增加多模态模型识别功能。

exp_multimodal/exp_multimodal2.py

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+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\回锅肉\img01.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()
+
+