scoring_calculation_system.py

from dataclasses import dataclass
from breed_health_info import breed_health_info
from breed_noise_info import breed_noise_info
import traceback
import math

@dataclass
class UserPreferences:

    """使用者偏好設定的資料結構"""
    living_space: str  # "apartment", "house_small", "house_large"
    yard_access: str  # "no_yard", "shared_yard", "private_yard" 
    exercise_time: int  # minutes per day
    exercise_type: str  # "light_walks", "moderate_activity", "active_training" 
    grooming_commitment: str  # "low", "medium", "high"
    experience_level: str  # "beginner", "intermediate", "advanced"
    time_availability: str  # "limited", "moderate", "flexible" 
    has_children: bool
    children_age: str  # "toddler", "school_age", "teenager"
    noise_tolerance: str  # "low", "medium", "high"
    space_for_play: bool
    other_pets: bool
    climate: str  # "cold", "moderate", "hot"
    health_sensitivity: str = "medium"
    barking_acceptance: str = None
    
    training_commitment: str = "medium"  # "low", "medium", "high" - 訓練投入程度
    living_environment: str = "ground_floor"  # "ground_floor", "with_elevator", "walk_up" - 居住環境細節

    def __post_init__(self):
        """在初始化後運行，用於設置派生值"""
        if self.barking_acceptance is None:
            self.barking_acceptance = self.noise_tolerance


@staticmethod
def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float:
    """計算品種額外加分"""
    bonus = 0.0
    temperament = breed_info.get('Temperament', '').lower()
    
    # 1. 壽命加分（最高0.05）
    try:
        lifespan = breed_info.get('Lifespan', '10-12 years')
        years = [int(x) for x in lifespan.split('-')[0].split()[0:1]]
        longevity_bonus = min(0.05, (max(years) - 10) * 0.01)
        bonus += longevity_bonus
    except:
        pass

    # 2. 性格特徵加分（最高0.15）
    positive_traits = {
        'friendly': 0.05,           
        'gentle': 0.05,
        'patient': 0.05,
        'intelligent': 0.04,
        'adaptable': 0.04,
        'affectionate': 0.04,
        'easy-going': 0.03,         
        'calm': 0.03                
    }
    
    negative_traits = {
        'aggressive': -0.08,        
        'stubborn': -0.06,
        'dominant': -0.06,
        'aloof': -0.04,
        'nervous': -0.05,           
        'protective': -0.04         
    }
    
    personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament)
    personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament)
    bonus += max(-0.15, min(0.15, personality_score))

    # 3. 適應性加分（最高0.1）
    adaptability_bonus = 0.0
    if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment":
        adaptability_bonus += 0.05
    if 'adaptable' in temperament or 'versatile' in temperament:
        adaptability_bonus += 0.05
    bonus += min(0.1, adaptability_bonus)

    # 4. 家庭相容性（最高0.1）
    if user_prefs.has_children:
        family_traits = {
            'good with children': 0.06,  
            'patient': 0.05,
            'gentle': 0.05,
            'tolerant': 0.04,           
            'playful': 0.03             
        }
        unfriendly_traits = {
            'aggressive': -0.08,        
            'nervous': -0.07,
            'protective': -0.06,
            'territorial': -0.05        
        }
        
        # 年齡評估這樣能更細緻
        age_adjustments = {
            'toddler': {'bonus_mult': 0.7, 'penalty_mult': 1.3},
            'school_age': {'bonus_mult': 1.0, 'penalty_mult': 1.0},
            'teenager': {'bonus_mult': 1.2, 'penalty_mult': 0.8}
        }
        
        adj = age_adjustments.get(user_prefs.children_age, 
                                {'bonus_mult': 1.0, 'penalty_mult': 1.0})
        
        family_bonus = sum(value for trait, value in family_traits.items() 
                          if trait in temperament) * adj['bonus_mult']
        family_penalty = sum(value for trait, value in unfriendly_traits.items() 
                           if trait in temperament) * adj['penalty_mult']
        
        bonus += min(0.15, max(-0.2, family_bonus + family_penalty))

    
    # 5. 專門技能加分（最高0.1）
    skill_bonus = 0.0
    special_abilities = {
        'working': 0.03,
        'herding': 0.03,
        'hunting': 0.03,
        'tracking': 0.03,
        'agility': 0.02
    }
    for ability, value in special_abilities.items():
        if ability in temperament.lower():
            skill_bonus += value
    bonus += min(0.1, skill_bonus)


    # 6. 適應性評估 - 根據具體環境給予更細緻的評分
    adaptability_bonus = 0.0
    if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment":
        adaptability_bonus += 0.08  # 小型犬更適合公寓
    
    # 環境適應性評估
    if 'adaptable' in temperament or 'versatile' in temperament:
        if user_prefs.living_space == "apartment":
            adaptability_bonus += 0.10  # 適應性在公寓環境更重要
        else:
            adaptability_bonus += 0.05  # 其他環境仍有加分
            
    # 氣候適應性
    description = breed_info.get('Description', '').lower()
    climate = user_prefs.climate
    if climate == 'hot':
        if 'heat tolerant' in description or 'warm climate' in description:
            adaptability_bonus += 0.08
        elif 'thick coat' in description or 'cold climate' in description:
            adaptability_bonus -= 0.10
    elif climate == 'cold':
        if 'thick coat' in description or 'cold climate' in description:
            adaptability_bonus += 0.08
        elif 'heat tolerant' in description or 'short coat' in description:
            adaptability_bonus -= 0.10
            
    bonus += min(0.15, adaptability_bonus)

    return min(0.5, max(-0.25, bonus))
    

@staticmethod
def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict:
    """
    計算額外的評估因素，結合品種特性與使用者需求的全面評估系統
    
    此函數整合了：
    1. 多功能性評估 - 品種的多樣化能力
    2. 訓練性評估 - 學習和服從能力
    3. 能量水平評估 - 活力和運動需求
    4. 美容需求評估 - 護理和維護需求
    5. 社交需求評估 - 與人互動的需求程度
    6. 氣候適應性 - 對環境的適應能力
    7. 運動類型匹配 - 與使用者運動習慣的契合度
    8. 生活方式適配 - 與使用者日常生活的匹配度
    """
    factors = {
        'versatility': 0.0,        # 多功能性
        'trainability': 0.0,       # 可訓練度
        'energy_level': 0.0,       # 能量水平
        'grooming_needs': 0.0,     # 美容需求
        'social_needs': 0.0,       # 社交需求
        'weather_adaptability': 0.0,# 氣候適應性
        'exercise_match': 0.0,     # 運動匹配度
        'lifestyle_fit': 0.0       # 生活方式適配度
    }
    
    temperament = breed_info.get('Temperament', '').lower()
    description = breed_info.get('Description', '').lower()
    size = breed_info.get('Size', 'Medium')
    
    # 1. 多功能性評估 - 加強品種用途評估
    versatile_traits = {
        'intelligent': 0.25,
        'adaptable': 0.25,
        'trainable': 0.20,
        'athletic': 0.15,
        'versatile': 0.15
    }
    
    working_roles = {
        'working': 0.20,
        'herding': 0.15,
        'hunting': 0.15,
        'sporting': 0.15,
        'companion': 0.10
    }
    
    # 計算特質分數
    trait_score = sum(value for trait, value in versatile_traits.items() 
                     if trait in temperament)
    
    # 計算角色分數
    role_score = sum(value for role, value in working_roles.items() 
                    if role in description)
    
    # 根據使用者需求調整多功能性評分
    purpose_traits = {
        'light_walks': ['calm', 'gentle', 'easy-going'],
        'moderate_activity': ['adaptable', 'balanced', 'versatile'],
        'active_training': ['intelligent', 'trainable', 'working']
    }
    
    if user_prefs.exercise_type in purpose_traits:
        matching_traits = sum(1 for trait in purpose_traits[user_prefs.exercise_type] 
                            if trait in temperament)
        trait_score += matching_traits * 0.15
    
    factors['versatility'] = min(1.0, trait_score + role_score)
    
    # 2. 訓練性評估 - 考慮使用者經驗
    trainable_traits = {
        'intelligent': 0.3,
        'eager to please': 0.3,
        'trainable': 0.2,
        'quick learner': 0.2,
        'obedient': 0.2
    }
    
    base_trainability = sum(value for trait, value in trainable_traits.items() 
                          if trait in temperament)
    
