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| #importing the libraries | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.metrics import accuracy_score | |
| #loading the dataset | |
| df = pd.read_csv('Sleep_health_and_lifestyle_dataset.csv') | |
| # df.head() | |
| #checking for missing values | |
| df.isnull().sum() | |
| #replacing the null values with 'None' in the column 'Sleep Disorder' | |
| df['Sleep Disorder'].fillna('None', inplace=True) | |
| #drop column Person ID | |
| df.drop('Person ID', axis=1, inplace=True) | |
| #spliting the blood pressure into two columns | |
| df['systolic_bp'] = df['Blood Pressure'].apply(lambda x: x.split('/')[0]) | |
| df['diastolic_bp'] = df['Blood Pressure'].apply(lambda x: x.split('/')[1]) | |
| #droping the blood pressure column | |
| df.drop('Blood Pressure', axis=1, inplace=True) | |
| #replacing normal weight with normal in BMI column | |
| df['BMI Category'] = df['BMI Category'].replace('Normal Weight', 'Normal') | |
| df['Gender'].replace({'Male':0, 'Female':1}, inplace=True) | |
| df['Occupation'].replace({'Software Engineer':0, 'Doctor':1,'Sales Representative':2,'Teacher':3,'Nurse':4,'Engineer':5,'Accountant':6,'Scientist':7,'Lawyer':8,'Salesperson':9,'Manager':10}, inplace=True) | |
| df['BMI Category'].replace({'Normal':0, 'Overweight':1, 'Obese':2}, inplace=True) | |
| df['Sleep Disorder'].replace({'None':0,'Insomnia':1, 'Sleep Apnea':2}, inplace=True) | |
| from sklearn.model_selection import train_test_split | |
| X_train, X_test, y_train, y_test = train_test_split(df.drop('Sleep Disorder',axis=1), df['Sleep Disorder'], test_size=0.2, random_state=42) | |
| from sklearn.ensemble import RandomForestClassifier | |
| rfc = RandomForestClassifier() | |
| rfc.fit(X_train, y_train) | |
| def func(a, b, c, d, e, f, g, h, i, j, k, l): | |
| x_test = [[a, b, c, d, e, f, g, h, i, j, k, l]] | |
| result = rfc.predict(x_test)[0] | |
| dict = { | |
| 0 : "None", | |
| 1: "Insomnia", | |
| 2 : "Sleep Apnea" | |
| } | |
| if result==0: | |
| str = "Yay! You are totally fit 😃😎" | |
| elif result==1: | |
| str = "You may be suffering from Sleep Insomnia 😐😐" | |
| else: | |
| str="You may be suffering from Sleep Apnea 🙁🙁" | |
| return f"{dict[result]}\n\n{str}" | |
| import gradio as gr | |
| demo = gr.Interface( | |
| fn= func, | |
| inputs=[ | |
| gr.Textbox(label="Gender", placeholder="Enter 0 for Male and 1 for Female", elem_id="gender",type='text'), | |
| gr.Textbox(label="Age", placeholder="Enter Your Age", elem_id="gender",type='text'), | |
| gr.Textbox(label="Occupation", placeholder="Enter from 0 - 10", elem_id="gender",type='text'), | |
| gr.Textbox(label="Sleep Duration(in Hrs)", placeholder="Enter Your Sleep Duration", elem_id="gender",type='text'), | |
| gr.Textbox(label="Quality of Sleep", placeholder="Enter Your Quality of Sleep", elem_id="gender",type='text'), | |
| gr.Textbox(label="Physical Activity Level", placeholder="Enter Your Physical Activity Level", elem_id="gender",type='text'), | |
| gr.Textbox(label="Stress Level", placeholder="Enter Your Stress Level", elem_id="gender",type='text'), | |
| gr.Textbox(label="BMI Category", placeholder="Enter 0 for Normal 1 for Overweight and 2 for Obeses", elem_id="gender",type='text'), | |
| gr.Textbox(label="Systolic Blood Pressure", placeholder="Enter Your Systolic Blood Pressure", elem_id="gender",type='text'), | |
| gr.Textbox(label="Diastolic Blood Pressure", placeholder="Enter Your Diastolic Blood Pressure", elem_id="gender",type='text'), | |
| gr.Textbox(label="Heart Rate", placeholder="Enter Your Heart Rate", elem_id="gender",type='text'), | |
| gr.Textbox(label="Daily Steps Count", placeholder="Enter Your Daily Steps Count", elem_id="gender",type='text') | |
| ], | |
| outputs='text', | |
| theme=gr.themes.Soft(), | |
| title="<h1 id=title-first> Welcome to HEALTHSURE <br> <span id=title-second>Predict Your Sleep Disorder here using a ML Model</span> </h1>", | |
| description="<p id=desc>◾ Please Enter the Data in following way(Important)<br>◾ Gender: <span id=desc-info>Male=0 Female=1</span><br>◾ Occupation: <span id=desc-info> Software Engineer=0 Doctor=1 Sales Representative=2 Teacher=3 Nurse=4 <br> Engineer=5 Accontant=6 Scientist=7 Lawyer=8 SalesPerson=9 Manager=10</span> <br>◾ BMI Category: <span id=desc-info>Normal=0 OverWeight=1 Obese=2 </span></p> <br> <p id=desc>**Some Examples are given at bottom You can try them by clicking on it.<br>**Enter only Numeric Value</p>", | |
| css=""" | |
| .gradio-container {background-color: #daeefe}" | |
| #gender { background-color : teal !important; } | |
| #gender textarea {background-color: #ecf7fd; font-size : 15px; color : black; | |
| font-weight : bold; !important;} | |
| #desc {font-weight : bold; color : black !important;} | |
| #desc-info{font-weight:normal;} | |
| h1 {text-align : center; font-size: 40px !important;} | |
| #title-first {color:black; !important;} | |
| #title-second {color:green; font-size: 17px !important;} | |
| #a-tag { color : white !important;} | |
| #a-tag:hover {text-decoration : none !important;} | |
| """, | |
| examples=[[0, 44, 9, 6.3, 6, 45, 7, 1, 130,85, 72, 6000],[1, 31, 4, 7.9, 8, 75, 4, 0, 117, 76, 69, 6800],[1, 49, 4, 6.1, 6, 90, 8, 1, 140,95, 75, 10000]] | |
| ) | |
| demo.launch(inline=False) |