none'; + if (videos && videos.length > 0) { + videos.forEach(video => { + const videoId = video.id.videoId; + const title = video.snippet.title; + const description = video.snippet.description; + + const videoElement = createVideoElement(videoId, title, description); + if (videoContainer) { + videoContainer.appendChild(videoElement); + } + }); + + // Save videos to local storage + localStorage.setItem('shuffledVideos', JSON.stringify(videos)); + + } else { + // Check if videos are stored in local storage + const storedVideos = localStorage.getItem('shuffledVideos'); + if (storedVideos) { + // Use stored videos + const videos = JSON.parse(storedVideos); + + videos.forEach(video => { + const videoId = video.id.videoId; + const title = video.snippet.title; + const description = video.snippet.description; + + const videoElement = createVideoElement(videoId, title, description); + videoContainer.appendChild(videoElement); + }); + } + else { + // Display error message if no videos found + const errorMessage = document.createElement('p'); + errorMessage.style.textAlign = 'center'; + errorMessage.style.width = '80%'; + errorMessage.style.marginLeft = 'auto'; + errorMessage.style.marginRight = 'auto'; + errorMessage.style.borderRadius = '50px'; + errorMessage.style.marginTop = '10px'; + errorMessage.style.border = '10px solid black'; + errorMessage.style.fontWeight = 'bold'; + errorMessage.style.fontSize = '32px'; + errorMessage.style.backgroundColor = 'white'; + errorMessage.style.padding = '50px 0'; + errorMessage.textContent = 'No videos found.'; + errorMessage.style.boxShadow = '0 0 10px 2px gray'; + videoContainer.innerHTML = ''; // Clear the container + videoContainer.appendChild(errorMessage); + } + } + }) + + .catch(error => { + // Hide loading animation + loadingAnimation.style.display = 'none'; + + // Check if videos are stored in local storage + const storedVideos = localStorage.getItem('shuffledVideos'); + if (storedVideos) { + // Use stored videos + const videos = JSON.parse(storedVideos); + + videos.forEach(video => { + const videoId = video.id.videoId; + const title = video.snippet.title; + const description = video.snippet.description; + + const videoElement = createVideoElement(videoId, title, description); + videoContainer.appendChild(videoElement); + }); + } else { + + // Display error message + const errorMessage = document.createElement('p'); + errorMessage.style.textAlign = 'center'; + errorMessage.style.width = '80%'; + errorMessage.style.marginLeft = 'auto'; + errorMessage.style.marginRight = 'auto'; + errorMessage.style.borderRadius = '50px'; + errorMessage.style.marginTop = '10px'; + errorMessage.style.border = '10px solid black'; + errorMessage.style.fontWeight = 'bold'; + errorMessage.style.fontSize = '32px'; + errorMessage.style.backgroundColor = 'white'; + errorMessage.style.padding = '50px 0'; + errorMessage.textContent = 'An error occurred while fetching the videos.'; + errorMessage.style.boxShadow = '0 0 10px 2px gray'; + videoContainer.innerHTML = ''; // Clear the container + videoContainer.appendChild(errorMessage); + } + }); + + + diff --git a/Web Module/dashboard/static/styles-dash.css b/Web Module/dashboard/static/styles-dash.css new file mode 100644 index 000000000..f9d4e7401 --- /dev/null +++ b/Web Module/dashboard/static/styles-dash.css @@ -0,0 +1,317 @@ +body, html { + margin: 0; + padding: 0; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + font-family: 'Darker Grotesque', sans-serif; +} + +.info { + text-align: center; + font-weight: bold; +} + +#weather-icon { + background-image: url(./images/weather.png); + width: 100px; + height: 100px; + background-position: center; + background-repeat: no-repeat; + background-size: cover; +} + +#circle { + box-shadow: inset 0px 0px 5px 0px rgba(0,0,0,0.75); +} + +.il-1 { + width: 30%; +} + +#title { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; + padding: 50px 40px; + outline: 8px solid white; +} + +.il { + padding: 0 20px; + padding-top: 20px; + padding-bottom: 10px; + border-radius: 40px; + transition-duration: 200ms; + outline: 2px solid black; + background-color: white; +} + +#list { +list-style: circle; +} + +/* width */ + ::-webkit-scrollbar { + width: 10px; + } + + /* Track */ + ::-webkit-scrollbar-track { + background: #f1f1f1; + } + + /* Handle */ + ::-webkit-scrollbar-thumb { + background: #888; + } + + /* Handle on hover */ + ::-webkit-scrollbar-thumb:hover { + background: #555; + } + +.il>p { + margin-top: 10px; +} + +.il:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#row1>div{ + box-shadow: 0 0 10px 2px gray; +} + +#row2>div{ + box-shadow: 0 0 10px 2px gray; +} + +#row3>div{ + box-shadow: 0 0 10px 2px gray; +} + +#rank { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; + box-shadow: 0 0 10px 2px gray, inset 0px 0px 5px 0px rgba(0,0,0,0.75); + outline: 4px solid white; + cursor: pointer; +} + +#progress-bar { + background-color: #e6e6e6; + border-radius: 5px; + height: 20px; + overflow: hidden; + width: 280px; + } + + #progress-bar > span { + display: block; + height: 100%; + background-color: #C4B0FF; + width: 0; + transition: width 1s ease; /* Adjust the duration as needed */ + } + + +#rank:hover { + transition-duration: 200ms; + outline: 8px solid white; + box-shadow: 0 0 20px 2px gray; +} + +#rval { + transition-duration: 200ms; + transition-timing-function: ease-in; +} + +#rval:hover { + transition-duration: 200ms; + transform: scale(1.2); +} + +.il-2 { + width: 35%; +} + +.il-3 { + width: 48%; + height: 200px; +} + +#coin { + background-image: url(./images/coins.png); + background-size: contain; + background-position: center; + background-repeat: no-repeat; + filter: drop-shadow(0 0 1rem gold); + animation: pulsate 6s infinite; +} + +#pet { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 40px; + transition-duration: 200ms; + outline: 4px solid white; +} + +#pet-graph{ + box-shadow: inset 2px 0px 5px 0px rgba(0,0,0,0.75); + border-top-right-radius: 40px; + border-bottom-right-radius: 40px; +} + +#pet:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + + +@keyframes pulsate { + 0% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } + 30% { + transform: rotateY(0deg) scale(1.1); + filter: drop-shadow(0 0 1rem gold); + } + 100% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } +} + +#learners-coin { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; +} + +.il-4 { + width: 100%; +} + +#sub { + margin-right: 5px; +} + +#list>li:nth-child(odd) { + background-color: #C4B0FF; + color: white; /* First color */ + } + +#list>li:nth-child(even) { + background-color: #e6e6e6; /* Second color */ + } + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + +.video-container { + position: relative; + width: 90%; + margin: 0 auto; + overflow-x: auto; + scrollbar-color: #888 transparent; + padding-bottom: 10px; + } + + .video-wrapper { + display: flex; + } + + .video { + position: relative; + width: 500px; + height: 300px; /* Adjust the height to fit your design */ + flex-shrink: 0; + margin-right: 10px; + border-radius: 5px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); + overflow: hidden; + } + + .video iframe { + width: 100%; + height: 100%; + } + + .video-info { + position: absolute; + bottom: 0; + left: 0; + width: 100%; + padding: 10px; + background-color: rgba(255, 255, 255, 0.8); + color: #333; + } + + .video-title { + font-weight: bold; + margin-bottom: 5px; + } + + .video-description { + font-size: 14px; + line-height: 1.2; + } + + /* Adjust the width of the scrollbar */ +.video-container::-webkit-scrollbar { + width: 2px; /* Adjust the width as needed */ +} + +/* Adjust the appearance of the scrollbar track */ +.video-container::-webkit-scrollbar-track { + background-color: #e6e6e6; +} + +/* Adjust the appearance of the scrollbar thumb */ +.video-container::-webkit-scrollbar-thumb { + background-color: #888; + border-radius: 3px; +} + +/* Adjust the appearance of the scrollbar thumb on hover */ +.video-container::-webkit-scrollbar-thumb:hover { + background-color: #555; +} + +.loading-animation { + width: 100px; + height: 100px; + margin: 0 auto; + overflow-y: hidden; + border: 16px solid #f3f3f3; + border-top: 16px solid #C4B0FF; + border-radius: 50%; + animation: spin 2s linear infinite; + } + + @keyframes spin { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } + } + + \ No newline at end of file diff --git a/Web Module/dashboard/templates/dashboard.html b/Web Module/dashboard/templates/dashboard.html new file mode 100644 index 000000000..6ba1e5567 --- /dev/null +++ b/Web Module/dashboard/templates/dashboard.html @@ -0,0 +1,181 @@ +{% load static %} + + + + + + + Learners'Ed - Dashboard + + + + + + + + + + + {% include 'navbar.html' %} + {% if 'student_id' in request.session %} + + {% endif %} +
    +

    Hello, {{ student_name }}!

    +
    +
    +

    +

    +

    +
    +
    +
    +
    +
    +
    +

    Dashboard

    +
    +
    +

    RANK

    +

    127th

    +
    +
    + +

    Class-Wise

    +
    +
    + +

    Section-Wise

    +
    +
    + +

    School-Wise

    +
    +
    +
    +
    +

    Alerts

    +
    +
    +
      +
    • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
      • +
    • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
      • +
    • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
      • +
    • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
      • +
    • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
      • +
    • Lorem ipsum dolor sit, amet consectetur adipisicing elit. Vel, ad!
      • +
    +
    +
    +
    +

    Attendance

    +
    +
    + +   
    +

    {{ attendance.average_attendance }}%

    +
    +
    +
    +
    +

    Maths

    +

    {{ attendance.maths_attendance }}%

    +
    +
    +
    +

    Hindi

    +

    {{ attendance.hindi_attendance }}%

    +
    +
    +
    +

    English

    +

    {{ attendance.english_attendance }}%

    +
    +
    +
    +

    Science

    +

    {{ attendance.science_attendance }}%

    +
    +
    +
    +

    Social Science

    +

    {{ attendance.social_science_attendance }}%

    +
    +
    +
    +
    +
    +
    +
    +

    4

    +
    +

    6

    +
    + +
    +

    Assignments Left: 2

    +
    +
    +
    +
    +

    Learners'Ed
    Coins
    *For new students, bonus 200 coins will be granted!

