Update

backend/model_histo.py

@@ -401,37 +401,35 @@ class BreastCancerClassifier:
             batch = images[i:i + batch_size]
             processed_batch = self.preprocess_image_batch(batch)
             
-            try:
-                # Handle different model interface types
-                if hasattr(self.path_foundation, 'signatures') and "serving_default" in self.path_foundation.signatures:
-                    # TensorFlow SavedModel format
-                    infer = self.path_foundation.signatures["serving_default"]
-                    batch_embeddings = infer(tf.constant(processed_batch))
-                elif hasattr(self.path_foundation, 'predict'):
-                    # Standard Keras model
-                    batch_embeddings = self.path_foundation.predict(processed_batch, verbose=0)
+        # Calculate batch number before try block to ensure it's available in except block
+        batch_num = i // batch_size + 1
+        
+        try:
+            # Handle different model interface types
+            if hasattr(self.path_foundation, 'signatures') and "serving_default" in self.path_foundation.signatures:
+                # TensorFlow SavedModel format
+                infer = self.path_foundation.signatures["serving_default"]
+                batch_embeddings = infer(tf.constant(processed_batch))
+            elif hasattr(self.path_foundation, 'predict'):
+                # Standard Keras model
+                batch_embeddings = self.path_foundation.predict(processed_batch, verbose=0)
+            else:
+                # Direct callable
+                batch_embeddings = self.path_foundation(processed_batch)
+            
+            # Handle different output formats
+            if isinstance(batch_embeddings, dict):
+                key = list(batch_embeddings.keys())[0]
+                if hasattr(batch_embeddings[key], 'numpy'):
+                    batch_embeddings = batch_embeddings[key].numpy()
                 else:
-                    # Direct callable
-                    batch_embeddings = self.path_foundation(processed_batch)
-                
-                # Handle different output formats
-                if isinstance(batch_embeddings, dict):
-                    key = list(batch_embeddings.keys())[0]
-                    if hasattr(batch_embeddings[key], 'numpy'):
-                        batch_embeddings = batch_embeddings[key].numpy()
-                    else:
-                        batch_embeddings = batch_embeddings[key]
-                elif hasattr(batch_embeddings, 'numpy'):
-                    batch_embeddings = batch_embeddings.numpy()
-                
-                embeddings.append(batch_embeddings)
-                
-                # Progress reporting
-                batch_num = i // batch_size + 1
-                if batch_num % 10 == 0:
-                    print(f"Processed batch {batch_num}/{num_batches}")
-                    
-            except Exception as e:
+                    batch_embeddings = batch_embeddings[key]
+            elif hasattr(batch_embeddings, 'numpy'):
+                batch_embeddings = batch_embeddings.numpy()
+            
+            embeddings.append(batch_embeddings)
+            
+            # Progress reporting
                 print(f"Error processing batch {batch_num}: {e}")
                 continue
         

frontend/src/components/UploadSection.tsx

@@ -59,8 +59,8 @@ export function UploadSection({
       "/histo/hist1.png",
       "/histo/hist2.png",
       "/histo/hist3.jpg",
-      "/histo/hist4.jpg",
-      "/histo/hist5.jpg",
+      "/histo/hist4.tif",
+      "/histo/hist5.tif",
     ],
   };