Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -22,7 +22,7 @@ class UserPreferences:
     climate: str  # "cold", "moderate", "hot"
     health_sensitivity: str = "medium"
     barking_acceptance: str = None
-    
+    size_preference: str = "no_preference"  # "no_preference", "small", "medium", "large", "giant"
     training_commitment: str = "medium"  # "low", "medium", "high" - 訓練投入程度
     living_environment: str = "ground_floor"  # "ground_floor", "with_elevator", "walk_up" - 居住環境細節
 
@@ -30,6 +30,35 @@ class UserPreferences:
         if self.barking_acceptance is None:
             self.barking_acceptance = self.noise_tolerance
 
+def apply_size_filter(breed_score: float, user_preference: str, breed_size: str) -> float:
+    """
+    強過濾機制，基於用戶的體型偏好過濾品種
+    
+    Parameters:
+        breed_score (float): 原始品種評分
+        user_preference (str): 用戶偏好的體型
+        breed_size (str): 品種的實際體型
+    
+    Returns:
+        float: 過濾後的評分，如果體型不符合會返回 0
+    """
+    if user_preference == "no_preference":
+        return breed_score
+    
+    # 標準化 size 字串以進行比較
+    breed_size = breed_size.lower().strip()
+    user_preference = user_preference.lower().strip()
+    
+    # 特殊處理 "varies" 的情況
+    if breed_size == "varies":
+        return breed_score * 0.5  # 給予一個折扣係數，因為不確定性
+        
+    # 如果用戶有明確體型偏好但品種不符合，返回 0
+    if user_preference != breed_size:
+        return 0
+        
+    return breed_score        
+
 
 @staticmethod
 def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float:
@@ -1248,8 +1277,16 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
         adaptability_bonus = calculate_environmental_fit(breed_info, user_prefs)
 
         # 整合最終分數和加成
-        final_score = (final_score * 0.9) + (adaptability_bonus * 0.1)
-        final_score = amplify_score_extreme(final_score)
+        combined_score = (final_score * 0.9) + (adaptability_bonus * 0.1)
+        
+        # 體型過濾
+        filtered_score = apply_size_filter(
+            breed_score=combined_score,
+            user_preference=user_prefs.size_preference,
+            breed_size=breed_info['Size']
+        )
+        
+        final_score = amplify_score_extreme(filtered_score)
 
         # 更新並返回完整的評分結果
         scores.update({