Create app.py

app.py

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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>&nbsp; &nbsp;&nbsp;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)