from datetime import datetime

from pydantic import BaseModel, Field


class CreateEvaluationRequest(BaseModel):
    """评价生成入参：指定输出分数类型。"""

    score_type: str = Field(default="percentage", pattern="^(percentage|five_point)$")


class DimensionScore(BaseModel):
    """维度评分：保存单个评分维度的分数、满分和评价。"""

    dimension: str
    score: float
    max_score: float
    comment: str
    evidence: list[str] = Field(default_factory=list)
    deductions: list[str] = Field(default_factory=list)
    improvement: str = ""


class ScoreDetailItem(BaseModel):
    """评分明细：对应 training_score_detail 的单条评分细则。"""

    id: int | None = None
    record_id: int | None = None
    rule_id: int | None = None
    dimension: str
    score: float | None = None
    deducted_reason: str | None = None
    evidence_message_ids: list = Field(default_factory=list)
    ai_confidence: float | None = None
    comment: str | None = None


class EvaluationResponse(BaseModel):
    """评价报告响应：返回结构化 AI 评价报告。"""

    evaluation_id: int
    score_type: str
    total_score: float
    dimension_scores: list[DimensionScore]
    score_details: list[ScoreDetailItem] = Field(default_factory=list)
    errors: list[dict]
    improvement_plan: list[str]
    evidence_summary: list[str]
    guideline_refs: list[dict]
    overall_comment: str


class EvaluationListItem(BaseModel):
    """历史评价列表项：按 user_id 查询完整训练后的评价记录。"""

    evaluation_id: int
    case_title: str
    score_type: str
    total_score: float
    created_at: datetime
    pdf_exported: bool


class EvaluationListResponse(BaseModel):
    """历史评价列表响应。"""

    items: list[EvaluationListItem]


class ExportPdfResponse(BaseModel):
    """PDF 导出响应：返回导出记录和本地文件路径。"""

    export_id: int
    file_path: str


class EvaluationDetailResponse(EvaluationResponse):
    """评价详情响应：在报告详情页使用。"""

    session_id: int
    case_id: int
    case_title: str
    created_at: datetime
    pdf_file_path: str | None = None