{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5114c17a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading......\n", "Running on local URL: http://127.0.0.1:7866/\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(,\n", " 'http://127.0.0.1:7866/',\n", " None)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pickle\n", "import pandas as pd\n", "import numpy as np\n", "import gradio as gr\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "print('Loading......')\n", "\n", "# load the saved model\n", "rfc_saved = pickle.load(open('rfc.pickle','rb'))\n", "\n", "full_pipeline_saved = pickle.load(open('full_pipeline.pickle','rb'))\n", "\n", "\n", "# function to check the heart disease risk\n", "def CheckHeartDisease(age,sex,ChestPainType,RestingBP,Cholesterol,\n", " FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope):\n", " try:\n", " df_model = pd.DataFrame([],columns=['Age','Sex','ChestPainType','RestingBP','Cholesterol','FastingBS','RestingECG','MaxHR','ExerciseAngina', 'Oldpeak','ST_Slope'])\n", "\n", " df_model.loc[0] = [age,sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope]\n", " \n", " # preprocess the person details\n", " X_processed = full_pipeline_saved.transform(df_model)\n", " \n", " # do the prediction\n", " y_pred = rfc_saved.predict(X_processed)\n", " \n", " # plot risk of heart disease based on sex\n", " df = pd.read_csv('heart.csv')\n", " target = df['HeartDisease'].replace([0,1],['Low','High'])\n", " data = pd.crosstab(index=df['Sex'],\n", " columns=target)\n", " \n", " data.plot(kind='bar',stacked=True)\n", " fig1 = plt.gcf()\n", " plt.close()\n", " \n", " # plot count of person within given age range, with heart disease risk\n", " bins=[0,30,50,80]\n", " sns.countplot(x=pd.cut(df.Age,bins=bins),hue=target,color='r')\n", " fig2 = plt.gcf()\n", " plt.close()\n", "\n", " # plot graph based on ChestPainType\n", " sns.countplot(x=target,hue=df.ChestPainType)\n", " plt.xticks(np.arange(2), ['No', 'Yes']) \n", " fig3 = plt.gcf()\n", "\n", " if y_pred[0]==0:\n", " return 'No Heart Disease',fig1,fig2,fig3\n", " else:\n", " return 'High Chances of Heart Disease',fig1,fig2,fig3\n", " \n", " except:\n", " return 'Wrong inputs',fig1,fig2,fig3\n", "\n", "# create GUI\n", "iface = gr.Interface(\n", " CheckHeartDisease, # its the function to be called with below parameters\n", " [\n", " gr.inputs.Number(label='Age (0-115)'), \n", " gr.inputs.Dropdown(['M','F'],default='M'), \n", " gr.inputs.Dropdown(['ATA', 'NAP', 'ASY','TA'],default='TA'),\n", " gr.inputs.Number(label='RESTINGBP (0-200)'), \n", " gr.inputs.Number(label='CHOLESTEROL (0-603)'), \n", " gr.inputs.Number(label='FASTINGBS (0-1)'), \n", " gr.inputs.Dropdown(['Normal', 'ST' ,'LVH'],default='ST'),\n", " gr.inputs.Number(label='MAXHR (60-202)'), \n", "\n", " gr.inputs.Dropdown(['Y','N'],default='Y'),\n", " gr.inputs.Number(label='OLDPEAK (-2.6 to 6.2)'),\n", " gr.inputs.Dropdown(['Up', 'Flat', 'Down'],default='Up')\n", " ],\n", " [gr.outputs.Textbox(),\"plot\",\"plot\",\"plot\"]\n", " \n", " , live=False,layout='vertical',title='Get Your Heart Disease Status',\n", ")\n", "\n", "iface.launch() # launch the gui\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5d0cfc37", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }