HD_Ensemble_Streamlit.ipynb

{
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "-I6V6uttuo2S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa39a2c6-c5b2-4f31-c486-dc39a9e14c07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing collected packages: watchdog, validators, smmap, pydeck, gitdb, gitpython, streamlit\n",
            "Successfully installed gitdb-4.0.11 gitpython-3.1.41 pydeck-0.8.1b0 smmap-5.0.1 streamlit-1.31.0 validators-0.22.0 watchdog-3.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install streamlit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "j47IROxUx33o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4cdaf1f-bb7b-4315-d588-31cf595982b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing collected packages: streamlit_option_menu\n",
            "Successfully installed streamlit_option_menu-0.3.12\n"
          ]
        }
      ],
      "source": [
        "!pip install streamlit_option_menu"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "def getLocalFiles():\n",
        "    _files = files.upload()\n",
        "    if len(_files) >0:\n",
        "       for k,v in _files.items():\n",
        "         open(k,'wb').write(v)\n",
        "getLocalFiles()"
      ],
      "metadata": {
        "id": "p443i7johvcW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "387b0131-9e90-46a1-cfdb-24b22b8b2bd0"
      },
      "execution_count": 3,
      "outputs": [
        {
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
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              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
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              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
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              "          100 :\n",
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              "      percent.textContent = `${percentDone}% done`;\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
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              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
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              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving heart_disease_SVEnsemble_model.sav to heart_disease_SVEnsemble_model.sav\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "9vL3ZrDevAJT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8085ef2b-626b-4339-b9dd-e9a52f36edc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing app.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile app.py\n",
        "import pickle\n",
        "import streamlit as st\n",
        "from streamlit_option_menu import option_menu\n",
        "\n",
        "#load model\n",
        "heart_ensemble_model=pickle.load(open('heart_disease_SVEnsemble_model.sav','rb'))\n",
        "\n",
        "# sidebar for navigation\n",
        "with st.sidebar:\n",
        "\n",
        "    selected = option_menu('Disease Prediction System',\n",
        "                          ['Heart Disease Prediction'],\n",
        "                          icons=['activity','heart','person'],\n",
        "                          default_index=0)\n",
        "\n",
        "# Heart Disease Prediction Page\n",
        "if (selected == 'Heart Disease Prediction'):\n",
        "\n",
        "    # page title\n",
        "    st.title('Heart Disease Prediction using Ensembled ML')\n",
        "\n",
        "    col1, col2, col3 = st.columns(3)\n",
        "\n",
        "    with col1:\n",
        "        age = st.number_input('Age')\n",
        "\n",
        "    with col2:\n",
        "        sex = st.number_input('Sex')\n",
        "\n",
        "    with col3:\n",
        "        cp = st.number_input('Chest Pain types')\n",
        "\n",
        "    with col1:\n",
        "        trestbps = st.number_input('Resting Blood Pressure')\n",
        "\n",
        "    with col2:\n",
        "        chol = st.number_input('Serum Cholestoral in mg/dl')\n",
        "\n",
        "    with col3:\n",
        "        fbs = st.number_input('Fasting Blood Sugar > 120 mg/dl')\n",
        "\n",
        "    with col1:\n",
        "        restecg = st.number_input('Resting Electrocardiographic results')\n",
        "\n",
        "    with col2:\n",
        "        thalach = st.number_input('Maximum Heart Rate achieved')\n",
        "\n",
        "    with col3:\n",
        "        exang = st.number_input('Exercise Induced Angina')\n",
        "\n",
        "    with col1:\n",
        "        oldpeak = st.number_input('ST depression induced by exercise')\n",
        "\n",
        "    with col2:\n",
        "        slope = st.number_input('Slope of the peak exercise ST segment')\n",
        "\n",
        "    with col3:\n",
        "        ca = st.number_input('Major vessels colored by flourosopy')\n",
        "\n",
        "    with col1:\n",
        "        thal = st.number_input('thal: 0 = normal; 1 = fixed defect; 2 = reversable defect')\n",
        "\n",
        "    # code for Prediction\n",
        "    heart_diagnosis = ''\n",
        "\n",
        "    # creating a button for Prediction\n",
        "    if st.button('Heart Disease Test Result'):\n",
        "        heart_prediction = heart_ensemble_model.predict()\n",
        "\n",
        "        if (heart_prediction == 1):\n",
        "          heart_diagnosis = 'The person is having heart disease'\n",
        "        else:\n",
        "          heart_diagnosis = 'The person does not have any heart disease'\n",
        "\n",
        "    st.success(heart_diagnosis)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YykgGHWqxk8S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "654acae5-96f1-453b-bf23-25ca76dea611"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34.90.154.16\n"
          ]
        }
      ],
      "source": [
        "! wget -q -O - ipv4.icanhazip.com"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Open the your url and enter the code received from command\n",
        "\n",
        "!wget -q -O - ipv4.icanhazip.com"
      ],
      "metadata": {
        "id": "xD7djGBYerwm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gw88uQlnvY9I",
        "outputId": "edafd060-bb4c-416a-a177-a57c5b0e4bac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.\n",
            "\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://34.90.154.16:8501\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[K\u001b[?25hnpx: installed 22 in 5.022s\n",
            "your url is: https://late-wolves-fail.loca.lt\n",
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:160: UserWarning: [05:55:16] WARNING: /workspace/src/common/error_msg.h:80: If you are loading a serialized model (like pickle in Python, RDS in R) or\n",
            "configuration generated by an older version of XGBoost, please export the model by calling\n",
            "`Booster.save_model` from that version first, then load it back in current version. See:\n",
            "\n",
            "    https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html\n",
            "\n",
            "for more details about differences between saving model and serializing.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        }
      ],
      "source": [
        "! streamlit run app.py & npx localtunnel --port 8501"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}