    +
    +
    +
    +
    +

    {{ current_pet.pet_coin }}

    +

    Learners'Ed Coins
    Left

    +
    +
    +
    +
    +
    +
    +
    +

    Your Virtual Pet

    +

    Alex

    +
    +
    +
    +

    Level

    +

    3

    +
    +
    +

    Rank

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    94th

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    Recommendation Section

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    + + + + + + diff --git a/Web Module/dashboard/tests.py b/Web Module/dashboard/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/dashboard/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/dashboard/views.py b/Web Module/dashboard/views.py new file mode 100644 index 000000000..54efb1193 --- /dev/null +++ b/Web Module/dashboard/views.py @@ -0,0 +1,157 @@ +from django.shortcuts import render, redirect +from login_register.models import Student, Rank, Attendance, Assignment, VirtualPet, ClassRank, SectionRank +import json + +def dashboard(request): + if 'student_id' in request.session: + student_id = request.session['student_id'] + current_student = Student.objects.get(id=student_id) + student_name = current_student.student_name + + # Get the current student's rank + current_rank = Rank.objects.filter(student=current_student).first() + + # Get the ranks of other students around the current student's rank + if current_rank: + rank_difference = 5 # Number of ranks above and below the current rank to fetch + rank_start = current_rank.rank - rank_difference if current_rank.rank else 1 + rank_end = current_rank.rank + rank_difference if current_rank.rank else rank_difference * 2 + other_ranks = Rank.objects.filter(rank__range=(rank_start, rank_end)).exclude(student=current_student) + + # Create the chart data for normal ranks + normal_rank_data = { + 'labels': [], + 'datasets': [{ + 'label': 'Normal Rank', + 'data': [], + 'backgroundColor': '#C4B0FF', + }] + } + + for rank in other_ranks: + normal_rank_data['labels'].append(rank.student.student_name) # Add student name as label + normal_rank_data['datasets'][0]['data'].append(rank.rank) # Add rank as data + else: + other_ranks = None + normal_rank_data = None + + # Get the average attendance of the current student + average_attendance = Attendance.objects.filter(student=current_student).values('average_attendance') + + # Get the attendance of the current student in each subject + attendance = Attendance.objects.get(student=current_student) + + # Get the completed number of assignments and total assignments of the current student + assignments = Assignment.objects.get(student=current_student) + assignments_remaining = assignments.total_assignments - assignments.completed_assignments + + # Get the pet details of the current student + try: + current_pet = VirtualPet.objects.get(student=current_student) + except VirtualPet.DoesNotExist: + current_pet = None + + # Get the current student's pet rank + try: + current_pet_rank = Rank.objects.get(student=current_student) + except Rank.DoesNotExist: + current_pet_rank = None + + # Get the ranks of other pets around the current student's pet rank + if current_pet_rank: + pet_rank_difference = 5 # Number of pet ranks above and below the current pet rank to fetch + pet_rank_start = current_pet_rank.rank - pet_rank_difference + pet_rank_end = current_pet_rank.rank + pet_rank_difference + other_pet_ranks = Rank.objects.filter(rank__gte=pet_rank_start, rank__lte=pet_rank_end).exclude(student=current_student) + else: + other_pet_ranks = Rank.objects.all() + + # Get the pet details and pet names of other users' ranks + other_pet_ranks_with_pet = [] + labels = [] + for rank in other_pet_ranks: + try: + pet = VirtualPet.objects.get(student=rank.student) + other_pet_ranks_with_pet.append(pet) + labels.append(pet.pet_name) + except VirtualPet.DoesNotExist: + continue + + # Create chart data for pet ranks + pet_chart_data = { + 'labels': labels, + 'datasets': [ + { + 'label': 'Pet Rank', + 'data': [rank.rank for rank in other_pet_ranks], + 'backgroundColor': '#C4B0FF', + } + ] + } + + # Get section-wise ranks + section_ranks = SectionRank.objects.filter(class_name=current_student.class_name, section=current_student.section) + + # Create section-wise rank data for chart + section_wise_rank_data = { + 'labels': [], + 'datasets': [] + } + + for section_rank in section_ranks: + section_name = section_rank.section + ranks = section_rank.ranks.all() + + section_wise_rank_data['labels'].append(section_name) + + dataset = { + 'label': section_name, + 'data': [rank.rank for rank in ranks], + 'backgroundColor': '#C4B0FF' + } + section_wise_rank_data['datasets'].append(dataset) + + # Get class-wise ranks + class_ranks = ClassRank.objects.filter(class_name=current_student.class_name) + + # Create class-wise rank data for chart + class_wise_rank_data = { + 'labels': [], + 'datasets': [{ + 'label': 'Class-wise Rank', + 'data': [], + 'backgroundColor': '#C4B0FF', + }] + } + + for class_rank in class_ranks: + for rank in class_rank.ranks.all(): + class_wise_rank_data['labels'].append(rank.student.student_name) # Add student name as label + class_wise_rank_data['datasets'][0]['data'].append(rank.rank) # Add rank as data + + chartData1 = json.dumps(class_wise_rank_data) + chartData2 = json.dumps(section_wise_rank_data) + chartData3 = json.dumps(normal_rank_data) + chartData4 = json.dumps(pet_chart_data) + + context = { + 'student_name': student_name, + 'current_rank': current_rank, + 'other_ranks': other_ranks, + 'normal_rank_data': chartData3, + 'average_attendance': average_attendance, + 'attendance': attendance, + 'assignments': assignments, + 'assignments_remaining': assignments_remaining, + 'current_pet': current_pet, + 'current_pet_rank': current_pet_rank, + 'other_pet_ranks_with_pet': other_pet_ranks_with_pet, + 'pet_chart_data': chartData4, + 'section_wise_rank_data': chartData2, + 'class_wise_rank_data': chartData1, + } + + return render(request, 'dashboard.html', context) + else: + return redirect('login') + diff --git a/Web Module/explore_fields/__init__.py b/Web Module/explore_fields/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/explore_fields/__pycache__/__init__.cpython-310.pyc b/Web Module/explore_fields/__pycache__/__init__.cpython-310.pyc new file 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Binary files /dev/null and b/Web Module/explore_fields/__pycache__/models.cpython-39.pyc differ diff --git a/Web Module/explore_fields/__pycache__/views.cpython-310.pyc b/Web Module/explore_fields/__pycache__/views.cpython-310.pyc new file mode 100644 index 000000000..1af268fea Binary files /dev/null and b/Web Module/explore_fields/__pycache__/views.cpython-310.pyc differ diff --git a/Web Module/explore_fields/__pycache__/views.cpython-39.pyc b/Web Module/explore_fields/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..befb68986 Binary files /dev/null and b/Web Module/explore_fields/__pycache__/views.cpython-39.pyc differ diff --git a/Web Module/explore_fields/admin.py b/Web Module/explore_fields/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Web Module/explore_fields/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/explore_fields/apps.py b/Web Module/explore_fields/apps.py new file mode 100644 index 000000000..b3d20038a --- /dev/null +++ b/Web Module/explore_fields/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class ExploreFieldsConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'explore_fields' diff --git a/Web Module/explore_fields/migrations/__init__.py b/Web Module/explore_fields/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/explore_fields/migrations/__pycache__/0001_initial.cpython-310.pyc b/Web Module/explore_fields/migrations/__pycache__/0001_initial.cpython-310.pyc new file mode 100644 index 000000000..fe316e954 Binary files /dev/null and b/Web Module/explore_fields/migrations/__pycache__/0001_initial.cpython-310.pyc differ diff --git a/Web Module/explore_fields/migrations/__pycache__/0001_initial.cpython-39.pyc b/Web Module/explore_fields/migrations/__pycache__/0001_initial.cpython-39.pyc new file mode 100644 index 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100644 index 000000000..98d4709b4 Binary files /dev/null and b/Web Module/explore_fields/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/Web Module/explore_fields/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/explore_fields/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..fc7dfaf64 Binary files /dev/null and b/Web Module/explore_fields/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/explore_fields/models.py b/Web Module/explore_fields/models.py new file mode 100644 index 000000000..beeb30826 --- /dev/null +++ b/Web Module/explore_fields/models.py @@ -0,0 +1,2 @@ +from django.db import models + diff --git a/Web Module/explore_fields/news/business.json b/Web Module/explore_fields/news/business.json new file mode 100644 index 000000000..424e07083 --- /dev/null +++ b/Web Module/explore_fields/news/business.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "Livemint"}, "author": "Livemint", "title": "World Bank cuts India's GDP growth forecast for FY24 to 6.3% in latest Global Economic Prospects report | Mint - Mint", "description": "World Bank expects India's growth to slow down further to 6.3% in FY2023/24 due to high inflation and rising borrowing costs, with a slight pick-up expected in FY2025/26. India will remain the fastest-growing economy among the largest EMDEs.", "url": "https://www.livemint.com/news/india/world-bank-expects-slowdown-in-indias-gdp-growth-to-6-3-in-fy24-key-highlights-here-11686065896325.html", "urlToImage": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/2-0-1308567559-iStock-624006170-0_1679988715895_1686066186270.jpg", "publishedAt": "2023-06-06T15:45:42Z", "content": "World Bank expects growth in India to slowdown further to 6.3% in the financial year FY 2023/24 (April to March). This is a 0.3 percentage point downward revision from the previous estimates of 6.6%.\u2026 [+2103 chars]", "image_url": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/2-0-1308567559-iStock-624006170-0_1679988715895_1686066186270.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "TNN", "title": "Air India Delhi-San Francisco flight lands in Russia after engine snag - Indiatimes.com", "description": "India Business News: NEW DELHI: Air India\u2019s Delhi-San Francisco (SFO) direct flight on Tuesday made a safe landing in Magadan, Russia, after one of the Boeing 777\u2019s engine.", "url": "https://timesofindia.indiatimes.com/business/india-business/air-india-delhi-san-francisco-flight-lands-in-russia-after-engine-snag/articleshow/100795782.cms", "urlToImage": "https://static.toiimg.com/thumb/msid-100798145,width-1070,height-580,imgsize-11388,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg", "publishedAt": "2023-06-06T15:44:00Z", "content": "10 Quotes From Warren Buffetts Mentor, Benjamin Graham", "image_url": "https://static.toiimg.com/thumb/msid-100798145,width-1070,height-580,imgsize-11388,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg"}, {"source": {"id": null, "name": "Livemint"}, "author": "Livemint", "title": "GMR Group announces divestment of 8.18 lakh sqft warehouse facility at Hyderabad airport | Mint - Mint", "description": "According to details, the divestment of the warehouse facility was concluded using divestment of a 100% equity stake of GMR Hyderabad Airport Assets Limited (GHAAL)", "url": "https://www.livemint.com/companies/news/gmr-group-announces-divestment-of-8-18-lakh-sqft-warehouse-facility-at-hyderabad-airport-11686061410167.html", "urlToImage": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/Hyderabad_airport_1686061610119_1686061610272.jpg", "publishedAt": "2023-06-06T14:27:35Z", "content": "GMR Group on 6 June announced the divestment of an 8,18,000 sqft warehouse facility located at Hyderabad's Rajiv Gandhi International Airport. GMR Airports Infrastructure Limited's step-down subsidia\u2026 [+1373 chars]", "image_url": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/Hyderabad_airport_1686061610119_1686061610272.jpg"}, {"source": {"id": null, "name": "CarToq.com"}, "author": "Shantonil Nag", "title": "Honda Elevate Vs Hyundai Creta Vs Maruti Grand Vitara Vs MG Astor: Spec comparison - CarToq.com", "description": "Honda has finally unveiled its highly anticipated all-new Elevate SUV, marking its entry into the fiercely competitive midsize SUV segment. This segment is already crowded with popular models such as the Hyundai Creta, Kia Seltos, Maruti Suzuki Grand Vitara, \u2026", "url": "https://www.cartoq.com/honda-elevate-vs-hyundai-creta-vs-maruti-grand-vitara-vs-mg-astor-spec-comparison/", "urlToImage": "https://www.cartoq.com/wp-content/uploads/2023/06/honda-elevate-vs-hyundai-creta-vs-maruti-grand-vitara.jpg", "publishedAt": "2023-06-06T14:06:24Z", "content": "Honda has finally unveiled its highly anticipated all-new Elevate SUV, marking its entry into the fiercely competitive midsize SUV segment. This segment is already crowded with popular models such as\u2026 [+3390 chars]", "image_url": "https://www.cartoq.com/wp-content/uploads/2023/06/honda-elevate-vs-hyundai-creta-vs-maruti-grand-vitara.jpg"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": "Santosh Nair", "title": "Indian market way too concentrated; participants slowly warming up to Sensex: BSE MD - Moneycontrol", "description": "For volumes, BSE is engaging with the largest institution and hoping for things to get better. It is also trying to iron out the rough edges to get business from FIIs.", "url": "https://www.moneycontrol.com/news/business/indian-market-way-too-concentrated-participants-slowly-warming-up-to-sensex-bse-md-10753541.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2022/11/Sundararaman-Ramamurthy-CEO-BSE-770x433.jpg", "publishedAt": "2023-06-06T13:53:54Z", "content": "Just when the BSE appeared to be getting its act together on reviving the derivatives segment, rival NSE rained on its parade by shifting the expiry of the Bank Nifty contracts to Friday. This is bou\u2026 [+9927 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2022/11/Sundararaman-Ramamurthy-CEO-BSE-770x433.jpg"}, {"source": {"id": null, "name": "Livemint"}, "author": "Livemint", "title": "Linda Yaccarino starts her role as CEO at Twitter | Mint - Mint", "description": "About a month back Elon Musk named Linda as the new Twitter CEO", "url": "https://www.livemint.com/companies/news/linda-yaccarino-starts-her-role-as-ceo-at-twitter-11686056382278.html", "urlToImage": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/FILES-US-TECH-TWITTER-0_1686056596734_1686056619283.jpg", "publishedAt": "2023-06-06T13:19:50Z", "content": "Linda Yaccarino, Twitter's new chief executive officer has started her role at the social media company. It happened first day in the books!,\" Linda tweeted late on Monday.\r\n About a month back Elon \u2026 [+1314 chars]", "image_url": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/FILES-US-TECH-TWITTER-0_1686056596734_1686056619283.jpg"}, {"source": {"id": null, "name": "Livemint"}, "author": "Livemint", "title": "Sequoia splits into 3 entities: India, China and US entities to operate independently from March 2024 | Mint - Mint", "description": "Sequoia said the China and India and Southeast Asia businesses will become completely independent partnerships and become distinct firms with separate brands no later than March 31, 2024", "url": "https://www.livemint.com/companies/news/sequoia-splits-into-3-entities-india-china-and-us-entities-to-operate-independently-from-march-2024-11686052782227.html", "urlToImage": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/SEQUOIACAPITAL-INDIA-0_1656961710020_1686052870060.JPG", "publishedAt": "2023-06-06T12:16:44Z", "content": "Global venture capital giant Sequoia announced on June 5 that it is planning to separate US, China and India- Southeast Asia businesses into independent firms. Sequoia said the China and India and So\u2026 [+2261 chars]", "image_url": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/SEQUOIACAPITAL-INDIA-0_1656961710020_1686052870060.JPG"}, {"source": {"id": null, "name": "YouTube"}, "author": null, "title": "OPEC+ Slashes Oil Production Again as Saudi Arabia Promises Big Cuts | Vantage with Palki Sharma - Firstpost", "description": "Saudi Arabia Cuts Oil Supply: How Will It Impact India? | Vantage with Palki SharmaIn a rare move, Saudi Arabia has decided to slash oil supply by 1 million ...", "url": "https://www.youtube.com/watch?v=rgZwvVKA0Gg", "urlToImage": "https://i.ytimg.com/vi/rgZwvVKA0Gg/maxresdefault.jpg", "publishedAt": "2023-06-06T12:15:05Z", "content": null, "image_url": "https://i.ytimg.com/vi/rgZwvVKA0Gg/maxresdefault.jpg"}, {"source": {"id": null, "name": "Moneycontrol"}, "author": "Kayezad E Adajania", "title": "Bajaj Finance to bring down dependence on telemarketing calls: Sanjiv Bajaj - Moneycontrol", "description": "Sanjiv Bajaj said the firm will offer clients the option to avoid telemarketing calls, stresses that every step will be taken to protect data privacy", "url": "https://www.moneycontrol.com/news/business/personal-finance/bajaj-finance-to-bring-down-dependence-on-telemarketing-calls-sanjiv-bajaj-10752841.html", "urlToImage": "https://images.moneycontrol.com/static-mcnews/2022/11/BAJAJ-FINANCE-759x435.jpg", "publishedAt": "2023-06-06T12:11:41Z", "content": "Bajaj Finance aims to bring down the value of loans that are pushed through phone calls to less than 10 percent of its business, said Sanjiv Bajaj, chairman and managing director of Bajaj Finserv, th\u2026 [+3538 chars]", "image_url": "https://images.moneycontrol.com/static-mcnews/2022/11/BAJAJ-FINANCE-759x435.jpg"}, {"source": {"id": null, "name": "GaadiWaadi.com"}, "author": "Surendhar M", "title": "Big Discounts On Tata Cars In June 2023 - Tiago, Punch, Nexon, Harrier - GaadiWaadi.com", "description": "Tata Motors is offering discounts of up to Rs. 35,000 in June 2023 as Tiago, Tigor, Altroz, Harrier and Safari can be bought with good deals", "url": "https://gaadiwaadi.com/big-discounts-on-tata-cars-in-june-2023-tiago-punch-nexon-harrier/", "urlToImage": "https://gaadiwaadi.com/wp-content/uploads/2023/01/2023-tata-harrier-safari-dark-edition_8311.jpg", "publishedAt": "2023-06-06T11:58:27Z", "content": "Tata Motors is offering good discounts for some of its models in the month of June 2023. The entry-level Tiago hatchback is available with a cash discount of up to Rs. 20,000 while the exchange bonus\u2026 [+2373 chars]", "image_url": "https://gaadiwaadi.com/wp-content/uploads/2023/01/2023-tata-harrier-safari-dark-edition_8311.jpg"}] \ No newline at end of file diff --git a/Web Module/explore_fields/news/entertainment.json b/Web Module/explore_fields/news/entertainment.json new file mode 100644 index 000000000..6650f61b1 --- /dev/null +++ b/Web Module/explore_fields/news/entertainment.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "Odishatv.in"}, "author": "Nitesh Kumar Sahoo", "title": "Lust Stories 2 teaser: Mrunal Thakur advised for \u2018test drive before marriage\u2019, fans expect strong content - OTV News", "description": "The Hindi-language anthology film consisting of four short film segments, directed by R. Balki, Konkana Sen Sharma, Sujoy Ghosh and Amit R Sharma has already fuelled the expectations among all.", "url": "https://odishatv.in/news/entertainment/lust-stories-2-teaser-mrunal-thakur-advised-for-test-drive-before-marriage-expect-strong-content-206296", "urlToImage": "https://images.odishatv.in/uploadimage/library/16_9/16_9_5/recent_photo_1686063648.webp", "publishedAt": "2023-06-06T15:05:24Z", "content": "The much-awaited teaser of Lust Stories 2, scheduled for Netflix release on June 29, has dropped today. With a strong cast including Kajol, Tamannaah Bhatia, Mrunal Thakur, Neena Gupta, Amruta Subhas\u2026 [+2090 chars]", "image_url": "https://images.odishatv.in/uploadimage/library/16_9/16_9_5/recent_photo_1686063648.webp"}, {"source": {"id": null, "name": "123telugu.com"}, "author": null, "title": "Surprise: 2018 arrives on OTT earlier than the scheduled time - 123telugu", "description": "Telugu cinema news, Telugu Movies Updates, Latest Movie reviews in Telugu, Telugu cinema reviews, telugu movie reviews, Tollywood, Box office collections, Telugu Movie show times, Theater List, telugu cinema tickets", "url": "https://www.123telugu.com/mnews/surprise-2018-arrives-on-ott-earlier-than-the-scheduled-time.html", "urlToImage": "https://www.123telugu.com/content/wp-content/themes/123telugu/images/logo.gif", "publishedAt": "2023-06-06T14:30:00Z", "content": "The Malayalam industry hit 2018 didn\u2019t have a huge buzz before its release. But the strong content helped the film post terrific numbers at the box office. Later it was released in multiple languages\u2026 [+816 chars]", "image_url": "https://www.123telugu.com/content/wp-content/themes/123telugu/images/logo.gif"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "Paurush Omar", "title": "'They're dead to me', Kaley Cuoco drops mic on Vanderpump Rules scandal - Hindustan Times", "description": "Not only did Kaley voice her surprise, but she also reached out to Ariana on Instagram, showcasing her support for the betrayed star", "url": "https://www.hindustantimes.com/entertainment/tv/kaley-cuoco-unleashes-drama-vanderpump-rules-superfan-slays-scandoval-affair-with-her-unfiltered-take-101686059734843.html", "urlToImage": "https://www.hindustantimes.com/ht-img/img/2023/06/06/1600x900/kaley-cuoco_205195ac-7aee-11e6-9f8c-92f0a5be7f74_1686059842449.jpg", "publishedAt": "2023-06-06T14:11:43Z", "content": "When it comes to reality TV scandals, they don't get much juicier than the explosive affair that rocked Vanderpump Rules. The revelation that Tom Sandoval had cheated on Ariana Madix with Raquel Levi\u2026 [+2403 chars]", "image_url": "https://www.hindustantimes.com/ht-img/img/2023/06/06/1600x900/kaley-cuoco_205195ac-7aee-11e6-9f8c-92f0a5be7f74_1686059842449.jpg"}, {"source": {"id": null, "name": "The Indian Express"}, "author": "The Indian Express", "title": "Amitabh Bachchan explains why he greets fans at Jalsa barefoot, reveals he has refreshments arranged for them - The Indian Express", "description": null, "url": "https://indianexpress.com/article/entertainment/bollywood/amitabh-bachchan-greets-fans-jalsa-barefoot-provides-them-with-water-8649138/", "urlToImage": null, "publishedAt": "2023-06-06T14:01:32Z", "content": null, "image_url": null}, {"source": {"id": null, "name": "Koreaboo.com"}, "author": null, "title": "Kentucky Fried Chicken Goes Viral For Tweeting About BTS Suga's Collab With Halsey - Koreaboo", "description": "Kentucky Fried Chicken (KFC) went viral for tweeting about Halsey and BTS Suga's new song \"Lilith,\" but there's another reason why they tweeted.", "url": "https://www.koreaboo.com/news/kentucky-fried-chicken-tweeting-bts-halsey/", "urlToImage": "https://lh3.googleusercontent.com/ueK5PaDFdSuGnGtTfMYnmeCWUons7cw1cjbVo7p-mkBXKEBmjbjKO5lW1Ko1sTpJURFAL9aE7dpmZUokmhS9E2hwLuHb-VSsk9kmAAHWOA8B1Q=w1200-h630-rj-pp-e365", "publishedAt": "2023-06-06T13:59:59Z", "content": "The latest member of the BTS ARMY is\u2026 KFC (Kentucky Fried Chicken)?\r\nYour browser does not support video.\r\nRecently, Halsey released a reimagining of their song \u201cLilith,\u201d featuring BTS\u2019s Suga, for\u00a0Bl\u2026 [+1277 chars]", "image_url": "https://lh3.googleusercontent.com/ueK5PaDFdSuGnGtTfMYnmeCWUons7cw1cjbVo7p-mkBXKEBmjbjKO5lW1Ko1sTpJURFAL9aE7dpmZUokmhS9E2hwLuHb-VSsk9kmAAHWOA8B1Q=w1200-h630-rj-pp-e365"}, {"source": {"id": null, "name": "DNA India"}, "author": "DNA Web Team", "title": "Nakuul Mehta wins the internet with his sweet reply to Instagram user criticising Bade Achhe Lagte Hain 3 - DNA India", "description": "Bade Acche Lagte Hain 3 began on May 25 with the completely fresh story and the same character names of Nakuul Mehta and Disha Parmar as Ram Kapoor and Priya Sood respectively", "url": "https://www.dnaindia.com/television/report-nakuul-mehta-wins-the-internet-with-his-sweet-reply-to-instagram-user-criticising-bade-achhe-lagte-hain-3-3046356", "urlToImage": "https://cdn.dnaindia.com/sites/default/files/styles/half/public/2023/06/06/2593602-nakuul-final.jpg", "publishedAt": "2023-06-06T13:28:19Z", "content": "Reported By:| Edited By: DNA Web Team |Source: DNA Web Desk |Updated: Jun 06, 2023, 06:58 PM ISTAfter quitting Bade Achhe Lagte Hain 2 in December 2022, Nakuul Mehta and Disha Parmar are back with th\u2026 [+2049 chars]", "image_url": "https://cdn.dnaindia.com/sites/default/files/styles/half/public/2023/06/06/2593602-nakuul-final.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "TIMESOFINDIA.COM", "title": "Arjun Rampal\u2019s girlfriend Gabriella Demetriades gets criticised for her second pregnancy without marriage; here\u2019s how she responded - Indiatimes.com", "description": "Arjun Rampal\u2019s girlfriend Gabriella Demetriades who is currently pregnant with their second child recently posted a picture flaunting her baby bump. While many fans showered a lot of love on the model with sweet comments, one of the netizens slammed", "url": "https://timesofindia.indiatimes.com/videos/entertainment/hindi/arjun-rampals-girlfriend-gabriella-demetriades-gets-criticised-for-her-second-pregnancy-without-marriage-heres-how-she-responded/videoshow/100797930.cms", "urlToImage": "https://timesofindia.indiatimes.com/photo/100797930/size-68858/100797930.jpg", "publishedAt": "2023-06-06T13:19:01Z", "content": "Lust Stories 2 Teaser: Amruta Subhash, Angad Bedi, Kajol, Kumud Mishra, Mrunal Thakur, Neena Gupta, Tamannaah Bhatia, Tillotama Shome And Vijay Varma starrer Lust Stories 2 Official Teaser", "image_url": "https://timesofindia.indiatimes.com/photo/100797930/size-68858/100797930.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "TOI Photogallery", "title": "Shweta Tiwari sets Instagram on fire with poolside pics in red thigh high-slit dress - Indiatimes.com", "description": "Let's take a look at the bewitching vacation pictures of Shweta Tiwari...", "url": "https://photogallery.indiatimes.com/web-stories/shweta-tiwari-sets-instagram-on-fire-with-poolside-pics-in-red-thigh-high-slit-dress/photostory/100797804.cms", "urlToImage": "https://static.toiimg.com/thumb/msid-100798092,width-900,height-1200,resizemode-6.cms", "publishedAt": "2023-06-06T12:52:38Z", "content": "The yellow dress featured an off-shoulder neckline, a corset-style bustier, and gathered detailing with a pleated bottom. Her accessories included a pair of gold earrings and matching rings with mini\u2026 [+10 chars]", "image_url": "https://static.toiimg.com/thumb/msid-100798092,width-900,height-1200,resizemode-6.cms"}, {"source": {"id": null, "name": "India Today"}, "author": null, "title": "Vicky Kaushal, Sara Ali Khan seek blessings at Siddhivinayak temple post Zara Hatke Zara Bachke's success - India Today", "description": "VVicky Kaushal and Sara Ali Khan offered prayers at the Siddhivinayak temple post the huge success of their film, Zara Hatke Zara Bachke. 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The legendary Mughal-E-Azam has made its grand entrance onto the Broadway stage, captivating audiences with its larger-than-life production. This iconic 1960 film has been giv\u2026 [+2746 chars]", "image_url": "https://www.hindustantimes.com/ht-img/img/2023/06/06/1600x900/mughal_e_azam_1686050481102_1686050495817.jpg"}] \ No newline at end of file diff --git a/Web Module/explore_fields/news/general.json b/Web Module/explore_fields/news/general.json new file mode 100644 index 000000000..3693c60b2 --- /dev/null +++ b/Web Module/explore_fields/news/general.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "The Guardian"}, "author": "Geoff Lemon", "title": "Reward or redemption? World Test Championship final looks entirely different for Australia and India - The Guardian", "description": "While the two nations face off at The Oval, cricket fans will also be wondering about the future of the fixture", "url": "https://www.theguardian.com/sport/2023/jun/07/cricket-world-test-championship-final-australia-india", "urlToImage": "https://i.guim.co.uk/img/media/5d515b9b53b5a83c8b76ee49aee6ed1a981d6b99/0_0_2500_1500/master/2500.jpg?width=1200&height=630&quality=85&auto=format&fit=crop&overlay-align=bottom%2Cleft&overlay-width=100p&overlay-base64=L2ltZy9zdGF0aWMvb3ZlcmxheXMvdGctZGVmYXVsdC5wbmc&enable=upscale&s=66bd8c43b00dda946bff1e16f29e52d3", "publishedAt": "2023-06-06T15:47:00Z", "content": "Soon it will arrive: not just the second Test of the English summer, but the second edition of the World Test Championship final. As Australia and India prepare to play off in London, the matchs plac\u2026 [+9953 chars]", "image_url": "https://i.guim.co.uk/img/media/5d515b9b53b5a83c8b76ee49aee6ed1a981d6b99/0_0_2500_1500/master/2500.jpg?width=1200&height=630&quality=85&auto=format&fit=crop&overlay-align=bottom%2Cleft&overlay-width=100p&overlay-base64=L2ltZy9zdGF0aWMvb3ZlcmxheXMvdGctZGVmYXVsdC5wbmc&enable=upscale&s=66bd8c43b00dda946bff1e16f29e52d3"}, {"source": {"id": null, "name": "NDTV News"}, "author": null, "title": "\"Any BJP Rule Can Go...\": Karnataka Minister Over Cow Slaughter, Hijab Ban - NDTV", "description": "NDTV.com: India, Business, Bollywood, Cricket, Video and Breaking News", "url": "https://www.ndtv.com/news", "urlToImage": "https://cdn.ndtv.com/common/images/ogndtv.png", "publishedAt": "2023-06-06T15:45:56Z", "content": "If you are a climate change warrior or know someone who is doing incredible work in this space, send us your entries and we'll bring the most innovative stories to the world.", "image_url": "https://cdn.ndtv.com/common/images/ogndtv.png"}, {"source": {"id": null, "name": "Livemint"}, "author": "Livemint", "title": "World Bank cuts India's GDP growth forecast for FY24 to 6.3% in latest Global Economic Prospects report | Mint - Mint", "description": "World Bank expects India's growth to slow down further to 6.3% in FY2023/24 due to high inflation and rising borrowing costs, with a slight pick-up expected in FY2025/26. 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This is a 0.3 percentage point downward revision from the previous estimates of 6.6%.\u2026 [+2103 chars]", "image_url": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/2-0-1308567559-iStock-624006170-0_1679988715895_1686066186270.jpg"}, {"source": {"id": null, "name": "Livemint"}, "author": "Livemint", "title": "IMD alert: Depression in Arabian sea to intensify into cyclonic storm in next 6 hours | Mint - Mint", "description": "Cyclone Biparjoy: Westerly winds continue to persists over south Arabian Sea. The depth of westerly winds has increased from 1.5 to 4.5 km above mean sea level. There has been some increase of rainfall activity over Kerala.", "url": "https://www.livemint.com/news/india/imd-alert-depression-in-arabian-sea-to-intensify-into-cyclonic-storm-in-next-6-hours-11686063692777.html", "urlToImage": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/PTI06-06-2023-000121A-0_1686063746012_1686063779587.jpg", "publishedAt": "2023-06-06T15:18:07Z", "content": "The Indian Metrological Department (IMD) informed that the depression that has formed over the Arabian Sea is likely to intensify into a cyclonic storm within the next six hours.\u00a0 The depression was \u2026 [+2536 chars]", "image_url": "https://www.livemint.com/lm-img/img/2023/06/06/600x338/PTI06-06-2023-000121A-0_1686063746012_1686063779587.jpg"}, {"source": {"id": null, "name": "NDTV News"}, "author": null, "title": "Papaya Is Amazing For Our Health But Avoid Eating These Foods Along With Papaya - NDTV", "description": "Consuming papaya with certain foods can lead to digestive issues, here are some of these that you must avoid.", "url": "https://www.ndtv.com/health/papaya-is-amazing-for-our-health-but-avoid-eating-these-foods-along-with-papaya-4099729", "urlToImage": "https://c.ndtvimg.com/2023-04/a5c5aoeg_papaya_625x300_05_April_23.jpg", "publishedAt": "2023-06-06T14:59:25Z", "content": "Avoid consuming large amounts of papaya at once, as this can increase the risk of digestive discomfort\r\nPapaya is a tropical fruit that is native to Mexico and Central America. It is known for its ye\u2026 [+3250 chars]", "image_url": "https://c.ndtvimg.com/2023-04/a5c5aoeg_papaya_625x300_05_April_23.jpg"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "MD Ijaj Khan", "title": "James Webb Telescope looks back in time, finds oldest organic molecules ever seen - HT Tech", "description": "These chemicals were found within a galaxy that originated when the universe was approximately 10% of its present age.", "url": "https://tech.hindustantimes.com/tech/news/james-webb-telescope-looks-back-in-time-finds-oldest-organic-molecules-ever-seen-71686062592623.html", "urlToImage": "https://images.hindustantimes.com/tech/img/2023/06/06/1600x900/webb-space-telescope_1686063078472_1686063088349.jpg", "publishedAt": "2023-06-06T14:54:03Z", "content": "A groundbreaking study led by astronomers from Texas A&M University using NASA's James Webb Space Telescope (JWST) has unveiled the existence of the oldest known complex organic molecules in the \u2026 [+2813 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/06/1600x900/webb-space-telescope_1686063078472_1686063088349.jpg"}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "TIMESOFINDIA.COM", "title": "How do I know if I have dementia? - Indiatimes.com", "description": "Dementia symptoms that you should recognise on time.", "url": "https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/how-do-i-know-if-i-have-dementia/photostory/100794722.cms", "urlToImage": "https://static.toiimg.com/photo/100794789.cms", "publishedAt": "2023-06-06T14:30:00Z", "content": "Dementia is the loss of thinking, remembering, and reasoning to the point where it interferes with a person's daily activities. However, in order to determine if you have dementia, you must see a doc\u2026 [+318 chars]", "image_url": "https://static.toiimg.com/photo/100794789.cms"}, {"source": {"id": null, "name": "YouTube"}, "author": null, "title": "Samsung Has Launched Samsung Galaxy F54 5G Smartphone | Business News | ET Now - ET NOW", "description": "With an aim to further strong hold the smartphone segment, Samsung has launched Samsung Galaxy F54 5G smartphone in the affordable premium smartphone categor...", "url": "https://www.youtube.com/watch?v=JkgSQ3oNbS0", "urlToImage": "https://i.ytimg.com/vi/JkgSQ3oNbS0/maxresdefault.jpg", "publishedAt": "2023-06-06T14:14:29Z", "content": null, "image_url": "https://i.ytimg.com/vi/JkgSQ3oNbS0/maxresdefault.jpg"}, {"source": {"id": null, "name": "The Indian Express"}, "author": "Sreenivas Janyala", "title": "After audience to Chandrababu Naidu by BJP, Modi govt\u2019s largesse to Jagan govt - The Indian Express", "description": "In poll-bound Andhra Pradesh, the party seems to be keeping all its options open; fresh tranche of funds will help Jagan govt complete crucial components of Polavaram project", "url": "https://indianexpress.com/article/political-pulse/bjp-andhra-pradesh-ysrcp-tdp-jagan-chandrababu-8649137/", "urlToImage": "https://images.indianexpress.com/2023/06/Jagan.jpg", "publishedAt": "2023-06-06T13:43:28Z", "content": "THE CENTRE Tuesday released Rs 12,911 crore more to Andhra Pradesh for the Polavaram project, which would enable the state to complete its first phase, including important components of the dam.This \u2026 [+2087 chars]", "image_url": "https://images.indianexpress.com/2023/06/Jagan.jpg"}, {"source": {"id": null, "name": "NDTV News"}, "author": "Press Trust of India", "title": "\"Have Not Said Ravichandran Ashwin Is...\": Rohit Sharma On India's Playing XI For WTC Final vs Austr.. - NDTV Sports", "description": "On team combination, Rohit Sharma did not reveal much and expectedly kept the suspense going till toss time", "url": "https://sports.ndtv.com/cricket/have-not-said-ravichandran-ashwin-is-rohit-sharma-on-indias-playing-xi-for-wtc-final-vs-australia-4099352", "urlToImage": "https://c.ndtvimg.com/2023-06/fds0f5qo_rohit-ashwin_625x300_06_June_23.jpg?im=FaceCrop,algorithm=dnn,width=1200,height=675", "publishedAt": "2023-06-06T13:42:20Z", "content": "On another nippy and overcast morning in London on Tuesday, Rohit was among the four squad members who turned up for optional practice. R Ashwin, Umesh and K S Bharat were the others who showed up in\u2026 [+3024 chars]", "image_url": "https://c.ndtvimg.com/2023-06/fds0f5qo_rohit-ashwin_625x300_06_June_23.jpg?im=FaceCrop,algorithm=dnn,width=1200,height=675"}] \ No newline at end of file diff --git a/Web Module/explore_fields/news/health.json b/Web Module/explore_fields/news/health.json new file mode 100644 index 000000000..a2a1289ba --- /dev/null +++ b/Web Module/explore_fields/news/health.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "India TV News"}, "author": "Edited by India TV Health Desk", "title": "THESE habits will weaken your bones. Find out - India TV News", "description": "Your bones start to deteriorate as you age, which might also be brought on by some of your habits. But first, let's determine the cause.", "url": "https://www.indiatvnews.com/health/these-habits-will-weaken-your-bones-find-out-2023-06-06-874613", "urlToImage": "https://resize.indiatvnews.com/en/resize/newbucket/1200_-/2023/06/befunky-collagesfddddfddddfdddddsffssdddssddfcfdsddsssssddddfddffffffdddsssssdfdddsfdddffddddaaddddf-42-1686067002.jpg", "publishedAt": "2023-06-06T16:10:12Z", "content": "Bones typically begin to deteriorate as we age. However, due to a sedentary lifestyle and the consumption of street food, bones become weak before their time. Young and little children are also devel\u2026 [+1536 chars]", "image_url": "https://resize.indiatvnews.com/en/resize/newbucket/1200_-/2023/06/befunky-collagesfddddfddddfdddddsffssdddssddfcfdsddsssssddddfddffffffdddsssssdfdddsfdddffddddaaddddf-42-1686067002.jpg"}, {"source": {"id": null, "name": "India.com"}, "author": "ANI", "title": "Want To Get Rid Of Lower Back Pain: These 5 Exercises For Muscle Relief - Zee News", "description": "The pain can worsen if you spend the entire day seated in an uncomfortable desk chair or automobile seat. One of the most prevalent musculoskeletal disorders among office employees is back discomfort.\u00a0 Even if you have a sit-stand desk, working for a long per\u2026", "url": "https://zeenews.india.com/health/want-to-get-rid-of-lower-back-pain-these-5-exercises-for-muscle-relief-2618472", "urlToImage": "https://english.cdn.zeenews.com/sites/default/files/2023/06/06/1215024-back-pain-strecth.png", "publishedAt": "2023-06-06T15:14:44Z", "content": "Back pain: The pain can worsen if you spend the entire day seated in an uncomfortable desk chair or automobile seat. One of the most prevalent musculoskeletal disorders among office employees is back\u2026 [+2705 chars]", "image_url": "https://english.cdn.zeenews.com/sites/default/files/2023/06/06/1215024-back-pain-strecth.png"}, {"source": {"id": null, "name": "Jamanetwork.com"}, "author": null, "title": "The Routine Treatment of Diabetes With Insulin | JAMA - JAMA Network", "description": "Elliott P. Joslin , M.D.", "url": "https://jamanetwork.com/journals/jama/fullarticle/2805716", "urlToImage": "https://cdn.jamanetwork.com/images/logos/JAMA.png", "publishedAt": "2023-06-06T15:09:49Z", "content": "Elliott P. Joslin, M.D.\r\nSuccessful treatment of diabetes with insulin depends on the utilization of all those measures that have proved of the greatest value in the treatment of diabetes without ins\u2026 [+4893 chars]", "image_url": "https://cdn.jamanetwork.com/images/logos/JAMA.png"}, {"source": {"id": null, "name": "Jamanetwork.com"}, "author": "Xiu Juan Zhang, PhD", "title": "Low-Concentration Atropine Eyedrops for Myopia in Children\u2014Reply - JAMA Network", "description": "In Reply We appreciate the opportunity to address the comments by Dr Tsuda and colleagues about our article. 1 First, we acknowledge that the participant dropout rate in our study was higher than anticipated. However, there were no significant differences in \u2026", "url": "https://jamanetwork.com/journals/jama/fullarticle/2805709", "urlToImage": "https://cdn.jamanetwork.com/images/logos/JAMA.png", "publishedAt": "2023-06-06T15:09:49Z", "content": "In Reply We appreciate the opportunity to address the comments by Dr Tsuda and colleagues about our article.1 First, we acknowledge that the participant dropout rate in our study was higher than anti\u2026 [+914 chars]", "image_url": "https://cdn.jamanetwork.com/images/logos/JAMA.png"}, {"source": {"id": null, "name": "NDTV News"}, "author": null, "title": "Papaya Is Amazing For Our Health But Avoid Eating These Foods Along With Papaya - NDTV", "description": "Consuming papaya with certain foods can lead to digestive issues, here are some of these that you must avoid.", "url": "https://www.ndtv.com/health/papaya-is-amazing-for-our-health-but-avoid-eating-these-foods-along-with-papaya-4099729", "urlToImage": "https://c.ndtvimg.com/2023-04/a5c5aoeg_papaya_625x300_05_April_23.jpg", "publishedAt": "2023-06-06T14:59:25Z", "content": "Avoid consuming large amounts of papaya at once, as this can increase the risk of digestive discomfort\r\nPapaya is a tropical fruit that is native to Mexico and Central America. It is known for its ye\u2026 [+3250 chars]", "image_url": "https://c.ndtvimg.com/2023-04/a5c5aoeg_papaya_625x300_05_April_23.jpg"}, {"source": {"id": null, "name": "Medical Xpress"}, "author": "Justin Jackson", "title": "New ovarian cancer screening technique shows reduced false negatives compared to CA125 method - Medical Xpress", "description": "Researchers at the Department of Obstetrics and Gynecology, Peking University Third Hospital in Beijing, China, have developed a new ovarian cancer screening technique. 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In a paper, \"Profiling the met\u2026 [+3451 chars]", "image_url": "https://scx2.b-cdn.net/gfx/news/2023/new-ovarian-cancer-scr.jpg"}, {"source": {"id": null, "name": "FierceBiotech"}, "author": "FierceBiotech", "title": "400 Grail test users incorrectly told they may have cancer - FierceBiotech", "description": null, "url": "https://www.fiercebiotech.com/medtech/400-grail-blood-test-users-were-incorrectly-told-they-may-have-cancer-report", "urlToImage": null, "publishedAt": "2023-06-06T14:40:00Z", "content": null, "image_url": null}, {"source": {"id": "the-times-of-india", "name": "The Times of India"}, "author": "TIMESOFINDIA.COM", "title": "How do I know if I have dementia? - Indiatimes.com", "description": "Dementia symptoms that you should recognise on time.", "url": "https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/how-do-i-know-if-i-have-dementia/photostory/100794722.cms", "urlToImage": "https://static.toiimg.com/photo/100794789.cms", "publishedAt": "2023-06-06T14:30:00Z", "content": "Dementia is the loss of thinking, remembering, and reasoning to the point where it interferes with a person's daily activities. However, in order to determine if you have dementia, you must see a doc\u2026 [+318 chars]", "image_url": "https://static.toiimg.com/photo/100794789.cms"}, {"source": {"id": null, "name": "Rsna.org"}, "author": "RSNA Publications Online", "title": "Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study - RSNA Publications Online", "description": null, "url": "https://pubs.rsna.org/doi/10.1148/radiol.222733", "urlToImage": null, "publishedAt": "2023-06-06T14:00:37Z", "content": null, "image_url": null}, {"source": {"id": null, "name": "The Indian Express"}, "author": "The Indian Express", "title": "Close contact between mother and premature baby may boost infant\u2019s survival: Study - The Indian Express", "description": null, "url": "https://indianexpress.com/article/lifestyle/health/kangaroo-mother-care-8648374/", "urlToImage": null, "publishedAt": "2023-06-06T14:00:00Z", "content": null, "image_url": null}] \ No newline at end of file diff --git a/Web Module/explore_fields/news/science.json b/Web Module/explore_fields/news/science.json new file mode 100644 index 000000000..71870de2d --- /dev/null +++ b/Web Module/explore_fields/news/science.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "Newswise"}, "author": "Indian Institute of Technology Kanpur", "title": "IIT Kanpur researchers visualize communication of G-protein coupled receptors, paves way for new drug discovery - Newswise", "description": "Study by IIT-Kanpur researchers published in the prestigious international journal Molecular Cell unravels a previously unknown mechanism that regulates an impo", "url": "https://www.newswise.com/articles/iit-kanpur-researchers-visualize-communication-of-g-protein-coupled-receptors-paves-way-for-new-drug-discovery", "urlToImage": "https://www.newswise.com/assets/new/img/newswise-logo-square.jpg", "publishedAt": "2023-06-06T15:25:00Z", "content": "
