breastcancerdetection.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "19epbpMNCTDIjSF9YxHeOgBYZxf8sGtF7",
      "authorship_tag": "ABX9TyMlDPg0fr95EwKAtLn7Y6z1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nienhcar/Breastcancerdetection/blob/main/breastcancerdetection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LE1UYOgUdUdn"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "read_file = pd.read_excel (\"/content/maindataset.xlsx\")\n",
        "read_file.to_csv (\"breastcancer.csv\", index = None,header=True)\n",
        "\n",
        "\n",
        "dataset = pd.read_csv('breastcancer.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "FPX7reBWe5OS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4fF5jINe5QZ",
        "outputId": "2709ae55-fa39-4fbd-d76a-f71d4a31db2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
            " ...\n",
            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "loKMeFnCe5Sr",
        "outputId": "86aaa7ea-bc80-47cf-c7cf-66bf88154ac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "PnwCXqjCe5T1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "GbJJzj0Ne5W8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puR2qpeNe5X_",
        "outputId": "0d81ce29-fb53-4e19-b7e4-2c0122453d5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.44075296 -0.43531947 -1.36208497 ...  0.9320124   2.09724217\n",
            "   1.88645014]\n",
            " [ 1.97409619  1.73302577  2.09167167 ...  2.6989469   1.89116053\n",
            "   2.49783848]\n",
            " [-1.39998202 -1.24962228 -1.34520926 ... -0.97023893  0.59760192\n",
            "   0.0578942 ]\n",
            " ...\n",
            " [ 0.04880192 -0.55500086 -0.06512547 ... -1.23903365 -0.70863864\n",
            "  -1.27145475]\n",
            " [-0.03896885  0.10207345 -0.03137406 ...  1.05001236  0.43432185\n",
            "   1.21336207]\n",
            " [-0.54860557  0.31327591 -0.60350155 ... -0.61102866 -0.3345212\n",
            "  -0.84628745]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRWJ-5UJe5bl",
        "outputId": "153942ff-89af-4087-910a-9b1ace346fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.46649743 -0.13728933 -0.44421138 ... -0.19435087  0.17275669\n",
            "   0.20372995]\n",
            " [ 1.36536344  0.49866473  1.30551088 ...  0.99177862 -0.561211\n",
            "  -1.00838949]\n",
            " [ 0.38006578  0.06921974  0.40410139 ...  0.57035018 -0.10783139\n",
            "  -0.20629287]\n",
            " ...\n",
            " [-0.73547237 -0.99852603 -0.74138839 ... -0.27741059 -0.3820785\n",
            "  -0.32408328]\n",
            " [ 0.02898271  2.0334026   0.0274851  ... -0.49027026 -1.60905688\n",
            "  -0.33137507]\n",
            " [ 1.87216885  2.80077153  1.80354992 ...  0.7925579  -0.05868885\n",
            "  -0.09467243]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 42)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "NJAlF0M8e5d5",
        "outputId": "1ebd18a3-eeac-4ed4-975b-828edb0f187e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classifier.predict(sc.transform([[11.34,18.33,75.66,567.8,0.089045,0.06553,0.1334,0.1234,0.01256,0.2556,0.07896,0.9443,0.993,9.227,0.004562,0.08668,0.1,0.0236,0.03453,0.0045612,28.4,28.03,206.7,2306,0.1701,0.6969,0.9658,0.281,0.455,0.09789]])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVZv68fme5ie",
        "outputId": "f2815a88-dbec-4ba7-f3f6-a6f884a4f587"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test), 1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmrrXpQHe5kM",
        "outputId": "51364ffd-9bb4-4cc0-d9a2-cdd82fe9d8e0"
      },
      "execution_count": null,
      "outputs": [
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            " [1 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkZLwSSJe5nG",
        "outputId": "62393761-da55-44ae-e2ec-999c1d323af3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[70  1]\n",
            " [ 2 41]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9736842105263158"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EUyUkKCXe5of"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q streamlit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-oc6P8ze5rx",
        "outputId": "c3e72b7f-5833-4b3c-b0e5-a52c8d310eea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m68.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.8/164.8 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m184.3/184.3 kB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m97.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for validators (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "\n",
        "import streamlit as st\n",
        "\n",
        "st.write('Hello, *World!* :sunglasses:')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZqRPar9e5up",
        "outputId": "104c46af-02f9-41c8-fc72-680b55158f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!npm install localtunnel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJgCGcHdxFku",
