Update app.py

app.py

@@ -13,33 +13,34 @@ app.config['UPLOAD_FOLDER'] = 'uploads'
 # Create uploads folder if it doesn't exist
 os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 
-# Load the model with custom objects to handle compatibility
+# Load the model with compatibility handling
 print("Loading model...")
 import warnings
 warnings.filterwarnings('ignore')
 
-# Custom object scope to handle old Keras model format
-from tensorflow.keras.layers import InputLayer
-import tensorflow.keras.backend as K
+# Set TensorFlow to use less memory
+os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
 
-# Patch for old model format compatibility
-def custom_input_layer(*args, **kwargs):
-    # Convert old 'batch_shape' to new 'shape'
-    if 'batch_shape' in kwargs:
-        batch_shape = kwargs.pop('batch_shape')
-        if batch_shape is not None and len(batch_shape) > 1:
-            kwargs['shape'] = batch_shape[1:]
-    return InputLayer(*args, **kwargs)
+# Try loading with h5py directly for better compatibility
+import h5py
 
-# Try loading with custom objects
 try:
-    with tf.keras.utils.custom_object_scope({'InputLayer': custom_input_layer}):
-        model = load_model('cancer_model.h5', compile=False)
-    print("Model loaded successfully!")
+    # Use legacy loader which is more compatible
+    from tensorflow.keras.saving import legacy
+    model = legacy.load_model.load_model('cancer_model.h5', compile=False)
+    print("Model loaded successfully with legacy loader!")
 except Exception as e:
-    print(f"Error loading model: {e}")
-    print("Please ensure the model file is compatible with TensorFlow 2.13.0")
-    raise
+    print(f"Legacy loader failed: {e}")
+    try:
+        # Fallback: try standard loader
+        model = load_model('cancer_model.h5', compile=False)
+        print("Model loaded successfully!")
+    except Exception as e2:
+        print(f"Standard loader also failed: {e2}")
+        print("\nPlease re-save your model using:")
+        print("  model.save('cancer_model.h5', save_format='h5')")
+        print("Or convert to SavedModel format for better compatibility")
+        raise
 
 def resize_with_padding(img, target_size):
     """Resize image while maintaining aspect ratio and add padding"""