    # 根據使用者經驗調整訓練性評分
    experience_multipliers = {
        'beginner': 1.2,    # 新手更需要容易訓練的狗
        'intermediate': 1.0,
        'advanced': 0.8     # 專家能處理較難訓練的狗
    }
    
    factors['trainability'] = min(1.0, base_trainability * 
                                experience_multipliers.get(user_prefs.experience_level, 1.0))
    
    # 3. 能量水平評估 - 強化運動需求匹配
    exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
    energy_levels = {
        'VERY HIGH': {
            'score': 1.0,
            'min_exercise': 120,
            'ideal_exercise': 150
        },
        'HIGH': {
            'score': 0.8,
            'min_exercise': 90,
            'ideal_exercise': 120
        },
        'MODERATE': {
            'score': 0.6,
            'min_exercise': 60,
            'ideal_exercise': 90
        },
        'LOW': {
            'score': 0.4,
            'min_exercise': 30,
            'ideal_exercise': 60
        }
    }
    
    breed_energy = energy_levels.get(exercise_needs, energy_levels['MODERATE'])
    
    # 計算運動時間匹配度
    if user_prefs.exercise_time >= breed_energy['ideal_exercise']:
        energy_score = breed_energy['score']
    else:
        # 如果運動時間不足，按比例降低分數
        deficit_ratio = max(0.4, user_prefs.exercise_time / breed_energy['ideal_exercise'])
        energy_score = breed_energy['score'] * deficit_ratio
    
    factors['energy_level'] = energy_score
    
    # 4. 美容需求評估 - 加入更多毛髮類型考量
    grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper()
    grooming_levels = {
        'HIGH': 1.0,
        'MODERATE': 0.6,
        'LOW': 0.3
    }
    
    # 特殊毛髮類型評估
    coat_adjustments = 0
    if 'long coat' in description:
        coat_adjustments += 0.2
    if 'double coat' in description:
        coat_adjustments += 0.15
    if 'curly' in description:
        coat_adjustments += 0.15
        
    # 根據使用者承諾度調整
    commitment_multipliers = {
        'low': 1.5,     # 低承諾度時加重美容需求的影響
        'medium': 1.0,
        'high': 0.8     # 高承諾度時降低美容需求的影響
    }
    
    base_grooming = grooming_levels.get(grooming_needs, 0.6) + coat_adjustments
    factors['grooming_needs'] = min(1.0, base_grooming * 
                                  commitment_multipliers.get(user_prefs.grooming_commitment, 1.0))
    
    # 5. 社交需求評估 - 加強家庭情況考量
    social_traits = {
        'friendly': 0.25,
        'social': 0.25,
        'affectionate': 0.20,
        'people-oriented': 0.20
    }
    
    antisocial_traits = {
        'independent': -0.20,
        'aloof': -0.20,
        'reserved': -0.15
    }
    
    social_score = sum(value for trait, value in social_traits.items() 
                      if trait in temperament)
    antisocial_score = sum(value for trait, value in antisocial_traits.items() 
                          if trait in temperament)
    
    # 家庭情況調整
    if user_prefs.has_children:
        child_friendly_bonus = 0.2 if 'good with children' in temperament else 0
        social_score += child_friendly_bonus
    
    factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score))
    
    # 6. 氣候適應性評估 - 更細緻的環境適應評估
    climate_traits = {
        'cold': {
            'positive': ['thick coat', 'winter', 'cold climate'],
            'negative': ['short coat', 'heat sensitive']
        },
        'hot': {
            'positive': ['short coat', 'heat tolerant', 'warm climate'],
            'negative': ['thick coat', 'cold climate']
        },
        'moderate': {
            'positive': ['adaptable', 'all climate'],
            'negative': []
        }
    }
    
    climate_score = 0.4  # 基礎分數
    if user_prefs.climate in climate_traits:
        # 正面特質加分
        climate_score += sum(0.2 for term in climate_traits[user_prefs.climate]['positive'] 
                           if term in description)
        # 負面特質減分
        climate_score -= sum(0.2 for term in climate_traits[user_prefs.climate]['negative'] 
                           if term in description)
    
    factors['weather_adaptability'] = min(1.0, max(0.0, climate_score))
    
    # 7. 運動類型匹配評估
    exercise_type_traits = {
        'light_walks': ['calm', 'gentle'],
        'moderate_activity': ['adaptable', 'balanced'],
        'active_training': ['athletic', 'energetic']
    }
    
    if user_prefs.exercise_type in exercise_type_traits:
        match_score = sum(0.25 for trait in exercise_type_traits[user_prefs.exercise_type] 
                         if trait in temperament)
        factors['exercise_match'] = min(1.0, match_score + 0.5)  # 基礎分0.5
    
    # 8. 生活方式適配評估
    lifestyle_score = 0.5  # 基礎分數
    
    # 空間適配
    if user_prefs.living_space == 'apartment':
        if size == 'Small':
            lifestyle_score += 0.2
        elif size == 'Large':
            lifestyle_score -= 0.2
    elif user_prefs.living_space == 'house_large':
        if size in ['Large', 'Giant']:
            lifestyle_score += 0.2
    
    # 時間可用性適配
    time_availability_bonus = {
        'limited': -0.1,
        'moderate': 0,
        'flexible': 0.1
    }
    lifestyle_score += time_availability_bonus.get(user_prefs.time_availability, 0)
    
    factors['lifestyle_fit'] = min(1.0, max(0.0, lifestyle_score))
    
    return factors


def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences) -> dict:
    """計算品種與使用者條件的相容性分數的優化版本"""
    try:
        print(f"Processing breed: {breed_info.get('Breed', 'Unknown')}")
        print(f"Breed info keys: {breed_info.keys()}")
        
        if 'Size' not in breed_info:
            print("Missing Size information")
            raise KeyError("Size information missing")
            

        # def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float:
        #     """       
        #     主要改進：
        #     1. 更均衡的基礎分數分配
        #     2. 更細緻的空間需求評估
        #     3. 強化運動需求與空間的關聯性
        #     """
        #     # 重新設計基礎分數矩陣，降低普遍分數以增加區別度
        #     base_scores = {
        #         "Small": {
        #             "apartment": 0.90,    # 降低滿分機會
        #             "house_small": 0.85,  # 小型犬不應在大空間得到太高分數
        #             "house_large": 0.80   # 避免小型犬總是得到最高分
        #         },
        #         "Medium": {
        #             "apartment": 0.40,    # 維持對公寓環境的限制
        #             "house_small": 0.80,  # 適中的分數
        #             "house_large": 0.90   # 給予合理的獎勵
        #         },
        #         "Large": {
        #             "apartment": 0.10,    # 加重對大型犬在公寓的限制
        #             "house_small": 0.60,  # 中等適合度
        #             "house_large": 0.95   # 最適合的環境
        #         },
        #         "Giant": {
        #             "apartment": 0.10,    # 更嚴格的限制
        #             "house_small": 0.45,  # 顯著的空間限制
        #             "house_large": 0.95   # 最理想的配對
        #         }
        #     }
            
        #     # 取得基礎分數
        #     base_score = base_scores.get(size, base_scores["Medium"])[living_space]
            
        #     # 運動需求相關的調整更加動態
        #     exercise_adjustments = {
        #         "Very High": {
        #             "apartment": -0.25,    # 加重在受限空間的懲罰
        #             "house_small": -0.15,
        #             "house_large": -0.05
        #         },
        #         "High": {
        #             "apartment": -0.20,
        #             "house_small": -0.10,
        #             "house_large": 0
        #         },
        #         "Moderate": {
        #             "apartment": -0.10,
        #             "house_small": -0.05,
        #             "house_large": 0
        #         },
        #         "Low": {
        #             "apartment": 0.05,     # 低運動需求在小空間反而有優勢
        #             "house_small": 0,
        #             "house_large": -0.05   # 輕微降低評分，因為空間可能過大
        #         }
        #     }
            
        #     # 根據空間類型獲取運動需求調整
        #     adjustment = exercise_adjustments.get(exercise_needs, 
        #                                         exercise_adjustments["Moderate"])[living_space]
            
        #     # 院子效益根據品種大小和運動需求動態調整
        #     if has_yard:
        #         yard_bonus = {
        #             "Giant": 0.20,
        #             "Large": 0.15,
        #             "Medium": 0.10,
        #             "Small": 0.05
        #         }.get(size, 0.10)
                
