app.py

import numpy as np
import gradio as gr
import pickle
from sklearn.preprocessing import normalize

model = pickle.load(open("model.pkl", 'rb'))

def predict(Age, CigsPerDay, Cholestrol, SysBP, DIaBP, BMI, HeartRate, GlucoseLevel, Gender, BpMedication, PrevalentStroke, Smoker):
    '''
    For making predictions
    '''
    list_to_be_normalised = np.array([Age, CigsPerDay, Cholestrol, SysBP, DIaBP, BMI, HeartRate, GlucoseLevel]).reshape(1, -1)
    normalized = normalize(list_to_be_normalised)
    boolean = [Gender, BpMedication, PrevalentStroke, Smoker]
    final_features = np.append(normalized, boolean).reshape(1, -1)
    prediction = model.predict(final_features)

    if prediction == 1:
        return "problem"
    else:
        return "healthy"
    
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.inputs.Number(label="Age (Enter Age)", default=40),
        gr.inputs.Number(label="CigsPerDay (Enter Cigarettes Per Day)", default=10),
        gr.inputs.Number(label="Cholesterol (Enter Cholesterol Level)", default=200),
        gr.inputs.Number(label="SysBP (Enter Systolic Blood Pressure)", default=120),
        gr.inputs.Number(label="DIaBP (Enter Diastolic Blood Pressure)", default=80),
        gr.inputs.Number(label="BMI (Enter Body Mass Index)", default=25),
        gr.inputs.Number(label="HeartRate (Enter Heart Rate)", default=70),
        gr.inputs.Number(label="GlucoseLevel (Enter Glucose Level)", default=100),
        gr.inputs.Checkbox(label="Gender (Select Gender)"),
        gr.inputs.Checkbox(label="BpMedication (Select Blood Pressure Medication)"),
        gr.inputs.Checkbox(label="PrevalentStroke (Select Prevalent Stroke)"),
        gr.inputs.Checkbox(label="Smoker (Select Smoker)"),
    ],
    outputs=gr.outputs.Textbox(label="Prediction"),
)

iface.launch()