Update breed_recommendation.py

breed_recommendation.py

@@ -122,10 +122,6 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
                 get_recommendations_btn = gr.Button("Find My Perfect Match! 🔍", variant="primary")
                 recommendation_output = gr.HTML(label="Breed Recommendations")
 
-            with gr.Tab("Find by Description"):
-                description_input, description_search_btn, description_output, loading_msg = create_description_search_tab()
-
-
         def on_find_match_click(*args):
             try:
                 user_prefs = UserPreferences(
@@ -181,549 +177,6 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
                 import traceback
                 print(traceback.format_exc())
                 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
-                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 = []
-
-                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', '')
-                    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)
-
-                    scores = {
-                            '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:
-                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:
-            """住宿空間適應性評分"""
-            if living_space == "apartment":
-                scores = {
-                    'Tiny': 0.6,         
-                    'Small': 0.8,        
-                    'Medium': 1.0,       
-                    'Medium-Large': 0.6, 
-                    'Large': 0.4,        
-                    'Giant': 0.2       
-                }
-            else:  # house
-                scores = {
-                    'Tiny': 0.4,         
-                    'Small': 0.6,        
-                    'Medium': 0.8,       
-                    'Medium-Large': 1.0, 
-                    'Large': 0.9,        
-                    'Giant': 0.7         
-                }
-            return scores.get(size, 0.5)
-
-        def _calculate_exercise_compatibility(exercise_needs: str, exercise_time: int) -> float:
-            """運動需求相容性評分"""
-            # 轉換運動時間到評分標準
-            if exercise_time >= 120:  # 高運動量
-                scores = {
-                    'Very High': 1.0,
-                    'High': 0.8,
-                    'Moderate': 0.5,
-                    'Low': 0.2
-                }
-            elif exercise_time >= 60:  # 中等運動量
-                scores = {
-                    'Very High': 0.5,
-                    'High': 0.7,
-                    'Moderate': 1.0,
-                    'Low': 0.8
-                }
-            else:  # 低運動量
-                scores = {
-                    'Very High': 0.2,
-                    'High': 0.4,
-                    'Moderate': 0.7,
-                    'Low': 1.0
-                }
-            return scores.get(exercise_needs, 0.5)
-
-        def _calculate_grooming_compatibility(grooming_needs: str, grooming_commitment: str) -> float:
-            """美容需求相容性評分"""
-            if grooming_commitment == "high":
-                scores = {
-                    'High': 1.0,
-                    'Moderate': 0.8,
-                    'Low': 0.5
-                }
-            elif grooming_commitment == "medium":
-                scores = {
-                    'High': 0.6,
-                    'Moderate': 1.0,
-                    'Low': 0.8
-                }
-            else:  # low
-                scores = {
-                    'High': 0.3,
-                    'Moderate': 0.6,
-                    'Low': 1.0
-                }
-            return scores.get(grooming_needs, 0.5)
-
-        def _calculate_experience_compatibility(care_level: str, experience_level: str) -> float:
-            if experience_level == "experienced":
-                care_scores = {
-                    'High': 1.0,
-                    'Moderate': 0.8,
-                    'Low': 0.6
-                }
-            elif experience_level == "intermediate":
-                care_scores = {
-                    'High': 0.6,
-                    'Moderate': 1.0,
-                    'Low': 0.8
-                }
-            else:  # beginner
-                care_scores = {
-                    'High': 0.3,
-                    'Moderate': 0.7,
-                    'Low': 1.0
-                }
-            return care_scores.get(care_level, 0.7)
-
-        def show_loading():
-            return [gr.update(value=""), gr.update(visible=True)]
-
 
         get_recommendations_btn.click(
             fn=on_find_match_click,
@@ -742,15 +195,6 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
             outputs=recommendation_output
         )
 
-        description_search_btn.click(
-            fn=show_loading,  # 先顯示加載消息
-            outputs=[description_output, loading_msg]
-        ).then(  # 然後執行搜索
-            fn=on_description_search,
-            inputs=[description_input],
-            outputs=[description_output, loading_msg]
-        )
-
     return {
         'living_space': living_space,
         'exercise_time': exercise_time,
@@ -760,7 +204,4 @@ def create_recommendation_tab(UserPreferences, get_breed_recommendations, format
         'noise_tolerance': noise_tolerance,
         'get_recommendations_btn': get_recommendations_btn,
         'recommendation_output': recommendation_output,
-        'description_input': description_input,
-        'description_search_btn': description_search_btn,
-        'description_output': description_output
-    }
+    }