Update breed_recommendation.py

breed_recommendation.py

@@ -9,6 +9,47 @@ from scoring_calculation_system import UserPreferences, calculate_compatibility_
 from recommendation_html_format import format_recommendation_html, get_breed_recommendations
 from search_history import create_history_tab, create_history_component
 
+
+def filter_breed_matches(user_prefs: UserPreferences, top_n: int = 10) -> List[Dict]:
+    """
+    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"):