Update breed_recommendation.py

breed_recommendation.py

@@ -157,21 +157,6 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
                     'scores': rec['scores']
                 } for rec in recommendations]
 
-                # # 保存到歷史記錄，也需要更新保存的偏好設定
-                # history_component.save_search(
-                #     user_preferences={
-                #         'living_space': args[0],
-                #         'exercise_time': args[1],
-                #         'grooming_commitment': args[2],
-                #         'experience_level': args[3],
-                #         'has_children': args[4],
-                #         'noise_tolerance': args[5],
-                #         'health_sensitivity': "medium",
-                #         'barking_acceptance': args[5]
-                #     },
-                #     results=history_results
-                # )
-
                 history_component.save_search(
                     user_preferences={
                         'living_space': args[0],
@@ -198,6 +183,226 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
                 return "Error getting recommendations"
         
 
+        # def on_description_search(description: str):
+        #     try:
+        #         # 初始化匹配器
+        #         matcher = SmartBreedMatcher(dog_data)
+        #         breed_recommendations = matcher.match_user_preference(description, top_n=10)
+
+        #         # 從描述中提取用戶偏好
+        #         user_prefs = UserPreferences(
+        #         living_space="apartment" if any(word in description.lower()
+        #             for word in ["apartment", "flat", "condo"]) else "house_small",
+        #         yard_access="no_yard" if any(word in description.lower()
+        #             for word in ["apartment", "flat", "condo"]) else "private_yard",
+        #         exercise_time=120 if any(word in description.lower()
+        #             for word in ["active", "exercise", "running", "athletic", "high energy"]) else 60,
+        #         exercise_type="active_training" if any(word in description.lower()
+        #             for word in ["training", "running", "jogging", "hiking"]) else "moderate_activity",
+        #         grooming_commitment="high" if any(word in description.lower()
+        #             for word in ["grooming", "brush", "maintain"]) else "medium",
+        #         experience_level="experienced" if any(word in description.lower()
+        #             for word in ["experienced", "trained", "professional"]) else "intermediate",
+        #         time_availability="flexible" if any(word in description.lower()
+        #             for word in ["time", "available", "flexible", "home"]) else "moderate",
+        #         has_children=any(word in description.lower()
+        #             for word in ["children", "kids", "family", "child"]),
+        #         children_age="school_age" if any(word in description.lower()
+        #             for word in ["school", "elementary"]) else "teenager" if any(word in description.lower()
+        #             for word in ["teen", "teenager"]) else "toddler" if any(word in description.lower()
+        #             for word in ["baby", "toddler"]) else None,
+        #         noise_tolerance="low" if any(word in description.lower()
+        #             for word in ["quiet", "peaceful", "silent"]) else "medium",
+        #         space_for_play=any(word in description.lower()
+        #             for word in ["yard", "garden", "outdoor", "space"]),
+        #         other_pets=any(word in description.lower()
+        #             for word in ["other pets", "cats", "dogs"]),
+        #         climate="moderate",
+        #         health_sensitivity="high" if any(word in description.lower()
+        #             for word in ["health", "medical", "sensitive"]) else "medium",
+        #         barking_acceptance="low" if any(word in description.lower()
+        #             for word in ["quiet", "no barking"]) else "medium"
+        #     )
+
+        #         final_recommendations = []
+
+        #         for smart_rec in breed_recommendations:
+        #             breed_name = smart_rec['breed']
+        #             breed_info = get_dog_description(breed_name)
+        #             if not isinstance(breed_info, dict):
+        #                 continue
+
+        #             # 獲取基礎分數
+        #             base_score = smart_rec.get('base_score', 0.7)
+        #             similarity = smart_rec.get('similarity', 0)
+        #             is_preferred = smart_rec.get('is_preferred', False)
+
+        #             bonus_reasons = []
+        #             bonus_score = 0
+
+        #             # 1. 尺寸評估
+        #             size = breed_info.get('Size', '')
+        #             if size in ['Small', 'Tiny']:
+        #                 if "apartment" in description.lower():
+        #                     bonus_score += 0.05
+        #                     bonus_reasons.append("Suitable size for apartment (+5%)")
+        #                 else:
+        #                     bonus_score -= 0.25
+        #                     bonus_reasons.append("Size too small (-25%)")
+        #             elif size == 'Medium':
+        #                 bonus_score += 0.15
+        #                 bonus_reasons.append("Ideal size (+15%)")
+        #             elif size == 'Large':
+        #                 if "apartment" in description.lower():
+        #                     bonus_score -= 0.05
+        #                     bonus_reasons.append("May be too large for apartment (-5%)")
+        #             elif size == 'Giant':
+        #                 bonus_score -= 0.20
+        #                 bonus_reasons.append("Size too large (-20%)")
+
+        #             # 2. 運動需求評估
+        #             exercise_needs = breed_info.get('Exercise_Needs', '')
+        #             if any(word in description.lower() for word in ['active', 'energetic', 'running']):
+        #                 if exercise_needs in ['High', 'Very High']:
+        #                     bonus_score += 0.20
+        #                     bonus_reasons.append("Exercise needs match (+20%)")
