"""
开放式VLM识别 + 向量匹配 主程序
用于测试"开放识别+向量检索"方案的性能
"""
import argparse
import json
import os
import sys
import time

# 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__))))

from exp_multimodal.vlm_classifier import classify_image_openset
from exp_multimodal.text_embedder import OllamaEmbedder
from exp_multimodal.vector_matcher import DishNameMatcher
from exp_multimodal.vlm_providers.ollama_provider import OllamaProvider


def main():
    ap = argparse.ArgumentParser(description="开放式菜品识别测试")
    ap.add_argument(
        "--image",
        default=r"D:\MyProjects\PythonProjects\FoodClassifier\dataset\DishClassification\test\红烧肉\img04.png",
        help="测试图片路径"
    )
    ap.add_argument(
        "--index_dir",
        default=None,
        help="FAISS索引目录（默认: faiss_vector_db/dish_names）"
    )
    ap.add_argument(
        "--vlm_url",
        default="http://192.168.1.250:11434",
        help="VLM服务地址（Ollama）"
    )
    ap.add_argument(
        "--vlm_model",
        default="qwen2.5vl:32b",
        help="VLM模型名称"
    )
    ap.add_argument(
        "--embedder_url",
        default="http://192.168.1.250:11434",
        help="Embedding服务地址（Ollama）"
    )
    ap.add_argument(
        "--embedder_model",
        default="quentinz/bge-large-zh-v1.5",
        help="Embedding模型名称"
    )
    ap.add_argument(
        "--top_k",
        type=int,
        default=3,
        help="向量检索返回的候选数"
    )
    ap.add_argument(
        "--min_score",
        type=float,
        default=0.5,
        help="最低匹配分数阈值"
    )
    
    args = ap.parse_args()
    
    # 确定索引目录
    if args.index_dir:
        index_dir = args.index_dir
    else:
        project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        index_dir = os.path.join(project_root, "faiss_vector_db", "dish_names")
    
    print("=" * 80)
    print(f"[Main] 开放式VLM识别 + 向量匹配测试")
    print("=" * 80)
    print(f"[Main] Image: {args.image}")
    print(f"[Main] Image exists: {os.path.exists(args.image)}")
    if os.path.exists(args.image):
        print(f"[Main] Image size: {os.path.getsize(args.image)} bytes")
    print(f"[Main] Index dir: {index_dir}")
    print(f"[Main] VLM: {args.vlm_url} / {args.vlm_model}")
    print(f"[Main] Embedder: {args.embedder_url} / {args.embedder_model}")
    print(f"[Main] Top-K: {args.top_k}, Min score: {args.min_score}")
    print("=" * 80)
    
    # 初始化组件
    print(f"\n[Main] 初始化VLM Provider...")
    vlm_provider = OllamaProvider(
        base_url=args.vlm_url,
        model=args.vlm_model,
    )
    
    print(f"\n[Main] 初始化Embedder...")
    embedder = OllamaEmbedder(
        base_url=args.embedder_url,
        model=args.embedder_model,
    )
    
    print(f"\n[Main] 加载FAISS索引...")
    matcher = DishNameMatcher(index_dir)
    
    # 执行识别
    print(f"\n[Main] 开始识别...")
    print("=" * 80)
    
    t_start = time.time()
    result = classify_image_openset(
        image_path=args.image,
        provider=vlm_provider,
        embedder=embedder,
        matcher=matcher,
        top_k=args.top_k,
        min_match_score=args.min_score,
    )
    t_elapsed = time.time() - t_start
    
    print("=" * 80)
    print(f"\n[Main] ✅ 识别完成！耗时: {t_elapsed:.2f}秒")
    print("=" * 80)
    print("\n识别结果：")
    print(json.dumps(result, ensure_ascii=False, indent=2))
    print("=" * 80)
    
    # 简要总结
    print(f"\n📊 性能总结:")
    print(f"  - 总耗时: {t_elapsed:.2f}秒")
    print(f"  - VLM原始输出: {result['raw_dish']}")
    print(f"  - 清洗后菜名: {result['cleaned_dish']}")
    print(f"  - VLM置信度: {result['vlm_confidence']:.4f}")
    print(f"  - 最佳匹配: {result['best_match']}")
    print(f"  - 最终置信度: {result['final_confidence']:.4f}")
    print(f"  - 候选数量: {len(result['candidates'])}")
    
    if t_elapsed <= 3.0:
        print(f"\n✅ 性能达标！({t_elapsed:.2f}s ≤ 3.0s)")
    else:
        print(f"\n⚠️ 性能未达标 ({t_elapsed:.2f}s > 3.0s)")


if __name__ == "__main__":
    main()