Update breed_recommendation.py

breed_recommendation.py

@@ -10,46 +10,6 @@ from recommendation_html_format import format_recommendation_html, get_breed_rec
 from search_history import create_history_tab, create_history_component
 
 
-def filter_breed_matches(user_prefs: UserPreferences, top_n: int = 10):
-    """
-    根據使用者偏好篩選並推薦狗狗品種。
-    
-    Parameters:
-        user_prefs: 使用者偏好設定
-        top_n: 要返回的推薦數量
-        
-    Returns:
-        List[Dict]: 排序後的推薦品種列表
-    """
-    all_breeds = []
-    for breed_info in breed_database:
-        score = calculate_compatibility_score(breed_info, user_prefs)
-        if score is not None:  # 只添加未被過濾的品種
-            all_breeds.append({
-                'breed': breed_info['Breed'],
-                'final_score': score['overall'],
-                'base_score': score.get('base_score', 0),
-                'bonus_score': score.get('bonus_score', 0),
-                'size': breed_info['Size'],
-                'scores': score
-            })
-    
-    # 根據體型偏好過濾
-    if user_prefs.size_preference != "no_preference":
-        filtered_breeds = [b for b in all_breeds if b['size'].lower() == user_prefs.size_preference.lower()]
-        # 如果符合體型的品種太少，調整返回數量
-        if len(filtered_breeds) < 5:  # 設定最少要有5種品種
-            top_n = len(filtered_breeds)
-    else:
-        filtered_breeds = all_breeds
-    
-    # 為每個品種添加排名
-    sorted_breeds = sorted(filtered_breeds, key=lambda x: x['final_score'], reverse=True)
-    for i, breed in enumerate(sorted_breeds, 1):
-        breed['rank'] = i
-    
-    return sorted_breeds[:top_n]
-
 def create_recommendation_tab(UserPreferences, get_breed_recommendations, format_recommendation_html, history_component):
 
     with gr.TabItem("Breed Recommendation"):
@@ -214,23 +174,23 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
         def on_find_match_click(*args):
             try:
                 user_prefs = UserPreferences(
-                    living_space=args[0],
-                    yard_access=args[1],       
-                    exercise_time=args[2],
-                    exercise_type=args[3],      
-                    grooming_commitment=args[4],
-                    size_preference=args[5],
-                    experience_level=args[6],
-                    time_availability=args[7],   
-                    has_children=args[8],
-                    children_age=args[9] if args[8] else None,
-                    noise_tolerance=args[10],
-                    space_for_play=True if args[0] != "apartment" else False,
-                    other_pets=False,
-                    climate="moderate",
-                    health_sensitivity="medium",
-                    barking_acceptance=args[10]
-                )
+                living_space=args[0],
+                yard_access=args[1],       
+                exercise_time=args[2],
+                exercise_type=args[3],      
+                grooming_commitment=args[4],
+                size_preference=args[5],     
+                experience_level=args[6],    
+                time_availability=args[7],   
+                has_children=args[8],
+                children_age=args[9] if args[8] else None,
+                noise_tolerance=args[10],
+                space_for_play=True if args[0] != "apartment" else False,
+                other_pets=False,
+                climate="moderate",
+                health_sensitivity="medium",
+                barking_acceptance=args[10]
+            )
 
                 recommendations = get_breed_recommendations(user_prefs, top_n=10)