Update smart_breed_matcher.py

smart_breed_matcher.py

@@ -72,45 +72,6 @@ class SmartBreedMatcher:
         
         return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_n]
 
-    def _calculate_breed_similarity(self, breed1_features: Dict, breed2_features: Dict) -> float:
-        """計算兩個品種之間的相似度，包含健康因素"""
-        # 計算描述文本的相似度
-        desc1_embedding = self.model.encode(breed1_features['description'])
-        desc2_embedding = self.model.encode(breed2_features['description'])
-        description_similarity = float(util.pytorch_cos_sim(desc1_embedding, desc2_embedding))
-        
-        # 基本特徵相似度
-        size_similarity = 1.0 if breed1_features['size'] == breed2_features['size'] else 0.5
-        exercise_similarity = 1.0 if breed1_features['exercise'] == breed2_features['exercise'] else 0.5
-        
-        # 性格相似度
-        temp1_embedding = self.model.encode(breed1_features['temperament'])
-        temp2_embedding = self.model.encode(breed2_features['temperament'])
-        temperament_similarity = float(util.pytorch_cos_sim(temp1_embedding, temp2_embedding))
-        
-        # 健康分數相似度
-        health_score1 = self._calculate_health_score(breed1_features['breed_name'])
-        health_score2 = self._calculate_health_score(breed2_features['breed_name'])
-        health_similarity = 1.0 - abs(health_score1 - health_score2)
-        
-        # 加權計算
-        weights = {
-            'description': 0.3,
-            'temperament': 0.25,
-            'exercise': 0.15,
-            'size': 0.1,
-            'health': 0.2
-        }
-        
-        final_similarity = (
-            description_similarity * weights['description'] +
-            temperament_similarity * weights['temperament'] +
-            exercise_similarity * weights['exercise'] +
-            size_similarity * weights['size'] +
-            health_similarity * weights['health']
-        )
-        
-        return final_similarity
 
     def _calculate_breed_similarity(self, breed1_features: Dict, breed2_features: Dict) -> float:
         """計算兩個品種之間的相似度，包含健康和噪音因素"""