Update smart_breed_matcher.py

smart_breed_matcher.py

@@ -18,19 +18,40 @@ def gpu_init_wrapper(func):
         return func(*args, **kwargs)
     return wrapper
 
-
 class SmartBreedMatcher:
+    def _safe_prediction(self, func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            try:
+                return func(*args, **kwargs)
+            except RuntimeError as e:
+                if "CUDA" in str(e):
+                    print("GPU 操作失敗，嘗試使用 CPU")
+                    return func(*args, **kwargs)
+                raise
+        return wrapper
+
     def __init__(self, dog_data: List[Tuple]):
         self.dog_data = dog_data
-        self.model = SentenceTransformer('all-mpnet-base-v2')
+        self.model = None
         self._embedding_cache = {}
         self._clear_cache()
 
+    def _initialize_model(self):
+        """延遲初始化模型，只在需要時才創建"""
+        if self.model is None:
+            self.model = SentenceTransformer('all-mpnet-base-v2')
+
     def _clear_cache(self):
         self._embedding_cache = {}
 
 
+    @spaces.GPU
     def _get_cached_embedding(self, text: str) -> torch.Tensor:
+        """使用 GPU 裝飾器確保在正確的時機初始化 CUDA"""
+        if self.model is None:
+            self._initialize_model()
+            
         if text not in self._embedding_cache:
             self._embedding_cache[text] = self.model.encode(text)
         return self._embedding_cache[text]
@@ -75,6 +96,8 @@ class SmartBreedMatcher:
             List[Tuple[str, float]]: 相似品種列表，包含品種名稱和相似度分數
         """
         try:
+            if self.model is None:
+                self._initialize_model()
             target_breed = next((breed for breed in self.dog_data if breed[1] == breed_name), None)
             if not target_breed:
                 return []
@@ -868,8 +891,11 @@ class SmartBreedMatcher:
             }
 
     @gpu_init_wrapper
+    @_safe_prediction
     def match_user_preference(self, description: str, top_n: int = 10) -> List[Dict]:
         try:
+            if self.model is None:
+                self._initialize_model()
             # 獲取場景權重
             weights = self._detect_scenario(description)
             matches = []