test.py

# test.py
# 放在 RECOMMEND_SERVICE/ 同一層執行：
#   python test.py saved_requests/request_xxx.json
# 或不帶參數（自動抓 saved_requests/ 最新的 .json）：
#   python test.py

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path
from typing import Any, Dict, Optional

# 你的專案模組（同層）
from recommender import (
    ALPHA_SKIN,
    COLOR_DB,
    DOUBLE_NEUTRAL_EXTRA_BONUS,
    NEUTRAL_COLOR_BONUS,
    _is_neutral_color,
    color_harmony_scores_for_combo,
    extract_skin_rgb,
    get_color_combos_for_user,
    rgb_to_hsl,
    run_recommend_model,
    skin_compatibility_for_outfit,
)
from body_type_classifier import classify_male_body_type, classify_female_body_type


DEFAULT_GENDER = "male"  # 跟你 API 裡的行為一致：沒給 gender 就先用 male


def extract_gender(report: Dict[str, Any]) -> Optional[str]:
    """
    從 report 抓性別。找不到就回 DEFAULT_GENDER。
    """
    gender = (
        report.get("gender")
        or report.get("sex")
        or report.get("Gender")
        or report.get("user_gender")
        or report.get("body_gender")  # 若前面已經有，就沿用
    )

    if isinstance(gender, str):
        g = gender.strip().lower()
        if g in ("male", "m", "boy", "man", "男", "男性"):
            return "male"
        if g in ("female", "f", "girl", "woman", "女", "女性"):
            return "female"

    # 找不到就用預設
    if DEFAULT_GENDER is not None:
        print(f"[BodyType] report 中沒有可用的 gender 欄位，暫時使用 DEFAULT_GENDER={DEFAULT_GENDER}")
        return DEFAULT_GENDER

    return None


def extract_body_measurements(report: Dict[str, Any]) -> Optional[Dict[str, float]]:
    """
    從 report 取得 body_measurements dict。
    你提供的 request JSON 結構是 report["body_measurements"] = {...}
    """
    bm = report.get("body_measurements")
    if isinstance(bm, dict) and bm:
        return bm  # type: ignore[return-value]
    return None


def attach_body_type(report: Dict[str, Any]) -> None:
    """
    先依 body_measurements + gender 判斷身形，寫回 report：
      report["body_type"]
      report["body_gender"]
    """
    body_measurements = extract_body_measurements(report)
    gender = extract_gender(report)

    if not body_measurements or not gender:
        print("[BodyType] 無法判斷：缺少 body_measurements 或 gender（且 DEFAULT_GENDER=None）")
        return

    try:
        if gender == "male":
            body_type = classify_male_body_type(body_measurements)
        else:
            body_type = classify_female_body_type(body_measurements)

        print(f"[BodyType] gender={gender} → body_type={body_type}")

        report["body_type"] = body_type
        report["body_gender"] = gender

    except Exception as e:
        print(f"[BodyType] 判斷身形時發生錯誤: {e}")


def pick_default_request_file(base_dir: Path) -> Path:
    """
    不帶參數時：自動挑 saved_requests/ 裡最新修改的 .json
    """
    req_dir = base_dir / "saved_requests"
    if not req_dir.exists():
        raise FileNotFoundError(f"找不到資料夾：{req_dir}")

    candidates = sorted(req_dir.glob("*.json"), key=lambda p: p.stat().st_mtime, reverse=True)
    if not candidates:
        raise FileNotFoundError(f"{req_dir} 底下沒有任何 .json 檔可以測試")
    return candidates[0]


def load_request_json(path: Path) -> Dict[str, Any]:
    with path.open("r", encoding="utf-8") as f:
        data = json.load(f)
    if not isinstance(data, dict):
        raise ValueError("request JSON 的最外層必須是 dict")
    return data


def print_top10_color_combos(report: Dict[str, Any]) -> None:
    """
    印出該使用者顏色組合前 10 名（含分數）。
    分數公式與 recommender 內一致：
            S_color = max(S_sim, S_comp, S_cont) + ALPHA_SKIN * S_skin + neutral_bonus
    """
    gender = (
        report.get("body_gender")
        or report.get("gender")
        or report.get("sex")
    )

    skin_info = report.get("skin_analysis", {}) or {}
    if not isinstance(skin_info, dict):
        skin_info = {}

