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app.py

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+import os
+import logging
+from flask import Flask, render_template, request, jsonify
+from flask_socketio import SocketIO
+import joblib
+import torch
+import numpy as np
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = Flask(__name__)
+socketio = SocketIO(app, cors_allowed_origins="*")
+
+# Load models
+try:
+    # Try loading from the current directory first
+    HEART_MODEL_PATH = os.path.join('heart', 'models', 'heart_model.joblib')
+    logger.info(f"Attempting to load heart model from {HEART_MODEL_PATH}")
+    heart_model = joblib.load(HEART_MODEL_PATH)
+    logger.info("Heart model loaded successfully")
+except:
+    try:
+        # If that fails, try loading from the absolute path
+        HEART_MODEL_PATH = os.path.join(os.path.dirname(__file__), 'heart', 'models', 'heart_model.joblib')
+        logger.info(f"Attempting to load heart model from {HEART_MODEL_PATH}")
+        heart_model = joblib.load(HEART_MODEL_PATH)
+        logger.info("Heart model loaded successfully")
+    except Exception as e:
+        logger.error(f"Could not load heart model: {str(e)}")
+        heart_model = None
+
+try:
+    MODEL_DIR = os.path.join(os.path.dirname(__file__), 'models')
+    logger.info(f"Attempting to load autoencoder from {MODEL_DIR}")
+    autoencoder = torch.load(os.path.join(MODEL_DIR, 'best_model.pth'))
+    autoencoder.eval()
+    logger.info("Autoencoder model loaded successfully")
+except Exception as e:
+    logger.error(f"Could not load autoencoder model: {str(e)}")
+    autoencoder = None
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/health')
+def health():
+    status = {
+        'status': 'healthy',
+        'models': {
+            'heart_model': heart_model is not None,
+            'autoencoder': autoencoder is not None
+        }
+    }
+    return jsonify(status)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        data = request.get_json()
+        if not data:
+            return jsonify({'status': 'error', 'message': 'No data provided'}), 400
+        
+        # Add your prediction logic here
+        logger.info("Processing prediction request")
+        result = {'status': 'success', 'prediction': 'normal'}
+        logger.info(f"Prediction completed: {result}")
+        return jsonify(result)
+    except Exception as e:
+        logger.error(f"Error during prediction: {str(e)}")
+        return jsonify({'status': 'error', 'message': str(e)}), 500
+
+@socketio.on('connect')
+def handle_connect():
+    logger.info('Client connected')
+
+@socketio.on('disconnect')
+def handle_disconnect():
+    logger.info('Client disconnected')
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 7860))
+    logger.info(f"Starting server on port {port}")
+    socketio.run(app, host='0.0.0.0', port=port)