"""AI 总结 schema — Pydantic 校验模型、质量评估、DB 展平。"""

from __future__ import annotations

import json
from pydantic import BaseModel, Field, ValidationError, field_validator

from app.utils import sanitize_html, utc_now


# ── 子模型 ──────────────────────────────────────────────────────────────


class PrerequisitesSchema(BaseModel):
    concepts: list[dict] = Field(default_factory=list)


class MotivationSchema(BaseModel):
    problem: str
    goal: str = ""
    gap: str = ""

    @field_validator("problem")
    @classmethod
    def non_empty_problem(cls, v: str) -> str:
        if not v or not v.strip():
            raise ValueError("motivation.problem cannot be empty")
        return v.strip()


class MethodSchema(BaseModel):
    overview: str = ""
    key_idea: str
    steps: str = ""
    novelty: str = ""

    @field_validator("key_idea")
    @classmethod
    def non_empty_key_idea(cls, v: str) -> str:
        if not v or not v.strip():
            raise ValueError("method.key_idea cannot be empty")
        return v.strip()


class ResultsSchema(BaseModel):
    main_findings: str = ""
    benchmarks: list[str | dict] = Field(default_factory=list)
    limitations: str = ""


class ImprovementsSchema(BaseModel):
    weaknesses: str = ""
    future_work: str = ""
    reproducibility: str = ""


# ── 顶层 schema ─────────────────────────────────────────────────────────


class SummarySchema(BaseModel):
    model_config = {"extra": "ignore"}

    title_zh: str
    one_line: str
    tags: list[str]
    difficulty: str = ""
    paper_date: str | None = None
    prerequisites: PrerequisitesSchema = Field(default_factory=PrerequisitesSchema)
    motivation: MotivationSchema
    method: MethodSchema
    results: ResultsSchema = Field(default_factory=ResultsSchema)
    improvements: ImprovementsSchema = Field(default_factory=ImprovementsSchema)
    figures: list[dict] = Field(default_factory=list)

    @field_validator("title_zh", "one_line")
    @classmethod
    def non_empty_text(cls, v: str) -> str:
        if not v or not v.strip():
            raise ValueError("field cannot be empty")
        return v.strip()

    @field_validator("tags")
    @classmethod
    def non_empty_tags(cls, v: list[str]) -> list[str]:
        tags = [tag.strip() for tag in v if tag and tag.strip()]
        if not tags:
            raise ValueError("tags cannot be empty")
        return tags


# ── 质量评估 ────────────────────────────────────────────────────────────


def assess_quality(schema: SummarySchema) -> str:
    """评估总结质量：normal / degraded / low。"""
    # low：内容空洞的启发式判断
    if len(schema.one_line.strip()) < 10 or len(schema.method.key_idea.strip()) < 10:
        return "low"

    # 检查重要字段是否缺失
    missing_important = 0
    if not schema.motivation.goal.strip():
        missing_important += 1
    if not schema.motivation.gap.strip():
        missing_important += 1
    if not schema.method.overview.strip():
        missing_important += 1
    if not schema.results.main_findings.strip():
        missing_important += 1

    if missing_important == 0:
        return "normal"
    return "degraded"


# ── DB 展平 ─────────────────────────────────────────────────────────────


def flatten_for_db(schema: SummarySchema) -> dict:
    """将 SummarySchema 展平为 paper_summaries 表的列值 dict。

    所有供前端用 |safe 渲染的文本字段均经过 HTML 清洗。
    """
    # 清洗 prerequisites 嵌套文本
    prereqs = schema.prerequisites.model_dump()
    for c in prereqs.get("concepts", []):
        if isinstance(c, dict):
            for key in ("explanation", "why_matters"):
                if key in c and c[key]:
                    c[key] = sanitize_html(c[key])

    return {
        "one_line": sanitize_html(schema.one_line),
        "difficulty": schema.difficulty,
        "prerequisites_json": json.dumps(prereqs, ensure_ascii=False),
        "motivation_problem": sanitize_html(schema.motivation.problem),
        "motivation_goal": sanitize_html(schema.motivation.goal),
        "motivation_gap": sanitize_html(schema.motivation.gap),
        "method_overview": sanitize_html(schema.method.overview),
        "method_key_idea": sanitize_html(schema.method.key_idea),
        "method_steps_json": sanitize_html(schema.method.steps),
        "method_novelty": sanitize_html(schema.method.novelty),
        "results_main_json": sanitize_html(schema.results.main_findings),
        "results_benchmarks_json": json.dumps(
            schema.results.benchmarks, ensure_ascii=False
        ),
        "limitations_json": sanitize_html(schema.results.limitations),
        "weaknesses_json": sanitize_html(schema.improvements.weaknesses),
        "future_work_json": sanitize_html(schema.improvements.future_work),
        "reproducibility": sanitize_html(schema.improvements.reproducibility),
        "figures_json": json.dumps(schema.figures, ensure_ascii=False),
        "full_json": schema.model_dump_json(ensure_ascii=False),
        "updated_at": utc_now(),
    }


# ── 错误分类 ────────────────────────────────────────────────────────────

_REQUIRED_FIELDS = {"title_zh", "one_line", "tags", "problem", "key_idea"}


def classify_validation_error(exc: ValidationError) -> str:
    """区分 field_missing（必填缺失）和 schema_error（类型不合法等）。"""
    for err in exc.errors():
        field_name = err["loc"][-1] if err["loc"] else ""
        if field_name in _REQUIRED_FIELDS and err["type"] in (
            "missing",
            "value_error",
        ):
            return "field_missing"
    return "schema_error"