import argparse
import json
import os
import sys
from typing import Dict

# 兼容：支持直接运行脚本或用 -m 模块方式运行
if __name__ == "__main__" and __package__ is None:
    sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

# 统一使用绝对导入，避免相对导入在脚本直跑时失败
from exp_multimodal.labels import build_labels
from exp_multimodal.vlm_classifier import classify_image
from exp_multimodal.ollama_client import OLLAMA_URL, DEFAULT_MODEL


FEWSHOT_HINTS: Dict[str, str] = {
    # 可选：仅对菜品模式提供少量文字提示，帮助区分相似菜
    # "西红柿鸡蛋": "红黄对比明显，蛋块与番茄块同炒，汤汁偏红",
    # "麻婆豆腐": "豆腐块+红油辣椒+花椒，肉末点缀",
}



def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--mode", choices=["dish", "whole", "processed"], default="dish")
    ap.add_argument("--image", default=r"D:\MyProjects\PythonProjects\FoodClassifier\dataset\DishClassification\test\红烧肉\img04.png")
    ap.add_argument("--alias_map", default=None)

    args = ap.parse_args()

    print(
        f"[Main] mode={args.mode} image={args.image} alias_map={args.alias_map} "
        f"env_OLLAMA_URL={os.getenv('OLLAMA_URL')} env_VLM_MODEL={os.getenv('VLM_MODEL')} "
        f"defaults url={OLLAMA_URL} model={DEFAULT_MODEL}"
    )
    print(f"[Main] Image exists={os.path.exists(args.image)} size={os.path.getsize(args.image) if os.path.exists(args.image) else 'N/A'}")

    labels = build_labels(args.mode, args.alias_map)
    ingredient_only = args.mode in {"whole", "processed"}
    fewshot = FEWSHOT_HINTS if args.mode == "dish" else None
    print(f"[Main] Built labels count={len(labels)} ingredient_only={ingredient_only} fewshot={bool(fewshot)}")

    res = classify_image(
        args.image,
        labels,
        fewshot_hints=fewshot,
        ingredient_only=ingredient_only,
    )

    print(
        json.dumps(
            {"mode": args.mode, "result": res, "num_labels": len(labels)},
            ensure_ascii=False,
        )
    )


if __name__ == "__main__":
    main()