adjusted pruning

app.py

@@ -22,17 +22,22 @@ class ImageSearchSystem:
     def __init__(self):
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         logger.info(f"Using device: {self.device}")
-        
+
         # Load CLIP model
         self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
         self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16").to(self.device)
 
-          # Prune the model
+        # Prune the model (access vision module directly)
         parameters_to_prune = (
-            (self.model.visual.encoder.layers, 'weight'),
+            (self.model.vision.transformer.encoder.layers, 'attention.self.query.weight'),
+            (self.model.vision.transformer.encoder.layers, 'attention.self.key.weight'),
+            (self.model.vision.transformer.encoder.layers, 'attention.self.value.weight')
         )
+
+        # Prune the weights
         prune.l1_unstructured(parameters_to_prune, amount=0.2)
-        
+
+
         # Initialize dataset
         self.image_paths = []
         self.index = None