app.py

import gradio as gr
import joblib
import numpy as np

# Load trained model
model = joblib.load("xgboost_model.pkl")

# 🔧 Preprocessing Function
def preprocess(age, gender, bmi, smoking, alcohol, diabetes, hypertension, family_history, cognitive_score, apoe4):
    gender = 1 if gender == "Male" else 0
    smoking = {"Non-Smoker": 0, "Smoker": 1, "Former Smoker": 2}[smoking]
    alcohol = {"None": 0, "Moderate": 1, "Heavy": 2}[alcohol]
    diabetes = 1 if diabetes == "Yes" else 0
    hypertension = 1 if hypertension == "Yes" else 0
    family_history = 1 if family_history == "Yes" else 0
    apoe4 = 1 if apoe4 == "Yes" else 0

    return np.array([[age, gender, bmi, smoking, alcohol, diabetes, hypertension, family_history, cognitive_score, apoe4]])

# 🧠 Prediction Function
def predict_alzheimers(age, gender, bmi, smoking, alcohol, diabetes, hypertension, family_history, cognitive_score, apoe4):
    features = preprocess(age, gender, bmi, smoking, alcohol, diabetes, hypertension, family_history, cognitive_score, apoe4)

    # Debug: print features being sent to model
    print("🛠️ Processed input sent to model:", features)

    # Predict probability of class 1 (positive)
    proba = model.predict_proba(features)[0][1]
    proba_percent = round(proba * 100, 2)

    if proba >= 0.5:
        return f"🧠 Likely Alzheimer’s Positive ({proba_percent}%)"
    else:
        return f"✅ Likely Alzheimer’s Negative ({100 - proba_percent}%)"

# 🎨 Gradio Interface
interface = gr.Interface(
    fn=predict_alzheimers,
    inputs=[
        gr.Number(label="Age"),
        gr.Radio(["Male", "Female"], label="Gender"),
        gr.Number(label="BMI"),
        gr.Radio(["Non-Smoker", "Smoker", "Former Smoker"], label="Smoking Status"),
        gr.Radio(["None", "Moderate", "Heavy"], label="Alcohol Consumption"),
        gr.Radio(["Yes", "No"], label="Diabetes"),
        gr.Radio(["Yes", "No"], label="Hypertension"),
        gr.Radio(["Yes", "No"], label="Family History of Alzheimer’s"),
        gr.Number(label="Cognitive Test Score"),
        gr.Radio(["Yes", "No"], label="APOE-ε4 allele Present?")
    ],
    outputs="text",
    title="Alzheimer’s Prediction App",
    description="🔍 Enter details to predict Alzheimer’s risk using XGBoost model"
)

if __name__ == "__main__":
    interface.launch()