"""AI 분류 워커 — taxonomy 기반 도메인/문서타입/태그/요약 생성"""

import yaml
from datetime import datetime, timezone
from pathlib import Path

from sqlalchemy.ext.asyncio import AsyncSession

from ai.client import AIClient, parse_json_response, strip_thinking
from core.config import settings
from core.utils import setup_logger
from models.document import Document

logger = setup_logger("classify_worker")

MAX_CLASSIFY_TEXT = 8000

# config.yaml에서 taxonomy 로딩
_config_path = Path(__file__).resolve().parent.parent / "config.yaml"
_config = yaml.safe_load(_config_path.read_text(encoding="utf-8"))

DOCUMENT_TYPES = set(_config.get("document_types", []))


def _get_taxonomy_leaf_paths(taxonomy: dict, prefix: str = "") -> set[str]:
    """taxonomy dict에서 모든 유효한 경로를 추출"""
    paths = set()
    for key, value in taxonomy.items():
        current = f"{prefix}/{key}" if prefix else key
        if isinstance(value, dict):
            if not value:
                paths.add(current)
            else:
                paths.update(_get_taxonomy_leaf_paths(value, current))
        elif isinstance(value, list):
            if not value:
                paths.add(current)
            else:
                for leaf in value:
                    paths.add(f"{current}/{leaf}")
                paths.add(current)  # 2단계도 허용 (leaf가 없는 경우용)
        else:
            paths.add(current)
    return paths


VALID_DOMAIN_PATHS = _get_taxonomy_leaf_paths(_config.get("taxonomy", {}))


def _validate_domain(domain: str) -> str:
    """domain이 taxonomy에 존재하는지 검증, 없으면 최대한 가까운 경로 찾기"""
    if domain in VALID_DOMAIN_PATHS:
        return domain

    # 부분 매칭 시도 (2단계까지)
    parts = domain.split("/")
    for i in range(len(parts), 0, -1):
        partial = "/".join(parts[:i])
        if partial in VALID_DOMAIN_PATHS:
            logger.warning(f"[분류] domain '{domain}' → '{partial}' (부분 매칭)")
            return partial

    logger.warning(f"[분류] domain '{domain}' taxonomy에 없음, General/Reading_Notes로 대체")
    return "General/Reading_Notes"


async def process(document_id: int, session: AsyncSession) -> None:
    """문서 AI 분류 + 요약"""
    doc = await session.get(Document, document_id)
    if not doc:
        raise ValueError(f"문서 ID {document_id}를 찾을 수 없음")

    if not doc.extracted_text:
        raise ValueError(f"문서 ID {document_id}: extracted_text가 비어있음")

    client = AIClient()
    try:
        # ─── 분류 ───
        truncated = doc.extracted_text[:MAX_CLASSIFY_TEXT]
        raw_response = await client.classify(truncated)
        parsed = parse_json_response(raw_response)

        if not parsed:
            raise ValueError(f"AI 응답에서 JSON 추출 실패: {raw_response[:200]}")

        # domain 검증
        domain = _validate_domain(parsed.get("domain", ""))
        doc.ai_domain = domain

        # sub_group은 domain 경로에서 추출 (호환성)
        parts = domain.split("/")
        doc.ai_sub_group = parts[1] if len(parts) > 1 else ""

        # document_type 검증
        doc_type = parsed.get("document_type", "")
        doc.document_type = doc_type if doc_type in DOCUMENT_TYPES else "Note"

        # confidence
        confidence = parsed.get("confidence", 0.5)
        doc.ai_confidence = max(0.0, min(1.0, float(confidence)))

        # importance
        importance = parsed.get("importance", "medium")
        doc.importance = importance if importance in ("high", "medium", "low") else "medium"

        # tags
        doc.ai_tags = parsed.get("tags", [])[:5]

        # source/origin
        if parsed.get("sourceChannel") and not doc.source_channel:
            doc.source_channel = parsed["sourceChannel"]
        if parsed.get("dataOrigin") and not doc.data_origin:
            doc.data_origin = parsed["dataOrigin"]

        # ─── 요약 ───
        summary = await client.summarize(doc.extracted_text[:15000])
        doc.ai_summary = strip_thinking(summary)

        # ─── 메타데이터 ───
        doc.ai_model_version = "qwen3.5-35b-a3b"
        doc.ai_processed_at = datetime.now(timezone.utc)

        logger.info(
            f"[분류] document_id={document_id}: "
            f"domain={domain}, type={doc.document_type}, "
            f"confidence={doc.ai_confidence:.2f}, tags={doc.ai_tags}"
        )

    finally:
        await client.close()

    # _move_to_knowledge 제거됨 — 파일은 원본 위치 유지, 분류는 DB 메타데이터만