3218485270
b7f429db89
1) 模板导出：识别「基码表-胸围」填 sku.bust（多个胸围列都填）；申报价格模糊匹配多列统一按加价后价格填写；详情图文不再拼接 img_url_2；SPU 款式来源统一填「现货款」；商品产地国家简称映射（沙特→沙特阿拉伯） 2) 模特性别分组：model_features 按男女分组，按模板类目含男/女固定取对应性别模特（含 Pinterest 模式 pipeline） 3) 三合一提示词：去掉 DESIGN CONTENT 四要素描述（设计已由设计稿提供） 4) 生图尺寸：全部改为读 config 不再硬编码（设计图 compose.design_size / 合成图 compose.size / 种草图 seed_shot.size）
323 lines
15 KiB
Python
323 lines
15 KiB
Python
"""Pinterest 参考模式节点 3/3：从图池取图 → 多并发 LLM 分析 → 原创设计简报（pinterest_analyze）。
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图池机制：
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- 从持久化图池（image_pool.json）取「未消费」图片（md5 不在 used_images.json）。
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- 大图先压缩（内存占用过大 → 缩放/重编码）再送 LLM。
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- 多并发分析（每批 analyze_per_term 张，并发 analyze_concurrency 线程）。
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- 每张被分析的图片 md5 一律拉黑（used_images.json）——合适→产出简报→生成设计（设计 md5 全局拉黑见 compose）；
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不合适→图片 md5 已拉黑→下一轮自动取下一张，不重复分析。
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- 图池无未消费图片时返回空，由路由触发新一轮搜索。
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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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import concurrent.futures
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import re
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from pathlib import Path
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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.pinterest import (
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compress_image,
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load_image_pool,
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load_used_images,
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pool_unused_images,
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save_used_images,
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)
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from graph.validate import with_fallback
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# 明显不适合 T 恤印花的简报主体（启发式过滤；真实判定交给 LLM 搜索词引导）
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_BRIEF_UNSUITABLE = re.compile(
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r"\b(landscape|panorama|scenery|cityscape|street scene|interior|room decor|"
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r"food photography|meal|dinner plate|recipe|makeup|nails|manicure|"
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r"weather forecast|map|directions|photorealistic scene|realistic portrait)\b",
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re.IGNORECASE,
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)
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def _brief_suitable(b: Dict[str, Any]) -> bool:
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"""简报是否适合做 T 恤印花：非侵权（blocked 拦截）+ 有主体 + 非明显非印花概念。"""
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if str(b.get("risk_level") or "").strip().lower() == "blocked":
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return False
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motif = str(b.get("motif") or "").strip()
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if not motif:
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return False
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if _BRIEF_UNSUITABLE.search(motif):
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return False
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return True
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def _enrich_briefs(raw_briefs: List[Dict[str, Any]], country: str,
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existing_topics: List[str] = None) -> 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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existing_topics: 已累计简报的 topic 列表；用它初始化计数实现跨轮次去重——
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直接搜固定词时每轮 LLM 都返回相同 topic，若每轮从 #1 重新计数，
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30 个产品会因 topic 重复只用到前几个唯一设计（其余全复制）。
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"""
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from graph.classify import classify
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import re as _re
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seen_topics: Dict[str, int] = {}
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# 已累计简报按「基础词」计数（去掉 #N 后缀），保证跨轮次序号连续递增
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for t in existing_topics or []:
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key = _re.sub(r"\s+#\d+$", "", str(t).strip().lower())
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if key:
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seen_topics[key] = seen_topics.get(key, 0) + 1
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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": str(b.get("risk_level") or "safe").strip().lower() or "safe",
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"safe_for_print": bool(b.get("safe_for_print", True)),
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"suitable_for_print": bool(b.get("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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"image_prompt": str(b.get("image_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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"source_md5": str(b.get("source_md5") or "").strip().lower(),
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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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country = state["country"]
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config = state["config"]
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output_dir = state["output_dir"]
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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", 1))
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concurrency = int(pcfg.get("analyze_concurrency", 3))
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max_designs = int(pcfg.get("max_designs", 10))
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n_ref = max(1, int(pcfg.get("ref_images_per_design", 1)))
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provider = str(pcfg.get("provider") or "openai").strip().lower()
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# 按需分析：只取补齐到目标所需的图片数（batch_size 为上限，不超额分析），并按 md5 去重，
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# 保证同一图片内容（md5）不会同时被多条简报使用
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target = int(state.get("pinterest_target") or 0)
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existing = state.get("briefs") or []
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remaining = max(0, target - len(existing))
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batch_size = int(pcfg.get("analyze_batch", 0))
