Update breed_recommendation.py

breed_recommendation.py

@@ -146,63 +146,132 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
                         
                     # 計算基礎相容性分數
                     compatibility_scores = calculate_compatibility_score(breed_info, user_prefs)
-                    
-                    # 最終分數計算
+
+                    bonus_reasons = []
+                    bonus_score = 0
                     is_preferred = smart_rec.get('is_preferred', False)
-                    base_score = compatibility_scores.get('overall', 0.7)
-                    smart_score = smart_rec['score']
-                    
-                    # 根據是否為偏好品種調整分數
+                    similarity = smart_rec.get('similarity', 0)
+        
+                    # 用戶直接提到的品種
                     if is_preferred:
-                        final_score = 0.95  # 確保最高分
-                    else:
-                        # 相似品種的分數計算
-                        final_score = min(0.90, (base_score * 0.6 + smart_score * 0.4))
+                        bonus_score = 0.15  # 15% bonus
+                        bonus_reasons.append("Directly mentioned breed (+15%)")
+                    # 高相似度品種
+                    elif similarity > 0.8:
+                        bonus_score = 0.10  # 10% bonus
+                        bonus_reasons.append("Very similar to preferred breed (+10%)")
+                    # 中等相似度品種
+                    elif similarity > 0.6:
+                        bonus_score = 0.05  # 5% bonus
+                        bonus_reasons.append("Similar to preferred breed (+5%)")
+        
+                    # 基於品種特性的額外加分
+                    temperament = breed_info.get('Temperament', '').lower()
+                    if any(trait in temperament for trait in ['friendly', 'gentle', 'affectionate']):
+                        bonus_score += 0.02  # 2% bonus
+                        bonus_reasons.append("Positive temperament traits (+2%)")
+                    
+                    if breed_info.get('Good with Children') == 'Yes' and user_prefs.has_children:
+                        bonus_score += 0.03  # 3% bonus
+                        bonus_reasons.append("Excellent with children (+3%)")
+        
+                    # 基礎分數和最終分數計算
+                    base_score = compatibility_scores.get('overall', 0.7)
+                    final_score = min(0.95, base_score + bonus_score)  # 確保不超過95%
                     
                     final_recommendations.append({
-                        'rank': 0,  # 稍後更新
+                        'rank': 0,
                         'breed': breed_name,
                         'base_score': round(base_score, 4),
-                        'smart_match_score': round(smart_score, 4),
+                        'bonus_score': round(bonus_score, 4),
                         'final_score': round(final_score, 4),
                         'scores': compatibility_scores,
-                        'match_reason': smart_rec['reason'],
+                        'match_reason': ' • '.join(bonus_reasons) if bonus_reasons else "Standard match",
                         'info': breed_info,
                         'noise_info': breed_noise_info.get(breed_name, {}),
                         'health_info': breed_health_info.get(breed_name, {})
                     })
                 
-                # 根據final_score重新排序
+                # 根據最終分數排序
                 final_recommendations.sort(key=lambda x: (-x['final_score'], x['breed']))
                 
                 # 更新排名
                 for i, rec in enumerate(final_recommendations, 1):
                     rec['rank'] = i
-                
+                        
                 # 驗證排序
                 print("\nFinal Rankings:")
                 for rec in final_recommendations:
                     print(f"#{rec['rank']} {rec['breed']}")
                     print(f"Base Score: {rec['base_score']:.4f}")
-                    print(f"Smart Match Score: {rec['smart_match_score']:.4f}")
+                    print(f"Bonus Score: {rec['bonus_score']:.4f}")
                     print(f"Final Score: {rec['final_score']:.4f}")
                     print(f"Reason: {rec['match_reason']}\n")
-                    
-                    # 確保分數按降序排列
-                    if rec['rank'] > 1:
-                        prev_score = final_recommendations[rec['rank']-2]['final_score']
-                        if rec['final_score'] > prev_score:
-                            print(f"Warning: Ranking inconsistency detected!")
-                            print(f"#{rec['rank']-1} score: {prev_score:.4f}")
-                            print(f"#{rec['rank']} score: {rec['final_score']:.4f}")
-
+                
                 result = format_recommendation_html(final_recommendations)
                 return [gr.update(value=result), gr.update(visible=False)]
                 
-                
             except Exception as e:
                 error_msg = f"Error processing your description. Details: {str(e)}"
                 return [gr.update(value=error_msg), gr.update(visible=False)]
+                    
+            #         # 最終分數計算
+            #         is_preferred = smart_rec.get('is_preferred', False)
+            #         base_score = compatibility_scores.get('overall', 0.7)
+            #         smart_score = smart_rec['score']
+                    
+            #         # 根據是否為偏好品種調整分數
+            #         if is_preferred:
+            #             final_score = 0.95  # 確保最高分
+            #         else:
+            #             # 相似品種的分數計算
+            #             final_score = min(0.90, (base_score * 0.6 + smart_score * 0.4))
+                    
+            #         final_recommendations.append({
+            #             'rank': 0,  # 稍後更新
+            #             'breed': breed_name,
+            #             'base_score': round(base_score, 4),
+            #             'smart_match_score': round(smart_score, 4),
+            #             'final_score': round(final_score, 4),
+            #             'scores': compatibility_scores,
+            #             'match_reason': smart_rec['reason'],
+            #             'info': breed_info,
+            #             'noise_info': breed_noise_info.get(breed_name, {}),
+            #             'health_info': breed_health_info.get(breed_name, {})
+            #         })
+                
+            #     # 根據final_score重新排序
+            #     final_recommendations.sort(key=lambda x: (-x['final_score'], x['breed']))
+                
+            #     # 更新排名
+            #     for i, rec in enumerate(final_recommendations, 1):
+            #         rec['rank'] = i
+                
+            #     # 驗證排序
+            #     print("\nFinal Rankings:")
+            #     for rec in final_recommendations:
+            #         print(f"#{rec['rank']} {rec['breed']}")
+            #         print(f"Base Score: {rec['base_score']:.4f}")
+            #         print(f"Smart Match Score: {rec['smart_match_score']:.4f}")
+            #         print(f"Final Score: {rec['final_score']:.4f}")
+            #         print(f"Reason: {rec['match_reason']}\n")
+                    
+            #         # 確保分數按降序排列
+            #         if rec['rank'] > 1:
+            #             prev_score = final_recommendations[rec['rank']-2]['final_score']
+            #             if rec['final_score'] > prev_score:
+            #                 print(f"Warning: Ranking inconsistency detected!")
+            #                 print(f"#{rec['rank']-1} score: {prev_score:.4f}")
+            #                 print(f"#{rec['rank']} score: {rec['final_score']:.4f}")
+
+            #     result = format_recommendation_html(final_recommendations)
+            #     return [gr.update(value=result), gr.update(visible=False)]
+                
+                
+            # except Exception as e:
+            #     error_msg = f"Error processing your description. Details: {str(e)}"
+            #     return [gr.update(value=error_msg), gr.update(visible=False)]
+                
 
         def show_loading():
             return [gr.update(value=""), gr.update(visible=True)]