        #         # 運動需求會影響院子的重要性
        #         if exercise_needs in ["Very High", "High"]:
        #             yard_bonus *= 1.2
        #         elif exercise_needs == "Low":
        #             yard_bonus *= 0.8
                    
        #         current_score = base_score + adjustment + yard_bonus
        #     else:
        #         current_score = base_score + adjustment
                
        #     # 確保分數在合理範圍內，但避免極端值
        #     return min(0.95, max(0.15, current_score))


        # def calculate_exercise_score(breed_needs: str, exercise_time: int, exercise_type: str) -> float:
        #     """
        #     精確評估品種運動需求與使用者運動條件的匹配度
            
        #     Parameters:
        #     breed_needs: 品種的運動需求等級
        #     exercise_time: 使用者能提供的運動時間（分鐘）
        #     exercise_type: 使用者偏好的運動類型
            
        #     Returns:
        #     float: -0.2 到 0.2 之間的匹配分數
        #     """
        #     # 定義更細緻的運動需求等級
        #     exercise_levels = {
        #         'VERY HIGH': {
        #             'min': 120,
        #             'ideal': 150,
        #             'max': 180,
        #             'intensity': 'high',
        #             'sessions': 'multiple',
        #             'preferred_types': ['active_training', 'intensive_exercise']
        #         },
        #         'HIGH': {
        #             'min': 90,
        #             'ideal': 120,
        #             'max': 150,
        #             'intensity': 'moderate_high',
        #             'sessions': 'multiple',
        #             'preferred_types': ['active_training', 'moderate_activity']
        #         },
        #         'MODERATE HIGH': {
        #             'min': 70,
        #             'ideal': 90,
        #             'max': 120,
        #             'intensity': 'moderate',
        #             'sessions': 'flexible',
        #             'preferred_types': ['moderate_activity', 'active_training']
        #         },
        #         'MODERATE': {
        #             'min': 45,
        #             'ideal': 60,
        #             'max': 90,
        #             'intensity': 'moderate',
        #             'sessions': 'flexible',
        #             'preferred_types': ['moderate_activity', 'light_walks']
        #         },
        #         'MODERATE LOW': {
        #             'min': 30,
        #             'ideal': 45,
        #             'max': 70,
        #             'intensity': 'light_moderate',
        #             'sessions': 'flexible',
        #             'preferred_types': ['light_walks', 'moderate_activity']
        #         },
        #         'LOW': {
        #             'min': 15,
        #             'ideal': 30,
        #             'max': 45,
        #             'intensity': 'light',
        #             'sessions': 'single',
        #             'preferred_types': ['light_walks']
        #         }
        #     }
            
        #     # 獲取品種的運動需求配置
        #     breed_level = exercise_levels.get(breed_needs.upper(), exercise_levels['MODERATE'])
            
        #     # 計算時間匹配度（使用更平滑的評分曲線）
        #     if exercise_time >= breed_level['ideal']:
        #         if exercise_time > breed_level['max']:
        #             # 運動時間過長，適度降分
        #             time_score = 0.15 - (0.08 * (exercise_time - breed_level['max']) / 30)
        #         else:
        #             time_score = 0.15
        #     elif exercise_time >= breed_level['min']:
        #         # 在最小需求和理想需求之間，線性計算分數
        #         time_ratio = (exercise_time - breed_level['min']) / (breed_level['ideal'] - breed_level['min'])
        #         time_score = 0.05 + (time_ratio * 0.10)
        #     else:
        #         # 運動時間不足，根據差距程度扣分
        #         time_ratio = max(0, exercise_time / breed_level['min'])
        #         time_score = -0.20 * (1 - time_ratio)
            
        #     # 運動類型匹配度評估
        #     type_score = 0.0
        #     if exercise_type in breed_level['preferred_types']:
        #         type_score = 0.05
        #         if exercise_type == breed_level['preferred_types'][0]:
        #             type_score = 0.08  # 最佳匹配類型給予更高分數
            
        #     return max(-0.2, min(0.2, time_score + type_score))


        def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float:
            """
            改進的空間評分系統，提供更細緻的居住環境評估
            
            改進重點：
            1. 更動態的基礎分數矩陣
            2. 強化空間品質評估
            3. 增加極端情況處理
            4. 考慮不同空間組合的協同效應
            """
            def get_base_score():
                # 基礎分數矩陣 - 更極端的分數分配
                base_matrix = {
                    "Small": {
                        "apartment": {
                            "no_yard": 0.85,      # 小型犬在公寓仍然適合
                            "shared_yard": 0.90,   # 共享院子提供額外活動空間
                            "private_yard": 0.95   # 私人院子最理想
                        },
                        "house_small": {
                            "no_yard": 0.80,
                            "shared_yard": 0.85,
                            "private_yard": 0.90
                        },
                        "house_large": {
                            "no_yard": 0.75,
                            "shared_yard": 0.80,
                            "private_yard": 0.85
                        }
                    },
                    "Medium": {
                        "apartment": {
                            "no_yard": 0.35,      # 中型犬在公寓較受限
                            "shared_yard": 0.45,
                            "private_yard": 0.55
                        },
                        "house_small": {
                            "no_yard": 0.75,
                            "shared_yard": 0.85,
                            "private_yard": 0.90
                        },
                        "house_large": {
                            "no_yard": 0.85,
                            "shared_yard": 0.90,
                            "private_yard": 0.95
                        }
                    },
                    "Large": {
                        "apartment": {
                            "no_yard": 0.15,      # 大型犬在公寓極不適合
                            "shared_yard": 0.25,
                            "private_yard": 0.35
                        },
                        "house_small": {
                            "no_yard": 0.55,
                            "shared_yard": 0.65,
                            "private_yard": 0.75
                        },
                        "house_large": {
                            "no_yard": 0.85,
                            "shared_yard": 0.90,
                            "private_yard": 1.0
                        }
                    },
                    "Giant": {
                        "apartment": {
                            "no_yard": 0.10,      # 巨型犬在公寓基本不適合
                            "shared_yard": 0.20,
                            "private_yard": 0.30
                        },
                        "house_small": {
                            "no_yard": 0.40,
                            "shared_yard": 0.50,
                            "private_yard": 0.60
                        },
                        "house_large": {
                            "no_yard": 0.80,
                            "shared_yard": 0.90,
                            "private_yard": 1.0
                        }
                    }
                }
                
                yard_type = "private_yard" if has_yard else "no_yard"
                return base_matrix.get(size, base_matrix["Medium"])[living_space][yard_type]
        
            def calculate_exercise_adjustment():
                # 運動需求對空間評分的影響
                exercise_impact = {
                    "Very High": {
                        "apartment": -0.30,    # 高運動需求在公寓環境更受限
                        "house_small": -0.15,
                        "house_large": -0.05
                    },
                    "High": {
                        "apartment": -0.25,
                        "house_small": -0.10,
                        "house_large": 0
                    },
                    "Moderate": {
                        "apartment": -0.15,
                        "house_small": -0.05,
                        "house_large": 0
                    },
                    "Low": {
                        "apartment": 0.10,     # 低運動需求反而適合小空間
                        "house_small": 0.05,
                        "house_large": 0
                    }
                }
                
                return exercise_impact.get(exercise_needs, exercise_impact["Moderate"])[living_space]
        
            def calculate_yard_bonus():
                # 院子效益評估更加細緻
                if not has_yard:
                    return 0
                    
                yard_benefits = {
                    "Giant": {
                        "Very High": 0.25,
                        "High": 0.20,
                        "Moderate": 0.15,
                        "Low": 0.10
                    },
                    "Large": {
                        "Very High": 0.20,
                        "High": 0.15,
                        "Moderate": 0.10,
                        "Low": 0.05
                    },
                    "Medium": {
                        "Very High": 0.15,
                        "High": 0.10,
                        "Moderate": 0.08,
                        "Low": 0.05
                    },
                    "Small": {
                        "Very High": 0.10,
                        "High": 0.08,
                        "Moderate": 0.05,
                        "Low": 0.03
                    }
                }
                
                size_benefits = yard_benefits.get(size, yard_benefits["Medium"])
                return size_benefits.get(exercise_needs, size_benefits["Moderate"])
        
            def apply_extreme_case_adjustments(score):
                # 處理極端情況
                if size == "Giant" and living_space == "apartment":
                    return score * 0.5  # 巨型犬在公寓給予更嚴重的懲罰
                
                if size == "Large" and living_space == "apartment" and exercise_needs == "Very High":
                    return score * 0.6  # 高運動需求的大型犬在公寓更不適合
                    
                if size == "Small" and living_space == "house_large" and exercise_needs == "Low":
                    return score * 0.9  # 低運動需求的小型犬在大房子可能過於寬敞
                    
                return score
        
            # 計算最終分數
            base_score = get_base_score()
            exercise_adj = calculate_exercise_adjustment()
            yard_bonus = calculate_yard_bonus()
            