        "outputId": "2182fb35-3aa2-4a27-f0b6-17ca250793c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K\u001b[?25h\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35msaveError\u001b[0m ENOENT: no such file or directory, open '/content/package.json'\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[34;40mnotice\u001b[0m\u001b[35m\u001b[0m created a lockfile as package-lock.json. You should commit this file.\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35menoent\u001b[0m ENOENT: no such file or directory, open '/content/package.json'\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No description\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No repository field.\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No README data\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No license field.\n",
            "\u001b[0m\n",
            "+ localtunnel@2.0.2\n",
            "added 22 packages from 22 contributors and audited 22 packages in 1.731s\n",
            "\n",
            "3 packages are looking for funding\n",
            "  run `npm fund` for details\n",
            "\n",
            "found \u001b[92m0\u001b[0m vulnerabilities\n",
            "\n",
            "\u001b[K\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run app.py &>/content/logs.txt &"
      ],
      "metadata": {
        "id": "PECKVqQnxFmI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!npx localtunnel --port 8501 & curl ipv4.icanhazip.com"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTSMBrxDxFpI",
        "outputId": "59c1c105-dbde-4aaf-868d-30070f8ed130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35.247.76.119\n",
            "\u001b[K\u001b[?25hnpx: installed 22 in 1.993s\n",
            "your url is: https://modern-pots-train.loca.lt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import streamlit as st\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RxhproG0xFr_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i-vZXQWL4lAq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile breastcancer.py\n",
        "\n",
        "import streamlit as st\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "def user_report():\n",
        "  radius_mean= st.sidebar.slider('radius_mean', 0.00,25.00,3.00 )\n",
        "  Texture_mean= st.sidebar.slider('Texture_mean', 0.00,30.00, 5.00 )\n",
        "  perimeter_mean = st.sidebar.slider('perimeter_mean', 0.00,150.00, 70.00 )\n",
        "  area_mean = st.sidebar.slider('area_mean', 0.0,1500.0,1326.00 )\n",
        "  smoothness_mean = st.sidebar.slider('smoothness_mean', 0.0000000,2.0000000, 0.1000000)\n",
        "  compactness_mean = st.sidebar.slider('compactness_mean', 0.00000,2.00000, 0.2130000)\n",
        "  concavity_mean = st.sidebar.slider('concavity_mean', 0.0000000,2.0000000, 0.3000000 )\n",
        "  concave_points_mean = st.sidebar.slider('concave points_mean', 0.0000000,2.0000000, 0.3000000 )\n",
        "  symmetry_mean = st.sidebar.slider('symmetry_mean', 0.0000000,2.0000000, 0.3000000 )\n",
        "  fractal_dimension_mean = st.sidebar.slider('fractal_dimension_mean', 0.000000,1.000000, 0.030000 )\n",
        "  radius_se = st.sidebar.slider('radius_se', 0.000000,2.000000, 0.300000 )\n",
        "  texture_se = st.sidebar.slider('texture_se', 0.000000,2.000000, 0.900000 )\n",
        "  perimeter_se = st.sidebar.slider('perimeter_se', 0.0000,10.000, 8.563 )\n",
        "  area_se = st.sidebar.slider('area_se', 0.00,200.00, 153.60 )\n",
        "  smoothness_se = st.sidebar.slider('smoothness_se', 0.0000000,1.0000000, 0.00639000 )\n",
        "  compactness_se = st.sidebar.slider('compactness_se', 0.0000000,1.0000000, 0.0345700 )\n",
        "  concavity_se = st.sidebar.slider('concavity_se', 0.000000,1.000000, 0.05904 )\n",
        "  concave_points_se = st.sidebar.slider('concave points_se', 0.000000,1.000000, 0.0383 )\n",
        "  symmetry_se = st.sidebar.slider('symmetry_se', 0.000000,1.000000, 0.01756 )\n",
        "  fractal_dimension_se = st.sidebar.slider('fractal_dimension_se', 0.000000,1.000000, 0.00619 )\n",
        "  radius_worst = st.sidebar.slider('radius_worst', 0.0,70.0, 26.32 )\n",
        "  texture_worst = st.sidebar.slider('texture_worst', 0.0,60.0, 30.73 )\n",
        "  perimeter_worst = st.sidebar.slider('perimeter_worst', 0.0,250.0, 184.6 )\n",
        "  area_worst = st.sidebar.slider('area_worst', 0.0,2500.0, 2019.3 )\n",
        "  smoothness_worst = st.sidebar.slider('smoothness_worst', 0.000000,2.000000, 0.1457 )\n",
        "  compactness_worst = st.sidebar.slider('compactness_worst', 0.000000,2.000000, 0.6656 )\n",
        "  concavity_worst = st.sidebar.slider('concavity_worst', 0.000000,2.000000, 0.7 )\n",
        "  concave_points_worst = st.sidebar.slider('concave points_worst', 0.000000,2.000000, 0.2393)\n",
        "  symmetry_worst = st.sidebar.slider('symmetry_worst', 0.000000,2.000000, 0.3938 )\n",
        "  fractal_dimension_worst = st.sidebar.slider('fractal_dimension_worst', 0.0000000,1.0000000, 0.0834)\n",
        "\n",
        "  user_report_data = {\n",
