--

Author: codewithdark-git

Diff for: .gitattributes

@@ -9,7 +9,7 @@
 
 # Ignore local python virtual environment
 
-.venv
+venv
 env/
 
 # Ignore streamlit_chat_app.py conversations pickle

Diff for: .gitignore

@@ -1,20 +1,20 @@
-# Use an official Python runtime as a parent image
-FROM python:3.10-slim
+# Use the official Node.js image as a base
+FROM node:14
 
-# Set the working directory in the container
+# Set the working directory
 WORKDIR /app
 
-# Copy the current directory contents into the container at /app
-COPY . /app
+# Copy package.json and package-lock.json
+COPY frontend/package*.json ./
 
-# Install any needed packages specified in requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt
+# Install dependencies
+RUN npm install
 
-# Make port 8501 available to the world outside this container
-EXPOSE 8501
+# Copy the rest of the application code
+COPY . .
 
-# Define environment variable
-ENV NAME DARKCODER
+# Expose the port the app runs on
+EXPOSE 3000
 
-# Run app.py when the container launches
-CMD ["streamlit", "run", "app.py", "--server.port", "8501"]
+# Command to run the application
+CMD ["npm", "run", "dev"]

Diff for: Dockerfile

@@ -0,0 +1 @@
+# App initialization

Diff for: README.md

@@ -0,0 +1,18 @@
+from flask import Blueprint, request, jsonify
+from app.core.llm_pipeline import process_query
+
+chat_bp = Blueprint('chat', __name__)
+
+@chat_bp.route('/chat', methods=['POST'])
+def chat():
+    """
+    Process a chat query and return a response.
+    """
+    data = request.json
+    query = data.get('query')
+
+    if not query:
+        return jsonify({'error': 'No query provided'}), 400
+
+    response = process_query(query)
+    return jsonify({'response': response}), 200

Diff for: app.py

@@ -0,0 +1,31 @@
+from flask import Blueprint, request, jsonify
+from app.core.vector_store import VectorStore
+from app.core.llm_pipeline import process_query
+import os
+
+upload_bp = Blueprint('upload', __name__)
+
+@upload_bp.route('/upload', methods=['POST'])
+def upload_document():
+    """
+    Upload a document and process it into the vector store.
+    """
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file part'}), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({'error': 'No selected file'}), 400
+
+    # Save the file temporarily
+    file_path = os.path.join('temp', file.filename)
+    file.save(file_path)
+
+    # Process the file into the vector store
+    vector_store = VectorStore()
+    vector_store.add_document(file_path)
+
+    # Clean up the temporary file
+    os.remove(file_path)
+
+    return jsonify({'message': 'File uploaded and processed successfully'}), 200

Diff for: backend/app/__init__.py

@@ -0,0 +1,16 @@
+from flask import Flask
+from app.api.upload import upload_bp
+from app.api.endpoints import chat_bp
+
+def create_app():
+    app = Flask(__name__)
+
+    # Register blueprints
+    app.register_blueprint(upload_bp, url_prefix='/api')
+    app.register_blueprint(chat_bp, url_prefix='/api')
+
+    return app
+
+if __name__ == "__main__":
+    app = create_app()
+    app.run(debug=True)

Diff for: backend/app/api/endpoints.py

@@ -0,0 +1,9 @@
+from langchain.llms import Groq
+
+def process_query(query):
+    """
+    Process a query using the Groq model and return the response.
+    """
+    model = Groq(model_name="Llama-3")  # or Mixtral
+    response = model.generate(query)
+    return response

Diff for: backend/app/api/upload.py

@@ -0,0 +1,7 @@
+from app.core.vector_store import VectorStore
+
+def generate_embedding(text):
+    # Generate embeddings for the given text
+    vector_store = VectorStore()
+    embedding = vector_store.store_embedding(text)
+    return embedding

Diff for: backend/app/app.py

@@ -0,0 +1,14 @@
+from langchain.vectorstores import VectorStore
+from langchain.embeddings import OpenAIEmbeddings
+
+class CustomVectorStore(VectorStore):
+    def __init__(self):
+        self.embeddings = OpenAIEmbeddings()
+        self.documents = []
+
+    def add_document(self, file_path):
+        # Process the document and add it to the vector store
+        with open(file_path, 'r') as file:
+            content = file.read()
+            embedding = self.embeddings.embed(content)
+            self.documents.append((content, embedding))

Diff for: backend/app/core/llm_pipeline.py

@@ -0,0 +1,13 @@
+from sqlalchemy import Column, Integer, String
+from app.database import Base
+
+class Document(Base):
+    __tablename__ = 'documents'
+
+    id = Column(Integer, primary_key=True, index=True)
+    title = Column(String, index=True)
+    content = Column(String)
+    embedding = Column(String)
+
+    def __repr__(self):
+        return f"<Document(title={self.title}, id={self.id})>"

Diff for: backend/app/core/text_pipeline.py

@@ -0,0 +1,7 @@
+import openai
+
+def handle_audio_upload(file):
+    # Process the audio file and convert to text using OpenAI Whisper
+    audio_content = file.read()
+    response = openai.Audio.transcribe("whisper-1", audio_content)
+    return response['text']

Diff for: backend/app/core/vector_store.py

@@ -0,0 +1,7 @@
+import openai
+
+def handle_video_upload(file):
+    # Process the video file and convert to text using OpenAI Whisper
+    video_content = file.read()
+    response = openai.Audio.transcribe("whisper-1", video_content)
+    return response['text']

Diff for: backend/app/models/document.py

@@ -0,0 +1 @@
+# Configuration settings

Diff for: backend/app/services/text_to_speech.py

@@ -0,0 +1,9 @@
+Flask
+langchain
+langchain-groq
+openai
+chromadb
+sqlalchemy
+protobuf==3.20.3
+typing-extensions==4.5.0
+tokenizers==0.13.3