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dashboard.ipynb

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+{
+ "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",
+       "        <iframe\n",
+       "            width=\"900\"\n",
+       "            height=\"500\"\n",
+       "            src=\"http://127.0.0.1:7866/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x26e56928e50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<fastapi.applications.FastAPI at 0x26e43876550>,\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
+}