from pydantic import BaseModel, Field
from typing import Optional


class CitationResult(BaseModel):
    """LLM查询品牌引用结果"""
    cited: bool = Field(..., description="是否检测到品牌引用")
    position: Optional[int] = Field(None, description="引用位置（1-based）")
    citation_text: Optional[str] = Field(None, description="引用文本片段")
    sentiment: str = Field(..., description="情感类型: positive/neutral/negative")
    confidence: float = Field(..., ge=0.0, le=1.0, description="置信度 0.0-1.0")


class ScoringResult(BaseModel):
    """评分结果"""
    mention_rate_score: float = Field(..., ge=0.0, le=100.0, description="提及率得分 0-100")
    sov_score: float = Field(..., ge=0.0, le=100.0, description="SOV得分 0-100")
    quality_score: float = Field(..., ge=0.0, le=100.0, description="引用质量得分 0-100")
    overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分 0-100")

    class Config:
        json_schema_extra = {
            "example": {
                "mention_rate_score": 66.67,
                "sov_score": 50.0,
                "quality_score": 78.5,
                "overall_score": 64.67
            }
        }


class BrandScoreResponse(BaseModel):
    """品牌评分响应"""
    mention_rate_score: float = Field(..., ge=0.0, le=100.0, description="提及率得分 0-100")
    sov_score: float = Field(..., ge=0.0, le=100.0, description="SOV得分 0-100")
    quality_score: float = Field(..., ge=0.0, le=100.0, description="引用质量得分 0-100")
    overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分 0-100")


# ============================================================
# V2 评分Schema
# ============================================================

class DimensionScoreResponse(BaseModel):
    """单个维度评分响应"""
    name: str = Field(..., description="维度名称")
    score: float = Field(..., ge=0.0, description="该维度得分")
    max_score: float = Field(..., description="该维度满分")
    percentage: float = Field(..., ge=0.0, le=100.0, description="得分率百分比")
    detail: dict = Field(default_factory=dict, description="评分细节")


class BrandScoreV2Response(BaseModel):
    """品牌可见性评分V2响应"""
    overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分 0-100")
    health_level: str = Field(..., description="健康等级: excellent/good/pass/danger")
    mention_rate: DimensionScoreResponse = Field(..., description="提及率维度 (25分)")
    recommendation_rank: DimensionScoreResponse = Field(..., description="推荐排名维度 (25分)")
    sentiment_score: DimensionScoreResponse = Field(..., description="情感倾向维度 (20分)")
    citation_quality: DimensionScoreResponse = Field(..., description="引用质量维度 (15分)")
    competitive_position: DimensionScoreResponse = Field(..., description="竞品对比维度 (15分)")

    # V1兼容字段（保留旧字段，方便前端平滑迁移）
    mention_rate_score: float = Field(..., description="[V1兼容] 提及率得分 0-100")
    sov_score: float = Field(..., description="[V1兼容] SOV得分 0-100")
    quality_score: float = Field(..., description="[V1兼容] 引用质量得分 0-100")

    class Config:
        json_schema_extra = {
            "example": {
                "overall_score": 68.5,
                "health_level": "good",
                "mention_rate": {
                    "name": "提及率",
                    "score": 20.0,
                    "max_score": 25.0,
                    "percentage": 80.0,
                    "detail": {"mentioned_count": 8, "total_queries": 10, "rate": 0.8},
                },
                "recommendation_rank": {
                    "name": "推荐排名",
                    "score": 18.5,
                    "max_score": 25.0,
                    "percentage": 74.0,
                    "detail": {"avg_position": 2.5, "valid_count": 8},
                },
                "sentiment_score": {
                    "name": "情感倾向",
                    "score": 15.0,
                    "max_score": 20.0,
                    "percentage": 75.0,
                    "detail": {"positive": 5, "neutral": 3, "negative": 0},
                },
                "citation_quality": {
                    "name": "引用质量",
                    "score": 10.0,
                    "max_score": 15.0,
                    "percentage": 66.7,
                    "detail": {"cited_count": 8, "avg_depth_score": 0.667},
                },
                "competitive_position": {
                    "name": "竞品对比",
                    "score": 5.0,
                    "max_score": 15.0,
                    "percentage": 33.3,
                    "detail": {"brand_mentions": 8, "competitor_count": 3, "ahead_count": 1},
                },
                "mention_rate_score": 80.0,
                "sov_score": 50.0,
                "quality_score": 66.7,
            }
        }