+        #                 elif exercise_needs == 'Low':
+        #                     bonus_score -= 0.15
+        #                     bonus_reasons.append("Insufficient exercise level (-15%)")
+        #             else:
+        #                 if exercise_needs == 'Moderate':
+        #                     bonus_score += 0.10
+        #                     bonus_reasons.append("Moderate exercise needs (+10%)")
+
+        #             # 3. 美容需求評估
+        #             grooming = breed_info.get('Grooming_Needs', '')
+        #             if user_prefs.grooming_commitment == "high":
+        #                 if grooming == 'High':
+        #                     bonus_score += 0.10
+        #                     bonus_reasons.append("High grooming match (+10%)")
+        #             else:
+        #                 if grooming == 'High':
+        #                     bonus_score -= 0.15
+        #                     bonus_reasons.append("High grooming needs (-15%)")
+        #                 elif grooming == 'Low':
+        #                     bonus_score += 0.10
+        #                     bonus_reasons.append("Low grooming needs (+10%)")
+
+        #             # 4. 家庭適應性評估
+        #             if user_prefs.has_children:
+        #                 if breed_info.get('Good_With_Children'):
+        #                     bonus_score += 0.15
+        #                     bonus_reasons.append("Excellent with children (+15%)")
+        #                 temperament = breed_info.get('Temperament', '').lower()
+        #                 if any(trait in temperament for trait in ['gentle', 'patient', 'friendly']):
+        #                     bonus_score += 0.05
+        #                     bonus_reasons.append("Family-friendly temperament (+5%)")
+
+        #             # 5. 噪音評估
+        #             if user_prefs.noise_tolerance == "low":
+        #                 noise_level = breed_noise_info.get(breed_name, {}).get('noise_level', 'Unknown')
+        #                 if noise_level == 'High':
+        #                     bonus_score -= 0.10
+        #                     bonus_reasons.append("High noise level (-10%)")
+        #                 elif noise_level == 'Low':
+        #                     bonus_score += 0.10
+        #                     bonus_reasons.append("Low noise level (+10%)")
+
+        #             # 6. 健康考慮
+        #             if user_prefs.health_sensitivity == "high":
+        #                 health_score = smart_rec.get('health_score', 0.5)
+        #                 if health_score > 0.8:
+        #                     bonus_score += 0.10
+        #                     bonus_reasons.append("Excellent health score (+10%)")
+        #                 elif health_score < 0.5:
+        #                     bonus_score -= 0.10
+        #                     bonus_reasons.append("Health concerns (-10%)")
+
+        #             # 7. 品種偏好獎勵
+        #             if is_preferred:
+        #                 bonus_score += 0.15
+        #                 bonus_reasons.append("Directly mentioned breed (+15%)")
+        #             elif similarity > 0.8:
+        #                 bonus_score += 0.10
+        #                 bonus_reasons.append("Very similar to preferred breed (+10%)")
+
+        #             # 計算最終分數
+        #             final_score = min(0.95, base_score + bonus_score)
+
+        #             space_score = _calculate_space_compatibility(
+        #                 breed_info.get('Size', 'Medium'),
+        #                 user_prefs.living_space
+        #             )
+
+        #             exercise_score = _calculate_exercise_compatibility(
+        #                 breed_info.get('Exercise_Needs', 'Moderate'),
+        #                 user_prefs.exercise_time
+        #             )
+
+        #             grooming_score = _calculate_grooming_compatibility(
+        #                 breed_info.get('Grooming_Needs', 'Moderate'),
+        #                 user_prefs.grooming_commitment
+        #             )
+
+        #             experience_score = _calculate_experience_compatibility(
+        #                 breed_info.get('Care_Level', 'Moderate'),
+        #                 user_prefs.experience_level
+        #             )
+
+        #             scores = {
+        #                 'overall': final_score,
+        #                 'space': space_score,
+        #                 'exercise': exercise_score,
+        #                 'grooming': grooming_score,
+        #                 'experience': experience_score,
+        #                 'noise': smart_rec.get('scores', {}).get('noise', 0.0),
+        #                 'health': smart_rec.get('health_score', 0.5),
+        #                 'temperament': smart_rec.get('scores', {}).get('temperament', 0.0)
+        #             }
+
+
+        #             final_recommendations.append({
+        #                 'rank': 0,
+        #                 'breed': breed_name,
+        #                 'scores': scores,
+        #                 'base_score': round(base_score, 4),
+        #                 'bonus_score': round(bonus_score, 4),
+        #                 'final_score': round(final_score, 4),
+        #                 '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
+
+        #         # 保存到歷史記錄
+        #         history_results = [{
+        #             'breed': rec['breed'],
+        #             'rank': rec['rank'],
+        #             'final_score': rec['final_score']
+        #         } for rec in final_recommendations[:10]]
+
+        #         history_component.save_search(
+        #             user_preferences=None,
+        #             results=history_results,
+        #             search_type="description",
+        #             description=description
+        #         )
+
+        #         result = format_recommendation_html(final_recommendations, is_description_search=True)
+        #         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 on_description_search(description: str):
             try:
                 # 初始化匹配器
@@ -241,19 +446,28 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
 