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if batch_size <= 0:
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# 自动：一次分析补齐到「目标所需」或「每词简报上限」的较小值（每张图→1条简报），
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# 让 pipeline 队列一次有足够任务，时刻保持并发生成（避免每轮只推 6 条导致线程空转）
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batch_size = min(remaining, max_designs)
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need = min(batch_size, remaining) if remaining > 0 else 0
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# 1) 图池取未消费图片（md5 不在 used_images）；无 → 返回空，路由触发搜索
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pool = load_image_pool(output_dir, country)
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used = load_used_images(output_dir, country)
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unused = pool_unused_images(pool, used)
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if not unused:
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print("[pinterest_analyze] 图池无未消费图片，跳过分析（路由将触发新一轮搜索）")
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return {"pinterest_briefs": [], "briefs": state.get("briefs") or [],
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"errors": errors}
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if need <= 0:
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print("[pinterest_analyze] 简报已达标，无需分析")
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return {"pinterest_briefs": [], "briefs": state.get("briefs") or [],
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"errors": errors}
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seen_md5: set = set()
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batch: List[Dict[str, Any]] = []
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for img in unused:
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m = str(img.get("md5") or "").strip().lower()
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if m and m in seen_md5:
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continue # 同一图片内容（md5）不重复分析
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seen_md5.add(m)
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batch.append(img)
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if len(batch) >= need:
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break
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print(f"[pinterest_analyze] 图池取 {len(batch)} 张未消费图片分析（按需 {need}，"
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f"池剩余未消费 {len(unused) - len(batch)} 张，已消费 {len(used)} 张）")
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def _assign_refs(res: List[Dict[str, Any]], chunk: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""按简报的 image_index（LLM 返回）匹配它实际分析的图，写入 source_md5 + ref_images。
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全局 id 校验：简报必须带 image_index（对应输入第几张图，0-based）；
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无 image_index（mock 兜底）→ 回退按顺序；无效/越界/重复 → 丢弃该简报（避免错位）。
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这样 analyze_per_term 可 >1 一次分析多张图提速，简报仍严格对应各自的图。
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"""
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chunk_paths = [img["path"] for img in chunk]
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chunk_md5s = [str(img.get("md5") or "").strip().lower() for img in chunk]
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used_idx: set = set()
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out: List[Dict[str, Any]] = []
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for i, b in enumerate(res):
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if not isinstance(b, dict):
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continue
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try:
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idx = int(b.get("image_index"))
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except (TypeError, ValueError):
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idx = i # 无 image_index → 回退按顺序
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if idx < 0 or idx >= len(chunk_paths) or idx in used_idx:
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print(f"[pinterest_analyze] 简报 image_index={idx} 无效/重复，丢弃（避免图-简报错位）")
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continue
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used_idx.add(idx)
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refs: List[str] = []
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for k in range(n_ref):
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src = chunk_paths[(idx + k) % len(chunk_paths)]
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if src not in refs:
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refs.append(src)
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b["ref_images"] = refs
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b["source_md5"] = chunk_md5s[idx] if idx < len(chunk_md5s) else ""
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out.append(b)
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return out
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# 2) 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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# 3) 大图先压缩（内存占用过大 → 缩放/重编码），再按批分组
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compressed_map: Dict[str, str] = {}
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for img in batch:
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compressed_map[img["path"]] = compress_image(img["path"])
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chunks: List[List[Dict[str, Any]]] = [
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batch[i:i + analyze_per_term] for i in range(0, len(batch), analyze_per_term)
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]
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# 4) 多并发分析（每线程分析一个 chunk；LLM 后端只读 self._cfg，线程安全）
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raw_briefs: List[Dict[str, Any]] = []
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def _analyze_chunk(chunk: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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term = str(chunk[0].get("term") or "")
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paths = [compressed_map.get(img["path"], img["path"]) for img in chunk]
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if llm is not None and hasattr(llm, "analyze_pinterest_images"):
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try:
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def _on_400():
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pipe = state.get("pinterest_pipeline")
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if pipe is not None and hasattr(pipe, "record_400"):
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if pipe.record_400():
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pipe._abort_current_term()
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res = llm.analyze_pinterest_images(paths, term, country, on_400=_on_400) or []
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# 把每条简报的来源图路径回填为原始图（压缩图仅用于分析，参考图用原图）
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return _assign_refs(res, chunk)
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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}] batch@{len(chunk)}: {e}", "trace": ""})
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print(f"[pinterest_analyze] 「{term}」分析失败: {e}")
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return []
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return []
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workers = max(1, min(concurrency, len(chunks)))
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if len(chunks) > 1:
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print(f"[pinterest_analyze] 并发分析 {len(chunks)} 批（{workers} 线程，每批 {analyze_per_term} 张）…")
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with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as _ex:
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_futs = [_ex.submit(_analyze_chunk, c) for c in chunks]
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for _f in concurrent.futures.as_completed(_futs):
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raw_briefs.extend(_f.result())
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# 5) 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:
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try:
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mock = get_backend("mock")
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for chunk in chunks:
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term = str(chunk[0].get("term") or "")
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paths = [compressed_map.get(img["path"], img["path"]) for img in chunk]
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res = mock.analyze_pinterest_images(paths, term, country) or []
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_assign_refs(res, chunk)
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raw_briefs.extend(res)
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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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# 6) 本批所有图片 md5 一律拉黑（已消费，不再复用）——合适/不合适都拉黑
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for img in batch:
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if img.get("md5"):
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used.add(str(img["md5"]).lower())
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save_used_images(output_dir, country, used)
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# 7) 简报过滤：只留适合印花的（有主体 + 非明显非印花概念）
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kept: List[Dict[str, Any]] = []
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for b in raw_briefs:
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if isinstance(b, dict) and _brief_suitable(b):
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kept.append(b)
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if len(kept) < len(raw_briefs):
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print(f"[pinterest_analyze] 简报过滤：{len(raw_briefs)} → {len(kept)} 条适合印花")
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# 8) 上限 + 去重（同 motif+style 指纹只留一条）
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kept = kept[: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 kept:
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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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kept = uniq
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# 9) 富化 → screened → prompt_node 装配提示词 → 标准 briefs（追加到累计，按需截断到目标数）
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existing_topics = [str(b.get("topic", "")).strip() for b in (state.get("briefs") or [])]
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screened = _enrich_briefs(kept, country, existing_topics)
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new_briefs: List[Dict[str, Any]] = []
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if screened:
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r = prompt_node({**state, "screened": screened})
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new_briefs = r.get("briefs") or []
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old_count = len(state.get("briefs") or [])
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accumulated = list(state.get("briefs") or [])
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accumulated.extend(new_briefs)
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target = int(state.get("pinterest_target") or 0)
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if target > 0 and len(accumulated) > target:
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accumulated = accumulated[:target]
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print(f"[pinterest_analyze] 简报已达目标 {target} 条，截断多余部分")
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# 推送实际新增且保留的简报到并发生成流水线（简报池）：边分析边生成设计/三合一/种草图
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pushed = accumulated[old_count:]
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pipe = state.get("pinterest_pipeline")
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if pipe is not None and pushed:
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if getattr(pipe, "is_400_aborted", lambda: False)():
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print("[pinterest_analyze] 当前种子词 400 超限已放弃，本轮简报不推送")
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pushed = []
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else:
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try:
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pipe.add_briefs(pushed)
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except Exception as e: # noqa: BLE001
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print(f"[pinterest_analyze] 简报入池失败: {e}")
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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": len(batch),
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"pool_unused": len(unused),
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"used_images": len(used),
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"briefs": len(new_briefs),
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"accumulated": len(accumulated),
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}
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print(f"[pinterest_analyze] 本轮分析 {len(batch)} 张图 → 简报 {len(new_briefs)} 条，"
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f"累计 {len(accumulated)} 条（{country}）")
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return {"pinterest_briefs": kept, "briefs": accumulated, "stats": stats, "errors": errors}
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