app.py

import gradio as gr
import numpy as np
import pickle

# Load the trained model
with open('heart_disease_model.pkl', 'rb') as file:
    model = pickle.load(file)

# Define the prediction function
def predict_heart_disease(male, age, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, BMI):
    male = 1 if male == "Male" else 0
    currentSmoker = 1 if currentSmoker else 0
    BPMeds = 1 if BPMeds else 0
    prevalentStroke = 1 if prevalentStroke else 0
    prevalentHyp = 1 if prevalentHyp else 0
    diabetes = 1 if diabetes else 0
    input_data = np.array([[male, age, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, BMI]])
    prediction = model.predict(input_data)
    return 'Possible risk of Heart Disease' if prediction[0] == 1 else 'No possible risk of Heart Disease'

# Define the input components
inputs = [
    gr.Radio(choices=["Male","Female"], label="Sex"),
    gr.Number(label="Age (13 to 80)", minimum=13, maximum=80),
    gr.Checkbox(label="Do you smoke?"),
    gr.Number(label="Number of ciggerates a day"),
    gr.Checkbox(label="Do you take BP medicines?"),
    gr.Checkbox(label="Have you had strokes previously?"),
    gr.Checkbox(label="Have you had High BP previously?"),
    gr.Checkbox(label="Do you have Diabetes?"),
    gr.Number(value=float, label="BMI (13 to 40)", minimum=13, maximum=40),

]

# Define the output component
outputs = gr.Textbox(label="Prediction")

# Create and launch the Gradio interface
gr.Interface(fn=predict_heart_disease, inputs=inputs, outputs=outputs).launch()