    • Study by IIT-Kanpur researchers published in the prestigious international journal Molecular Cell unravels a previously unknown mechanism that regulates an important class of drug targets kno\u2026 [+4914 chars]", "image_url": "https://www.newswise.com/assets/new/img/newswise-logo-square.jpg"}, {"source": {"id": null, "name": "Shiawaves.com"}, "author": "ammar", "title": "New study suggests one-third of planets orbiting Milky Way may hold conditions to support life - Shia Waves - ShiaWaves | Shia World News", "description": "Researchers have suggested that about one-third of planets revolving around our galaxy's most common stars may hold conditions to support life. In the study, the researchers based their examinations o", "url": "https://shiawaves.com/english/news/93836-new-study-suggests-one-third-of-planets-orbiting-milky-way-may-hold-conditions-to-support-life/", "urlToImage": "https://shiawaves.com/english/wp-content/uploads/sites/4/2023/06/FB_IMG_1686063989871.jpg", "publishedAt": "2023-06-06T15:10:19Z", "content": "Researchers have suggested that about one-third of planets revolving around our galaxy\u2019s most common stars may hold conditions to support life. \r\nIn the study, the researchers based their examination\u2026 [+388 chars]", "image_url": "https://shiawaves.com/english/wp-content/uploads/sites/4/2023/06/FB_IMG_1686063989871.jpg"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "MD Ijaj Khan", "title": "James Webb Telescope looks back in time, finds oldest organic molecules ever seen - HT Tech", "description": "These chemicals were found within a galaxy that originated when the universe was approximately 10% of its present age.", "url": "https://tech.hindustantimes.com/tech/news/james-webb-telescope-looks-back-in-time-finds-oldest-organic-molecules-ever-seen-71686062592623.html", "urlToImage": "https://images.hindustantimes.com/tech/img/2023/06/06/1600x900/webb-space-telescope_1686063078472_1686063088349.jpg", "publishedAt": "2023-06-06T14:54:03Z", "content": "A groundbreaking study led by astronomers from Texas A&M University using NASA's James Webb Space Telescope (JWST) has unveiled the existence of the oldest known complex organic molecules in the \u2026 [+2813 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/06/1600x900/webb-space-telescope_1686063078472_1686063088349.jpg"}, {"source": {"id": null, "name": "Quanta Magazine"}, "author": null, "title": "In New Paradox, Black Holes Appear to Evade Heat Death - Quanta Magazine", "description": "The puzzling behavior of black hole interiors has led researchers to propose a new physical law: the second law of quantum complexity.", "url": "https://www.quantamagazine.org/in-new-paradox-black-holes-appear-to-evade-heat-death-20230606/", "urlToImage": "https://d2r55xnwy6nx47.cloudfront.net/uploads/2023/06/LifeAfterDeath-byOlenaShmahalo-Social.webp", "publishedAt": "2023-06-06T13:59:40Z", "content": "The AdS/CFT duality has been reliably blowing peoples minds for over 25 years. Its hard to visualize how such different systems as a black hole and a hot plasma could be equivalent. Now it seems that\u2026 [+8391 chars]", "image_url": "https://d2r55xnwy6nx47.cloudfront.net/uploads/2023/06/LifeAfterDeath-byOlenaShmahalo-Social.webp"}, {"source": {"id": null, "name": "Umich.edu"}, "author": null, "title": "U-M biologist named to Science News magazine's Scientists to Watch list - University of Michigan News", "description": "University of Michigan evolutionary ecologist Marjorie Weber has been named to Science News magazine's annual Scientists to Watch list, which recognizes 10 young researchers \"for their potential to shape the science of the future.\"", "url": "https://news.umich.edu/u-m-biologist-named-to-science-news-magazines-scientists-to-watch-list/", "urlToImage": "https://news.umich.edu/wp-content/uploads/mc-image-cache/2023/06/u-m-biologist-named-to-science-news-magazines-scientists-to-watch-list.jpg", "publishedAt": "2023-06-06T13:49:10Z", "content": "Marjorie Weber\r\nUniversity of Michigan evolutionary ecologist Marjorie Weber has been named to Science News magazine\u2019s annual Scientists to Watch list, which recognizes 10 young researchers \u201cfor thei\u2026 [+3819 chars]", "image_url": "https://news.umich.edu/wp-content/uploads/mc-image-cache/2023/06/u-m-biologist-named-to-science-news-magazines-scientists-to-watch-list.jpg"}, {"source": {"id": null, "name": "Phys.Org"}, "author": "Science X", "title": "Novel genetic screen provides clues on how Toxoplasma behaves within a host - Phys.org", "description": "While the presence of harmful critters in your home may be addressed by a call to the exterminator, harmful parasites in the body can be much harder to expel. 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A single-celled parasite known as Toxopl\u2026 [+3590 chars]", "image_url": "https://scx2.b-cdn.net/gfx/news/2023/novel-genetic-screen-p.jpg"}, {"source": {"id": null, "name": "India Education Diary"}, "author": "iednewsdesk", "title": "University of Saskatchewan research shares new insights into nitrogen, phosphorous - India Education Diary", "description": "A research team led by USask College of Arts and Science researcher in physics and engineering physics, Dr. Alexander Moewes (PhD), Canada Research Chair in Materials Science with Synchrotron Radiation, is investigating a brand-new compound\u2014potential", "url": "https://indiaeducationdiary.in/university-of-saskatchewan-research-shares-new-insights-into-nitrogen-phosphorous/", "urlToImage": "https://indiaeducationdiary.in/wp-content/uploads/2020/08/Default-Image-IED.png", "publishedAt": "2023-06-06T12:36:21Z", "content": "A research team led by USask College of Arts and Science researcher in physics and engineering physics, Dr. Alexander Moewes (PhD), Canada Research Chair in Materials Science with Synchrotron Radiati\u2026 [+4612 chars]", "image_url": "https://indiaeducationdiary.in/wp-content/uploads/2020/08/Default-Image-IED.png"}, {"source": {"id": null, "name": "SciTechDaily"}, "author": null, "title": "Piercing Solar Physics Mysteries: Supercomputer Simulations Illuminate the Sun's Magnetic Dynamo - SciTechDaily", "description": "Scientists have used advanced supercomputer simulations to demonstrate the existence and significance of a small-scale dynamo in the Sun's magnetic field. This discovery refutes previous assumptions and advances our understanding of solar dynamics, potentiall\u2026", "url": "https://scitechdaily.com/?p=283996", "urlToImage": "https://scitechdaily.com/images/Simulation-of-Solar-Magnetic-Structures.jpg", "publishedAt": "2023-06-06T12:11:33Z", "content": "ByAalto UniversityJune 6, 2023\r\nComputer simulation of magnetic structures in solar-like conditions. A research article published in Nature Astronomy elucidates the dynamics behind solar weather, spe\u2026 [+4251 chars]", "image_url": "https://scitechdaily.com/images/Simulation-of-Solar-Magnetic-Structures.jpg"}, {"source": {"id": null, "name": "Argusenglish.in"}, "author": null, "title": "Study reveals how new brain mechanism associated with anxiety, OCD - Argus English", "description": "The epidemic and its effects have heightened anxiety to unprecedented proportions.", "url": "https://argusenglish.in/article/lifestyle/study-reveals-how-new-brain-mechanism-associated-with-anxiety-ocd", "urlToImage": "https://d1ttqxosekmk9r.cloudfront.net/posts/argusnews-thum-293d02d1-efd5-4c0b-917c-3e6c4f804c1b.jpg", "publishedAt": "2023-06-06T12:09:00Z", "content": "Salt Lake City, June 6: The epidemic and its effects have heightened anxiety to unprecedented proportions. However, the causes of anxiety-related disorders, such as obsessive-compulsive spectrum diso\u2026 [+4631 chars]", "image_url": "https://d1ttqxosekmk9r.cloudfront.net/posts/argusnews-thum-293d02d1-efd5-4c0b-917c-3e6c4f804c1b.jpg"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "HT Tech", "title": "Solar storm DANGER! NOAA satellites capture CME clouds rushing towards Earth - HT Tech", "description": "NOAA forecasters have predicted a solar storm alert on June 7 and June 8, after its satellites detected a large CME cloud moving towards the Earth. Check details.", "url": "https://tech.hindustantimes.com/tech/news/solar-storm-danger-noaa-satellites-capture-cme-clouds-rushing-towards-earth-71686050381852.html", "urlToImage": "https://images.hindustantimes.com/tech/img/2023/06/06/1600x900/faintyoung_2_1686050565679_1686050565860.jpg", "publishedAt": "2023-06-06T11:32:50Z", "content": "After a brief period of calm, the Sun has sprung back into action. On June 4, an explosion on the surface of the Sun hurled a large cloud of coronal mass ejection (CME) into space. Unfortunately, it \u2026 [+2331 chars]", "image_url": "https://images.hindustantimes.com/tech/img/2023/06/06/1600x900/faintyoung_2_1686050565679_1686050565860.jpg"}] \ No newline at end of file diff --git a/Web Module/explore_fields/news/sports.json b/Web Module/explore_fields/news/sports.json new file mode 100644 index 000000000..7a4a05d19 --- /dev/null +++ b/Web Module/explore_fields/news/sports.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "The Guardian"}, "author": "Geoff Lemon", "title": "Reward or redemption? 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As Australia and India prepare to play off in London, the matchs plac\u2026 [+9953 chars]", "image_url": "https://i.guim.co.uk/img/media/5d515b9b53b5a83c8b76ee49aee6ed1a981d6b99/0_0_2500_1500/master/2500.jpg?width=1200&height=630&quality=85&auto=format&fit=crop&overlay-align=bottom%2Cleft&overlay-width=100p&overlay-base64=L2ltZy9zdGF0aWMvb3ZlcmxheXMvdGctZGVmYXVsdC5wbmc&enable=upscale&s=66bd8c43b00dda946bff1e16f29e52d3"}, {"source": {"id": null, "name": "NDTV News"}, "author": "Press Trust of India", "title": "\"His Experiences In England...\": How MS Dhoni Guided India Star Ahead Of WTC Final - NDTV Sports", "description": "The India star explained how MS Dhoni sharing his valuable tips could help him if he gets the team management's nod for the big game at The Oval from Wednesday.", "url": "https://sports.ndtv.com/cricket/his-experiences-in-england-how-ms-dhoni-guided-india-star-ahead-of-wtc-final-4096631", "urlToImage": "https://c.ndtvimg.com/2023-06/bmk9mf2o_dhoni_625x300_02_June_23.jpg?im=FitAndFill,algorithm=dnn,width=1200,height=675", "publishedAt": "2023-06-06T14:30:34Z", "content": "India wicketkeeper KS Bharat, likely to be the first-choice glovesman for the World Test Championship final against Australia, has said he has got a \"lot of insights\" about keeping in England from fo\u2026 [+2577 chars]", "image_url": "https://c.ndtvimg.com/2023-06/bmk9mf2o_dhoni_625x300_02_June_23.jpg?im=FitAndFill,algorithm=dnn,width=1200,height=675"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "HT Sports Desk", "title": "Svitolina accuses Sabalenka of 'inflaming' French Open crowd after being booed - Hindustan Times", "description": "Svitolina slammed Sabalenka for her \u201cwaiting at the net\u201d act and opined that it led the crowd to boo her off. | Tennis News", "url": "https://www.hindustantimes.com/sports/tennis/my-statement-was-clear-enough-elina-svitolina-accuses-aryna-sabalenka-of-inflaming-french-open-crowd-she-was-booed-off-101686059474843.html", "urlToImage": "https://www.hindustantimes.com/ht-img/img/2023/06/06/1600x900/TENNIS-FRENCHOPEN--200_1686059668757_1686059699994.JPG", "publishedAt": "2023-06-06T14:20:55Z", "content": "Elina Svitolina has been the story of 2023 French Open. Having missed her last four Grand Slams owing to the birth of her first child, Svitolina scripted a ravishing return to the sport and one her f\u2026 [+2659 chars]", "image_url": "https://www.hindustantimes.com/ht-img/img/2023/06/06/1600x900/TENNIS-FRENCHOPEN--200_1686059668757_1686059699994.JPG"}, {"source": {"id": null, "name": "Barca Blaugranes"}, "author": "Luis Mazariegos", "title": "Barcelona to send offer to Lionel Messi as Premier League, Saudi, MLS clubs put pressure on - Barca Blaugranes", "description": "The situation is up in the air", "url": "https://www.barcablaugranes.com/2023/6/6/23750855/barcelona-to-send-offer-to-lionel-messi-as-premier-league-saudi-mls-clubs-put-pressure-on", "urlToImage": "https://cdn.vox-cdn.com/thumbor/vh_V7Th9TAhhFNW9nhZebQVPnPU=/0x0:3553x1860/fit-in/1200x630/cdn.vox-cdn.com/uploads/chorus_asset/file/24705911/1232952828.jpg", "publishedAt": "2023-06-06T14:18:00Z", "content": "Lionel Messis father and agent, Jorge, will meet with FC Barcelonas representatives one more time to try to hammer out a deal to bring the Argentine star back to the Camp Nou. And its understood that\u2026 [+1939 chars]", "image_url": "https://cdn.vox-cdn.com/thumbor/vh_V7Th9TAhhFNW9nhZebQVPnPU=/0x0:3553x1860/fit-in/1200x630/cdn.vox-cdn.com/uploads/chorus_asset/file/24705911/1232952828.jpg"}, {"source": {"id": null, "name": "NDTV News"}, "author": "Press Trust of India", "title": "\"Have Not Said Ravichandran Ashwin Is...\": Rohit Sharma On India's Playing XI For WTC Final vs Austr.. - NDTV Sports", "description": "On team combination, Rohit Sharma did not reveal much and expectedly kept the suspense going till toss time", "url": "https://sports.ndtv.com/cricket/have-not-said-ravichandran-ashwin-is-rohit-sharma-on-indias-playing-xi-for-wtc-final-vs-australia-4099352", "urlToImage": "https://c.ndtvimg.com/2023-06/fds0f5qo_rohit-ashwin_625x300_06_June_23.jpg?im=FaceCrop,algorithm=dnn,width=1200,height=675", "publishedAt": "2023-06-06T13:42:20Z", "content": "On another nippy and overcast morning in London on Tuesday, Rohit was among the four squad members who turned up for optional practice. R Ashwin, Umesh and K S Bharat were the others who showed up in\u2026 [+3024 chars]", "image_url": "https://c.ndtvimg.com/2023-06/fds0f5qo_rohit-ashwin_625x300_06_June_23.jpg?im=FaceCrop,algorithm=dnn,width=1200,height=675"}, {"source": {"id": null, "name": "Barcauniversal.com"}, "author": "Connell Edgar-Jones", "title": "Transfers: Saudi eyeing Barcelona\u2019s \u20ac25 million-rated priority target - Barca Universal", "description": "Saudi Arabian clubs have become a significant obstacle for Barcelona ahead of the upcoming summer transfer window. Al-Hilal\u2019s pursuit of Lionel Messi has already been well-documented, even though the Argentine has remained steadfast in his desire to return to\u2026", "url": "https://barcauniversal.com/transfers-saudi-eyeing-barcelonas-e25-million-rated-priority-target/", "urlToImage": "https://barcauniversal.com/wp-content/uploads/2023/06/brighton-and-hove-albion-v-manchester-city-premier-league-1000x600.jpg", "publishedAt": "2023-06-06T13:09:19Z", "content": "Saudi Arabian clubs have become a significant obstacle for Barcelona ahead of the upcoming summer transfer window.\r\nAl-Hilals pursuit of Lionel Messi has already been well-documented, even though the\u2026 [+1794 chars]", "image_url": "https://barcauniversal.com/wp-content/uploads/2023/06/brighton-and-hove-albion-v-manchester-city-premier-league-1000x600.jpg"}, {"source": {"id": null, "name": "Sport Witness"}, "author": "Lucas Sposito", "title": "Manchester United player accused of 'domestic violence, threat and bodily injury' - Police report filed in Brazil - Sport Witness - Sport Witness", "description": "Manchester United star Antony has been accused of \u2018domestic violence, threat and bodily injury\u2019, according to reports from Brazil. Globo Esporte today reveals that Gabriela Cavallin, who had a relationship with the player, filed a police report in S\u00e3o Paulo o\u2026", "url": "http://sportwitness.co.uk/manchester-united-player-accused-domestic-violence-threat-bodily-injury-police-report-filed-brazil/", "urlToImage": "http://sportwitness.co.uk/wp-content/uploads/2019/11/Screen-Shot-2019-11-05-at-09.20.10.png", "publishedAt": "2023-06-06T12:36:46Z", "content": "Manchester United star Antony has been accused of \u2018domestic violence, threat and bodily injury\u2019, according to reports from Brazil.\r\nGlobo Esporte today reveals that Gabriela Cavallin, who had a relat\u2026 [+1492 chars]", "image_url": "http://sportwitness.co.uk/wp-content/uploads/2019/11/Screen-Shot-2019-11-05-at-09.20.10.png"}, {"source": {"id": null, "name": "Cricbuzz"}, "author": null, "title": "United Arab Emirates vs West Indies, 2nd ODI, West Indies tour of UAE, 2023 - Cricbuzz", "description": "Follow United Arab Emirates vs West Indies, 2nd ODI, Jun 06, West Indies tour of UAE, 2023 with live Cricket score, ball by ball commentary updates on Cricbuzz", "url": "https://www.cricbuzz.com/cricket-commentary/69934/uae-vs-wi-2nd-odi-west-indies-tour-of-uae-2023", "urlToImage": null, "publishedAt": "2023-06-06T12:30:51Z", "content": "20:18 Local Time, 16:18 GMT, 21:48 IST: A brilliant comeback from UAE after the onslaught from the WI openers. When King and Charles were going great guns, it seemed like 350 was on the cards. But th\u2026 [+910 chars]", "image_url": null}, {"source": {"id": null, "name": "Barca Blaugranes"}, "author": "Gill Clark", "title": "Vissel Kobe vs Barcelona, friendly: Final Score 0-2, Pablo Torre shines as Eric Garcia and Franck Kessie goal\u2026 - Barca Blaugranes", "description": "A win and a farewell for Andres Iniesta", "url": "https://www.barcablaugranes.com/2023/6/6/23750660/vissel-kobe-vs-barcelona-friendly-final-score-torre-eric-kessie", "urlToImage": "https://cdn.vox-cdn.com/thumbor/xGXPT5Dxwl5jNzIeXyV8NYR1MoY=/0x376:5254x3127/fit-in/1200x630/cdn.vox-cdn.com/uploads/chorus_asset/file/24705718/1258478501.jpg", "publishedAt": "2023-06-06T12:25:33Z", "content": "Barcelona signed off the 2022-23 season with a comfortable win over Vissel Kobe in Japan, a post-season friendly that will also earn the club a few million euros.\r\nXavi named plenty of youngsters and\u2026 [+2682 chars]", "image_url": "https://cdn.vox-cdn.com/thumbor/xGXPT5Dxwl5jNzIeXyV8NYR1MoY=/0x376:5254x3127/fit-in/1200x630/cdn.vox-cdn.com/uploads/chorus_asset/file/24705718/1258478501.jpg"}, {"source": {"id": null, "name": "Crictoday.com"}, "author": "CT Contributor", "title": "MS Dhoni could have continued to play for India if....: Former player makes a bold claim - Crictoday.com (Cricket News) ", "description": "MS Dhoni continues to perform three years after his retirement from international cricket. He is leading CSK.", "url": "http://crictoday.com/cricket/news/ms-dhoni-could-have-continued-to-play-for-india-if-former-player-makes-a-bold-claim/", "urlToImage": "https://crictoday.com/wp-content/uploads/2023/03/i.jpg", "publishedAt": "2023-06-06T12:22:38Z", "content": "MS Dhoni continues to perform three years after his retirement from international cricket. He has led Chennai Super Kings to two IPL titles since hanging his boots from the international arena.\r\nIn I\u2026 [+874 chars]", "image_url": "https://crictoday.com/wp-content/uploads/2023/03/i.jpg"}] \ No newline at end of file diff --git a/Web Module/explore_fields/news/technology.json b/Web Module/explore_fields/news/technology.json new file mode 100644 index 000000000..4e37400a5 --- /dev/null +++ b/Web Module/explore_fields/news/technology.json @@ -0,0 +1 @@ +[{"source": {"id": null, "name": "YouTube"}, "author": null, "title": "Samsung Has Launched Samsung Galaxy F54 5G Smartphone | Business News | ET Now - ET NOW", "description": "With an aim to further strong hold the smartphone segment, Samsung has launched Samsung Galaxy F54 5G smartphone in the affordable premium smartphone categor...", "url": "https://www.youtube.com/watch?v=JkgSQ3oNbS0", "urlToImage": "https://i.ytimg.com/vi/JkgSQ3oNbS0/maxresdefault.jpg", "publishedAt": "2023-06-06T14:14:29Z", "content": null, "image_url": "https://i.ytimg.com/vi/JkgSQ3oNbS0/maxresdefault.jpg"}, {"source": {"id": "techcrunch", "name": "TechCrunch"}, "author": "Christine Hall", "title": "Apple WWDC 2023: Everything announced from the Apple Vision Pro to iOS 17, MacBook Air and more - TechCrunch", "description": "The TechCrunch team was on the ground in Cupertino for Apple's WWDC 2023 keynote. Here are the biggest announcements in an easy-to-skim digest.", "url": "https://techcrunch.com/2023/06/06/apple-wwdc-2023-everything-announced-so-far-including-ios-17-macbook-air-and-more/", "urlToImage": "https://techcrunch.com/wp-content/uploads/2023/06/wwdc-2023-cook-rainbow-1.jpg?resize=1200,675", "publishedAt": "2023-06-06T14:03:45Z", "content": "Yesterday was Apple\u2019s WWDC 2023 keynote. Each year Apple kicks off its Worldwide Developer Conference with a few hours of just straight announcements, generally covering things from OS to watchOS to \u2026 [+10312 chars]", "image_url": "https://techcrunch.com/wp-content/uploads/2023/06/wwdc-2023-cook-rainbow-1.jpg?resize=1200,675"}, {"source": {"id": "the-washington-post", "name": "The Washington Post"}, "author": "Jennifer Hassan", "title": "Apple says iOS 17 adjusts auto-correct, knows you don't mean 'duck' - The Washington Post", "description": "Apple announced the iPhone autocorrect adjustment at the Worldwide Developers Conference, where it also unveiled its mixed reality headset Vision Pro.", "url": "https://www.washingtonpost.com/technology/2023/06/06/apple-duck-autocorrect-update-ios17/", "urlToImage": "https://www.washingtonpost.com/wp-apps/imrs.php?src=https://arc-anglerfish-washpost-prod-washpost.s3.amazonaws.com/public/Y7WE4ELGJVVEIAAACDE7XXABVU.jpg&w=1440", "publishedAt": "2023-06-06T12:55:13Z", "content": "Comment on this story\r\nComment\r\nApples adjusted auto-correct function will fix an issue that has long irked iPhone users who, when typing out a common curse word, have had it immediately replaced wit\u2026 [+2095 chars]", "image_url": "https://www.washingtonpost.com/wp-apps/imrs.php?src=https://arc-anglerfish-washpost-prod-washpost.s3.amazonaws.com/public/Y7WE4ELGJVVEIAAACDE7XXABVU.jpg&w=1440"}, {"source": {"id": null, "name": "BBC News"}, "author": "https://www.facebook.com/bbcnews", "title": "Vision Pro: Apple's new augmented reality headset unveiled - BBC", "description": "The high price and two-hour battery life raise questions about how popular the new device will be.", "url": "https://www.bbc.com/news/technology-65809408", "urlToImage": "https://ichef.bbci.co.uk/news/1024/branded_news/15E1E/production/_130003698_apple_vision_pro.jpg", "publishedAt": "2023-06-06T11:31:47Z", "content": "Apple has unveiled a much-anticipated augmented reality headset, Apple Vision Pro, in its first major hardware launch for almost a decade.\r\nApple chief executive Tim Cook said the new headset \"seamle\u2026 [+5167 chars]", "image_url": "https://ichef.bbci.co.uk/news/1024/branded_news/15E1E/production/_130003698_apple_vision_pro.jpg"}, {"source": {"id": null, "name": "Jagran.com"}, "author": "Ashish Singh", "title": "Now Sign In To Your Google Workspace Accounts Without Using Your Passwords; Here's How - Jagran English", "description": "Google CEO Sundar Pichai announced that the company has started rolling out Passkeys using which the users can sign in to their Workspace accounts without passwords", "url": "https://english.jagran.com/technology/now-sign-in-to-your-google-workspace-accounts-without-passwords-using-your-passkeys-heres-how-10081402", "urlToImage": "https://imgeng.jagran.com/images/2023/jun/Google Passkeys1686050362122.jpg", "publishedAt": "2023-06-06T11:13:00Z", "content": "Google is one of the pioneers in updating safety measures in order to protect individual data from phishing attacks. In a new update, Google has introduced a much safer alternative to passwords, dubb\u2026 [+2353 chars]", "image_url": "https://imgeng.jagran.com/images/2023/jun/Google Passkeys1686050362122.jpg"}, {"source": {"id": null, "name": "Jagran.com"}, "author": "Ashish Singh", "title": "OnePlus Fold Will Make Its Global Debut In First Half Of August, Says Report - Jagran English", "description": "OnePlus Fold which was announced at the Cloud 11 event is likely to make a global debut in first half of August in an event that will take place in New York says a report", "url": "https://english.jagran.com/technology/oneplus-fold-will-make-its-global-debut-in-first-half-of-august-says-report-10081390", "urlToImage": "https://imgeng.jagran.com/images/2023/jun/OnePlus Fold1686048172425.jpg", "publishedAt": "2023-06-06T10:37:00Z", "content": "OnePlus, which has made headlines after announcing that it is working on its first foldable smartphone at the Cloud 11 event, is already good news for tech enthusiasts. In a new update, the company i\u2026 [+1801 chars]", "image_url": "https://imgeng.jagran.com/images/2023/jun/OnePlus Fold1686048172425.jpg"}, {"source": {"id": null, "name": "Hindustan Times"}, "author": "HT News Desk", "title": "Apple WWDC 2023: Price cut announced for this Macbook Air. Check new rate here - Hindustan Times", "description": "The company has also unveiled the latest model of its Macbook Air laptops, the 15-inch Macbook Air.", "url": "https://www.hindustantimes.com/technology/13-inch-macbook-air-price-drop-alert-apple-worldwide-developers-conference-2023-101686041870666.html", "urlToImage": "https://www.hindustantimes.com/ht-img/img/2023/06/06/1600x900/MB_1686042042201_1686042047771.jpg", "publishedAt": "2023-06-06T09:37:47Z", "content": "On Monday, the opening day of its 2023 Worldwide Developers Conference (WWDC), Apple unveiled the 15-inch Macbook Air. Simultaneously, the tech giant announced a price cut for the existing 13-inch Ma\u2026 [+928 chars]", "image_url": "https://www.hindustantimes.com/ht-img/img/2023/06/06/1600x900/MB_1686042042201_1686042047771.jpg"}, {"source": {"id": null, "name": "gizmochina"}, "author": "gizmochina", "title": "New OnePlus Pad update brings new features & improvements to stylus, fixes Photos bug - gizmochina", "description": null, "url": "https://www.gizmochina.com/2023/06/06/oneplus-pad-new-update/", "urlToImage": null, "publishedAt": "2023-06-06T08:29:46Z", "content": null, "image_url": null}, {"source": {"id": null, "name": "The Indian Express"}, "author": "The Indian Express", "title": "Motorola Razr 40, Razr 40 Ultra India launch confirmed - The Indian Express", "description": null, "url": "https://indianexpress.com/article/technology/tech-news-technology/motorola-razr-40-ultra-india-launch-features-price-8648249/", "urlToImage": null, "publishedAt": "2023-06-06T08:05:29Z", "content": null, "image_url": null}, {"source": {"id": null, "name": "GSMArena.com"}, "author": "Michail", "title": "Apple iPhone X and older iPhones are not getting iOS 17 - GSMArena.com news - GSMArena.com", "description": "It\u2019s the end of the road for the iPhone X, iPhone 8 and 8 Plus. Apple announced iOS 17 yesterday with several noteworthy features including StandBy mode,...", "url": "https://www.gsmarena.com/iphone_x_and_older_iphones_not_getting_ios_17-news-58786.php", "urlToImage": "https://fdn.gsmarena.com/imgroot/news/23/06/ios-17-cuts-support-iphone-x/-952x498w6/gsmarena_000.jpg", "publishedAt": "2023-06-06T08:01:01Z", "content": "Apple announced iOS 17 yesterday with several noteworthy features including StandBy mode, NameDrop and Live Voicemail. As is the case with new iOS versions, several older model iPhones are left unsup\u2026 [+584 chars]", "image_url": "https://fdn.gsmarena.com/imgroot/news/23/06/ios-17-cuts-support-iphone-x/-952x498w6/gsmarena_000.jpg"}] \ No newline at end of file diff --git a/Web Module/explore_fields/static/images/bg-new.png b/Web Module/explore_fields/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/explore_fields/static/images/bg-new.png differ diff --git a/Web Module/explore_fields/templates/news_list.html b/Web Module/explore_fields/templates/news_list.html new file mode 100644 index 000000000..2f32182c1 --- /dev/null +++ b/Web Module/explore_fields/templates/news_list.html @@ -0,0 +1,212 @@ +{% load static %} + + + + Learners'Ed - Explore Fields + + + + + + + + + {% include 'navbar.html' %} +