    r, g, b = extract_skin_rgb(skin_info)
    skin_hsl = rgb_to_hsl(r, g, b)

    color_combos = get_color_combos_for_user(report, gender)
    if not color_combos:
        print("[ColorDebug] 沒有可用的顏色組合。")
        return

    scored = []
    for top_zh, bottom_zh in color_combos:
        c1 = COLOR_DB.get(top_zh)
        c2 = COLOR_DB.get(bottom_zh)
        if not c1 or not c2:
            continue

        hsl1 = c1["hsl"]
        hsl2 = c2["hsl"]
        s_sim, s_comp, s_cont = color_harmony_scores_for_combo([hsl1, hsl2])
        s_skin = skin_compatibility_for_outfit(skin_hsl, [hsl1, hsl2])
        neutral_bonus = 0.0
        if _is_neutral_color(top_zh) or _is_neutral_color(bottom_zh):
            neutral_bonus += NEUTRAL_COLOR_BONUS
        if _is_neutral_color(top_zh) and _is_neutral_color(bottom_zh):
            neutral_bonus += DOUBLE_NEUTRAL_EXTRA_BONUS

        s_color = max(s_sim, s_comp, s_cont) + ALPHA_SKIN * s_skin + neutral_bonus
        scored.append((s_color, top_zh, bottom_zh, c1["en"], c2["en"]))

    if not scored:
        print("[ColorDebug] 顏色組合分數計算失敗。")
        return

    scored.sort(key=lambda x: x[0], reverse=True)
    print("[ColorDebug] Top 10 顏色組合（含分數）：")
    for rank, (score, top_zh, bottom_zh, top_en, bottom_en) in enumerate(scored[:20], start=1):
        print(
            f"  {rank:02d}. ({top_zh}/{top_en}) + ({bottom_zh}/{bottom_en}) "
            f"=> score={score:.2f}"
        )


def main() -> int:
    base_dir = Path(__file__).resolve().parent

    parser = argparse.ArgumentParser(description="純 Python 測試：先判斷身形，再跑推薦，輸出到 output/")
    parser.add_argument(
        "input",
        nargs="?",
        help="request JSON 路徑（例如 saved_requests/request_xxx.json）。不填則自動抓 saved_requests/ 最新檔。",
    )
    args = parser.parse_args()

    # 決定輸入檔
    if args.input:
        input_path = (base_dir / args.input).resolve() if not Path(args.input).is_absolute() else Path(args.input)
    else:
        input_path = pick_default_request_file(base_dir)

    if not input_path.exists():
        print(f"[Error] 找不到輸入檔：{input_path}")
        return 2

    print(f"[Test] 使用輸入檔：{input_path}")

    # 讀 JSON
    data = load_request_json(input_path)

    # 解析 report / weather
    if "report" in data and "weather" in data:
        report = data["report"]
        weather = data["weather"]
    else:
        raise ValueError("request JSON 必須包含 'report' 與 'weather' 兩個 key（最外層）")

    if not isinstance(report, dict):
        raise ValueError("'report' 必須是 dict")
    if not isinstance(weather, dict):
        raise ValueError("'weather' 必須是 dict")

    # 方式 1：先做身形判斷（寫回 report）
    attach_body_type(report)

    # 額外印出顏色組合分數前 10 名
    print_top10_color_combos(report)

    # 跑推薦
    result = run_recommend_model(report, weather)

    # 輸出資料夾 output/
    out_dir = base_dir / "output"
    out_dir.mkdir(exist_ok=True)

    # 輸出檔名：跟輸入檔對應
    stem = input_path.stem  # e.g. request_anonymous_1763...
    out_path = out_dir / f"{stem}_output.json"

    with out_path.open("w", encoding="utf-8") as f:
        json.dump(result, f, ensure_ascii=False, indent=2)

    print(f"[Test] 推薦結果已輸出：{out_path}")
    print("[Test] 預覽（前幾項）：")
    # 簡單預覽
    preview_items = list(result.items())[:3]
    print(json.dumps(dict(preview_items), ensure_ascii=False, indent=2))

    return 0


if __name__ == "__main__":
    try:
        raise SystemExit(main())
    except Exception as e:
        print(f"[Fatal] 測試失敗：{e}")
        raise