app.py

import gradio as gr
import numpy as np
import joblib
import json

# Load updated model and scaler
model = joblib.load("epilepsy_rf_model_9features.pkl")
scaler = joblib.load("scaler_9features.pkl")

# Load updated feature names
with open("feature_names.json", "r") as f:
    feature_names = json.load(f)

# Define prediction function
def predict_epilepsy(*inputs):
    input_array = np.array(inputs).reshape(1, -1)
    scaled_input = scaler.transform(input_array)
    prediction = model.predict(scaled_input)[0]
    return "🧠 Epileptic" if prediction == 1 else "✅ Not Epileptic"

# Build Gradio UI
inputs = [gr.Number(label=feature) for feature in feature_names]

demo = gr.Interface(
    fn=predict_epilepsy,
    inputs=inputs,
    outputs="text",
    title="🧠 Epilepsy Seizure Prediction",
    description="Enter EEG signal features to predict if a patient has epilepsy.",
)

demo.launch()