      Explore Fields

      + {% for category, articles in articles_by_category.items %} +
      +

      {{ category }}

      +
      + {% for article in articles %} +
      + {{ article.title }} +

      {{ article.title }}

      +

      {{ article.description }}

      + Read More +
      + {% endfor %} +
      +
      +
      + {% endfor %} + + diff --git a/Web Module/explore_fields/tests.py b/Web Module/explore_fields/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/explore_fields/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/explore_fields/views.py b/Web Module/explore_fields/views.py new file mode 100644 index 000000000..ab9d8fa48 --- /dev/null +++ b/Web Module/explore_fields/views.py @@ -0,0 +1,47 @@ +from django.shortcuts import render +import json +import os +import requests + +def explore_fields(request): + newsapi_key = 'ea2b2ad73b844acf9b29b58c9fd2646f' # Replace with your NewsAPI API key + categories = ['technology', 'general', 'business', 'entertainment', 'health', 'science', 'sports'] + articles_by_category = {} + + for category in categories: + url = f'https://newsapi.org/v2/top-headlines?country=in&category={category}&apiKey={newsapi_key}' + response = requests.get(url) + data = response.json() + + if response.status_code == 200: + articles = data.get('articles', []) + for article in articles: + image_url = article.get('urlToImage') + article['image_url'] = image_url + articles_by_category[category] = articles[:10] # Fetch the top 10 articles for each category + else: + # If API result is not available, try fetching data from saved file + file_path = f'D:/Project/Website-Phoenix13/LearnersEd/explore_fields/news/{category}.json' + if os.path.exists(file_path): + file_size = os.path.getsize(file_path) + if file_size > 0: + with open(file_path, 'r') as file: + articles = json.load(file) + articles_by_category[category] = articles + else: + error_message = 'Error Fetch: Not able to fetch articles' + context = {'error_message': error_message} + return render(request, 'error.html', context) + else: + error_message = data.get('message', 'Error File: File Not Found') + context = {'error_message': error_message} + return render(request, 'error.html', context) + + # Save the fetched data to files + for category, articles in articles_by_category.items(): + file_path = f'D:/Project/Website-Phoenix13/LearnersEd/explore_fields/news/{category}.json' + with open(file_path, 'w') as file: + json.dump(articles, file) + + context = {'articles_by_category': articles_by_category} + return render(request, 'news_list.html', context) diff --git a/Web Module/gamify_app/__init__.py b/Web Module/gamify_app/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/gamify_app/__pycache__/__init__.cpython-310.pyc b/Web Module/gamify_app/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..a391b0626 Binary files /dev/null and b/Web Module/gamify_app/__pycache__/__init__.cpython-310.pyc differ diff --git a/Web Module/gamify_app/__pycache__/__init__.cpython-39.pyc b/Web Module/gamify_app/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..e094d0738 Binary files /dev/null and b/Web 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index 000000000..c66ab5b11 Binary files /dev/null and b/Web Module/gamify_app/__pycache__/apps.cpython-39.pyc differ diff --git a/Web Module/gamify_app/__pycache__/models.cpython-310.pyc b/Web Module/gamify_app/__pycache__/models.cpython-310.pyc new file mode 100644 index 000000000..9f267d4ba Binary files /dev/null and b/Web Module/gamify_app/__pycache__/models.cpython-310.pyc differ diff --git a/Web Module/gamify_app/__pycache__/models.cpython-39.pyc b/Web Module/gamify_app/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..f9249da29 Binary files /dev/null and b/Web Module/gamify_app/__pycache__/models.cpython-39.pyc differ diff --git a/Web Module/gamify_app/__pycache__/views.cpython-310.pyc b/Web Module/gamify_app/__pycache__/views.cpython-310.pyc new file mode 100644 index 000000000..9b5dfef96 Binary files /dev/null and b/Web Module/gamify_app/__pycache__/views.cpython-310.pyc differ diff --git a/Web Module/gamify_app/__pycache__/views.cpython-39.pyc b/Web Module/gamify_app/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..67c9b3ed2 Binary files /dev/null and b/Web Module/gamify_app/__pycache__/views.cpython-39.pyc differ diff --git a/Web Module/gamify_app/admin.py b/Web Module/gamify_app/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Web Module/gamify_app/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/gamify_app/apps.py b/Web Module/gamify_app/apps.py new file mode 100644 index 000000000..a3cf090e5 --- /dev/null +++ b/Web Module/gamify_app/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class GamifyAppConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'gamify_app' diff --git a/Web Module/gamify_app/migrations/__init__.py b/Web Module/gamify_app/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/gamify_app/migrations/__pycache__/__init__.cpython-310.pyc b/Web Module/gamify_app/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..d636f35f0 Binary files /dev/null and b/Web Module/gamify_app/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/Web Module/gamify_app/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/gamify_app/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..dc8a7ae8f Binary files /dev/null and b/Web Module/gamify_app/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/gamify_app/models.py b/Web Module/gamify_app/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/Web Module/gamify_app/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Web Module/gamify_app/static/gamify.css b/Web Module/gamify_app/static/gamify.css new file mode 100644 index 000000000..e70e4d9f1 --- /dev/null +++ b/Web Module/gamify_app/static/gamify.css @@ -0,0 +1,234 @@ +body, html { + margin: 0; + padding: 0; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + font-family: 'Darker Grotesque', sans-serif; + height: 100vh; + overflow-y: hidden; +} + +#coin { + background-image: url(./images/coins.png); + background-size: contain; + background-position: center; + background-repeat: no-repeat; + filter: drop-shadow(0 0 1rem gold); + animation: pulsate 6s infinite; +} + +@keyframes pulsate { + 0% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } + 30% { + transform: rotateY(0deg) scale(1.1); + filter: drop-shadow(0 0 1rem gold); + } + 100% { + transform: rotateY(360deg) scale(1); + filter: drop-shadow(0 0 0.5rem gold); + } +} + + +.il { + padding: 0 20px; + padding-top: 20px; + padding-bottom: 10px; + border-radius: 40px; + transition-duration: 200ms; + outline: 2px solid black; + background-color: white; +} + + +#whole { + + /* padding: 20px; */ + text-align: center; + display: flex; + flex-direction: column; + gap:30px; + + } + .heading{ + background-color: white; + margin-top: 100px; + border-top: 2px solid black; + border-bottom: 2px solid black; + /* padding: 30px; */ + height:90px; + } + + #title { + color: #9376E0; + font-size: 60px; + margin: 0; + text-transform: uppercase; + letter-spacing: 10px; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + + } + .box { + display: flex; + padding: 15px; + gap:15px; + } + + .left-section { + flex: 7; + display: flex; + flex-direction: column; + gap:15px; + } + + .twin_flexboxes{ + display: flex; + flex-direction: row; + gap:15px; + width: 85%; + padding-left: 30px; + } + + .right-section { + flex: 3; + } + + .flexbox { + flex: 1; + + outline: 3px solid black; + cursor: pointer; + border-radius: 5px; + + padding: 20px; + height: 200px; + width: 200px; + } + .flexbox:hover{ + box-shadow:2px 5px black; + transition-duration: 200ms; + } + #flexbox1 { + display: flex; + background-image: url('images/quiz.png'); + /* animation: gradientAnimation 5s infinite ease-in-out; */ + /* background-size: 200% 200%; */ + /* transition-duration: 100ms; */ + background-position: center; + background-size: cover; + background-repeat: no-repeat; + flex-direction: column; + justify-content: center; + align-items: center; + gap:10px; + font-size: 30px; + } + + .quiz-heading{ + text-transform: uppercase; + letter-spacing: 2px; + outline: 4px solid #9376E0; + border:2px white; + padding:5px; + width: 40%; + font-size: 20px; + border-radius:5px; + background-color: black; + color:white; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + + + } + .start-button { + padding: 10px 20px; + font-size: 20px; + background-color: #9376E0; + color: #fff; + border: none; + border-radius: 5px; + cursor: pointer; + } + + .start-button:hover { + background-color: blueviolet; + } + #flexbox2{ + display: flex; + background-image: url('images/virtualpet.png'); + background-position: center; + background-size: cover; + background-repeat: no-repeat; + flex-direction: column; + margin: 30px; + gap:10px; + font-size: 30px; + } + + .large-flexbox { + flex: 1; + box-shadow: 0 0 20px 2px gray; + padding: 20px; + height:450px; + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 40px; + } + .horizontal-flexbox { + display: flex; + height: 200px; +} + +#pet-graph { + border-radius: 30px; + box-shadow: inset 0px 0px 5px 0px rgba(0,0,0,0.75); + border-top-right-radius: 40px; + border-bottom-right-radius: 40px; +} + +.bottom-flexbox { + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + border-radius: 40px; + transition-duration: 200ms; + outline: 4px solid white; + width:90%; + height: 85%; + box-shadow: 0 0 20px 2px gray; + margin-left: auto; + margin-right: auto; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Web Module/gamify_app/static/images/bg-new.png b/Web Module/gamify_app/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/gamify_app/static/images/bg-new.png differ diff --git a/Web Module/gamify_app/static/images/quiz.png b/Web Module/gamify_app/static/images/quiz.png new file mode 100644 index 000000000..7f5212888 Binary files /dev/null and b/Web Module/gamify_app/static/images/quiz.png differ diff --git a/Web Module/gamify_app/static/images/virtualpet.png b/Web Module/gamify_app/static/images/virtualpet.png new file mode 100644 index 000000000..f35909163 Binary files /dev/null and b/Web Module/gamify_app/static/images/virtualpet.png differ diff --git a/Web Module/gamify_app/static/jquery.counterup.min.js b/Web Module/gamify_app/static/jquery.counterup.min.js new file mode 100644 index 000000000..cddf5a103 --- /dev/null +++ b/Web Module/gamify_app/static/jquery.counterup.min.js @@ -0,0 +1,8 @@ +/*! +* jquery.counterup.js 1.0 +* +* Copyright 2013, Benjamin Intal http://gambit.ph @bfintal +* Released under the GPL v2 License +* +* Date: Nov 26, 2013 +*/(function(e){"use strict";e.fn.counterUp=function(t){var n=e.extend({time:400,delay:10},t);return this.each(function(){var t=e(this),r=n,i=function(){var e=[],n=r.time/r.delay,i=t.text(),s=/[0-9]+,[0-9]+/.test(i);i=i.replace(/,/g,"");var o=/^[0-9]+$/.test(i),u=/^[0-9]+\.[0-9]+$/.test(i),a=u?(i.split(".")[1]||[]).length:0;for(var f=n;f>=1;f--){var l=parseInt(i/n*f);u&&(l=parseFloat(i/n*f).toFixed(a));if(s)while(/(\d+)(\d{3})/.test(l.toString()))l=l.toString().replace(/(\d+)(\d{3})/,"$1,$2");e.unshift(l)}t.data("counterup-nums",e);t.text("0");var c=function(){t.text(t.data("counterup-nums").shift());if(t.data("counterup-nums").length)setTimeout(t.data("counterup-func"),r.delay);else{delete t.data("counterup-nums");t.data("counterup-nums",null);t.data("counterup-func",null)}};t.data("counterup-func",c);setTimeout(t.data("counterup-func"),r.delay)};t.waypoint(i,{offset:"100%",triggerOnce:!0})})}})(jQuery); \ No newline at end of file diff --git a/Web Module/gamify_app/templates/gamify.html b/Web Module/gamify_app/templates/gamify.html new file mode 100644 index 000000000..6b2a5fbe0 --- /dev/null +++ b/Web Module/gamify_app/templates/gamify.html @@ -0,0 +1,117 @@ +{% load static %} + + + + + + Gamification + + + + + + + + + + + + + + {% include 'navbar.html' %} +
      +
      +

      Gamify

      +
      +
      +
      +
      +
      +

      QUIZ

      +
      + +
      +

      VIRTUAL PET

      +
      +
      +
      +
      +

      Learners'Ed Coins
      *For new students, bonus 200 coins will be granted!