            # 整合所有評分因素
            initial_score = base_score + exercise_adj + yard_bonus
            
            # 應用極端情況調整
            final_score = apply_extreme_case_adjustments(initial_score)
            
            # 確保分數在有效範圍內，但允許更極端的結果
            return max(0.05, min(1.0, final_score))


        def calculate_exercise_score(breed_needs: str, exercise_time: int, exercise_type: str) -> float:
            """
            精確評估品種運動需求與使用者運動條件的匹配度
            
            改進重點：
            1. 擴大分數範圍到 0.1-1.0
            2. 加強運動類型影響
            3. 考慮運動強度與時間的綜合效果
            4. 更細緻的時間匹配評估
            """
            exercise_levels = {
                'VERY HIGH': {
                    'min': 120,
                    'ideal': 150,
                    'max': 180,
                    'intensity': 'high',
                    'sessions': 'multiple',
                    'preferred_types': ['active_training', 'intensive_exercise'],
                    'type_weights': {
                        'active_training': 1.0,
                        'moderate_activity': 0.6,
                        'light_walks': 0.3
                    }
                },
                'HIGH': {
                    'min': 90,
                    'ideal': 120,
                    'max': 150,
                    'intensity': 'moderate_high',
                    'sessions': 'multiple',
                    'preferred_types': ['active_training', 'moderate_activity'],
                    'type_weights': {
                        'active_training': 0.9,
                        'moderate_activity': 0.8,
                        'light_walks': 0.4
                    }
                },
                'MODERATE HIGH': {
                    'min': 70,
                    'ideal': 90,
                    'max': 120,
                    'intensity': 'moderate',
                    'sessions': 'flexible',
                    'preferred_types': ['moderate_activity', 'active_training'],
                    'type_weights': {
                        'active_training': 0.8,
                        'moderate_activity': 0.9,
                        'light_walks': 0.5
                    }
                },
                'MODERATE': {
                    'min': 45,
                    'ideal': 60,
                    'max': 90,
                    'intensity': 'moderate',
                    'sessions': 'flexible',
                    'preferred_types': ['moderate_activity', 'light_walks'],
                    'type_weights': {
                        'active_training': 0.7,
                        'moderate_activity': 1.0,
                        'light_walks': 0.8
                    }
                },
                'MODERATE LOW': {
                    'min': 30,
                    'ideal': 45,
                    'max': 70,
                    'intensity': 'light_moderate',
                    'sessions': 'flexible',
                    'preferred_types': ['light_walks', 'moderate_activity'],
                    'type_weights': {
                        'active_training': 0.6,
                        'moderate_activity': 0.9,
                        'light_walks': 1.0
                    }
                },
                'LOW': {
                    'min': 15,
                    'ideal': 30,
                    'max': 45,
                    'intensity': 'light',
                    'sessions': 'single',
                    'preferred_types': ['light_walks'],
                    'type_weights': {
                        'active_training': 0.5,
                        'moderate_activity': 0.8,
                        'light_walks': 1.0
                    }
                }
            }
        
            breed_level = exercise_levels.get(breed_needs.upper(), exercise_levels['MODERATE'])
            
            # 時間匹配度評估（基礎分數）
            def calculate_time_score():
                if exercise_time >= breed_level['ideal']:
                    if exercise_time > breed_level['max']:
                        # 超出最大值的懲罰更明顯
                        excess = (exercise_time - breed_level['max']) / 30
                        return max(0.4, 1.0 - (excess * 0.2))
                    return 1.0  # 理想範圍內給予滿分
                elif exercise_time >= breed_level['min']:
                    # 在最小值和理想值之間使用更陡峭的曲線
                    progress = (exercise_time - breed_level['min']) / (breed_level['ideal'] - breed_level['min'])
                    return 0.5 + (progress * 0.5)
                else:
                    # 低於最小值時給予更嚴厲的懲罰
                    deficit_ratio = exercise_time / breed_level['min']
                    return max(0.1, deficit_ratio * 0.5)
        
            # 運動類型匹配度評估
            def calculate_type_score():
                type_weight = breed_level['type_weights'].get(exercise_type, 0.5)
                
                # 根據運動需求等級調整類型權重
                if breed_needs.upper() in ['VERY HIGH', 'HIGH']:
                    if exercise_type == 'light_walks':
                        type_weight *= 0.5  # 高需求品種做輕度運動的懲罰
                elif breed_needs.upper() == 'LOW':
                    if exercise_type == 'active_training':
                        type_weight *= 0.7  # 低需求品種做高強度運動的輕微懲罰
                
                return type_weight
        
            # 計算最終分數
            time_score = calculate_time_score()
            type_score = calculate_type_score()
            
            # 綜合評分，運動時間佔70%，類型佔30%
            final_score = (time_score * 0.7) + (type_score * 0.3)
            
            # 特殊情況調整
            if exercise_time < breed_level['min'] * 0.5:  # 運動時間嚴重不足
                final_score *= 0.5
            elif exercise_time > breed_level['max'] * 1.5:  # 運動時間過多
                final_score *= 0.7
        
            return max(0.1, min(1.0, final_score))


        def calculate_grooming_score(breed_needs: str, user_commitment: str, breed_size: str) -> float:
            """
            計算美容需求分數，強化美容維護需求與使用者承諾度的匹配評估。
            這個函數特別注意品種大小對美容工作的影響，以及不同程度的美容需求對時間投入的要求。
            """
            # 重新設計基礎分數矩陣，讓美容需求的差異更加明顯
            base_scores = {
                "High": {
                    "low": 0.20,      # 高需求對低承諾極不合適，顯著降低初始分數
                    "medium": 0.65,   # 中等承諾仍有挑戰
                    "high": 1.0       # 高承諾最適合
                },
                "Moderate": {
                    "low": 0.45,      # 中等需求對低承諾有困難
                    "medium": 0.85,   # 較好的匹配
                    "high": 0.95      # 高承諾會有餘力
                },
                "Low": {
                    "low": 0.90,      # 低需求對低承諾很合適
                    "medium": 0.85,   # 略微降低以反映可能過度投入
                    "high": 0.80      # 可能造成資源浪費
                }
            }
        
            # 取得基礎分數
            base_score = base_scores.get(breed_needs, base_scores["Moderate"])[user_commitment]
        
            # 根據品種大小調整美容工作量
            size_adjustments = {
                "Giant": {
                    "low": -0.35,     # 大型犬的美容工作量顯著增加
                    "medium": -0.20,
                    "high": -0.10
                },
                "Large": {
                    "low": -0.25,
                    "medium": -0.15,
                    "high": -0.05
                },
                "Medium": {
                    "low": -0.15,
                    "medium": -0.10,
                    "high": 0
                },
                "Small": {
                    "low": -0.10,
                    "medium": -0.05,
                    "high": 0
                }
            }
        
            # 應用體型調整
            size_adjustment = size_adjustments.get(breed_size, size_adjustments["Medium"])[user_commitment]
            current_score = base_score + size_adjustment
        
            # 特殊毛髮類型的額外調整
            def get_coat_adjustment(breed_description: str, commitment: str) -> float:
                """
                評估特殊毛髮類型所需的額外維護工作
                """
                adjustments = 0
                
                # 長毛品種需要更多維護
                if 'long coat' in breed_description.lower():
                    coat_penalties = {
                        'low': -0.20,
                        'medium': -0.15,
                        'high': -0.05
                    }
                    adjustments += coat_penalties[commitment]
                    
                # 雙層毛的品種掉毛量更大
                if 'double coat' in breed_description.lower():
                    double_coat_penalties = {
                        'low': -0.15,
                        'medium': -0.10,
                        'high': -0.05
                    }
                    adjustments += double_coat_penalties[commitment]
                    
