Update app.py

app.py

@@ -303,44 +303,34 @@ class MultimodalRAG:
         except Exception as e:
             logger.error(f"Error loading template embeddings: {e}")
 
-    def save_template_embeddings(self):
-        """Save template embeddings to disk"""
+    def encode_image(self, image: Image.Image) -> np.ndarray:
+        """Encode image using BLIP"""
         try:
-            template_path = TEMPLATE_DIR / "template_embeddings.npz"
-            np.savez(
-                template_path,
-                **self.template_embeddings
-            )
+            model, processor = self.model_manager.load_model("image_processor")
+            
+            # Process image
+            inputs = processor(images=image, return_tensors="pt").to(model.device)
+            
+            # Get image features using the proper method
+            with torch.no_grad():
+                outputs = model.get_image_features(**inputs)
+                image_features = outputs.last_hidden_state.mean(dim=1)  # Average pooling
+            
+            return image_features.cpu().numpy()
+                
         except Exception as e:
-            logger.error(f"Error saving template embeddings: {e}")
+            logger.error(f"Error encoding image: {str(e)}")
+            raise ModelError(f"Error encoding image: {str(e)}")
 
-    def encode_image(self, image: Image.Image) -> np.ndarray:
-    """Encode image using BLIP"""
-    try:
-        model, processor = self.model_manager.load_model("image_processor")
-        
-        # Process image
-        inputs = processor(images=image, return_tensors="pt").to(model.device)
-        
-        # Get image features using the proper method
-        with torch.no_grad():
-            outputs = model.get_image_features(**inputs)
-            image_features = outputs.last_hidden_state.mean(dim=1)  # Average pooling
-        
-        return image_features.cpu().numpy()
-            
-    except Exception as e:
-        logger.error(f"Error encoding image: {str(e)}")
-        raise ModelError(f"Error encoding image: {str(e)}")
-        
     def encode_text(self, text: str) -> np.ndarray:
         """Encode text using sentence-transformers"""
-        
         try:
             return self.text_encoder.encode(text)
         except Exception as e:
             raise ModelError(f"Error encoding text: {str(e)}")
 
+    # ... rest of the MultimodalRAG class methods ...
+
     def generate_code(self, description: str, template_code: str) -> str:
         """Generate code using StarCoder"""
         try: