Upload app.py with huggingface_hub

app.py

@@ -13,12 +13,28 @@ logger = logging.getLogger(__name__)
 app = Flask(__name__)
 socketio = SocketIO(app, cors_allowed_origins="*")
 
+# Create necessary directories
+def create_directories():
+    directories = [
+        'heart/models',
+        'models'
+    ]
+    for directory in directories:
+        try:
+            os.makedirs(directory, exist_ok=True)
+            logger.info(f"Created directory: {directory}")
+        except Exception as e:
+            logger.warning(f"Failed to create directory {directory}: {str(e)}")
+
 # Load models
 def load_models():
     global heart_model, autoencoder
     heart_model = None
     autoencoder = None
     
+    # Create directories first
+    create_directories()
+    
     # Define possible model paths
     model_paths = [
         os.path.join('heart', 'models', 'heart_model.joblib'),
@@ -30,9 +46,12 @@ def load_models():
     for path in model_paths:
         try:
             logger.info(f"Attempting to load heart model from {path}")
-            heart_model = joblib.load(path)
-            logger.info("Heart model loaded successfully")
-            break
+            if os.path.exists(path):
+                heart_model = joblib.load(path)
+                logger.info("Heart model loaded successfully")
+                break
+            else:
+                logger.warning(f"Model file not found at {path}")
         except Exception as e:
             logger.warning(f"Failed to load heart model from {path}: {str(e)}")
             continue
@@ -47,10 +66,13 @@ def load_models():
     for path in autoencoder_paths:
         try:
             logger.info(f"Attempting to load autoencoder from {path}")
-            autoencoder = torch.load(path)
-            autoencoder.eval()
-            logger.info("Autoencoder model loaded successfully")
-            break
+            if os.path.exists(path):
+                autoencoder = torch.load(path)
+                autoencoder.eval()
+                logger.info("Autoencoder model loaded successfully")
+                break
+            else:
+                logger.warning(f"Model file not found at {path}")
         except Exception as e:
             logger.warning(f"Failed to load autoencoder from {path}: {str(e)}")
             continue