                # 捲毛品種需要定期專業修剪
                if 'curly' in breed_description.lower():
                    curly_penalties = {
                        'low': -0.15,
                        'medium': -0.10,
                        'high': -0.05
                    }
                    adjustments += curly_penalties[commitment]
                    
                return adjustments
        
            # 季節性考量
            def get_seasonal_adjustment(breed_description: str, commitment: str) -> float:
                """
                評估季節性掉毛對美容需求的影響
                """
                if 'seasonal shedding' in breed_description.lower():
                    seasonal_penalties = {
                        'low': -0.15,
                        'medium': -0.10,
                        'high': -0.05
                    }
                    return seasonal_penalties[commitment]
                return 0
        
            # 專業美容需求評估
            def get_professional_grooming_adjustment(breed_description: str, commitment: str) -> float:
                """
                評估需要專業美容服務的影響
                """
                if 'professional grooming' in breed_description.lower():
                    grooming_penalties = {
                        'low': -0.20,
                        'medium': -0.15,
                        'high': -0.05
                    }
                    return grooming_penalties[commitment]
                return 0
        
            # 應用所有額外調整
            # 由於這些是示例調整，實際使用時需要根據品種描述信息進行調整
            coat_adjustment = get_coat_adjustment("", user_commitment)
            seasonal_adjustment = get_seasonal_adjustment("", user_commitment)
            professional_adjustment = get_professional_grooming_adjustment("", user_commitment)
            
            final_score = current_score + coat_adjustment + seasonal_adjustment + professional_adjustment
        
            # 確保分數在有意義的範圍內，但允許更大的差異
            return max(0.1, min(1.0, final_score))


        def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float:
            """
            計算使用者經驗與品種需求的匹配分數，加強經驗等級的影響力
            
            重要改進：
            1. 擴大基礎分數差異
            2. 加重困難特徵的懲罰
            3. 更細緻的品種特性評估
            """
            # 基礎分數矩陣 - 大幅擴大不同經驗等級的分數差異
            base_scores = {
                "High": {
                    "beginner": 0.15,      # 降低起始分，高難度品種對新手幾乎不推薦
                    "intermediate": 0.65,   # 中級玩家仍需謹慎
                    "advanced": 1.0        # 資深者能完全勝任
                },
                "Moderate": {
                    "beginner": 0.40,      # 適中難度對新手仍具挑戰
                    "intermediate": 0.85,   # 中級玩家較適合
                    "advanced": 0.95        # 資深者完全勝任
                },
                "Low": {
                    "beginner": 0.85,      # 新手友善品種
                    "intermediate": 0.90,   # 中級玩家幾乎完全勝任
                    "advanced": 0.85        # 資深者完全勝任
                }
            }
            
            # 取得基礎分數
            score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
            
            temperament_lower = temperament.lower()
            temperament_adjustments = 0.0
            
            # 根據經驗等級設定不同的特徵評估標準
            if user_experience == "beginner":
                # 新手不適合的特徵 - 更嚴格的懲罰
                difficult_traits = {
                    'stubborn': -0.30,        # 固執性格嚴重影響新手
                    'independent': -0.25,      # 獨立性高的品種不適合新手
                    'dominant': -0.25,         # 支配性強的品種需要經驗處理
                    'strong-willed': -0.20,    # 強勢性格需要技巧管理
                    'protective': -0.20,       # 保護性強需要適當訓練
                    'aloof': -0.15,           # 冷漠性格需要耐心培養
                    'energetic': -0.15,       # 活潑好動需要經驗引導
                    'aggressive': -0.35        # 攻擊傾向極不適合新手
                }
                
                # 新手友善的特徵 - 適度的獎勵
                easy_traits = {
                    'gentle': 0.05,            # 溫和性格適合新手
                    'friendly': 0.05,          # 友善性格容易相處
                    'eager to please': 0.08,   # 願意服從較容易訓練
                    'patient': 0.05,           # 耐心的特質有助於建立關係
                    'adaptable': 0.05,         # 適應性強較容易照顧
                    'calm': 0.06              # 冷靜的性格較好掌握
                }
                
                # 計算特徵調整
                for trait, penalty in difficult_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += penalty
                
                for trait, bonus in easy_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += bonus
                        
                # 品種類型特殊評估
                if 'terrier' in temperament_lower:
                    temperament_adjustments -= 0.20  # 梗類犬種通常不適合新手
                elif 'working' in temperament_lower:
                    temperament_adjustments -= 0.25  # 工作犬需要經驗豐富的主人
                elif 'guard' in temperament_lower:
                    temperament_adjustments -= 0.25  # 護衛犬需要專業訓練
                    
            elif user_experience == "intermediate":
                # 中級玩家的特徵評估
                moderate_traits = {
                    'stubborn': -0.15,        # 仍然需要注意，但懲罰較輕
                    'independent': -0.10,
                    'intelligent': 0.08,      # 聰明的特質可以好好發揮
                    'athletic': 0.06,         # 運動能力可以適當訓練
                    'versatile': 0.07,        # 多功能性可以開發
                    'protective': -0.08       # 保護性仍需注意
                }
                
                for trait, adjustment in moderate_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += adjustment
                        
            else:  # advanced
                # 資深玩家能夠應對挑戰性特徵
                advanced_traits = {
                    'stubborn': 0.05,         # 困難特徵反而成為優勢
                    'independent': 0.05,
                    'intelligent': 0.10,
                    'protective': 0.05,
                    'strong-willed': 0.05
                }
                
                for trait, bonus in advanced_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += bonus
            
            # 確保最終分數範圍更大，讓差異更明顯
            final_score = max(0.05, min(1.0, score + temperament_adjustments))
            
            return final_score

        def calculate_health_score(breed_name: str, user_prefs: UserPreferences) -> float:
            """
            計算品種健康分數，加強健康問題的影響力和與使用者敏感度的連結
            
            重要改進：
            1. 根據使用者的健康敏感度調整分數
            2. 更嚴格的健康問題評估
            3. 考慮多重健康問題的累積效應
            4. 加入遺傳疾病的特別考量
            """
            if breed_name not in breed_health_info:
                return 0.5
        
            health_notes = breed_health_info[breed_name]['health_notes'].lower()
            
            # 嚴重健康問題 - 加重扣分
            severe_conditions = {
                'hip dysplasia': -0.25,           # 髖關節發育不良，影響生活品質
                'heart disease': -0.25,           # 心臟疾病，需要長期治療
                'progressive retinal atrophy': -0.20,  # 進行性視網膜萎縮，導致失明
                'bloat': -0.22,                   # 胃扭轉，致命風險
                'epilepsy': -0.20,                # 癲癇，需要長期藥物控制
                'degenerative myelopathy': -0.20,  # 脊髓退化，影響行動能力
                'von willebrand disease': -0.18    # 血液凝固障礙
            }
            
            # 中度健康問題 - 適度扣分
            moderate_conditions = {
                'allergies': -0.12,               # 過敏問題，需要持續關注
                'eye problems': -0.15,            # 眼睛問題，可能需要手術
                'joint problems': -0.15,          # 關節問題，影響運動能力
                'hypothyroidism': -0.12,          # 甲狀腺功能低下，需要藥物治療
                'ear infections': -0.10,          # 耳道感染，需要定期清理
                'skin issues': -0.12              # 皮膚問題，需要特殊護理
            }
            
            # 輕微健康問題 - 輕微扣分
            minor_conditions = {
                'dental issues': -0.08,           # 牙齒問題，需要定期護理
                'weight gain tendency': -0.08,     # 易胖體質，需要控制飲食
                'minor allergies': -0.06,         # 輕微過敏，可控制
                'seasonal allergies': -0.06       # 季節性過敏
            }
        
            # 計算基礎健康分數
            health_score = 1.0
            
            # 健康問題累積效應計算
            condition_counts = {
                'severe': 0,
                'moderate': 0,
                'minor': 0
            }
            
            # 計算各等級健康問題的數量和影響
            for condition, penalty in severe_conditions.items():
                if condition in health_notes:
                    health_score += penalty
                    condition_counts['severe'] += 1
                    
            for condition, penalty in moderate_conditions.items():
                if condition in health_notes:
                    health_score += penalty
                    condition_counts['moderate'] += 1
                    
            for condition, penalty in minor_conditions.items():
                if condition in health_notes:
                    health_score += penalty
                    condition_counts['minor'] += 1
            
            # 多重問題的額外懲罰（累積效應）
            if condition_counts['severe'] > 1:
                health_score *= (0.85 ** (condition_counts['severe'] - 1))
            if condition_counts['moderate'] > 2:
                health_score *= (0.90 ** (condition_counts['moderate'] - 2))
            
            # 根據使用者健康敏感度調整分數
            sensitivity_multipliers = {
                'low': 1.1,      # 較不在意健康問題
                'medium': 1.0,   # 標準評估
                'high': 0.85     # 非常注重健康問題
            }
            
            health_score *= sensitivity_multipliers.get(user_prefs.health_sensitivity, 1.0)
        
            # 壽命影響評估
            try:
                lifespan = breed_health_info[breed_name].get('average_lifespan', '10-12')
                years = float(lifespan.split('-')[0])
                if years < 8:
                    health_score *= 0.85   # 短壽命顯著降低分數
                elif years < 10:
                    health_score *= 0.92   # 較短壽命輕微降低分數
                elif years > 13:
                    health_score *= 1.1    # 長壽命適度加分
            except:
                pass
        
            # 特殊健康優勢
            if 'generally healthy' in health_notes or 'hardy breed' in health_notes:
                health_score *= 1.15
            elif 'robust health' in health_notes or 'few health issues' in health_notes:
                health_score *= 1.1
        
            # 確保分數在合理範圍內，但允許更大的分數差異
            return max(0.1, min(1.0, health_score))
            

        def calculate_noise_score(breed_name: str, user_prefs: UserPreferences) -> float:
            """
            計算品種噪音分數，特別加強噪音程度與生活環境的關聯性評估
            """
            if breed_name not in breed_noise_info:
                return 0.5
        
            noise_info = breed_noise_info[breed_name]
            noise_level = noise_info['noise_level'].lower()
            noise_notes = noise_info['noise_notes'].lower()
        
            # 重新設計基礎噪音分數矩陣，考慮不同情境下的接受度
            base_scores = {
                'low': {
                    'low': 1.0,       # 安靜的狗對低容忍完美匹配
                    'medium': 0.95,   # 安靜的狗對一般容忍很好
                    'high': 0.90      # 安靜的狗對高容忍當然可以
                },
                'medium': {
                    'low': 0.60,      # 一般吠叫對低容忍較困難
                    'medium': 0.90,   # 一般吠叫對一般容忍可接受
                    'high': 0.95      # 一般吠叫對高容忍很好
                },
                'high': {
                    'low': 0.25,      # 愛叫的狗對低容忍極不適合
                    'medium': 0.65,   # 愛叫的狗對一般容忍有挑戰
                    'high': 0.90      # 愛叫的狗對高容忍可以接受
                },
                'varies': {
                    'low': 0.50,      # 不確定的情況對低容忍風險較大
                    'medium': 0.75,   # 不確定的情況對一般容忍可嘗試
                    'high': 0.85      # 不確定的情況對高容忍問題較小
                }
            }
        
            # 取得基礎分數
            base_score = base_scores.get(noise_level, {'low': 0.6, 'medium': 0.75, 'high': 0.85})[user_prefs.noise_tolerance]
        
            # 吠叫原因評估，根據環境調整懲罰程度
            barking_penalties = {
                'separation anxiety': {
                    'apartment': -0.30,    # 在公寓對鄰居影響更大
                    'house_small': -0.25,
                    'house_large': -0.20
                },
                'excessive barking': {
                    'apartment': -0.25,
                    'house_small': -0.20,
                    'house_large': -0.15
                },
                'territorial': {
                    'apartment': -0.20,    # 在公寓更容易被觸發
                    'house_small': -0.15,
                    'house_large': -0.10
                },
                'alert barking': {
                    'apartment': -0.15,    # 公寓環境刺激較多
                    'house_small': -0.10,
                    'house_large': -0.08
                },
                'attention seeking': {
                    'apartment': -0.15,
                    'house_small': -0.12,
                    'house_large': -0.10
                }
            }
        
            # 計算環境相關的吠叫懲罰
            living_space = user_prefs.living_space
            barking_penalty = 0
            for trigger, penalties in barking_penalties.items():
                if trigger in noise_notes:
                    barking_penalty += penalties.get(living_space, -0.15)
        
            # 特殊情況評估
            special_adjustments = 0
            if user_prefs.has_children:
                # 孩童年齡相關調整
                child_age_adjustments = {
                    'toddler': {
                        'high': -0.20,     # 幼童對吵鬧更敏感
                        'medium': -0.15,
                        'low': -0.05
                    },
                    'school_age': {
                        'high': -0.15,
                        'medium': -0.10,
                        'low': -0.05
                    },
                    'teenager': {
                        'high': -0.10,
                        'medium': -0.05,
                        'low': -0.02
                    }
                }
                
                # 根據孩童年齡和噪音等級調整
                age_adj = child_age_adjustments.get(user_prefs.children_age, 
                                                  child_age_adjustments['school_age'])
                special_adjustments += age_adj.get(noise_level, -0.10)
        
            # 訓練性補償評估
            trainability_bonus = 0
            if 'responds well to training' in noise_notes:
                trainability_bonus = 0.12
            elif 'can be trained' in noise_notes:
                trainability_bonus = 0.08
            elif 'difficult to train' in noise_notes:
                trainability_bonus = 0.02
        
            # 夜間吠叫特別考量
            if 'night barking' in noise_notes or 'howls' in noise_notes:
                if user_prefs.living_space == 'apartment':
                    special_adjustments -= 0.15
                elif user_prefs.living_space == 'house_small':
                    special_adjustments -= 0.10
                else:
                    special_adjustments -= 0.05
        
            # 計算最終分數，確保更大的分數範圍
            final_score = base_score + barking_penalty + special_adjustments + trainability_bonus
            return max(0.1, min(1.0, final_score))
            

        # 1. 計算基礎分數
        print("\n=== 開始計算品種相容性分數 ===")
        print(f"處理品種: {breed_info.get('Breed', 'Unknown')}")
        print(f"品種信息: {breed_info}")
        print(f"使用者偏好: {vars(user_prefs)}")

        # 計算所有基礎分數並整合到字典中
        scores = {
            'space': calculate_space_score(
                breed_info['Size'], 
                user_prefs.living_space,
                user_prefs.yard_access != 'no_yard',
                breed_info.get('Exercise Needs', 'Moderate')
            ),
            'exercise': calculate_exercise_score(
                breed_info.get('Exercise Needs', 'Moderate'),
                user_prefs.exercise_time,
                user_prefs.exercise_type
            ),
            'grooming': calculate_grooming_score(
                breed_info.get('Grooming Needs', 'Moderate'),
                user_prefs.grooming_commitment.lower(),
                breed_info['Size']
            ),
            'experience': calculate_experience_score(
                breed_info.get('Care Level', 'Moderate'),
                user_prefs.experience_level,
                breed_info.get('Temperament', '')
            ),
            'health': calculate_health_score(
                breed_info.get('Breed', ''),
                user_prefs
            ),
            'noise': calculate_noise_score(
                breed_info.get('Breed', ''),
                user_prefs
            )
        }

        final_score = calculate_breed_compatibility_score(
            scores=scores,
            user_prefs=user_prefs,
            breed_info=breed_info
        )

        # 計算環境適應性加成
        adaptability_bonus = calculate_environmental_fit(breed_info, user_prefs)

        # 整合最終分數和加成
        final_score = (final_score * 0.9) + (adaptability_bonus * 0.1)
        final_score = amplify_score_extreme(final_score)

        # 更新並返回完整的評分結果
        scores.update({
            'overall': final_score,
            'adaptability_bonus': adaptability_bonus
        })

        return scores

    except Exception as e:
        print(f"\n!!!!! 發生嚴重錯誤 !!!!!")
        print(f"錯誤類型: {type(e).__name__}")
        print(f"錯誤訊息: {str(e)}")
        print(f"完整錯誤追蹤:")
        print(traceback.format_exc())
        return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}


def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -> float:
    """計算品種與環境的適應性加成"""
    adaptability_score = 0.0
    description = breed_info.get('Description', '').lower()
    temperament = breed_info.get('Temperament', '').lower()
    
    # 環境適應性評估
    if user_prefs.living_space == 'apartment':
        if 'adaptable' in temperament or 'apartment' in description:
            adaptability_score += 0.1
        if breed_info.get('Size') == 'Small':
            adaptability_score += 0.05
    elif user_prefs.living_space == 'house_large':
        if 'active' in temperament or 'energetic' in description:
            adaptability_score += 0.1
            
    # 氣候適應性
    if user_prefs.climate in description or user_prefs.climate in temperament:
        adaptability_score += 0.05
        
    return min(0.2, adaptability_score)


# def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float:
#     """
#     改進的品種相容性評分系統
#     通過更細緻的特徵評估和動態權重調整，自然產生分數差異
#     """
#     # 評估關鍵特徵的匹配度，使用更極端的調整係數
#     def evaluate_key_features():
#         # 空間適配性評估
#         space_multiplier = 1.0
#         if user_prefs.living_space == 'apartment':
#             if breed_info['Size'] == 'Giant':
#                 space_multiplier = 0.3  # 嚴重不適合
#             elif breed_info['Size'] == 'Large':
#                 space_multiplier = 0.4  # 明顯不適合
#             elif breed_info['Size'] == 'Small':
#                 space_multiplier = 1.4  # 明顯優勢
        
#         # 運動需求評估
#         exercise_multiplier = 1.0
#         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
#         if exercise_needs == 'VERY HIGH':
#             if user_prefs.exercise_time < 60:
#                 exercise_multiplier = 0.3  # 嚴重不足
#             elif user_prefs.exercise_time > 150:
#                 exercise_multiplier = 1.5  # 完美匹配
#         elif exercise_needs == 'LOW' and user_prefs.exercise_time > 150:
#             exercise_multiplier = 0.5  # 運動過度

#         return space_multiplier, exercise_multiplier

#     # 計算經驗匹配度
#     def evaluate_experience():
#         exp_multiplier = 1.0
#         care_level = breed_info.get('Care Level', 'MODERATE')
        
#         if care_level == 'High':
#             if user_prefs.experience_level == 'beginner':
#                 exp_multiplier = 0.4
#             elif user_prefs.experience_level == 'advanced':
#                 exp_multiplier = 1.3
#         elif care_level == 'Low':
#             if user_prefs.experience_level == 'advanced':
#                 exp_multiplier = 0.9  # 略微降低評分，因為可能不夠有挑戰性
                
#         return exp_multiplier

#     # 取得特徵調整係數
#     space_mult, exercise_mult = evaluate_key_features()
#     exp_mult = evaluate_experience()

#     # 調整基礎分數
#     adjusted_scores = {
#         'space': scores['space'] * space_mult,
#         'exercise': scores['exercise'] * exercise_mult,
#         'experience': scores['experience'] * exp_mult,
#         'grooming': scores['grooming'],
#         'health': scores['health'],
#         'noise': scores['noise']
#     }

#     # 計算加權平均，關鍵特徵佔更大權重
#     weights = {
#         'space': 0.35,
#         'exercise': 0.30,
#         'experience': 0.20,
#         'grooming': 0.15,
#         'health': 0.10,
#         'noise': 0.10
#     }

#     # 動態調整權重
#     if user_prefs.has_children:
#         if user_prefs.children_age == 'toddler':
#             weights['noise'] *= 1.5     # 幼童對噪音更敏感
#             weights['experience'] *= 1.3 # 需要更有經驗的飼主
        
#     if user_prefs.living_space == 'apartment':
#         weights['space'] *= 1.4        # 公寓空間限制更重要
#         weights['noise'] *= 1.3        # 噪音問題更重要

#     # 運動時間極端情況
#     if user_prefs.exercise_time < 30:
#         weights['exercise'] *= 1.5     # 運動時間極少時加重權重
#     elif user_prefs.exercise_time > 150:
#         weights['exercise'] *= 1.3     # 運動時間充足時略微加重

#     # 正規化權重
#     total_weight = sum(weights.values())
#     normalized_weights = {k: v/total_weight for k, v in weights.items()}

#     # 計算最終分數
#     final_score = sum(adjusted_scores[k] * normalized_weights[k] for k in scores.keys())

#     # 品種特性加成
#     breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
    
#     # 整合最終分數，保持在0-1範圍內
#     return min(1.0, max(0.0, (final_score * 0.85) + (breed_bonus * 0.15)))
    

# def amplify_score_extreme(score: float) -> float:
#     """
#     改進的分數轉換函數，提供更大的分數區間和更明顯的差異
    
#     轉換邏輯：
#     - 極差匹配 (0.0-0.2) -> 50-60%
#     - 較差匹配 (0.2-0.4) -> 60-70%
#     - 中等匹配 (0.4-0.6) -> 70-82%
#     - 良好匹配 (0.6-0.8) -> 82-90%
#     - 優秀匹配 (0.8-1.0) -> 90-98%
#     """
#     if score < 0.2:
#         # 極差匹配：更低的起始分數
#         return 0.50 + (score / 0.2) * 0.10
#     elif score < 0.4:
#         # 較差匹配：緩慢增長
#         position = (score - 0.2) / 0.2
#         return 0.60 + position * 0.10
#     elif score < 0.6:
#         # 中等匹配：較大的分數增長
#         position = (score - 0.4) / 0.2
#         return 0.70 + position * 0.12
#     elif score < 0.8:
#         # 良好匹配：快速增長
#         position = (score - 0.6) / 0.2
#         return 0.82 + position * 0.08
#     else:
#         # 優秀匹配：達到更高分數
#         position = (score - 0.8) / 0.2
#         return 0.90 + position * 0.08


# def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float:
#     """改進的品種相容性評分系統"""
    
#     def evaluate_key_features():
#         # 空間適配性評估 - 更極端的調整
#         space_multiplier = 1.0
#         if user_prefs.living_space == 'apartment':
#             if breed_info['Size'] == 'Giant':
#                 space_multiplier = 0.2  # 更嚴重的懲罰
#             elif breed_info['Size'] == 'Large':
#                 space_multiplier = 0.3
#             elif breed_info['Size'] == 'Medium':
#                 space_multiplier = 0.7
#             elif breed_info['Size'] == 'Small':
#                 space_multiplier = 1.6  # 更大的獎勵
                
#         # 運動需求評估 - 更細緻的匹配
#         exercise_multiplier = 1.0
#         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
        
#         # 運動時間差異計算
#         time_diff_ratio = abs(user_prefs.exercise_time - get_ideal_exercise_time(exercise_needs)) / 60.0
        
#         if exercise_needs == 'VERY HIGH':
#             if user_prefs.exercise_time < 90:
#                 exercise_multiplier = max(0.2, 1.0 - time_diff_ratio)
#             elif user_prefs.exercise_time > 150:
#                 exercise_multiplier = min(2.0, 1.0 + time_diff_ratio/2)
#         elif exercise_needs == 'HIGH':
#             if user_prefs.exercise_time < 60:
#                 exercise_multiplier = max(0.3, 1.0 - time_diff_ratio)
#             elif user_prefs.exercise_time > 120:
#                 exercise_multiplier = min(1.8, 1.0 + time_diff_ratio/2)
#         elif exercise_needs == 'LOW':
#             if user_prefs.exercise_time > 120:
#                 exercise_multiplier = max(0.4, 1.0 - time_diff_ratio/2)

#         return space_multiplier, exercise_multiplier

#     def get_ideal_exercise_time(exercise_needs: str) -> int:
#         """獲取理想運動時間"""
#         return {
#             'VERY HIGH': 150,
#             'HIGH': 120,
#             'MODERATE HIGH': 90,
#             'MODERATE': 60,
#             'MODERATE LOW': 45,
#             'LOW': 30
#         }.get(exercise_needs, 60)

#     # 經驗匹配度評估 - 更強的影響力
#     def evaluate_experience():
#         exp_multiplier = 1.0
#         care_level = breed_info.get('Care Level', 'MODERATE')
        
#         if care_level == 'High':
#             if user_prefs.experience_level == 'beginner':
#                 exp_multiplier = 0.3  # 更嚴重的懲罰
#             elif user_prefs.experience_level == 'advanced':
#                 exp_multiplier = 1.5  # 更大的獎勵
#         elif care_level == 'Low':
#             if user_prefs.experience_level == 'advanced':
#                 exp_multiplier = 0.8
                
#         return exp_multiplier

#     # 計算調整係數
#     space_mult, exercise_mult = evaluate_key_features()
#     exp_mult = evaluate_experience()

#     # 調整基礎分數
#     adjusted_scores = {
#         'space': scores['space'] * space_mult,
#         'exercise': scores['exercise'] * exercise_mult,
#         'experience': scores['experience'] * exp_mult,
#         'grooming': scores['grooming'],
#         'health': scores['health'] * (1.5 if user_prefs.health_sensitivity == 'high' else 1.0),
#         'noise': scores['noise']
#     }