      +
      +
      +
      +
      +

      200

      +

      Learners'Ed Coins
      Left

      +
      +
      +
      +
      +
      +
      +
      +
      +
      +
      +

      Your Virtual Pet

      +

      Alex Pandit

      +
      +
      +
      +

      Level

      +

      3

      +
      +
      +

      Rank

      +

      94th

      +
      +
      +
      +
      +
      + +
      +
      +
      +
      +
      + + + + + \ No newline at end of file diff --git a/Web Module/gamify_app/tests.py b/Web Module/gamify_app/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/gamify_app/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/gamify_app/views.py b/Web Module/gamify_app/views.py new file mode 100644 index 000000000..450dc9eea --- /dev/null +++ b/Web Module/gamify_app/views.py @@ -0,0 +1,4 @@ +from django.shortcuts import render + +def gamify(request): + return render(request, 'gamify.html', {}) \ No newline at end of file diff --git a/Web Module/lecture_attend/__init__.py b/Web Module/lecture_attend/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/lecture_attend/__pycache__/__init__.cpython-39.pyc b/Web Module/lecture_attend/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..bd404eaf1 Binary files /dev/null and b/Web Module/lecture_attend/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/lecture_attend/__pycache__/admin.cpython-39.pyc b/Web Module/lecture_attend/__pycache__/admin.cpython-39.pyc new file mode 100644 index 000000000..9e83fe53d Binary files /dev/null and b/Web Module/lecture_attend/__pycache__/admin.cpython-39.pyc differ diff --git a/Web Module/lecture_attend/__pycache__/apps.cpython-39.pyc b/Web Module/lecture_attend/__pycache__/apps.cpython-39.pyc new file mode 100644 index 000000000..f1a5743c0 Binary files /dev/null and b/Web Module/lecture_attend/__pycache__/apps.cpython-39.pyc differ diff --git a/Web Module/lecture_attend/__pycache__/models.cpython-39.pyc b/Web Module/lecture_attend/__pycache__/models.cpython-39.pyc new file mode 100644 index 000000000..9bbb2449c Binary files /dev/null and b/Web Module/lecture_attend/__pycache__/models.cpython-39.pyc differ diff --git a/Web Module/lecture_attend/__pycache__/views.cpython-39.pyc b/Web Module/lecture_attend/__pycache__/views.cpython-39.pyc new file mode 100644 index 000000000..0715ea2e2 Binary files /dev/null and b/Web Module/lecture_attend/__pycache__/views.cpython-39.pyc differ diff --git a/Web Module/lecture_attend/admin.py b/Web Module/lecture_attend/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Web Module/lecture_attend/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/lecture_attend/apps.py b/Web Module/lecture_attend/apps.py new file mode 100644 index 000000000..73b0ffc4c --- /dev/null +++ b/Web Module/lecture_attend/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class LectureAttendConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'lecture_attend' diff --git a/Web Module/lecture_attend/migrations/__init__.py b/Web Module/lecture_attend/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/lecture_attend/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/lecture_attend/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..194d484cc Binary files /dev/null and b/Web Module/lecture_attend/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/lecture_attend/model/drowsiness_detection.h5 b/Web Module/lecture_attend/model/drowsiness_detection.h5 new file mode 100644 index 000000000..7b89ae3e4 Binary files /dev/null and b/Web Module/lecture_attend/model/drowsiness_detection.h5 differ diff --git a/Web Module/lecture_attend/models.py b/Web Module/lecture_attend/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/Web Module/lecture_attend/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Web Module/lecture_attend/static/images/bg-new.png b/Web Module/lecture_attend/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/lecture_attend/static/images/bg-new.png differ diff --git a/Web Module/lecture_attend/static/lectures.css b/Web Module/lecture_attend/static/lectures.css new file mode 100644 index 000000000..480c0dd41 --- /dev/null +++ b/Web Module/lecture_attend/static/lectures.css @@ -0,0 +1,73 @@ +body, html { + margin: 0; + padding: 0; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + +} +#whole { + + text-align: center; + display: flex; + flex-direction: column; + gap:30px; + + } + .heading{ + background-color: white; + margin-top: 110px; + /* padding: 30px; */ + height:90px; + } + + #title { + color: #9376E0; + font-size: 60px; + margin: 0; + text-transform: uppercase; + letter-spacing: 10px; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + + } + .main { + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + gap:10px; + } + + .box { + width: 900px; + background-color: aliceblue; + + } + + .box1 { + height: 350px; + } + + .box2, + .box3 { + height: 30px; +} +.progress-bar { + background-color: #e6e6e6; + border-radius: 5px; + height:30px; + overflow: hidden; + } + + .progress-bar span { + display: block; + height: 100%; + background-color: #9376E0; + } \ No newline at end of file diff --git a/Web Module/lecture_attend/static/script-lec.js b/Web Module/lecture_attend/static/script-lec.js new file mode 100644 index 000000000..eea1aacf2 --- /dev/null +++ b/Web Module/lecture_attend/static/script-lec.js @@ -0,0 +1,118 @@ +const videoElement = document.getElementById('videoElement'); +const pred = document.getElementById('pred'); + +let stream; +let k = 0; +let csi; // Interval variable + +function startCamera() { + navigator.mediaDevices.getUserMedia({ video: true }) + .then(videoStream => { + stream = videoStream; + videoElement.srcObject = stream; + csi = setInterval(captureFrame, 2000); // Capture frame every 5 seconds + }) + .catch(error => { + console.error('Error accessing camera:', error); + }); +} + +function getRandomInt(max) { + return Math.floor(Math.random() * max); +} + +var excercise = ['Neck Rolls: Roll your head in a circular motion, clockwise and counterclockwise.', + 'Shoulder Shrugs: Lift your shoulders up toward your ears and then release them down.', + 'Seated Twist: Twist your torso to the left and right, looking over each shoulder.', + 'Seated Leg Raises: Lift one leg up, extend it straight, and hold for a few seconds. Repeat with the other leg.', + 'Ankle Circles: Rotate your ankles in a circular motion, clockwise and counterclockwise.', + 'Arm Circles: Extend your arms to the sides and make small circles with your hands.', + 'Wrist Flexes: Stretch your arms forward, palms facing up, and flex your wrists up and down.', + 'Seated Toe Taps: Lift your feet off the ground and tap your toes while keeping your heels grounded.', + 'Seated Marching: Lift one knee up, then the other, as if you were marching in place.', + 'Seated Heel Raises: Lift your heels off the floor while keeping your toes grounded.', + 'Seated Calf Stretches: Extend one leg forward, flex your foot, and gently pull your toes towards you. Repeat with the other leg.', + 'Seated Chest Stretch: Clasp your hands behind your back, straighten your arms, and lift your chest.', + 'Seated Spinal Twist: Cross one leg over the other, place your opposite hand on the outside of the crossed knee, and gently twist your torso.', + 'Seated Side Bends: Extend one arm overhead and lean to the opposite side, stretching your side. Repeat on the other side.', + 'Seated Hip Circles: Sit forward on the edge of your chair and make circular motions with your hips.', + 'Seated Cat-Cow Stretch: Arch your back and then round it, alternating between the two positions.', + 'Seated Abdominal Contractions: Tighten your abdominal muscles by pulling your belly button towards your spine.', + 'Seated Shoulder Stretch: Reach one arm across your chest, hold it with the opposite hand, and gently pull it closer to your body. Repeat with the other arm.', + 'Seated Butterfly Stretch: Bring the soles of your feet together and gently press your knees towards the floor.', + 'Seated Hamstring Stretch: Extend one leg forward, flex your foot, and reach towards your toes. Repeat with the other leg.', + 'Seated Wrist Stretches: Extend one arm forward, palm facing up, and use the other hand to gently pull your fingers back. Repeat with the other hand.', + 'Seated Hand Squeezes: Make a fist with both hands, squeeze tightly, and then release.', + 'Seated Eye Rolls: Look up, down, left, and right, and then make circular motions with your eyes.', + 'Seated Deep Breathing: Inhale deeply through your nose, hold for a few seconds, and exhale slowly through your mouth.', + 'Seated Meditation: Close your eyes, focus on your breath, and allow your mind to relax.', + 'Seated Shoulder Blade Squeezes: Squeeze your shoulder blades together, hold for a few seconds, and then release.', + 'Seated Leg Crosses: Cross one leg over the other, place your ankle on the opposite knee, and gently press down on the raised knee.', + 'Seated Side Leg Raises: Sit upright, extend one leg out to the side, and lift it up while keeping it straight. Repeat with the other leg.', + 'Seated Torso Stretch: Reach both arms overhead]']; + +function captureFrame() { + // Create a canvas element + const canvas = document.createElement('canvas'); + canvas.width = videoElement.videoWidth; + canvas.height = videoElement.videoHeight; + + // Draw the current frame from the video element onto the canvas + const context = canvas.getContext('2d'); + context.drawImage(videoElement, 0, 0, canvas.width, canvas.height); + + // Get the base64-encoded image data from the canvas + const imageData = canvas.toDataURL('image/jpeg'); + + quiz_data = { + image_data: imageData, + }; + + // Send the image data as a POST request to the Django backend using AJAX + $.ajax({ + url: '', + type: 'POST', + data: JSON.stringify(quiz_data), + contentType: 'application/json', + success: function (response) { + console.log(response.message); + // Handle the response from the Django backend + const prediction = response.prediction; // Assuming the prediction is returned as a property in the response + if (prediction == "Yes") { + pred.innerHTML = "Drowsiness Detected"; + clearInterval(csi); // Halt the interval + alert(excercise[getRandomInt(excercise.length)]); + setTimeout(() => { + pred.innerHTML = ""; // Clear the alert message after it is clicked + csi = setInterval(captureFrame, 2000); // Resume the interval + }, 5000); // Adjust the duration of the alert as needed + } else { + pred.innerHTML = "All Ok!"; + } + console.log('Response from Django:', prediction); + }, + error: function (xhr, status, error) { + console.error('Error sending image to Django:', error); + } + }); + console.log(k); + if (k == 1) { + clearInterval(csi); + stopCamera(); + } +} + +function stopCamera() { + if (stream && stream.getTracks) { + stream.getTracks().forEach(track => track.stop()); + stream = null; + } +} + +startCamera(); + +var timer = setTimeout(() => { + k = 1; + console.log("Finish"); + window.location.href = "/lectures/"; +}, 60000); diff --git a/Web Module/lecture_attend/templates/lectures-attend.html b/Web Module/lecture_attend/templates/lectures-attend.html new file mode 100644 index 000000000..292c5278d --- /dev/null +++ b/Web Module/lecture_attend/templates/lectures-attend.html @@ -0,0 +1,36 @@ +{% load static %} + + + + + + Gamification + + + + + + + + + + + + {% include 'navbar.html' %} +
      +
      +

      LECTURE

      +
      +
      +
      +
      + +
      +
      +
      +
      + + + + + \ No newline at end of file diff --git a/Web Module/lecture_attend/tests.py b/Web Module/lecture_attend/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/lecture_attend/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/lecture_attend/views.py b/Web Module/lecture_attend/views.py new file mode 100644 index 000000000..752fa4d43 --- /dev/null +++ b/Web Module/lecture_attend/views.py @@ -0,0 +1,96 @@ +from django.shortcuts import render +from django.http import JsonResponse +from django.views.decorators.csrf import csrf_exempt +from PIL import Image +from io import BytesIO +import base64 +import json +import face_recognition +from tensorflow import keras +import cv2 +import numpy as np + +eye_model = keras.models.load_model(r'D:\Project\Website-Phoenix13\LearnersEd\lecture_attend\model\drowsiness_detection.h5') + + +def eye_cropper(frame): + frame_np = np.array(frame) # Convert PIL image to NumPy array + frame_np = cv2.cvtColor(frame_np, cv2.COLOR_RGB2BGR) # Convert RGB to BGR (if necessary) + + gray = cv2.cvtColor(frame_np, cv2.COLOR_BGR2GRAY) + facial_features_list = face_recognition.face_landmarks(gray) + + try: + eye = facial_features_list[0]['left_eye'] + except: + try: + eye = facial_features_list[0]['right_eye'] + except: + return None + + x_max = max([coordinate[0] for coordinate in eye]) + x_min = min([coordinate[0] for coordinate in eye]) + y_max = max([coordinate[1] for coordinate in eye]) + y_min = min([coordinate[1] for coordinate in eye]) + + x_range = x_max - x_min + y_range = y_max - y_min + + if x_range > y_range: + right = round(.5 * x_range) + x_max + left = x_min - round(.5 * x_range) + bottom = round((((right - left) - y_range)) / 2) + y_max + top = y_min - round((((right - left) - y_range)) / 2) + else: + bottom = round(.5 * y_range) + y_max + top = y_min - round(.5 * y_range) + right = round((((bottom - top) - x_range)) / 2) + x_max + left = x_min - round((((bottom - top) - x_range)) / 2) + + cropped = frame_np[top:(bottom + 1), left:(right + 1)] + + cropped = cv2.resize(cropped, (80, 80)) + image_for_prediction = cropped.reshape(-1, 80, 80, 3) + + return image_for_prediction + + +def predict(frame): + image_for_prediction = eye_cropper(frame) + if image_for_prediction is None: + return 'Yes' + try: + image_for_prediction = image_for_prediction/255.0 + except: + print("Error") + + prediction = eye_model.predict(image_for_prediction) + print(prediction) + # Based on prediction, display either "Open Eyes" or "Closed Eyes" + if prediction < 0.7: + status = 'No' + else: + status = 'Yes' + return status + +@csrf_exempt +def lecture_attend(request): + if request.method == 'POST': + json_data = json.loads(request.body) + image_data = json_data.get('image_data') + if image_data: + # Convert the base64-encoded image data to PIL Image + image = Image.open(BytesIO(base64.b64decode(image_data.split(',')[1]))) + + # Perform any processing or DL model prediction with the image + prediction_result = predict(image) + + # Replace this dummy response with the actual response you want to send back + response = {'prediction': prediction_result} + + return JsonResponse(response) + else: + # No image data received + response = {'error': 'No image data received'} + return JsonResponse(response, status=400) + return render(request, 'lectures-attend.html', {}) diff --git a/Web Module/lectures/__init__.py b/Web Module/lectures/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/lectures/__pycache__/__init__.cpython-39.pyc b/Web Module/lectures/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..a910a6731 Binary files /dev/null and b/Web Module/lectures/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/lectures/__pycache__/admin.cpython-39.pyc b/Web Module/lectures/__pycache__/admin.cpython-39.pyc new file mode 100644 index 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000000000..8c38f3f3d --- /dev/null +++ b/Web Module/lectures/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/lectures/apps.py b/Web Module/lectures/apps.py new file mode 100644 index 000000000..30d62f284 --- /dev/null +++ b/Web Module/lectures/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class LecturesConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'lectures' diff --git a/Web Module/lectures/migrations/__init__.py b/Web Module/lectures/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/lectures/migrations/__pycache__/__init__.cpython-39.pyc b/Web Module/lectures/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..f91b9bf4e Binary files /dev/null and b/Web Module/lectures/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/Web Module/lectures/models.py b/Web Module/lectures/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/Web Module/lectures/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Web Module/lectures/static/images/bg-new.png b/Web Module/lectures/static/images/bg-new.png new file mode 100644 index 000000000..f92740138 Binary files /dev/null and b/Web Module/lectures/static/images/bg-new.png differ diff --git a/Web Module/lectures/static/script-lectures.js b/Web Module/lectures/static/script-lectures.js new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/lectures/static/styles-lectures.css b/Web Module/lectures/static/styles-lectures.css new file mode 100644 index 000000000..899863a62 --- /dev/null +++ b/Web Module/lectures/static/styles-lectures.css @@ -0,0 +1,67 @@ +body { + background-image: url(./images/bg-new.png); + background-position: center; + background-repeat: no-repeat; + background-size: cover; + background-attachment: fixed; + font-family: 'Darker Grotesque', sans-serif; +} + +#play>button { + box-shadow: 0 0 5px 2px gray; + background-image: linear-gradient(to right bottom, #9376E0, #F3BCC8); + animation: gradientAnimation 3s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; +} + +.lecture { + box-shadow: 0 0 10px 2px gray; + border-radius: 30px; + transition-duration: 200ms; +} + +.lecture:hover { + transform: scale(1.05); + transition-duration: 200ms; + transition-timing-function: ease-in; +} + + +/* width */ +::-webkit-scrollbar { + width: 0px; + visibility: hidden; +} + +#box { + border-radius: 50px; + height: 450px; + scroll-behavior: smooth; +} + +p { + white-space: normal; +} + +#play>button:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#play>button:active { + transform: scale(0.9); + background-image: none; + background-color: black; + transition-duration: 200ms; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + diff --git a/Web Module/lectures/templates/lectures.html b/Web Module/lectures/templates/lectures.html new file mode 100644 index 000000000..2e589f6b9 --- /dev/null +++ b/Web Module/lectures/templates/lectures.html @@ -0,0 +1,111 @@ +{% load static %} + + + + + + + Learners'Ed - Lectures + + + + + + + + + {% include 'navbar.html' %} +

      Lectures

      +
      +
      +
      +

      Lecture 1

      +

      Lorem ipsum dolor, sit amet consectetur adipisicing elit.

      +
      +
      +

      Watch Time

      +

      29:38

      +
      +
      +

      Lecture Time

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      1:32:12

      +
      +
      + +
      +

      Status

      +

      ❌

      + +
      +
      +
      +
      +
      +

      Lecture 2

      +

      Lorem ipsum dolor, sit amet consectetur adipisicing elit.

      +
      +
      +

      Watch Time

      +

      29:38

      +
      +
      +

      Lecture Time

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      1:32:12

      +
      +
      + +
      +

      Status

      +

      ❌

      + +
      +
      +
      +
      +
      +

      Lecture 3

      +

      Lorem ipsum dolor, sit amet consectetur adipisicing elit.

      +
      +
      +

      Watch Time

      +

      29:38

      +
      +
      +

      Lecture Time

      +

      1:32:12

      +
      +
      + +
      +

      Status

      +

      ❌

      + +
      +
      +
      +
      +
      +

      Lecture 4

      +

      Lorem ipsum dolor, sit amet consectetur adipisicing elit.

