{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "tF2nUGnWA9wQ",
        "outputId": "7b7d0778-02fc-475d-e796-d9a52d773bcc"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndentationError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-bfb3c3b35877>\"\u001b[0;36m, line \u001b[0;32m21\u001b[0m\n\u001b[0;31m    with st.form(\"questionaire\"):\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "import pandas as pd\n",
        "import streamlit as st\n",
        "smoking_status = {'formerly smoked': 1,\n",
        "            'never smoked\t': 2,\n",
        "            'smokes': 3,\n",
        "            'Unknown': 4,\n",
        "            }\n",
        "\n",
        "model = joblib.load('model.joblib')\n",
        "unique_values = joblib.load('unique_values.joblib')\n",
        "unique_gender = unique_values[\"gender\"]\n",
        "unique_ever_married = unique_values[\"ever_married\"]\n",
        "unique_work_type = unique_values[\"work_type\"]\n",
        "unique_Residence_type = unique_values[\"Residence_type\"]\n",
        "unique_smoking_status = unique_values[\"smoking_status\"]\n",
        "\n",
        "\n",
        "def main():\n",
        "  st.title(\"Adult Income Analysis\")\n",
        "    with st.form(\"questionaire\"):\n",
        "      age = st.slider(\"age\", min_value=0, max_value=100)\n",
        "      gender = st.selectbox(\"gender\", unique_gender)\n",
        "      hypertension = st.slider(\"hypertension\", min_value=0, max_value=1)\n",
        "      heart_disease = st.slider(\"heart_disease\", min_value=0, max_value=1)\n",
        "      ever_married = st.selectbox(\"ever_married\", unique_ever_married)\n",
        "      work_type = st.selectbox(\"work_type\", unique_work_type)\n",
        "      Residence_type = st.selectbox(\"Residence_type\", unique_Residence_type)\n",
        "      avg_glucose_level = st.slider(\"avg_glucose_level\", min_value=0, max_value=300)\n",
        "      bmi = st.slider(\"bmi\", min_value=0, max_value=100)\n",
        "      smoking_status = st.selectbox(\"smoking_status\", unique_smoking_status)\n",
        "\n",
        "clicked = st.form_submit_button(\"Predict stroke\")\n",
        "if clicked:\n",
        "  result=model.predict(pd.DataFrame({\"age\": [age],\n",
        "                                      \"gender\": [gender],\n",
        "                                      \"hypertension\": [hypertension],\n",
        "                                      \"heart_disease\": [heart_disease],\n",
        "                                      \"ever_married\": [ever_married],\n",
        "                                      \"work_type\": [work_type],\n",
        "                                      \"Residence_type\": [Residence_type],\n",
        "                                      \"avg_glucose_level\": [avg_glucose_level],\n",
        "                                      \"bmi\": [bmi],\n",
        "                                      \"smoking_status\":[smoking_status]}))\n",
        "          result = '1' if result[0] == 1 else '0'\n",
        "          st.success('The predicted stroke is {}'.format(result))\n",
        "if __name__=='__main__':\n",
        "  main()"
      ]
    }
  ]
}