app.py

from flask import Flask, request, jsonify
import tensorflow as tf
import numpy as np
import os

app = Flask(__name__)

# Load the model
model_path = "sleep_cognition_model.h5"

if not os.path.exists(model_path):
    raise FileNotFoundError(f"⚠️ Model file not found: {model_path}. Please check the path.")

model = tf.keras.models.load_model(model_path)

@app.route("/", methods=["GET"])
def home():
    return jsonify({"message": "Sleep Cognition Model API is running!"})

@app.route("/predict", methods=["POST"])
def predict():
    try:
        data = request.json["input"]
        data = np.array(data).reshape(1, -1)  # Reshape input data if needed
        prediction = model.predict(data)
        return jsonify({"prediction": prediction.tolist()})
    except Exception as e:
        return jsonify({"error": str(e)}), 400

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000, debug=True)