"""키워드 추출 — 한글 명사 빈도 + Claude Haiku 정제."""

import json
import logging
import re
from collections import Counter
from typing import Any, Dict, List

from anthropic import Anthropic

from .config import ANTHROPIC_API_KEY, ANTHROPIC_MODEL_HAIKU, KEYWORDS_PER_CATEGORY
from . import db

logger = logging.getLogger(__name__)

_NOUN_RE = re.compile(r"[가-힣]{2,6}")
_STOPWORDS = {
    "있다", "없다", "이다", "되다", "그리고", "하지만", "통해", "위해", "오늘", "이번",
    "지난", "관련", "대해", "또한", "다만", "한편", "최근", "앞서", "현재", "진행",
    "발생", "결과", "이상", "이하", "여러", "다양", "방법", "경우", "이유", "필요",
}


def _count_nouns(text: str) -> Dict[str, int]:
    tokens = _NOUN_RE.findall(text or "")
    return Counter(tokens)


def _top_candidates(counts: Dict[str, int], n: int = 20) -> List[tuple]:
    filtered = [(k, c) for k, c in counts.items() if k not in _STOPWORDS]
    return sorted(filtered, key=lambda x: x[1], reverse=True)[:n]


def _refine_with_llm(category: str, candidates: List[tuple], articles: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Claude Haiku로 후보 정제. JSON 리스트 [{keyword, score(0~1), reason}] 반환."""
    if not ANTHROPIC_API_KEY:
        return [{"keyword": k, "score": min(1.0, c / 10), "reason": "freq"} for k, c in candidates[:KEYWORDS_PER_CATEGORY]]

    client = Anthropic(api_key=ANTHROPIC_API_KEY)
    titles = [a["title"] for a in articles[:15]]
    prompt = f"""너는 인스타그램 카드 뉴스 큐레이터다.
카테고리: {category}
빈도 상위 후보: {[k for k, _ in candidates]}
관련 기사 제목 일부:
{chr(10).join('- ' + t for t in titles)}

이 후보 중에서 인스타 카드 콘텐츠로 적합한 키워드를 score 내림차순으로 최대 {KEYWORDS_PER_CATEGORY}개 골라.
출력 형식 (JSON 배열만):
[{{"keyword": "...", "score": 0.0~1.0, "reason": "..."}}]
"""
    msg = client.messages.create(
        model=ANTHROPIC_MODEL_HAIKU,
        max_tokens=600,
        messages=[{"role": "user", "content": prompt}],
    )
    text = msg.content[0].text.strip()
    if text.startswith("```"):
        text = re.sub(r"^```(?:json)?\s*|\s*```$", "", text).strip()
    try:
        return json.loads(text)
    except Exception:
        logger.warning("LLM refine JSON parse failed, falling back to freq")
        return [{"keyword": k, "score": min(1.0, c / 10), "reason": "freq-fallback"} for k, c in candidates[:KEYWORDS_PER_CATEGORY]]


def extract_for_category(category: str, limit: int = KEYWORDS_PER_CATEGORY) -> List[Dict[str, Any]]:
    """카테고리 기사들에서 키워드를 뽑아 DB에 저장하고 결과 반환."""
    articles = db.list_news_articles(category=category, days=2)
    text_blob = "\n".join((a["title"] + " " + a.get("summary", "")) for a in articles)
    counts = _count_nouns(text_blob)
    candidates = _top_candidates(counts, n=20)
    refined = _refine_with_llm(category, candidates, articles)[:limit]

    saved: List[Dict[str, Any]] = []
    for kw in refined:
        kid = db.add_trending_keyword({
            "keyword": kw["keyword"],
            "category": category,
            "score": float(kw.get("score", 0.0)),
            "articles_count": sum(1 for a in articles if kw["keyword"] in a["title"]),
        })
        saved.append({"id": kid, **kw, "category": category})
    return saved


def extract_with_weights(weights: Dict[str, float], total_limit: int) -> List[Dict[str, Any]]:
    """카테고리 가중치 비율대로 키워드를 분배 추출."""
    from .config import DEFAULT_CATEGORY_SEEDS
    if not weights or sum(weights.values()) == 0:
        cats = list(DEFAULT_CATEGORY_SEEDS.keys())
        weights = {c: 1.0 for c in cats}

    total_weight = sum(weights.values())
    out: List[Dict[str, Any]] = []
    for category, w in weights.items():
        if w <= 0:
            continue
        per_cat = round(total_limit * w / total_weight)
        if per_cat <= 0:
            continue
        out.extend(extract_for_category(category, limit=per_cat))
    return out