Update app.py

app.py

@@ -9,6 +9,7 @@ import requests
 import io
 import logging
 import time
+import h5py
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
@@ -40,7 +41,7 @@ MODEL_CONFIGS = {
     }
 }
 
-# Performance metrics (for recommendation logic)
+# Performance metrics
 MODEL_METRICS = {
     "Custom CNN": {"accuracy": 95.2, "inference_time": 45, "model_size": 25.3},
     "MobileNetV2": {"accuracy": 92.8, "inference_time": 18, "model_size": 8.7},
@@ -49,18 +50,42 @@ MODEL_METRICS = {
 }
 
 def load_model(model_name):
-    """Load model from Hugging Face with error handling"""
+    """Load model from Hugging Face with enhanced error handling"""
     try:
         logger.info(f"Loading model: {model_name}")
         url = MODEL_CONFIGS[model_name]["url"]
         response = requests.get(url, timeout=60, stream=True)
         response.raise_for_status()
-        if len(response.content) < 1000:
-            return None, f"Model {model_name} download failed - file too small"
+
+        # Check content length
+        content_length = int(response.headers.get('content-length', 0))
+        if content_length < 1000:
+            logger.error(f"Model {model_name} file too small: {content_length} bytes")
+            return None, f"Model {model_name} file is too small ({content_length} bytes)"
+
+        # Read content into BytesIO
         model_bytes = io.BytesIO(response.content)
+
+        # Verify if the file is a valid HDF5 file
+        try:
+            with h5py.File(model_bytes, 'r') as f:
+                if 'keras_version' not in f.attrs:
+                    logger.error(f"Model {model_name} is not a valid HDF5 file")
+                    return None, f"Model {model_name} is not a valid HDF5 file"
+        except Exception as h5py_error:
+            logger.error(f"Invalid HDF5 file for {model_name}: {str(h5py_error)}")
+            return None, f"Invalid HDF5 file for {model_name}: {str(h5py_error)}"
+
+        # Reset BytesIO position
+        model_bytes.seek(0)
+
+        # Load the model
         model = tf.keras.models.load_model(model_bytes)
         logger.info(f"Successfully loaded model: {model_name}")
         return model, None
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Network error loading {model_name}: {str(e)}")
+        return None, f"Network error loading {model_name}: {str(e)}"
     except Exception as e:
         logger.error(f"Error loading {model_name}: {str(e)}")
         return None, f"Error loading {model_name}: {str(e)}"