"""质量控制器（Judge）：评估专家输出，决定是否升级大模型。

- RuleJudge：零依赖启发式（内容覆盖度 / 长度充分性 / 领域格式 / 安全提示），
  稳定可测，适合 MVP 与离线演示。
- LLMJudge：可选，基于 transformers 小模型或 API 的 LLM-as-Judge。

设计对齐实现方案：overall_score < judge_fallback_threshold -> 升级大模型。
"""
from __future__ import annotations

from dataclasses import dataclass, field
from typing import Dict, List

from .experts import extract_content_terms

# 各领域期望的响应长度范围（字符数）
_EXPECTED_LEN = {
    "code": (60, 2000),
    "math": (60, 2000),
    "legal": (80, 3000),
    "medical": (80, 3000),
    "general": (40, 2000),
}

# 领域格式检查：响应应包含的标记
_DOMAIN_FORMAT_HINTS = {
    "code": ["```", "def ", "function", "class "],
    "math": ["步骤", "推导", "=", "解", "step"],
    "legal": ["⚠", "法律", "意见", "合规", "contract", "law"],
    "medical": ["⚠", "就医", "医生", "症状", "诊断", "symptom"],
    "general": [],
}


@dataclass
class QualityEvaluation:
    overall_score: float
    scores: Dict[str, float] = field(default_factory=dict)
    needs_fallback: bool = False
    reasons: List[str] = field(default_factory=list)

    def to_dict(self) -> Dict:
        return {
            "overall_score": round(self.overall_score, 4),
            "scores": {k: round(v, 4) for k, v in self.scores.items()},
            "needs_fallback": self.needs_fallback,
            "reasons": self.reasons,
        }


class BaseJudge:
    async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
        raise NotImplementedError


class RuleJudge(BaseJudge):
    """启发式质量评估（零依赖）。"""

    def __init__(self, fallback_threshold: float = 0.70):
        self.fallback_threshold = fallback_threshold

    async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
        scores: Dict[str, float] = {}
        reasons: List[str] = []

        # 1) 内容覆盖度：查询中的内容词有多少出现在响应里
        terms = extract_content_terms(query)
        if terms:
            lowered = response.lower()  # lowercase 一次，避免逐词重复复制长响应
            hit = sum(1 for t in terms if t in lowered)
            coverage = hit / len(terms)
            scores["coverage"] = coverage
            if coverage < 0.4:
                reasons.append(f"内容覆盖度低 ({coverage:.0%})")
        else:
            scores["coverage"] = 1.0

        # 2) 长度充分性
        lo, hi = _EXPECTED_LEN.get(domain, (40, 2000))
        n = len(response)
        if n < lo:
            scores["length"] = max(0.0, n / lo)
            reasons.append(f"响应过短 ({n} 字符)")
        elif n > hi:
            scores["length"] = 0.8
            reasons.append(f"响应过长 ({n} 字符)")
        else:
            scores["length"] = 1.0

        # 3) 领域格式检查
        hints = _DOMAIN_FORMAT_HINTS.get(domain, [])
        if hints:
            hit_hints = sum(1 for h in hints if h in response)
            scores["format"] = min(1.0, 0.4 + 0.2 * hit_hints)
            if hit_hints == 0:
                reasons.append("缺少领域格式特征")
        else:
            scores["format"] = 1.0

        # 4) 安全/免责提示（法律、医疗领域应有警示语）
        if domain in ("legal", "medical") and ("⚠" not in response and "提示" not in response):
            scores["safety"] = 0.6
            reasons.append("缺少免责提示")
        else:
            scores["safety"] = 1.0

        weights = {"coverage": 0.4, "length": 0.2, "format": 0.2, "safety": 0.2}
        overall = sum(scores.get(k, 0.0) * w for k, w in weights.items())
        needs = overall < self.fallback_threshold
        if needs:
            reasons.append("质量分低于阈值，建议升级大模型")
        return QualityEvaluation(
            overall_score=round(overall, 4),
            scores=scores,
            needs_fallback=needs,
            reasons=reasons,
        )


class LLMJudge(BaseJudge):
    """可选：LLM-as-Judge（API 后端）。"""

    def __init__(self, model: str, base_url: str, api_key: str, fallback_threshold: float = 0.70):
        from .llm_client import OpenAICompatClient
        self.model = model
        self.fallback_threshold = fallback_threshold
        # 请求体与旧实现一致：不下发 temperature / max_tokens
        self._client = OpenAICompatClient(base_url, api_key, timeout=60.0)

    async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
        prompt = (
            f"你是质量评审员。评估以下回答对查询的满足程度，输出 0-1 分（相关性/正确性/完整性）。\n"
            f"查询: {query}\n领域: {domain}\n回答: {response[:2000]}\n"
            f"只输出一个 0 到 1 之间的数字。"
        )
        data = await self._client.chat(
            self.model,
            [{"role": "user", "content": prompt}],
            temperature=None,
            max_tokens=None,
        )
        try:
            score = float(self._client.completion_text(data).strip())
            score = max(0.0, min(1.0, score))
        except Exception:
            score = 0.5
        return QualityEvaluation(
            overall_score=score,
            scores={"llm_judge": score},
            needs_fallback=score < self.fallback_threshold,
        )


def build_judge(cfg: Dict, fallback_threshold: float = 0.70) -> BaseJudge:
    """cfg 为 judge 段配置。"""
    jtype = cfg.get("type", "rule")
    if jtype == "rule":
        return RuleJudge(fallback_threshold=fallback_threshold)
    if jtype == "llm":
        from .config import get_api_key
        api_key = cfg.get("api_key") or get_api_key(cfg)
        return LLMJudge(
            cfg.get("model", "deepseek-chat"),
            cfg.get("base_url", "https://api.deepseek.com/v1"),
            api_key or "",
            fallback_threshold=fallback_threshold,
        )
    raise ValueError(f"未知 judge 类型: {jtype}（支持 rule | llm）")