                 final_recommendations = []
 
-                for smart_rec in breed_recommendations:
-                    breed_name = smart_rec['breed']
-                    breed_info = get_dog_description(breed_name)
-                    if not isinstance(breed_info, dict):
-                        continue
-
-                    # 獲取基礎分數
-                    base_score = smart_rec.get('base_score', 0.7)
-                    similarity = smart_rec.get('similarity', 0)
-                    is_preferred = smart_rec.get('is_preferred', False)
-
-                    bonus_reasons = []
-                    bonus_score = 0
+                if not breed_recommendations:
+                    print("No direct matches found, applying fallback logic")
+                    # 使用 criteria 搜索的邏輯作為後備
+                    recommendations = get_breed_recommendations(user_prefs, top_n=10)
+                    if recommendations:
+                        final_recommendations.extend(recommendations)
+                else:
+                    # 保持原有的詳細評分系統
+                    for smart_rec in breed_recommendations:
+                        breed_name = smart_rec['breed']
+                        breed_info = get_dog_description(breed_name)
+                        
+                        if not isinstance(breed_info, dict):
+                            continue
+        
+                        # 獲取基礎分數
+                        base_score = smart_rec.get('base_score', 0.7)
+                        similarity = smart_rec.get('similarity', 0)
+                        is_preferred = smart_rec.get('is_preferred', False)
+        
+                        bonus_reasons = []
+                        bonus_score = 0
 
                     # 1. 尺寸評估
                     size = breed_info.get('Size', '')
@@ -344,78 +558,76 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
                     # 計算最終分數
                     final_score = min(0.95, base_score + bonus_score)
 
