import json
from typing import Dict, List, Optional

from .ollama_client import chat_vision
from .prompts import build_closedset_prompt


def classify_image(
    image_path: str,
    labels: List[str],
    fewshot_hints: Optional[Dict[str, str]] = None,
    ingredient_only: bool = False,
) -> Dict:
    if not labels:
        print("[VLM] Empty labels provided, return Unknown")
        return {"label": "Unknown", "confidence": 0.0}

    print(
        f"[VLM] Start classify image={image_path} labels={len(labels)} "
        f"ingredient_only={ingredient_only} fewshot={bool(fewshot_hints)}"
    )
    prompt = build_closedset_prompt(labels, fewshot_hints, ingredient_only)
    _preview_prompt = (prompt[:120]).replace("\n", " ")
    print(f"[VLM] Prompt length={len(prompt)} preview={_preview_prompt}...")

    print("[VLM] Calling chat_vision...")
    text = chat_vision(prompt, [image_path], temperature=0.1)
    _preview_text = (text[:120]).replace("\n", " ")
    print(f"[VLM] Received text length={len(text)} preview={_preview_text}...")

    # 主路径：严格 JSON 解析
    try:
        obj = json.loads(text)
        if isinstance(obj, dict) and "label" in obj:
            label = obj.get("label", "")
            # 纠偏：若输出不在封闭集，尝试包含匹配；否则 Unknown
            if label not in labels:
                for lb in labels:
                    if lb in str(label):
                        label = lb
                        break
                else:
                    label = "Unknown"
            conf = obj.get("confidence", 0.0)
            try:
                conf = float(conf)
            except Exception:
                conf = 0.0
            result = {"label": label, "confidence": conf}
            print(f"[VLM] Parsed JSON result={result}")
            return result
    except Exception as e:
        print(f"[VLM] JSON parse failed: {e}")

    # 回退：字符串包含匹配
    for lb in labels:
        if lb in text:
            print(f"[VLM] Fallback match label={lb}")
            return {"label": lb, "confidence": 0.5}

    print("[VLM] No match, return Unknown")
    return {"label": "Unknown", "confidence": 0.0}