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{
"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()"
]
}
]
} |