"""Pinterest 参考模式节点 3/3：LLM 多模态分析图片 → 原创设计简报（pinterest_analyze）。

对 pinterest_scrape 爬到的每个搜索词图片，调 LLM 多模态分析（analyze_pinterest_images）
提取视觉概念（风格/情绪/主体/配色/构图）→ 生成原创设计简报
（motif/art_style/color_palette/composition/concept/negative_prompt），
再经 prompt_node 装配最终 image/wearable/composite 提示词，产出标准 briefs 供 compose 用。

兜底链：LLM 多模态 → 纯文本降级（后端内部）→ mock 规则简报 → 空列表（下游跳过）。
带 with_fallback：任何异常都不中断。
"""
from typing import Any, Dict, List

from graph.llms import get_backend
from graph.nodes.prompt_node import prompt_node
from graph.validate import with_fallback


def _enrich_briefs(raw_briefs: List[Dict[str, Any]], country: str) -> List[Dict[str, Any]]:
    """富化原始简报 → screened 格式（唯一 topic / safe / 分类 / 分数），供 prompt_node 装配。

    同一搜索词的多张图会产出多条简报，topic 相同 → 追加序号保证唯一
    （product_node 按 topic 绑定简报，重复 topic 会互相覆盖）。
    """
    from graph.classify import classify
    seen_topics: Dict[str, int] = {}
    out: List[Dict[str, Any]] = []
    for i, b in enumerate(raw_briefs):
        if not isinstance(b, dict):
            continue
        term = str(b.get("topic") or "").strip() or f"pinterest {i + 1}"
        base = term
        n = seen_topics.get(base.lower(), 0)
        seen_topics[base.lower()] = n + 1
        topic = base if n == 0 else f"{base} #{n + 1}"
        motif = str(b.get("motif") or "").strip() or term
        if not motif:
            continue
        out.append({
            "country": country,
            "topic": topic,
            "risk_level": "safe",
            "safe_for_print": True,
            "suitable_for_print": True,
            "design_category": classify(term),
            "concept": str(b.get("concept") or "").strip() or f"围绕「{term}」的原创印花设计",
            "motif": motif,
            "art_style": str(b.get("art_style") or "").strip(),
            "color_palette": str(b.get("color_palette") or "").strip(),
            "composition": str(b.get("composition") or "").strip(),
            "negative_prompt": str(b.get("negative_prompt") or "").strip(),
            "ref_images": [str(p) for p in (b.get("ref_images") or []) if str(p)],
            "slogan": "",
            "score": 1.0,
            "confidence": 1.0,
            "source": "pinterest",
        })
    return out


@with_fallback("pinterest_analyze")
def pinterest_analyze_node(state: Dict[str, Any]) -> Dict[str, Any]:
    images: Dict[str, List[str]] = state.get("pinterest_images") or {}
    if not images:
        print("[pinterest_analyze] 无爬取图片，跳过分析")
        return {"pinterest_briefs": [], "briefs": [], "errors": state.get("errors") or []}

    country = state["country"]
    config = state["config"]
    errors = list(state.get("errors") or [])

    pcfg = config.get("pinterest") or {}
    analyze_per_term = int(pcfg.get("analyze_per_term", 6))
    max_designs = int(pcfg.get("max_designs", 10))
    provider = str(pcfg.get("provider") or "openai").strip().lower()

    # 1) LLM 后端（openai → 真多模态；mock → 规则兜底）
    llm = None
    if provider != "static":
        try:
            llm = get_backend(provider)
            if hasattr(llm, "bind_config"):
                llm.bind_config(config.get("llm_screen") or {})
            if provider not in ("mock",) and not getattr(llm, "has_key", False):
                print(f"[pinterest_analyze] {provider} 未配置 API key，降级 mock")
                llm = get_backend("mock")
        except Exception as e:  # noqa: BLE001
            print(f"[pinterest_analyze] LLM 初始化失败: {e}")
            llm = None

    # 2) 逐搜索词分析图片 → 原始设计简报
    raw_briefs: List[Dict[str, Any]] = []
    if llm is not None and hasattr(llm, "analyze_pinterest_images"):
        for term, paths in images.items():
            sample = list(paths)[:analyze_per_term]
            if not sample:
                continue
            try:
                res = llm.analyze_pinterest_images(sample, term, country)
                res = res or []
                raw_briefs.extend(res)
                print(f"[pinterest_analyze] 「{term}」分析 {len(sample)} 张图 → {len(res)} 条简报")
            except Exception as e:  # noqa: BLE001
                errors.append({"node": "pinterest_analyze", "type": type(e).__name__,
                               "message": f"term[{term}]: {e}", "trace": ""})
                print(f"[pinterest_analyze] 「{term}」分析失败: {e}")

    # 3) 兜底：LLM 无结果 → mock 规则简报（零 API 成本，保证有设计可生成）。
    #    注意必须切到 mock 后端，不能再调回失败的 llm（否则同样报错）。
    if not raw_briefs:
        try:
            mock = get_backend("mock")
            for term, paths in images.items():
                sample = list(paths)[:analyze_per_term]
                if sample:
                    raw_briefs.extend(mock.analyze_pinterest_images(sample, term, country) or [])
            print(f"[pinterest_analyze] 兜底：mock 规则简报 {len(raw_briefs)} 条")
        except Exception as e:  # noqa: BLE001
            print(f"[pinterest_analyze] mock 兜底失败: {e}")

    # 4) 上限 + 去重（同 motif+style 指纹只留一条）
    raw_briefs = raw_briefs[:max_designs]
    seen: set = set()
    uniq: List[Dict[str, Any]] = []
    for b in raw_briefs:
        if not isinstance(b, dict):
            continue
        fp = f"{str(b.get('motif', '')).strip().lower()}|{str(b.get('art_style', '')).strip().lower()}"
        if fp in seen:
            continue
        seen.add(fp)
        uniq.append(b)
    raw_briefs = uniq

    # 5) 富化 → screened → prompt_node 装配提示词 → 标准 briefs
    screened = _enrich_briefs(raw_briefs, country)
    if not screened:
        print("[pinterest_analyze] 无有效设计简报，跳过")
        return {"pinterest_briefs": [], "briefs": [], "errors": errors}

    r = prompt_node({**state, "screened": screened})
    briefs = r.get("briefs") or []

    stats = dict(state.get("stats") or {})
    stats["pinterest_analyze"] = {
        "provider": provider,
        "images_analyzed": sum(len(v) for v in images.values()),
        "briefs": len(briefs),
    }
    print(f"[pinterest_analyze] 设计简报 {len(briefs)} 条（{country}）")
    return {"pinterest_briefs": raw_briefs, "briefs": briefs, "stats": stats, "errors": errors}