"""
ClassificationAgent：将内容分类到 8 个章节类别之一。

原「LLM 返回 none / 零散档案启发式」不再跳过 Story：统一映射为 ``summary`` 章节，
仍走叙事流水线落库；与 StoryRoute 仍兼容（批次内 new/append 规划不变）。
Memory ingest 由 Celery 任务在批次级先行完成，与分类结果独立。
"""

from __future__ import annotations

import json
import re
from dataclasses import dataclass
from typing import Any

from pydantic import ValidationError

from app.agents.memoir.prompts import get_chapter_classification_json_prompt
from app.agents.memoir.schemas import ClassificationOutput
from app.agents.stage_constants import (
    CHAPTER_CATEGORIES,
    STAGE_KEYWORD_WEIGHTS,
    STAGE_TO_DEFAULT_CATEGORY,
)
from app.core.config import settings
from app.core.json_utils import extract_json_payload
from app.core.llm_call import LLMCallError, llm_json_call
from app.core.logging import get_logger

logger = get_logger(__name__)

# 模型判定 none 或启发式命中零散档案时，仍写入回忆录正文所用的兜底章节
_SUMMARY_FALLBACK_CATEGORY = "summary"

# 与「仅档案句式」组合使用；过短但明显为叙事句的仍交 LLM 判断
_FRAGMENT_SHORT_MAX_LEN = 48

# 整段仅为出生年份/年份声明（零散档案，不成故事）
_BIRTH_YEAR_LINE = re.compile(
    r"^[\s\u200b]*(?:我)?\d{4}\s*年\s*(出生|生的|生)?\s*[。.!！]?[\s\u200b]*$",
    re.UNICODE,
)

# 极短且为「我是某地人」式籍贯标签，无过程描写
_SHORT_HUKOU_STYLE = re.compile(
    r"^[\s\u200b]*(?:我)?是[\u4e00-\u9fff]{1,10}(人|籍)\s*[。.!！]?[\s\u200b]*$",
    re.UNICODE,
)


def _detect_stage(text: str, fallback_stage: str) -> str:
    """根据关键词检测消息所属的 5-stage 阶段（与 stage_constants.STAGE_KEYWORD_WEIGHTS 同源；匹配方式为子串，非加权）。"""
    message = (text or "").lower()
    for stage, pairs in STAGE_KEYWORD_WEIGHTS.items():
        if any(word in message for word, _w in pairs):
            return stage
    return fallback_stage


def _looks_like_fragment_only(text: str) -> bool:
    """
    保守启发式：明显为档案点/标签句。
    命中时仍进回忆录正文，章节映射为 ``summary``（与 LLM 返回 none 一致）。
    """
    s = (text or "").strip()
    if not s:
        return True
    if _BIRTH_YEAR_LINE.match(s):
        return True
    if len(s) <= _FRAGMENT_SHORT_MAX_LEN and _SHORT_HUKOU_STYLE.match(s):
        return True
    return False


def _normalize_llm_category(raw: str) -> str:
    """去掉模型偶发的引号、反引号包裹。"""
    s = (raw or "").strip().lower()
    if s.startswith("`"):
        s = s.strip("`").strip()
    if (s.startswith('"') and s.endswith('"')) or (
        s.startswith("'") and s.endswith("'")
    ):
        s = s[1:-1].strip()
    return s


@dataclass(frozen=True)
class ChapterClassifyResult:
    """章节分类结果；``llm_said_none`` 仅当走 LLM 且解析为 none 时为 True（fragment 启发式不为 True）。"""

    category: str
    llm_said_none: bool = False


def _parse_category_from_llm_response(raw: str) -> str:
    """优先解析 JSON ``{"category": "..."}``，失败则按纯文本 key 处理。"""
    s = (raw or "").strip()
    if not s:
        return ""
    try:
        data = json.loads(extract_json_payload(s))
        if isinstance(data, dict) and "category" in data:
            return _normalize_llm_category(str(data["category"]))
    except (json.JSONDecodeError, TypeError, ValueError):
        pass
    return _normalize_llm_category(s)


class ClassificationAgent:
    """将内容分类到 8 个章节类别之一；none/零散档案映射为 ``summary`` 仍进 Story。"""

    def classify(
        self,
        text: str,
        fallback_stage: str,
        llm: Any,
        *,
        segment_id: str | None = None,
    ) -> ChapterClassifyResult:
        """
        分类到 8 个章节类别之一。
        LLM 返回 none 或启发式为零散档案时，``category`` 为 ``summary``（仍可走回忆录流水线；
        ``llm_said_none`` 仅在 LLM 明确返回 none 时为 True，供空转抑制判断）。
        llm 需支持 .invoke(prompt) 同步调用。
        """
        if _looks_like_fragment_only(text):
            logger.info(
                "event=chapter_classification_summary_fallback reason=fragment_heuristic "
                "segment_id={} text_len={} category={}",
                segment_id or "",
                len(text or ""),
                _SUMMARY_FALLBACK_CATEGORY,
            )
            return ChapterClassifyResult(
                category=_SUMMARY_FALLBACK_CATEGORY,
                llm_said_none=False,
            )

        if llm:
            try:
                prompt = get_chapter_classification_json_prompt(text)
                out = llm_json_call(
                    llm,
                    prompt,
                    ClassificationOutput,
                    max_tokens=settings.memoir_classification_max_tokens,
                    agent="ClassificationAgent.classify",
                )
                category = _normalize_llm_category(out.category)
                if category == "none":
                    logger.info(
                        "event=chapter_classification_summary_fallback reason=llm_none "
                        "segment_id={} text_len={} category={}",
                        segment_id or "",
                        len(text or ""),
                        _SUMMARY_FALLBACK_CATEGORY,
                    )
                    return ChapterClassifyResult(
                        category=_SUMMARY_FALLBACK_CATEGORY,
                        llm_said_none=True,
                    )
                if category in CHAPTER_CATEGORIES:
                    return ChapterClassifyResult(category=category, llm_said_none=False)
            except (LLMCallError, ValidationError, ValueError, KeyError) as e:
                logger.warning("ClassificationAgent LLM 章节分类失败: {}", e)

        stage = _detect_stage(text, fallback_stage)
        cat = STAGE_TO_DEFAULT_CATEGORY.get(
            stage,
            STAGE_TO_DEFAULT_CATEGORY.get(fallback_stage, "childhood"),
        )
        return ChapterClassifyResult(category=cat, llm_said_none=False)