#     # 基礎權重
#     weights = {
#         'space': 0.25,
#         'exercise': 0.25,
#         'experience': 0.15,
#         'grooming': 0.15,
#         'health': 0.10,
#         'noise': 0.10
#     }

#     # 動態權重調整 - 更強的條件反應
#     if user_prefs.has_children:
#         if user_prefs.children_age == 'toddler':
#             weights['noise'] *= 2.0  # 更強的噪音影響
#             weights['experience'] *= 1.5
#             weights['health'] *= 1.3
#         elif user_prefs.children_age == 'school_age':
#             weights['noise'] *= 1.5
#             weights['experience'] *= 1.3

#     if user_prefs.living_space == 'apartment':
#         weights['space'] *= 1.8  # 更強的空間限制
#         weights['noise'] *= 1.6

#     # 運動時間極端情況
#     if user_prefs.exercise_time < 30:
#         weights['exercise'] *= 2.0
#     elif user_prefs.exercise_time > 150:
#         weights['exercise'] *= 1.5

#     # 正規化權重
#     total_weight = sum(weights.values())
#     normalized_weights = {k: v/total_weight for k, v in weights.items()}

#     # 計算基礎分數
#     base_score = sum(adjusted_scores[k] * normalized_weights[k] for k in scores.keys())
    
#     # 品種特性加成
#     breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
    
#     # 動態整合係數
#     bonus_weight = min(0.25, max(0.15, breed_bonus))  # 讓優秀特性有更大影響
    
#     # 完美匹配加成
#     if all(score >= 0.8 for score in adjusted_scores.values()):
#         base_score *= 1.2
    
#     # 極端不匹配懲罰
#     if any(score <= 0.3 for score in adjusted_scores.values()):
#         base_score *= 0.6

#     return min(1.0, max(0.0, (base_score * (1.0 - bonus_weight)) + (breed_bonus * bonus_weight)))


# def amplify_score_extreme(score: float) -> float:
#     """
#     改進的分數轉換函數，提供更動態的分數範圍
    
#     動態轉換邏輯：
#     - 極差匹配 (0.0-0.2) -> 45-58%
#     - 較差匹配 (0.2-0.4) -> 58-72%
#     - 中等匹配 (0.4-0.6) -> 72-85%
#     - 良好匹配 (0.6-0.8) -> 85-92%
#     - 優秀匹配 (0.8-0.9) -> 92-96%
#     - 完美匹配 (0.9-1.0) -> 96-99%
#     """
#     if score < 0.2:
#         return 0.45 + (score / 0.2) * 0.13
#     elif score < 0.4:
#         position = (score - 0.2) / 0.2
#         return 0.58 + position * 0.14
#     elif score < 0.6:
#         position = (score - 0.4) / 0.2
#         return 0.72 + position * 0.13
#     elif score < 0.8:
#         position = (score - 0.6) / 0.2
#         return 0.85 + position * 0.07
#     elif score < 0.9:
#         position = (score - 0.8) / 0.1
#         return 0.92 + position * 0.04
#     else:
#         position = (score - 0.9) / 0.1
#         return 0.96 + position * 0.03


def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float:
    """
    改進的品種相容性評分系統，提供更動態和精確的評分
    
    主要改進：
    1. 更動態的權重系統
    2. 更強的極端情況處理
    3. 更精確的品種特性評估
    """
    def evaluate_condition_extremity():
        """評估使用者條件的極端程度"""
        extremity_count = 0
        
        # 空間條件極端性
        if user_prefs.living_space == 'apartment' and breed_info['Size'] in ['Large', 'Giant']:
            extremity_count += 2
        elif user_prefs.living_space == 'house_large' and breed_info['Size'] == 'Small':
            extremity_count += 1
            
        # 運動需求極端性
        exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
        if exercise_needs == 'VERY HIGH' and user_prefs.exercise_time < 60:
            extremity_count += 2
        elif exercise_needs == 'LOW' and user_prefs.exercise_time > 150:
            extremity_count += 1
            
        # 經驗等級極端性
        care_level = breed_info.get('Care Level', 'MODERATE')
        if care_level == 'High' and user_prefs.experience_level == 'beginner':
            extremity_count += 2
            
        return extremity_count

    def calculate_dynamic_weights():
        """計算動態權重"""
        # 基礎權重
        weights = {
            'space': 0.20,
            'exercise': 0.20,
            'experience': 0.15,
            'grooming': 0.15,
            'health': 0.15,
            'noise': 0.15
        }
        
        # 根據生活環境調整權重
        if user_prefs.living_space == 'apartment':
            weights['space'] *= 2.0
            weights['noise'] *= 1.8
        
        # 根據家庭情況調整
        if user_prefs.has_children:
            if user_prefs.children_age == 'toddler':
                weights['noise'] *= 2.0
                weights['experience'] *= 1.8
                weights['health'] *= 1.5
            elif user_prefs.children_age == 'school_age':
                weights['noise'] *= 1.5
                weights['experience'] *= 1.3
        
        # 根據運動時間調整
        if user_prefs.exercise_time < 30:
            weights['exercise'] *= 2.5
        elif user_prefs.exercise_time > 150:
            weights['exercise'] *= 2.0
            
        # 根據健康敏感度調整
        if user_prefs.health_sensitivity == 'high':
            weights['health'] *= 1.8
            
        return weights

    # 計算條件極端程度
    extremity_level = evaluate_condition_extremity()
    
    # 計算動態權重
    weights = calculate_dynamic_weights()
    
    # 正規化權重
    total_weight = sum(weights.values())
    normalized_weights = {k: v/total_weight for k, v in weights.items()}
    
    # 計算加權分數
    weighted_scores = {
        k: scores[k] * normalized_weights[k] for k in scores.keys()
    }
    
    # 基礎分數
    base_score = sum(weighted_scores.values())
    
    # 品種特性加成
    breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
    
    # 根據極端程度調整最終分數
    if extremity_level >= 3:
        base_score *= 0.6  # 多個極端條件的嚴重懲罰
    elif extremity_level >= 2:
        base_score *= 0.8  # 較少極端條件的適度懲罰
    
    # 完美匹配加成
    if all(score >= 0.8 for score in scores.values()):
        base_score *= 1.3
    
    # 品種特性影響力隨匹配度增加
    bonus_weight = min(0.35, max(0.15, breed_bonus))
    
    # 最終分數計算
    final_score = (base_score * (1.0 - bonus_weight)) + (breed_bonus * bonus_weight)
    
    return min(1.0, max(0.0, final_score))


def amplify_score_extreme(score: float) -> float:
    """
    改進的分數轉換函數，提供更合理的分數分布
    
    特點：
    1. 更大的分數範圍
    2. 更平滑的轉換曲線
    3. 更準確的極端情況處理
    """
    def sigmoid_transform(x: float, steepness: float = 10) -> float:
        """使用 sigmoid 函數實現更平滑的轉換"""
        import math
        return 1 / (1 + math.exp(-steepness * (x - 0.5)))

    if score < 0.2:
        # 極差匹配：使用更低的起始分數
        base = 0.40
        range_score = 0.15
        position = score / 0.2
        return base + (sigmoid_transform(position) * range_score)
        
    elif score < 0.4:
        # 較差匹配：緩慢增長
        base = 0.55
        range_score = 0.15
        position = (score - 0.2) / 0.2
        return base + (sigmoid_transform(position) * range_score)
        
    elif score < 0.6:
        # 中等匹配：較大增長
        base = 0.70
        range_score = 0.15
        position = (score - 0.4) / 0.2
        return base + (sigmoid_transform(position) * range_score)
        
    elif score < 0.8:
        # 良好匹配：快速增長
        base = 0.85
        range_score = 0.10
        position = (score - 0.6) / 0.2
        return base + (sigmoid_transform(position) * range_score)
        
    elif score < 0.9:
        # 優秀匹配：接近最高分
        base = 0.95
        range_score = 0.03
        position = (score - 0.8) / 0.1
        return base + (sigmoid_transform(position) * range_score)
        
    else:
        # 完美匹配：可能達到最高分
        base = 0.98
        range_score = 0.02
        position = (score - 0.9) / 0.1
        return base + (sigmoid_transform(position) * range_score)