Create app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+
+# Load trained model
+model = joblib.load("xgboost_model.pkl")  # Make sure this file is in same directory
+
+# Prediction function
+def predict_alzheimers(age, gender, bmi, smoking, alcohol, diabetes, hypertension, family_history, cognitive_score, apoe4):
+    # Convert inputs
+    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
+
+    features = np.array([[age, gender, bmi, smoking, alcohol, diabetes, hypertension, family_history, cognitive_score, apoe4]])
+    prediction = model.predict(features)[0]
+    return "🧠 Likely Alzheimer’s Positive" if prediction == 1 else "✅ Likely Alzheimer’s Negative"
+
+# Gradio UI
+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 patient details to predict Alzheimer’s risk using XGBoost model"
+)
+
+if __name__ == "__main__":
+    interface.launch()