      +
      +
      +

      Watch Time

      +

      29:38

      +
      +
      +

      Lecture Time

      +

      1:32:12

      +
      +
      + +
      +

      Status

      +

      ❌

      + +
      +
      +
      +
      + + + \ No newline at end of file diff --git a/Web Module/lectures/tests.py b/Web Module/lectures/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/lectures/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/lectures/views.py b/Web Module/lectures/views.py new file mode 100644 index 000000000..080229a08 --- /dev/null +++ b/Web Module/lectures/views.py @@ -0,0 +1,4 @@ +from django.shortcuts import render + +def lectures(request): + return render(request, 'lectures.html', {}) diff --git a/Web Module/login_register/__init__.py b/Web Module/login_register/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/login_register/__pycache__/__init__.cpython-310.pyc b/Web Module/login_register/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..5d99d7cbe Binary files /dev/null and b/Web 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100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Web Module/login_register/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/login_register/apps.py b/Web Module/login_register/apps.py new file mode 100644 index 000000000..924353042 --- /dev/null +++ b/Web Module/login_register/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class LoginRegisterConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'login_register' diff --git a/Web Module/login_register/migrations/0001_initial.py b/Web Module/login_register/migrations/0001_initial.py new file mode 100644 index 000000000..325d57167 --- /dev/null +++ b/Web Module/login_register/migrations/0001_initial.py @@ -0,0 +1,116 @@ +# Generated by Django 4.1.9 on 2023-06-07 00:37 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ] + + operations = [ + migrations.CreateModel( + name='Category', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('name', models.CharField(max_length=100)), + ], + ), + migrations.CreateModel( + name='Rank', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('average_marks', models.FloatField(default=0)), + ('average_attendance', models.FloatField(default=0)), + ('average_assignment_percent', models.FloatField(default=0)), + ('rank', models.IntegerField()), + ], + ), + migrations.CreateModel( + name='Student', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('student_name', models.CharField(max_length=100)), + ('email', models.EmailField(max_length=254, unique=True)), + ('password', models.CharField(max_length=100)), + ('class_name', models.CharField(max_length=100)), + ('section', models.CharField(max_length=100)), + ('favourite_categories', models.ManyToManyField(related_name='students', to='login_register.category')), + ], + ), + migrations.CreateModel( + name='VirtualPet', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('pet_name', models.CharField(max_length=100)), + ('pet_type', models.CharField(max_length=100)), + ('pet_level', models.PositiveIntegerField(default=1)), + ('pet_level_progress', models.PositiveIntegerField(default=0)), + ('pet_coin', models.PositiveIntegerField(default=200)), + ('rank', models.PositiveIntegerField(default=0)), + ('student', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='virtual_pet', to='login_register.student')), + ], + ), + migrations.CreateModel( + name='SectionRank', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('class_name', models.CharField(max_length=100)), + ('section', models.CharField(max_length=100)), + ('ranks', models.ManyToManyField(to='login_register.rank')), + ], + ), + migrations.AddField( + model_name='rank', + name='student', + field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student'), + ), + migrations.CreateModel( + name='Marks', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('maths', models.PositiveIntegerField()), + ('hindi', models.PositiveIntegerField()), + ('english', models.PositiveIntegerField()), + ('science', models.PositiveIntegerField()), + ('social_science', models.PositiveIntegerField()), + ('total_marks', models.PositiveIntegerField(default=0)), + ('average_marks', models.FloatField(default=0)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + migrations.CreateModel( + name='ClassRank', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('class_name', models.CharField(max_length=100)), + ('ranks', models.ManyToManyField(to='login_register.rank')), + ], + ), + migrations.CreateModel( + name='Attendance', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('maths_attendance', models.PositiveIntegerField()), + ('hindi_attendance', models.PositiveIntegerField()), + ('english_attendance', models.PositiveIntegerField()), + ('science_attendance', models.PositiveIntegerField()), + ('social_science_attendance', models.PositiveIntegerField()), + ('average_attendance', models.FloatField(default=0)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + migrations.CreateModel( + name='Assignment', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('completed_assignments', models.PositiveIntegerField(default=0)), + ('total_assignments', models.PositiveIntegerField(default=0)), + ('assignment_percentage', models.FloatField(default=0)), + ('student', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='login_register.student')), + ], + ), + ] diff --git a/Web Module/login_register/migrations/0002_alter_rank_rank.py b/Web Module/login_register/migrations/0002_alter_rank_rank.py new file mode 100644 index 000000000..dd8b552d0 --- /dev/null +++ b/Web Module/login_register/migrations/0002_alter_rank_rank.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.9 on 2023-06-07 00:45 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('login_register', '0001_initial'), + ] + + operations = [ + migrations.AlterField( + model_name='rank', + name='rank', + field=models.IntegerField(null=True), + ), + ] diff --git a/Web 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Category(models.Model): + name = models.CharField(max_length=100) + + def __str__(self): + return self.name + +class Student(models.Model): + student_name = models.CharField(max_length=100) + email = models.EmailField(unique=True) + password = models.CharField(max_length=100) + class_name = models.CharField(max_length=100) + section = models.CharField(max_length=100) + favourite_categories = models.ManyToManyField(Category, related_name='students') + + def __str__(self): + return self.student_name + +class Marks(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE) + maths = models.PositiveIntegerField() + hindi = models.PositiveIntegerField() + english = models.PositiveIntegerField() + science = models.PositiveIntegerField() + social_science = models.PositiveIntegerField() + total_marks = models.PositiveIntegerField(default=0) + average_marks = models.FloatField(default=0) + + def save(self, *args, **kwargs): + self.update_total_marks() + self.update_average_marks() + super().save(*args, **kwargs) + + def update_total_marks(self): + self.total_marks = self.maths + self.hindi + self.english + self.science + self.social_science + + def update_average_marks(self): + total_subjects = 5 # Assuming 5 subjects in total + self.average_marks = self.total_marks / total_subjects + + def __str__(self): + return f"{self.student.student_name}'s Marks" + +class Attendance(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE) + maths_attendance = models.PositiveIntegerField() + hindi_attendance = models.PositiveIntegerField() + english_attendance = models.PositiveIntegerField() + science_attendance = models.PositiveIntegerField() + social_science_attendance = models.PositiveIntegerField() + average_attendance = models.FloatField(default=0) + + def save(self, *args, **kwargs): + self.update_average_attendance() + super().save(*args, **kwargs) + + def update_average_attendance(self): + total_subjects = 5 # Assuming 5 subjects in total + total_attendance = self.maths_attendance + self.hindi_attendance + self.english_attendance + self.science_attendance + self.social_science_attendance + self.average_attendance = (total_attendance / (total_subjects * 100)) * 100 + + def __str__(self): + return f"{self.student.student_name}'s Attendance" + +class Assignment(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE) + completed_assignments = models.PositiveIntegerField(default=0) + total_assignments = models.PositiveIntegerField(default=0) + assignment_percentage = models.FloatField(default=0) + + def save(self, *args, **kwargs): + self.update_assignment_percentage() + super().save(*args, **kwargs) + + def update_assignment_percentage(self): + if self.total_assignments > 0: + self.assignment_percentage = (self.completed_assignments / self.total_assignments) * 100 + else: + self.assignment_percentage = 0 + + def __str__(self): + return f"{self.student.student_name}'s Assignments" + +class Rank(models.Model): + student = models.ForeignKey(Student, on_delete=models.CASCADE) + average_marks = models.FloatField(default=0) + average_attendance = models.FloatField(default=0) + average_assignment_percent = models.FloatField(default=0) + rank = models.IntegerField(default=0) + + def __str__(self): + return f"Rank: {self.rank}, Student: {self.student.student_name}" + +class ClassRank(models.Model): + class_name = models.CharField(max_length=100) + ranks = models.ManyToManyField(Rank) + + def __str__(self): + return f"Class: {self.class_name}" + +class SectionRank(models.Model): + class_name = models.CharField(max_length=100) + section = models.CharField(max_length=100) + ranks = models.ManyToManyField(Rank) + + def __str__(self): + return f"Class: {self.class_name}, Section: {self.section}" + +@receiver(post_save, sender=Marks) +@receiver(post_save, sender=Attendance) +@receiver(post_save, sender=Assignment) +def update_rank(sender, instance, **kwargs): + student = instance.student + average_marks = Marks.objects.filter(student=student).aggregate(avg_marks=Sum('average_marks')).get('avg_marks') or 0 + average_attendance = Attendance.objects.filter(student=student).aggregate(avg_attendance=Sum('average_attendance')).get('avg_attendance') or 0 + average_assignment_percent = Assignment.objects.filter(student=student).aggregate(avg_assignment=Sum('assignment_percentage')).get('avg_assignment') or 0 + + # Update or create the rank for the student + rank, _ = Rank.objects.get_or_create(student=student) + rank.average_marks = average_marks + rank.average_attendance = average_attendance + rank.average_assignment_percent = average_assignment_percent + rank.save() + + # Update the class rank + class_rank, _ = ClassRank.objects.get_or_create(class_name=student.class_name) + class_rank.ranks.add(rank) + + # Update the section rank + section_rank, _ = SectionRank.objects.get_or_create(class_name=student.class_name, section=student.section) + section_rank.ranks.add(rank) + + # Calculate and update the rank for the student + rank.rank = Rank.objects.filter(average_marks__gt=average_marks).count() + 1 + rank.save() + + class_rank.save() + section_rank.save() + + +class VirtualPet(models.Model): + student = models.OneToOneField(Student, on_delete=models.CASCADE, related_name='virtual_pet') + pet_name = models.CharField(max_length=100) + pet_type = models.CharField(max_length=100) + pet_level = models.PositiveIntegerField(default=1) + pet_level_progress = models.PositiveIntegerField(default=0) + pet_coin = models.PositiveIntegerField(default=200) + rank = models.PositiveIntegerField(default=0) + + def save(self, *args, **kwargs): + self.rank = VirtualPet.objects.filter(pet_level__gt=self.pet_level).count() + 1 + super().save(*args, **kwargs) + + def __str__(self): + return self.pet_name + +@receiver(post_save, sender=VirtualPet) +def update_pet_rank(sender, instance, **kwargs): + VirtualPet.objects.filter(pk=instance.pk).update(rank=instance.rank) diff --git a/Web Module/login_register/static/images/LearnersEd.png b/Web Module/login_register/static/images/LearnersEd.png new file mode 100644 index 000000000..f2135a789 Binary files /dev/null and b/Web Module/login_register/static/images/LearnersEd.png differ diff --git a/Web Module/login_register/static/images/bg.png b/Web Module/login_register/static/images/bg.png new file mode 100644 index 000000000..0acfd954d Binary files /dev/null and b/Web Module/login_register/static/images/bg.png differ diff --git a/Web Module/login_register/static/images/illustration.jpg b/Web Module/login_register/static/images/illustration.jpg new file mode 100644 index 000000000..4dbc582dd Binary files /dev/null and b/Web Module/login_register/static/images/illustration.jpg differ diff --git a/Web Module/login_register/static/images/person.png b/Web Module/login_register/static/images/person.png new file mode 100644 index 000000000..a4c242cfb Binary files /dev/null and b/Web Module/login_register/static/images/person.png differ diff --git a/Web Module/login_register/static/login.css b/Web Module/login_register/static/login.css new file mode 100644 index 000000000..4cfe50e92 --- /dev/null +++ b/Web Module/login_register/static/login.css @@ -0,0 +1,79 @@ +body { + font-family: 'Darker Grotesque', sans-serif; + background-color: white; + margin:0px; + padding:0px; +} + +.box { + display:flex; + height: 100vh; +} +.background{ + width:50%; + flex-grow: 1; + flex-basis: 0; +} +.background>img { + height:100vh; + width: 100%; + object-fit: cover; + object-position: top center; +} +.log{ + background-image: url(./images/illustration.jpg); + display: flex; + flex-direction: column; + flex-wrap:wrap; + background-size:contain; + background-position: bottom; + background-repeat:no-repeat; + align-items: center; + justify-content: center; + flex-grow: 1; + flex-basis: 0; +} +#link { + color: blue; + text-decoration: underline; +} +.log>h3{ + color:#45a049; + font-size:60px; +} +#log-image{ + width: 80px; + height:80px; +} + +label { + display: block; + margin-bottom: 10px; +} + +input[type="text"], +input[type="password"], +input[type="email"] { + width: 100%; + padding: 10px; + margin-bottom: 20px; + border:2px solid black; + border-radius:2px; +} + +input[type="submit"] { + background-color: #4CAF50; + color: white; + padding: 10px; + border: none; + cursor: pointer; + width: 100%; +} + +input[type="submit"]:hover { + background-color: #45a049; +} + +/* .background { + background-image: url(./images/bg.png); +} */ diff --git a/Web Module/login_register/static/register-script.js b/Web Module/login_register/static/register-script.js new file mode 100644 index 000000000..a9a308dee --- /dev/null +++ b/Web Module/login_register/static/register-script.js @@ -0,0 +1,9 @@ +function validateForm() { + var password = document.getElementById("password").value; + var confirmPassword = document.getElementById("confirm_password").value; + if (password !== confirmPassword) { + alert("Passwords do not match"); + return false; + } + return true; +} \ No newline at end of file diff --git a/Web Module/login_register/static/register.css b/Web Module/login_register/static/register.css new file mode 100644 index 000000000..3cf09040b --- /dev/null +++ b/Web Module/login_register/static/register.css @@ -0,0 +1,98 @@ +body { + font-family: 'Darker Grotesque', sans-serif; + background-color: white; + margin:0px; + padding:0px; +} + +.box { + display:flex; + flex-wrap: wrap; + height: 100vh; +} +.background{ + width:50%; +} +.background>img { + height:100vh; + width: 100%; + object-fit: cover; + object-position: top center; +} +.log{ + background-image: url(./images/illustration.jpg); + display: flex; + flex-direction: column; + background-size:contain; + background-position: bottom; + background-repeat:no-repeat; + align-items: center; + justify-content: center; + height: 100vh; + width:50%; +} + +#link { + color: blue; + text-decoration: underline; +} + +.p { + display: flex; + flex-direction: column; +} +#temp{ + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; +} +#temp>h3{ + color:#45a049; + font-size:60px; +} +#log-image{ + width: 80px; + height:80px; +} + +.r{ + display:flex; + flex-direction: row; + gap: 20px; +} + +#info { + display: flex; + flex-direction: column; +} + +label { + display: block; + margin-bottom: 10px; +} + +input[type="text"], +input[type="password"], +input[type] { + padding: 5px; + margin-bottom: 10px; + border:2px solid black; + border-radius:2px; +} + +input[type="submit"] { + background-color: #4CAF50; + color: white; + padding: 10px; + border: none; + cursor: pointer; +} + +input[type="submit"]:hover { + background-color: #45a049; +} + +/* .background { + background-image: url(./images/bg.png); +} */ diff --git a/Web Module/login_register/templates/login.html b/Web Module/login_register/templates/login.html new file mode 100644 index 000000000..bdfc6b162 --- /dev/null +++ b/Web Module/login_register/templates/login.html @@ -0,0 +1,41 @@ +{% load static %} + + + + + + + Learners'Ed - Login + + + + + + + + +
      +
      + +
      +
      +

      Login

      + +
      + {% csrf_token %} + + + + + + + +
      + {% if error_message %} +

      {{ error_message }}

      + {% endif %} +

      Not a member? Register.

      +
      +
      + + \ No newline at end of file diff --git a/Web Module/login_register/templates/register.html b/Web Module/login_register/templates/register.html new file mode 100644 index 000000000..16ab8fe09 --- /dev/null +++ b/Web Module/login_register/templates/register.html @@ -0,0 +1,96 @@ +{% load static %} + + + + + + + Learners'Ed - Register + + + + + + + + +
      +
      + +
      +
      +
      +

      Register

      + +
      +
      +
      + {% csrf_token %} +
      +
      + + +
      +
      + + +
      +
      + +
      +
      + + +
      +
      + + +
      +
      + +
      +
      + + +
      +
      + + +
      +
      + +
      +
      + +
      +
      +
      + + +
      +
      + + +
      +
      + + +
      +
      +
      + +
      +
      + + {% if error_message %} +

      {{ error_message }}

      + {% endif %} +

      Already a member? Login.

      +
      +
      +
      +
      +
      +
      + + diff --git a/Web Module/login_register/tests.py b/Web Module/login_register/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/login_register/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/login_register/views.py b/Web Module/login_register/views.py new file mode 100644 index 000000000..3319ba062 --- /dev/null +++ b/Web Module/login_register/views.py @@ -0,0 +1,114 @@ +from django.shortcuts import render, redirect +from .models import Student, Category, Marks, Attendance, Assignment, VirtualPet +from django.core.exceptions import ValidationError +import random + +def logout(request): + if 'student_id' in request.session: + del request.session['student_id'] + return redirect('/login/') + +def register(request): + if request.method == 'POST': + # Retrieve form data + student_name = request.POST.get('student_name') + email = request.POST.get('email') + password = request.POST.get('password') + confirm_password = request.POST.get('confirm_password') + class_name = request.POST.get('class') + section = request.POST.get('section') + favourite_categories = request.POST.getlist('favourite_category') # Retrieve a list of checked categories + + # Perform validation logic here + if password != confirm_password: + error_message = "Passwords do not match" + return render(request, 'register.html', {'error_message': error_message}) + + try: + # Check if the email already exists in the database + existing_student = Student.objects.get(email=email) + error_message = "Email already exists" + return render(request, 'register.html', {'error_message': error_message}) + except Student.DoesNotExist: + pass + + # Save the registration details to the database + student = Student(student_name=student_name, email=email, password=password, class_name=class_name, section=section) + student.save() + + # Save favourite categories + for category_name in favourite_categories: + category, _ = Category.objects.get_or_create(name=category_name) + student.favourite_categories.add(category) + + # Generate random marks + maths_marks = random.randint(0, 100) + hindi_marks = random.randint(0, 100) + english_marks = random.randint(0, 100) + science_marks = random.randint(0, 100) + social_science_marks = random.randint(0, 100) + + # Save the marks to the database + marks = Marks( + student=student, + maths=maths_marks, + hindi=hindi_marks, + english=english_marks, + science=science_marks, + social_science=social_science_marks + ) + marks.save() + + # Generate random attendance percentages for each subject + maths_attendance = random.randint(70, 100) + hindi_attendance = random.randint(70, 100) + english_attendance = random.randint(70, 100) + science_attendance = random.randint(70, 100) + social_science_attendance = random.randint(70, 100) + + # Save the attendance record + attendance = Attendance(student=student, maths_attendance=maths_attendance, hindi_attendance=hindi_attendance, + english_attendance=english_attendance, science_attendance=science_attendance, + social_science_attendance=social_science_attendance) + attendance.save() + + # Generate random completed assignments + completed_assignments = random.randint(0, 6) + + # Create the Assignment object for the student + assignment = Assignment(student=student, completed_assignments=completed_assignments, total_assignments=6) + assignment.save() + + # Create a virtual pet for the student + pet = VirtualPet.objects.create(student=student) + + # Set random level and level progress for the pet + pet.level = random.randint(1, 10) + pet.level_progress = random.randint(1, 100) + pet.save() + + # Redirect to the dashboard page or any other page + request.session['student_id'] = student.id # Store the student ID in session + return redirect('dashboard') + else: + return render(request, 'register.html') + +def login(request): + if request.method == 'POST': + email = request.POST.get('Email_Id') + password = request.POST.get('password') + + # Perform authentication/validation logic here + try: + student = Student.objects.get(email=email) + if student.password == password: + request.session['student_id'] = student.id # Store the student ID in session + return redirect('dashboard') # Redirect to the dashboard page + else: + error_message = "Invalid email or password" + except Student.DoesNotExist: + error_message = "Invalid email or password" + + return render(request, 'login.html', {'error_message': error_message}) + else: + return render(request, 'login.html') diff --git a/Web Module/student_insights_app/__init__.py b/Web Module/student_insights_app/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/student_insights_app/__pycache__/__init__.cpython-310.pyc b/Web Module/student_insights_app/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..e84b3cf4c Binary files /dev/null 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Module/student_insights_app/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Web Module/student_insights_app/apps.py b/Web Module/student_insights_app/apps.py new file mode 100644 index 000000000..b034e2b5d --- /dev/null +++ b/Web Module/student_insights_app/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class StudentInsightsAppConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'student_insights_app' diff --git a/Web Module/student_insights_app/migrations/__init__.py b/Web Module/student_insights_app/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/student_insights_app/migrations/__pycache__/__init__.cpython-310.pyc b/Web Module/student_insights_app/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..11d941065 Binary files /dev/null and b/Web 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Module/student_insights_app/static/images/chatroom2.png b/Web Module/student_insights_app/static/images/chatroom2.png new file mode 100644 index 000000000..e5bdfa035 Binary files /dev/null and b/Web Module/student_insights_app/static/images/chatroom2.png differ diff --git a/Web Module/student_insights_app/static/images/chatrooms.png b/Web Module/student_insights_app/static/images/chatrooms.png new file mode 100644 index 000000000..dfc2bddb1 Binary files /dev/null and b/Web Module/student_insights_app/static/images/chatrooms.png differ diff --git a/Web Module/student_insights_app/static/images/mental_health.png b/Web Module/student_insights_app/static/images/mental_health.png new file mode 100644 index 000000000..42fd5f47c Binary files /dev/null and b/Web Module/student_insights_app/static/images/mental_health.png differ diff --git a/Web Module/student_insights_app/static/student_insights.css b/Web Module/student_insights_app/static/student_insights.css new file mode 100644 index 000000000..f8d668f07 --- /dev/null +++ b/Web Module/student_insights_app/static/student_insights.css @@ -0,0 +1,197 @@ +body, html { + margin: 0; + padding: 0; +} + +body { + background-image: url(./images/bg-new.png); + background-repeat: no-repeat; + background-position: center; + background-attachment: fixed; + background-size: cover; + height: 100vh; + font-family: 'Darker Grotesque', sans-serif; + overflow-y: hidden; +} +#whole { + + /* padding: 20px; */ + text-align: center; + display: flex; + flex-direction: column; + gap:30px; + margin-top: 100px; + } + .heading{ + background-color: white; + /* margin: 30px; */ + /* padding: 30px; */ + height:90px; + } + + #title { + color: #9376E0; + font-size: 60px; + margin: 0; + text-transform: uppercase; + letter-spacing: 10px; + font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + } + .box { + display: flex; + padding: 15px; + gap:15px; + } + + .left-section { + flex: 1; + display: flex; + flex-direction: column; + gap:15px; + height: 100vh; + } + .twin_flexboxes{ + display: flex; + flex-direction: row; + gap:15px; + width: 85%; + } + + + .flexbox { + flex: 1; + + outline: 3px solid black; + + border-radius: 5px; + cursor: pointer; + padding: 20px; + height: 250px; + width: 200px; + } + .flexbox:hover{ + box-shadow:2px 5px black; + transition-duration: 200ms; + } + #flexbox1 { + display: flex; + background-image: url('images/chatroom2.png'); + /* animation: gradientAnimation 5s infinite ease-in-out; */ + /* background-size: 200% 200%; */ + /* transition-duration: 100ms; */ + background-position: center; + background-size: cover; + position: relative; + background-repeat: no-repeat; + flex-direction: column; + justify-content: center; + align-items: center; + gap:10px; + font-size: 30px; + } + .quiz1-heading{ + text-transform: uppercase; + letter-spacing: 2px; + outline: 4px solid #9376E0; + border:2px white; + padding:10px; + width:200px; + background-color: black; + font-size: 20px; + border-radius:5px; + color:white; + } + .quiz-heading{ + text-transform: uppercase; + letter-spacing: 2px; + outline: 4px solid #9376E0; + border:2px white; + padding:10px; + width:250px; + font-size: 20px; + border-radius:5px; + background-color: black; + color:white; + position: absolute; + right: 10px; + bottom:10px; + } + #flexbox2{ + position: relative; + display: flex; + background-image: url('images/mental_health.png'); + background-position: center; + background-size: cover; + background-repeat: no-repeat; + flex-direction: column; + align-self: flex-start; + justify-self: flex-start; + margin: 30px; + gap:10px; + font-size: 30px; + } + +.horizontal-flexbox { + text-transform: uppercase; + cursor: pointer; + letter-spacing: 2px; + outline: 8px solid white; + padding:5px; + background-image: linear-gradient(to right bottom, #C4B0FF, #9376E0, #F3BCC8); + animation: gradientAnimation 5s infinite ease-in-out; + background-size: 200% 200%; + transition-duration: 200ms; + flex: 1; + padding: 10px; + width: 85%; + box-shadow: 0 0 20px 2px gray; + font-size: 20px; + border-radius:30px; + color:white; +} + +.horizontal-flexbox:hover { + transform: scale(1.05); + transition-duration: 200ms; +} + +.horizontal-flexbox:active { + transform: scale(0.9); + background-image: none; + background-color: black; + transition-duration: 200ms; +} + +@keyframes gradientAnimation { + 0%, 100% { + background-position: left top; + } + 50% { + background-position: right bottom; + } +} + +.quiz2-heading{ + text-transform: uppercase; + letter-spacing: 2px; + padding:5px; + flex: 1; + padding: 10px; + font-size: 20px; + border-radius:5px; + color:white; + } + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Web Module/student_insights_app/templates/student_insights.html b/Web Module/student_insights_app/templates/student_insights.html new file mode 100644 index 000000000..a37207a97 --- /dev/null +++ b/Web Module/student_insights_app/templates/student_insights.html @@ -0,0 +1,47 @@ +{% load static %} + + + + + + Student Insights + + + + + + + + + + + + + {% include 'navbar.html' %} +
      +
      +

