app.py

import joblib
import pandas as pd
import gradio as gr

# Load model
model = joblib.load("stroke_rf_model.pkl")

# Define prediction function
def predict_stroke(age, hypertension, heart_disease, glucose_level, bmi):
    data = {
        'age': [age],
        'hypertension': [hypertension],
        'heart_disease': [heart_disease],
        'avg_glucose_level': [glucose_level],
        'bmi': [bmi]
    }
    df = pd.DataFrame(data)
    prediction = model.predict(df)
    return "Stroke Risk" if prediction[0] == 1 else "No Stroke Risk"

# Create Gradio Interface
iface = gr.Interface(
    fn=predict_stroke,
    inputs=[
        gr.Number(label="Age"),
        gr.Radio(choices=[0, 1], label="Hypertension (0=No, 1=Yes)"),
        gr.Radio(choices=[0, 1], label="Heart Disease (0=No, 1=Yes)"),
        gr.Number(label="Average Glucose Level"),
        gr.Number(label="BMI")
    ],
    outputs="text",
    title="Stroke Prediction Model",
    description="Predict stroke risk based on health metrics."
)

# Launch the app
iface.launch()