-                    space_score = _calculate_space_compatibility(
-                        breed_info.get('Size', 'Medium'),
-                        user_prefs.living_space
-                    )
-
-                    exercise_score = _calculate_exercise_compatibility(
-                        breed_info.get('Exercise_Needs', 'Moderate'),
-                        user_prefs.exercise_time
-                    )
-
-                    grooming_score = _calculate_grooming_compatibility(
-                        breed_info.get('Grooming_Needs', 'Moderate'),
-                        user_prefs.grooming_commitment
-                    )
-
-                    experience_score = _calculate_experience_compatibility(
-                        breed_info.get('Care_Level', 'Moderate'),
-                        user_prefs.experience_level
-                    )
-
                     scores = {
-                        'overall': final_score,
-                        'space': space_score,
-                        'exercise': exercise_score,
-                        'grooming': grooming_score,
-                        'experience': experience_score,
-                        'noise': smart_rec.get('scores', {}).get('noise', 0.0),
-                        'health': smart_rec.get('health_score', 0.5),
-                        'temperament': smart_rec.get('scores', {}).get('temperament', 0.0)
-                    }
-
-
-                    final_recommendations.append({
-                        'rank': 0,
-                        'breed': breed_name,
-                        'scores': scores,
-                        'base_score': round(base_score, 4),
-                        'bonus_score': round(bonus_score, 4),
-                        'final_score': round(final_score, 4),
-                        '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
-
-                # 保存到歷史記錄
-                history_results = [{
-                    'breed': rec['breed'],
-                    'rank': rec['rank'],
-                    'final_score': rec['final_score']
-                } for rec in final_recommendations[:10]]
-
-                history_component.save_search(
-                    user_preferences=None,
-                    results=history_results,
-                    search_type="description",
-                    description=description
-                )
-
-                result = format_recommendation_html(final_recommendations, is_description_search=True)
-                return [gr.update(value=result), gr.update(visible=False)]
-
+                            'overall': final_score,
+                            'space': _calculate_space_compatibility(
+                                breed_info.get('Size', 'Medium'),
+                                user_prefs.living_space
+                            ),
+                            'exercise': _calculate_exercise_compatibility(
+                                breed_info.get('Exercise_Needs', 'Moderate'),
+                                user_prefs.exercise_time
+                            ),
+                            'grooming': _calculate_grooming_compatibility(
+                                breed_info.get('Grooming_Needs', 'Moderate'),
+                                user_prefs.grooming_commitment
+                            ),
+                            'experience': _calculate_experience_compatibility(
+                                breed_info.get('Care_Level', 'Moderate'),
+                                user_prefs.experience_level
+                            ),
+                            'noise': smart_rec.get('scores', {}).get('noise', 0.0),
+                            'health': smart_rec.get('health_score', 0.5),
+                            'temperament': smart_rec.get('scores', {}).get('temperament', 0.0)
+                        }
+        
+                        final_recommendations.append({
+                            'rank': 0,
+                            'breed': breed_name,
+                            'scores': scores,
+                            'base_score': round(base_score, 4),
+                            'bonus_score': round(bonus_score, 4),
+                            'final_score': round(final_score, 4),
+                            '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, {})
+                        })
+        
+                # 排序並更新排名
+                if final_recommendations:
+                    final_recommendations.sort(key=lambda x: (-x['final_score'], x['breed']))
+                    for i, rec in enumerate(final_recommendations, 1):
+                        rec['rank'] = i
+        
+                    # 保存搜索歷史
+                    history_results = [{
+                        'breed': rec['breed'],
+                        'rank': rec['rank'],
+                        'overall_score': rec['final_score'],
+                        'base_score': rec['base_score'],
+                        'bonus_score': rec['bonus_score'],
+                        'scores': rec['scores']
+                    } for rec in final_recommendations[:10]]
+        
+                    # 保存到歷史記錄
+                    history_component.save_search(
+                        user_preferences={'description': description},
+                        results=history_results,
+                        search_type="description"
+                    )
+        
+                    # 返回結果
+                    result = format_recommendation_html(final_recommendations, is_description_search=True)
+                    return result
+        
+                return "No matching breeds found. Please try a different description."
+        
             except Exception as e:
-                error_msg = f"Error processing your description. Details: {str(e)}"
-                return [gr.update(value=error_msg), gr.update(visible=False)]
+                print(f"Error in description search: {str(e)}")
+                import traceback
+                print(traceback.format_exc())
+                return "Error processing your description"
 
 
         def _calculate_space_compatibility(size: str, living_space: str) -> float: