新增 Pinterest 参考模式：独立于 Google Trends 的完整链路（12国种子词池 / LLM搜索词json_schema+防重复+已用词限100 / 并发爬图 / 多模态分析→原创简报 / 生图带爬取图参考图生图 / UI流程选择）
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"""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）。
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对 pinterest_scrape 爬到的每个搜索词图片，调 LLM 多模态分析（analyze_pinterest_images）
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提取视觉概念（风格/情绪/主体/配色/构图）→ 生成原创设计简报
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（motif/art_style/color_palette/composition/concept/negative_prompt），
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再经 prompt_node 装配最终 image/wearable/composite 提示词，产出标准 briefs 供 compose 用。
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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）。
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带 with_fallback：任何异常都不中断。
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"""
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from typing import Any, Dict, List
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from graph.llms import get_backend
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from graph.nodes.prompt_node import prompt_node
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from graph.validate import with_fallback
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def _enrich_briefs(raw_briefs: List[Dict[str, Any]], country: str) -> List[Dict[str, Any]]:
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"""富化原始简报 → screened 格式（唯一 topic / safe / 分类 / 分数），供 prompt_node 装配。
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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
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（product_node 按 topic 绑定简报，重复 topic 会互相覆盖）。
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"""
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from graph.classify import classify
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seen_topics: Dict[str, int] = {}
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out: List[Dict[str, Any]] = []
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for i, b in enumerate(raw_briefs):
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if not isinstance(b, dict):
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continue
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term = str(b.get("topic") or "").strip() or f"pinterest {i + 1}"
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base = term
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n = seen_topics.get(base.lower(), 0)
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seen_topics[base.lower()] = n + 1
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topic = base if n == 0 else f"{base} #{n + 1}"
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motif = str(b.get("motif") or "").strip() or term
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if not motif:
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continue
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out.append({
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"country": country,
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"topic": topic,
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"risk_level": "safe",
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"safe_for_print": True,
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"suitable_for_print": True,
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"design_category": classify(term),
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"concept": str(b.get("concept") or "").strip() or f"围绕「{term}」的原创印花设计",
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"motif": motif,
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"art_style": str(b.get("art_style") or "").strip(),
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"color_palette": str(b.get("color_palette") or "").strip(),
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"composition": str(b.get("composition") or "").strip(),
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"negative_prompt": str(b.get("negative_prompt") or "").strip(),
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"ref_images": [str(p) for p in (b.get("ref_images") or []) if str(p)],
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"slogan": "",
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"score": 1.0,
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"confidence": 1.0,
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"source": "pinterest",
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})
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return out
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@with_fallback("pinterest_analyze")
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def pinterest_analyze_node(state: Dict[str, Any]) -> Dict[str, Any]:
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images: Dict[str, List[str]] = state.get("pinterest_images") or {}
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if not images:
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print("[pinterest_analyze] 无爬取图片，跳过分析")
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return {"pinterest_briefs": [], "briefs": [], "errors": state.get("errors") or []}
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country = state["country"]
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config = state["config"]
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errors = list(state.get("errors") or [])
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pcfg = config.get("pinterest") or {}
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analyze_per_term = int(pcfg.get("analyze_per_term", 6))
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max_designs = int(pcfg.get("max_designs", 10))
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provider = str(pcfg.get("provider") or "openai").strip().lower()
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# 1) LLM 后端（openai → 真多模态；mock → 规则兜底）
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llm = None
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if provider != "static":
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try:
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llm = get_backend(provider)
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if hasattr(llm, "bind_config"):
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llm.bind_config(config.get("llm_screen") or {})
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if provider not in ("mock",) and not getattr(llm, "has_key", False):
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print(f"[pinterest_analyze] {provider} 未配置 API key，降级 mock")
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llm = get_backend("mock")
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except Exception as e: # noqa: BLE001
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print(f"[pinterest_analyze] LLM 初始化失败: {e}")
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llm = None
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# 2) 逐搜索词分析图片 → 原始设计简报
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raw_briefs: List[Dict[str, Any]] = []
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if llm is not None and hasattr(llm, "analyze_pinterest_images"):
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for term, paths in images.items():
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sample = list(paths)[:analyze_per_term]
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if not sample:
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continue
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try:
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res = llm.analyze_pinterest_images(sample, term, country)
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res = res or []
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raw_briefs.extend(res)
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print(f"[pinterest_analyze] 「{term}」分析 {len(sample)} 张图 → {len(res)} 条简报")
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except Exception as e: # noqa: BLE001
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errors.append({"node": "pinterest_analyze", "type": type(e).__name__,
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"message": f"term[{term}]: {e}", "trace": ""})
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print(f"[pinterest_analyze] 「{term}」分析失败: {e}")
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# 3) 兜底：LLM 无结果 → mock 规则简报（零 API 成本，保证有设计可生成）
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if not raw_briefs and llm is not None:
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try:
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for term, paths in images.items():
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sample = list(paths)[:analyze_per_term]
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if sample:
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raw_briefs.extend(llm.analyze_pinterest_images(sample, term, country) or [])
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print(f"[pinterest_analyze] 兜底：mock 规则简报 {len(raw_briefs)} 条")
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except Exception as e: # noqa: BLE001
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print(f"[pinterest_analyze] mock 兜底失败: {e}")
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# 4) 上限 + 去重（同 motif+style 指纹只留一条）
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raw_briefs = raw_briefs[:max_designs]
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seen: set = set()
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uniq: List[Dict[str, Any]] = []
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for b in raw_briefs:
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if not isinstance(b, dict):
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continue
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fp = f"{str(b.get('motif', '')).strip().lower()}|{str(b.get('art_style', '')).strip().lower()}"
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if fp in seen:
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continue
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seen.add(fp)
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uniq.append(b)
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raw_briefs = uniq
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# 5) 富化 → screened → prompt_node 装配提示词 → 标准 briefs
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screened = _enrich_briefs(raw_briefs, country)
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if not screened:
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print("[pinterest_analyze] 无有效设计简报，跳过")
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return {"pinterest_briefs": [], "briefs": [], "errors": errors}
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r = prompt_node({**state, "screened": screened})
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briefs = r.get("briefs") or []
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stats = dict(state.get("stats") or {})
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stats["pinterest_analyze"] = {
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"provider": provider,
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"images_analyzed": sum(len(v) for v in images.values()),
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"briefs": len(briefs),
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}
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print(f"[pinterest_analyze] 设计简报 {len(briefs)} 条（{country}）")
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return {"pinterest_briefs": raw_briefs, "briefs": briefs, "stats": stats, "errors": errors}
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