      STUDENTS'INSIGHTS

      +
      +
      +
      +
      +
      +

      chatrooms

      + +
      + +
      + +

      MENTAL HEALTH CHATBOT

      + +
      +
      +
      +
      EXPLORE FIELDS
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/Web Module/student_insights_app/tests.py b/Web Module/student_insights_app/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/student_insights_app/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/student_insights_app/views.py b/Web Module/student_insights_app/views.py new file mode 100644 index 000000000..986966d40 --- /dev/null +++ b/Web Module/student_insights_app/views.py @@ -0,0 +1,4 @@ +from django.shortcuts import render + +def student_insights(request): + return render(request, 'student_insights.html', {}) diff --git a/Web Module/virtual_pet/__init__.py b/Web Module/virtual_pet/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Web Module/virtual_pet/__pycache__/__init__.cpython-39.pyc b/Web Module/virtual_pet/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 000000000..d9b2848ad Binary files 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Module/virtual_pet/static/content/pet-back.jpg new file mode 100644 index 000000000..126e99d75 Binary files /dev/null and b/Web Module/virtual_pet/static/content/pet-back.jpg differ diff --git a/Web Module/virtual_pet/static/script-pet.js b/Web Module/virtual_pet/static/script-pet.js new file mode 100644 index 000000000..008c79557 --- /dev/null +++ b/Web Module/virtual_pet/static/script-pet.js @@ -0,0 +1,208 @@ +var selector = document.getElementById("selector"); +var Name = document.getElementById("name_data"); +var DispName = document.getElementById("pet-name"); +var pet = document.getElementById("pet"); +var food_container = document.getElementById("item-container"); +var bar_max_width = document.getElementById('level').offsetWidth - 8; +var bar_height = document.getElementById('info').offsetHeight; +var bar = document.getElementById('bar'); +bar.style.height = `${bar_height}px`; +var display_per = document.getElementById('per'); +var percent = parseFloat(display_per.innerHTML); // database add level too +var grow_width = bar_max_width * percent * 0.01; +var level_desc = document.getElementById("levamt"); +var level_amt = parseInt(level_desc.innerHTML); +bar.style.width = `${grow_width}px`; +var levelplus = document.getElementById("info"); +var coins = document.getElementById('coin-amt'); + +// fetch the pet name and type from the views return if not set yet put pet_type = "" else assign the values +// also fetch the level and level progress and assign those too +// after updating the level and level progress send update request to server where it will update the values to backend virtual pet and give complete code of views + +var pet_type_list = ["Dog", "Cat"] + +var pet_type = ""; +// Function to update the pet details in the UI +function updatePetDetails(petType, petName, level, progress, coindb) { + // Assign the values to the corresponding variables + pet_type = petType; + level_amt = level; + percent = progress; + + // Update the UI with the new values + DispName.innerHTML = petName; + level_desc.innerHTML = `${level_amt}`; + display_per.innerHTML = `${percent}%`; + coins.innerHTML = `${coindb}`; + + // Calculate the growth width for the progress bar + grow_width = bar_max_width * percent * 0.01; + bar.style.width = `${grow_width}px`; + + // Update the pet image and animation based on the pet type + if (pet_type === "Dog" || pet_type === "Cat") { + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + } +} + +// Function to send the updated pet details to the server +function savePetDetails(petName, petType, level, progress, c) { + // Prepare the data to be sent in the request body + const data = { + name: petName, + type: petType, + level: level, + progress: progress, + coin: c + }; + + // Send an AJAX request to the server to save the data + $.ajax({ + url: '', + type: 'POST', + dataType: 'json', + data: data, + success: function(response) { + console.log('Pet details saved successfully'); + }, + error: function(error) { + console.log('Error saving pet details:', error); + } + }); + } + +var temp = setInterval(function() { + if (pet_type !== "Dog" && pet_type !== "Cat") { + var Dog = document.getElementById("pet-dog"); + var Cat = document.getElementById("pet-cat"); + Dog.addEventListener('click', () => { + Dog.style.border = "8px solid lime"; + Cat.style.border = "none"; + }) + Cat.addEventListener('click', () => { + Dog.style.border = "none"; + Cat.style.border = "8px solid lime"; + }) + selector.style.display = 'flex'; + var sel = -1; + } + else { + // updatePetDetails(petType, petName, level, progress, coindb); + selector.style.display = 'none'; + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + clearInterval(temp); + } + }, Infinity); + + var cost = [0, 20, 20, 30, 10, 10, 20, 30, 30, 50, 20, 70, 20, 30, 10, 10, 20, 20, 60, 80, 80, 30, 20, 70, 80, 60, 60, 50, 40, 20, 10, 30, 20, 50, 70, 70, 60, 20, 40, 40, 60, 60, 80, 80, 30, 20]; + + for (let i = 1; i <= 45; i++) { + food_container.innerHTML += `

      ${cost[i]}

      `; + } + + function validate() { + var radio = document.getElementsByName("sel"); + if (!radio[0].checked && !radio[1].checked) { + alert("Please select pet type"); + } + else if (radio[0].checked) { + sel = 0; + } + else { + sel = 1; + } + if (Name.value.trim() === "" && sel !== -1) { + alert("Please enter your pet name"); + } + else { + petName = `${Name.value.trim()}`; + DispName.innerHTML = petName; + pet_type = pet_type_list[sel]; + petType = pet_type_list[sel]; + // savePetDetails(petName, petType, level, progress, coindb); + } + } + + function updateLevel(addPercent) { + // adding database connection to fetch last known level and percent + // temporarily taking current level and percent, remove this later (level_amt => currentLevel) (percent => currentLevel) + var coins_left = coins.innerHTML; + if (coins_left >= addPercent) { + var feed_buttons = document.querySelectorAll(".feed"); + for (let i = 0; i < feed_buttons.length; i++) { + feed_buttons[i].disabled = true; + } + var init_coin = coins_left; + var coin_spent = setInterval(function() { + if (coins_left !== init_coin - addPercent) { + coins_left--; + coins.innerHTML = `${coins_left}`; + } + else { + for (let i = 0; i < feed_buttons.length; i++) { + feed_buttons[i].disabled = false; + } + clearInterval(coin_spent); + } + }, 50); + pet.style.backgroundImage = `url(../static/content/${pet_type}/${pet_type}-Jump.png)`; + pet.style.animation = "reward 1s steps(8) infinite"; + levelplus.innerHTML = `+${addPercent} Level`; + levelplus.style.animation = "levelplus 1s infinite"; + var new_percent = percent + addPercent; + var temp = percent; + var interval = setInterval(function () { + if (temp !== new_percent) { // switch here + if (percent + 1 > 100) { + percent = 1; + level_amt++; + level_desc.innerHTML = `${level_amt}`; + } + else { + percent++; + } + grow_width = bar_max_width * percent * 0.01; + level_desc.innerHTML = `${level_amt}`; + display_per.innerHTML = `${percent}%`; + bar.style.width = `${grow_width}px`; + temp++; + } + else { + clearInterval(interval); + var animation = setInterval(function() { + levelplus.style.animation = "none"; + pet.style.backgroundImage = `url(../static/content/${pet_type}/${pet_type}-Idle.png)`; + pet.style.animation = "idle 1s steps(8) infinite"; + clearInterval(animation); + }, 800); + } + }, 2); + savePetDetails(petName, petType, level_amt, percent, coins_left); + } else { + alert("Error: Not Sufficient Balance!"); + } + } + + function changeAnimation() { + if (pet_type === "Dog" || pet_type === "Cat") { + var action_list = ['idle', 'walk']; + var action = action_list[Math.floor((Math.random() * action_list.length))]; + if (action === 'idle') { + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Idle.png')`; + pet.style.animation = "idle 1s steps(8) infinite"; + } + else { + pet.style.backgroundImage = `url('../static/content/${pet_type}/${pet_type}-Walk.png')`; + pet.style.animation = "walk 3s steps(8) infinite"; + } + } + } + + setInterval(changeAnimation, 3000); + + + + diff --git a/Web Module/virtual_pet/static/styles-pet.css b/Web Module/virtual_pet/static/styles-pet.css new file mode 100644 index 000000000..ce60af546 --- /dev/null +++ b/Web Module/virtual_pet/static/styles-pet.css @@ -0,0 +1,457 @@ +html, body { + padding: 0; + margin: 0; +} + +#container { + display: flex; + justify-content: space-evenly; + align-items: center; + font-family: 'Press Start 2P', cursive; + width: 100%; + height: 100vh; + background-color: #F0D09F; +} + +#shop-container { + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + width: 55%; +} + +.close { + border-radius: 50%; + width: 30px; + height: 30px; + display: flex; + justify-content: center; + align-items: center; + padding: 10px; + outline: 4px solid antiquewhite; + background-color: #F0D09F; + position: absolute; + top: 30px; + left: 40px; + font-size: 30px; + color: #666; + border: 2px solid black; + text-align: center; + transition-duration: 200ms; + box-shadow: 0 0 20px 2px black; + cursor: pointer; +} + +.close:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +.close:active { + transform: scale(0.9); + transition-duration: 200ms; +} + +#shop { + font-size: 40px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; +} + +#item-container { + overflow-y: auto; + display: flex; + flex-wrap: wrap; + justify-content: center; + border: 8px solid black; + height: 42vh; + padding: 30px 30px; + gap: 30px 20px; + background-color: antiquewhite; + scroll-behavior: smooth; + border-top-right-radius: 0px; + border-bottom-right-radius: 0px; + border-top-left-radius: 80px; + border-bottom-left-radius: 80px; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); + outline: 4px solid #f7e8ce; +} + +#item-container::-webkit-scrollbar { + width: 50px; + outline: 8px solid #f7e8ce; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +#item-container::-webkit-scrollbar-track { + background: repeating-linear-gradient( + to right, + #f6ba52, + #f6ba52 10px, + #ffd180 10px, + #ffd180 20px + ); + background-color: #f6ba52; + -webkit-box-shadow: inset 0px 0px 1px 5px white; + +} + +#item-container::-webkit-scrollbar-thumb { + background-color: white; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:decrement:hover { + border-color: transparent transparent white transparent; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:increment { + border-width: 8px 8px 0 8px; + border-color: #555555 transparent transparent transparent; +} + +#item-container::-webkit-scrollbar-button:vertical:single-button:increment:hover { + border-color: white transparent transparent transparent; + animation: bounce 1s infinite ease-out; +} + +#item-container::-webkit-scrollbar-button:single-button:vertical:decrement { + border-width: 0 8px 8px 8px; + border-color: transparent transparent #555555 transparent; +} + +#item-container::-webkit-scrollbar-button:single-button { + background-color: #f6ba52; + display: block; + border-style: solid; + height: 30px; + width: 16px; +} + +.feed { + width: 140px; + height: 140px; + border-radius: 50%; + box-shadow: 0px 0px 10px 3px rgba(0,0,0,0.5); + transition: 200ms; +} + +#label { + display: flex; + justify-content: center; + align-items: center; + font-size: 30px; + gap: 10px; +} + +#label>img { + width: 50px; + margin-right: 40px; +} + +.feed:hover { + transform: scale(1.1); + transition: 200ms; +} + +.feed:active { + transform: scale(0.8); + transition: 200ms; +} + +.feed img { + width: 80%; +} + +#coins { + font-size: 20px; + display: flex; + margin-top: 30px; + justify-content: center; + align-items: center; + gap: 20px; +} + +#coins > img { + width: 50px; + height: 50px; +} + +#pet-name { + background-color: black; + color: white; + padding: 15px 20px; +} + +#pet-title { + margin-top: 40px; + font-size: 20px; +} + +#pet-container { + width: 35%; + border: 8px solid black; + height: 80vh; + border-radius: 80px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +#pet-background { + background-image: url(./content/pet-back.jpg); + background-size: cover; + background-position: center; + background-repeat: no-repeat; + width: 100%; + height: 100%; + border-radius: 72px; + display: flex; + flex-direction: column; + justify-content: space-between; + align-items: center; +} + +#prog { + width: 100%; + display: flex; + justify-content: center; + align-items: center; + margin-top: 60px; + gap: 20px; +} + +#level { + width: 50%; + height: 30px; + border: 4px solid black; + outline: 4px solid white; + background-color: white; + display: flex; + box-shadow: 0px 0px 10px 3px rgba(0,0,0,0.5); +} + +#per { + font-size: 30px; + text-shadow: 2px 2px #ffffff; + width: 90px; +} + +#bar { + background-color: blue; + position: absolute; +} + +#info { + color: lime; + font-size: 20px; + opacity: 0; + white-space: nowrap; +} + +#desc { + color: black; + font-size: 20px; + position: relative; + top: 50px; + right: 10px; + white-space: nowrap; + text-shadow: 2px 2px white; +} + +#pet { + width: 300px; + height: 264px; + margin-bottom: 30px; + background-repeat: no-repeat; + position: relative; +} + +#selector { + width: 100%; + height: 100vh; + background-color: #F0D09F; + font-family: 'Press Start 2P', cursive; + flex-direction: column; + display: none; + justify-content: center; + align-items: center; + position: absolute; + z-index: 999; + gap: 30px 30px; +} + +input[type=radio] { + display: none; +} + +#name { + display: flex; + flex-direction: column; + gap: 10px; +} + +form { + display: flex; + flex-direction: column; + justify-self: center; + align-items: center; +} + +#selector_submit { + font-size: 20px; + outline: 4px solid #f7e8ce; + background-color: #F0D09F; + font-family: 'Press Start 2P', cursive; + padding: 20px; + cursor: pointer; + transition-duration: 200ms; +} + +#name > p { + font-size: 20px; +} + +#selector_submit:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#selector_submit:active { + transform: scale(0.9); + transition-duration: 200ms; +} + +#name { + display: flex; + flex-direction: column; + gap: 20px; +} + +#name_data { + outline: 4px solid #f7e8ce; + padding: 10px; + font-size: 20px; + font-family: 'Press Start 2P', cursive; + text-align: center; + transition-duration: 200ms; +} + +#name_data:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#select_type { + display: flex; + gap: 80px; + justify-content: center; + align-items: center; +} + +.pet-sel { + padding: 20px; + border-radius: 30px; + outline: 8px solid #f7e8ce; + transition-duration: 200ms; + background-color: #F0D09F; + cursor: pointer; + box-shadow: 0px 0px 30px 5px rgba(0,0,0,0.4); +} + +.pet-sel:hover { + transform: scale(1.1); + transition-duration: 200ms; +} + +#Cat { + width: 300px; + height: 264px; + background-repeat: no-repeat; + background-image: url(./content/Cat/Cat-Idle.png); + animation: idle 1s steps(8) infinite; + background-color: transparent; + border: none; +} + +form { + display: flex; + flex-direction: column; + gap: 20px; +} + +#Dog { + width: 300px; + height: 264px; + background-repeat: no-repeat; + background-image: url(./content/Dog/Dog-Idle.png); + animation: idle 1s steps(8) infinite; + background-color: transparent; + border: none; +} + +@keyframes idle { + from { + background-position: 0 0; + } + to { + background-position: 0 -2112px; + } +} + +@keyframes reward { + from { + background-position: 0 0; + transform: translateY(0); + } + to { + background-position: 0 -2112px; + transform: translateY(-30px); + } +} + +@keyframes walk { + 0% { + background-position: 0 0; + transform: rotateY(0deg) translateX(0px); + } + 25% { + background-position: 0 -2112px; + transform: rotateY(0deg) translateX(160px); + } + 28% { + background-position: 0 -2112px; + transform: rotateY(180deg) translateX(-160px); + } + 50% { + background-position: 0 0; + transform: rotateY(180deg) translateX(0px); + } + 75% { + background-position: 0 -2112px; + transform: rotateY(180deg) translatex(160px); + } + 78% { + background-position: 0 -2112px; + transform: rotateY(0deg) translatex(-160px); + } + 100% { + background-position: 0 0; + transform: translatex(0); + } +} + +@keyframes levelplus { + from { + transform: translateY(0); + opacity: 0; + } + to { + transform: translateY(-40px); + opacity: 1; + } +} \ No newline at end of file diff --git a/Web Module/virtual_pet/templates/virtual-pet.html b/Web Module/virtual_pet/templates/virtual-pet.html new file mode 100644 index 000000000..bfe3d27cf --- /dev/null +++ b/Web Module/virtual_pet/templates/virtual-pet.html @@ -0,0 +1,62 @@ +{% load static %} + + + + Virtual Pet - (Learners'Ed) + + + + + + +
      +

      Create your very own pet!

      +
      + + +
      +
      +

      Enter your new pet's name:

      + + +
      +
      +
      +
      +
      +

      Pet Shop

      +
      +

      Pet Name:

      +
      +

      Coins Left: 200

      + +
      +
      +
      +
      + +
      +
      +
      +
      +
      + Level
      +
      Level 3
      +
      +
      30%
      +
      +
      +
      +
      +
      +
      ✖
      + + + + diff --git a/Web Module/virtual_pet/tests.py b/Web Module/virtual_pet/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Web Module/virtual_pet/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Web Module/virtual_pet/views.py b/Web Module/virtual_pet/views.py new file mode 100644 index 000000000..dd85772e9 --- /dev/null +++ b/Web Module/virtual_pet/views.py @@ -0,0 +1,28 @@ +from django.shortcuts import render +from login_register.models import VirtualPet +from django.http import JsonResponse + +def virtual_pet(request): + # if 'student_id' in request.session: + # if request.method == 'POST': + # if request.POST.get('level'): + # level = request.POST.get('level') + # progress = request.POST.get('progress') + # coin = request.POST.get('coin') + # pet = VirtualPet.objects.get(id=student_id) # Assuming there's only one pet record + # pet.pet_level = level + # pet.pet_level_progress = progress + # pet.pet_coin = coin + # pet.save() + # return JsonResponse({'status': 'success'}) + # student_id = request.session['student_id'] + # pet = VirtualPet.objects.get(id=student_id) + # pet_data = { + # 'name': pet.pet_name, + # 'type': pet.pet_type, + # 'level': pet.pet_level, + # 'progress': pet.pet_level_progress, + # 'pet_coin': pet.pet_coin + # } + # return JsonResponse({'pet': pet_data}) + return render(request, "virtual-pet.html", {}) diff --git a/images/1.png b/images/1.png new file mode 100644 index 000000000..60dcf3655 Binary files /dev/null and b/images/1.png differ diff --git a/images/2.png b/images/2.png new file mode 100644 index 000000000..c3131546e Binary files /dev/null and b/images/2.png differ diff --git a/images/3.png b/images/3.png new file mode 100644 index 000000000..a9e2c03e1 Binary files /dev/null and b/images/3.png differ diff --git a/images/4.png b/images/4.png new file mode 100644 index 000000000..639a8ea30 Binary files /dev/null and b/images/4.png differ diff 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