Update server_medical_RAM_optimize.py

server_medical_RAM_optimize.py

@@ -46,6 +46,7 @@ class ImageSearchEngine:
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         print("Loading BiomedCLIP-PubMedBERT_256-vit_base_patch16_224...")
         self.model, preprocess_train, self.preprocess = open_clip.create_model_and_transforms('hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224')
+        self.tokenizer = open_clip.get_tokenizer('hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224')
         
         if not os.path.exists(h5_file_path):
              # Tạo file giả nếu không có để server không crash ngay (giúp debug)
@@ -63,7 +64,7 @@ class ImageSearchEngine:
         if os.path.exists(BIN_FILE_PATH):
             print(f"⚡ Loading Index from {BIN_FILE_PATH}...")
             self.index.load_index(BIN_FILE_PATH, max_elements=self.max_elements)
-            self.index.set_ef(200)
+            self.index.set_ef(400)
         else:
             print("⚠️ BIN file not found.")
 
@@ -71,19 +72,19 @@ class ImageSearchEngine:
         if isinstance(text, str):
             text = [text]
         
-        tokens = tokenizer(text).to(device)
+        tokens = self.tokenizer(text).to(device)
         
         with torch.no_grad():
-            text_features = model.encode_text(tokens)    
+            text_features = self.model.encode_text(tokens)    
             text_features = text_features / text_features.norm(dim=-1, keepdim=True)
             
         return text_features.cpu().numpy()
 
     def image_to_vector(self, image):
-        image_tensor = preprocess(image).unsqueeze(0).to(device)
+        image_tensor = self.preprocess(image).unsqueeze(0).to(device)
         
         with torch.no_grad():
-            image_features = model.encode_image(image_tensor)
+            image_features = self.model.encode_image(image_tensor)
             image_features = image_features / image_features.norm(dim=-1, keepdim=True)
         
         return image_features.cpu().numpy().astype(np.float32)[0]