class BrandScoreHistoryItem(BaseModel):
    """评分历史条目"""
    date: str = Field(..., description="日期 YYYY-MM-DD")
    mention_rate_score: float = Field(..., ge=0.0, le=100.0, description="提及率得分")
    sov_score: float = Field(..., ge=0.0, le=100.0, description="SOV得分")
    quality_score: float = Field(..., ge=0.0, le=100.0, description="引用质量得分")
    overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分")
    total_queries: int = Field(..., description="总查询次数")
    cited_count: int = Field(..., description="被引用次数")


class BrandScoreHistoryResponse(BaseModel):
    """品牌评分历史响应"""
    history: list[BrandScoreHistoryItem] = Field(default_factory=list)
    total: int = Field(..., description="历史记录总数")


class PlatformStats(BaseModel):
    """平台统计"""
    queries: int = Field(..., description="查询次数")
    citations: int = Field(..., description="引用次数")
    rate: float = Field(..., description="引用率")


class TrendItem(BaseModel):
    """趋势数据"""
    date: str = Field(..., description="日期")
    score: int = Field(..., description="评分/引用数")


class SentimentBreakdown(BaseModel):
    """情感分布"""
    positive: int = Field(0, description="正面引用数")
    neutral: int = Field(0, description="中性引用数")
    negative: int = Field(0, description="负面引用数")


class CitationsStatsResponse(BaseModel):
    """引用统计响应"""
    total_queries: int = Field(..., description="总查询次数")
    total_citations: int = Field(..., description="总引用次数")
    citation_rate: float = Field(..., description="引用率百分比")
    platform_breakdown: dict[str, PlatformStats] = Field(
        default_factory=dict, description="各平台统计"
    )
    sentiment_breakdown: SentimentBreakdown = Field(
        ..., description="情感分布"
    )
    trend: list[TrendItem] = Field(default_factory=list, description="趋势数据")


class DimensionCompareItem(BaseModel):
    """单维度对比项"""
    name: str = Field(..., description="维度名称")
    score: float = Field(..., description="得分")
    max_score: float = Field(..., description="满分")
    percentage: float = Field(..., description="得分率百分比")
    trend: str = Field("stable", description="趋势: up/down/stable")
    trend_value: float = Field(0.0, description="趋势变化值")


class CompareItem(BaseModel):
    """对比项"""
    entity_id: str = Field(..., description="品牌或竞品ID")
    entity_name: str = Field(..., description="品牌或竞品名称")
    entity_type: str = Field(..., description="类型: brand 或 competitor")
    mention_rate_score: float = Field(..., description="提及率得分")
    sov_score: float = Field(..., description="SOV得分")
    quality_score: float = Field(..., description="引用质量得分")
    overall_score: float = Field(..., description="综合评分")
    citation_count: int = Field(0, description="引用次数")
    # V2 多维度对比
    dimensions: list[DimensionCompareItem] = Field(
        default_factory=list, description="五维度评分对比"
    )
    # 趋势
    overall_trend: str = Field("stable", description="综合评分趋势: up/down/stable")
    overall_trend_value: float = Field(0.0, description="综合评分变化值")


class CompareResponse(BaseModel):
    """竞品对比响应"""
    brand_id: str = Field(..., description="品牌ID")
    brand_name: str = Field(..., description="品牌名称")
    items: list[CompareItem] = Field(default_factory=list, description="对比项列表（含品牌和竞品）")
    # 雷达图数据
    radar_data: list[dict] = Field(
        default_factory=list, description="雷达图数据（各维度各品牌得分）"
    )