"""mockup 7개 역분석 — 채널 규모별 3개월/12개월 target 성장률 공식."""

from integrations.llm.schemas.report import KPIMetric


def _round_clean(n: int) -> int:
    if n < 100:        return n
    if n < 1000:       return round(n / 100) * 100
    if n < 10_000:     return round(n / 500) * 500
    if n < 100_000:    return round(n / 1000) * 1000
    if n < 1_000_000:  return round(n / 5000) * 5000
    return round(n / 50_000) * 50_000


def _target_multiplier(current: int) -> tuple[float, float]:
    if current < 1_000:     return (2.5, 9.0)
    if current < 5_000:     return (1.7, 4.0)
    if current < 25_000:    return (1.5, 2.5)
    if current < 50_000:    return (1.3, 2.2)
    return (1.1, 1.9)


def _follower_kpi(metric: str, val: int | None, unit: str = "명") -> dict | None:
    if not val: return None
    m3, m12 = _target_multiplier(val)
    return {
        "metric":          metric,
        "current":         f"{val:,}{unit}",
        "target_3_month":  f"{_round_clean(int(val * m3)):,}{unit}",
        "target_12_month": f"{_round_clean(int(val * m12)):,}{unit}",
    }


def _blog_frequency(posts: list) -> tuple[str, str, str] | None:
    """RSS posts timestamp로 (current, target_3m, target_12m) 라벨 반환. target은 절대 downgrade 안 함."""
    from common.utils import parse_ts
    dts = sorted((d for d in (parse_ts(p.get("postDate")) for p in posts) if d), reverse=True)
    if len(dts) < 2: return None
    avg_gap = (dts[0] - dts[-1]).days / (len(dts) - 1)
    if avg_gap > 90:    current = f"방치 ({dts[0].strftime('%Y-%m')})"
    elif avg_gap <= 1:  current = f"주 {7 // max(int(avg_gap), 1)}회"
    elif avg_gap <= 3:  current = "주 2~3회"
    elif avg_gap <= 14: current = "주 1~2회"
    elif avg_gap <= 30: current = f"월 {max(30 // int(avg_gap), 1)}회"
    else:               current = "월 1회 미만"
    if avg_gap > 3:     return current, "주 2회", "주 3회"
    if avg_gap > 2:     return current, "주 3회", "주 5회"
    if avg_gap > 1:     return current, "주 5회", "주 7회"
    return current, f"{current} 유지", f"{current} 유지"


def build_kpi_dashboard(
    instagram: dict, facebook: dict, youtube: dict, gangnam_unni: dict, hospital: dict,
    naver_blog: dict | None = None,
) -> list[dict]:
    tiktok = hospital.get("tiktok")      or {}
    cafe   = hospital.get("naverCafe")   or {}
    # print("facebook", facebook)
    # print("instagram", instagram)

    kpis: list[dict] = []
    facebook_kpis = [_follower_kpi("Facebook " + fb["language"] + " 팔로워",  fb['raw_data'].get("followers") ) for fb in facebook]
    instagram_kpis = [_follower_kpi("Instagram " + ib["language"] + " 팔로워",  ib['raw_data'].get("followers")) for ib in instagram]
    print("facebook_kpis", facebook_kpis)
    print("instagram_kpis", instagram_kpis)

    kpis += [k for k in facebook_kpis if k]
    kpis += [k for k in instagram_kpis if k]
    for k in [
        _follower_kpi("YouTube 구독자",     youtube.get("subscribers")),
        _follower_kpi("TikTok 팔로워",       tiktok.get("followers")),
        _follower_kpi("Naver Cafe 회원 수",  cafe.get("memberCount")),
    ]:
        if k: kpis.append(k)

    if naver_blog:
        freq = _blog_frequency(naver_blog.get("posts") or [])
        if freq:
            cur, t3, t12 = freq
            kpis.append({
                "metric":          "네이버 블로그 포스팅 빈도",
                "current":         cur,
                "target_3_month":  t3,
                "target_12_month": t12,
            })

    gu_reviews = gangnam_unni.get("totalReviews")
    if gu_reviews:
        if gu_reviews < 1000:    rm3, rm12 = 2.0, 6.0
        elif gu_reviews < 5000:  rm3, rm12 = 1.10, 1.50
        else:                    rm3, rm12 = 1.07, 1.27
        kpis.append({
            "metric":          "강남언니 리뷰",
            "current":         f"{gu_reviews:,}개",
            "target_3_month":  f"{_round_clean(int(gu_reviews * rm3)):,}개",
            "target_12_month": f"{_round_clean(int(gu_reviews * rm12)):,}개",
        })
    print ("kpis", kpis)
    return [KPIMetric.model_validate(k).model_dump() for k in kpis]