        "      'radius_mean':radius_mean,\n",
        "      'Texture_mean':Texture_mean,\n",
        "      'perimeter_mean':perimeter_mean,\n",
        "      'area_mean':area_mean,\n",
        "      'smoothness_mean':smoothness_mean,\n",
        "      'compactness_mean':compactness_mean,\n",
        "      'concavity_mean':concavity_mean,\n",
        "      'concave_points_mean':concave_points_mean,\n",
        "      'symmetry_mean':symmetry_mean,\n",
        "      'fractal_dimension_mean':fractal_dimension_mean,\n",
        "      'radius_se':radius_se,\n",
        "      'texture_se':texture_se,\n",
        "      'perimeter_se':perimeter_se,\n",
        "      'area_se':area_se,\n",
        "      'smoothness_se':smoothness_se,\n",
        "      'compactness_se':compactness_se,\n",
        "      'concavity_se':concavity_se,\n",
        "      'concave_points_se':concave_points_se,\n",
        "      'symmetry_se':symmetry_se,\n",
        "      'fractal_dimension_se':fractal_dimension_se,\n",
        "      'radius_worst':radius_worst,\n",
        "      'texture_worst':texture_worst,\n",
        "      'perimeter_worst':perimeter_worst,\n",
        "      'area_worst':area_worst,\n",
        "      'smoothness_worst':smoothness_worst,\n",
        "      'compactness_worst':compactness_worst,\n",
        "      'concavity_worst':concavity_worst,\n",
        "      'concave_points_worst':concave_points_worst,\n",
        "      'symmetry_worst':symmetry_worst,\n",
        "      'fractal_dimension_worst':fractal_dimension_worst,\n",
        "\n",
        "  }\n",
        "  report_data = pd.DataFrame(user_report_data, index=[0])\n",
        "  return report_data\n",
        "\n",
        "read_file = pd.read_excel (\"/content/maindataset.xlsx\")\n",
        "read_file.to_csv (\"breastcancer.csv\", index = None,header=True)\n",
        "dataset = pd.read_csv('breastcancer.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "\n",
        "user_data = user_report()\n",
        "st.subheader('Patient Data')\n",
        "st.write(user_data)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "\n",
        "\n",
        "lr  = LogisticRegression()\n",
        "lr.fit(X_train, y_train)\n",
        "user_result = lr.predict(user_data)\n",
        "\n",
        "st.subheader('Your Report: ')\n",
        "output=''\n",
        "if user_result[0]==1:\n",
        "  output = 'You are have high risk of breast cancer'\n",
        "else:\n",
        "  output = 'You have low risk of breast cancer'\n",
        "st.title(output)\n",
        "st.subheader('Accuracy: ')\n",
        "st.write(str(accuracy_score(y_test, lr.predict(X_test))*100)+'%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FieF7hjAfda",
        "outputId": "6b527d0c-71d9-480f-dc56-94e726a0a3c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting breastcancer.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!npm install localtunnel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYQcU03Y4lC_",
        "outputId": "f235019a-729a-4bd3-ac56-e7f4869a1de0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K\u001b[?25h\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35msaveError\u001b[0m ENOENT: no such file or directory, open '/content/package.json'\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35menoent\u001b[0m ENOENT: no such file or directory, open '/content/package.json'\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No description\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No repository field.\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No README data\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No license field.\n",
            "\u001b[0m\n",
            "+ localtunnel@2.0.2\n",
            "updated 1 package and audited 36 packages in 0.41s\n",
            "\n",
            "3 packages are looking for funding\n",
            "  run `npm fund` for details\n",
            "\n",
            "found \u001b[92m0\u001b[0m vulnerabilities\n",
            "\n",
            "\u001b[K\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run breastcancer.py &>/content/logs.txt &"
      ],
      "metadata": {
        "id": "f_q7q7lS4lHG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!npx localtunnel --port 8501 & curl ipv4.icanhazip.com"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCzbGqf94lIq",
        "outputId": "4fcc87b9-d57c-4822-cc85-12a6627e1017"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35.247.76.119\n",
            "\u001b[K\u001b[?25hnpx: installed 22 in 2.394s\n",
            "your url is: https://beige-spies-carry.loca.lt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ooZkV4Nm4lKP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Q5Bs6Dn4lNO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkCbOKLB4lO1",
        "outputId": "5a71dc34-1403-4f25-f4a6-45d5870b3539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:432: UserWarning: X has feature names, but LogisticRegression was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}