v88 功能增强：产品落盘持久化 + 生图网关适配 + 模板导出优化
- 产品持久化：每完成一个产品立即追加写入 products_pending.jsonl，崩溃不丢已完成产品，finish 读盘合并后统一写模板 - 503 致命错误提前终止：compose/product/seed_shot 端到端识别，提前终止搜索分析，丢弃未完成简报，保留已完成落盘产品直接合成模板 - 模特分配：material_library 合格模特图按任务序号独立随机，同 SPU 多款不再共用同一模特 - 图像网关适配：execution_mode/background 默认不再传入 yunfei 等标准网关，base_url 需带 /v1；429/5xx/空响应退避重试 - Pinterest 分析：删除 term 注入与纯文本降级，失败直接放弃；图片上传前 PIL 完整性校验；suitable_for_print=False 过滤丢弃 - 模板导出：不再产生空白 xlsx，文件名=模板原文件名_已填写；写入前按货号末 3 位升序排序 - 删除对接文档.md，更新 README，gitignore 排除测试产物
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@@ -318,31 +318,56 @@ def build_pinterest_term_user_prompt(context: Dict[str, Any]) -> str:
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# —— Pinterest 参考模式：图片分析 → 原创设计简报（多模态）——
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# image_prompt 由 LLM 直接输出完整的英文生图提示词（多模态对图片的描述拼接），
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# 不再走「四要素 + 固定模板」装配；尺寸/白底等统一约束段由 prompt_node 自动追加。
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PINTEREST_ANALYZE_SYSTEM_PROMPT = """You are a POD (print-on-demand) T-shirt design analyst.
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You receive Pinterest reference images for one search term. For each image, extract the VISUAL CONCEPT
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(style, mood, motif, color palette, composition) that makes it appealing, then produce an ORIGINAL
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T-shirt print design brief that captures that VIBE WITHOUT copying the image.
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PINTEREST_ANALYZE_SYSTEM_PROMPT = """You are a POD T-shirt design analyst. Given ONE Pinterest reference image,
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judge whether it can inspire a T-shirt print, then write an ORIGINAL design brief
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capturing its vibe WITHOUT copying.
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Your image_prompt will be sent TOGETHER WITH this reference image to an image
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generator, so it must actively override visual imitation.
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RULES:
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- NEVER copy the image, never reproduce the exact artwork, characters, logos, or any text from it.
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- Extract only the abstract style/mood/motif concept as inspiration.
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- Produce an original, flat, print-ready design brief (no garment, no model, no background scene).
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- COPYRIGHT-SAFE: no brands, no logos, no characters, no celebrities, no real persons, no franchises.
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- AVOID: politics, religion, hate, violence, sexual content, alcohol, national flags.
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- motif: English, concrete central subject of the print (e.g. "a smiling cat with a fish", "geometric mountain layers").
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- art_style: English visual technique (e.g. "clean flat vector", "retro screen print").
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- color_palette: English colors (e.g. "sunset orange, cream, dusty blue").
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- composition: English layout (e.g. "centered emblem with balanced negative space").
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- concept: Chinese, one sentence describing the design idea.
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- negative_prompt: what to avoid (real people, likeness, characters, logos, text).
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- image_prompt: a COMPLETE, fluent English text-to-image prompt for generating the ORIGINAL flat print
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design artwork (the print itself, NOT a garment photo). Describe the motif, art style, colors, layout
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and mood in natural English, as a standalone print. Do NOT include garment / shirt / model / mannequin /
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background-scene / watermark words. Do NOT mention any size or white-background suffix — a fixed
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"small centered print on pure white" suffix will be appended automatically by the system.
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1. NO COPYING — never reproduce or closely imitate the reference's artwork,
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characters, layout or text. Deliberately change motif, arrangement and/or
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palette so the two read as clearly different works sharing only a general
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style. Distill inspiration into generic style words (retro, y2k, minimal,
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grunge, boho, kawaii...); never imitate an identifiable artist/studio/IP
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style.
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2. FORBIDDEN — brand logos, trademarks, slogans, mascots, copyrighted
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characters, real people/celebrities, movie/game/anime/band IP, lyrics,
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even stylized or silhouette versions. Avoid politics, religion, violence,
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sexual content, alcohol, drugs, gambling, flags, death/occult themes.
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3. FORM — ONE clear central subject with strong graphic composition;
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print-ready standalone artwork. ANY colors are fine — rich palettes,
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gradients and detailed shading are all acceptable. Photographic references
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may be rendered as detailed full-color illustrations, retro badges or
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vintage stickers.
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Return JSON with the field "designs" (array of objects with keys:
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motif, art_style, color_palette, composition, concept, negative_prompt, image_prompt)."""
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TEXT: short ORIGINAL English wording (1-6 words) allowed; wrap exact words in
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double quotes and demand exact spelling; integrate into composition. Never
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reuse/translate reference text; no brand/band/movie names or famous slogans.
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When unsure, omit.
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suitable_for_print: DEFAULT TRUE for graphics, illustrations, badges, vector
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art, typography posters, or prints on mockups (judge only the printed artwork).
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FALSE only for: subjectless photo scenery, memes/screenshots/collages,
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watermarked or very low-quality images, decor/food/candid photos with no
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usable motif. Even when FALSE, still fill all fields so downstream never breaks.
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image_prompt = two parts:
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1) mandatory opener, e.g.: "Use the attached reference image only as loose
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inspiration for overall mood, theme and era — do NOT reproduce, trace,
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rearrange, recolor or closely imitate any element, character, layout or
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text shown in it."
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2) the new design: [central motif] + [style] + [color treatment] +
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[composition] + [mood], plus quoted original text if used.
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NEVER mention shirts, apparel, models, scenes, sizes, backgrounds or
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watermarks — placement is handled externally.
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negative_prompt: copy of reference artwork, likenesses, characters, logos,
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trademarks, watermark, photorealistic shirt/product mockups, busy background;
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add garbled-lettering terms only if your design includes text.
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OUTPUT — ONLY valid JSON, no fences:
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{"designs":[{"suitable_for_print":<bool>,"negative_prompt":"<str>","image_prompt":"<str>"}]}"""
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PINTEREST_ANALYZE_SCHEMA = {
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"name": "pinterest_design_briefs",
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@@ -354,17 +379,11 @@ PINTEREST_ANALYZE_SCHEMA = {
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"items": {
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"type": "object",
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"properties": {
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"image_index": {"type": "integer"},
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"motif": {"type": "string"},
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"art_style": {"type": "string"},
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"color_palette": {"type": "string"},
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"composition": {"type": "string"},
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"concept": {"type": "string"},
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"suitable_for_print": {"type": "boolean"},
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"negative_prompt": {"type": "string"},
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"image_prompt": {"type": "string"},
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},
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"required": ["image_index", "motif", "art_style", "color_palette",
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"composition", "concept", "negative_prompt", "image_prompt"],
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"required": ["suitable_for_print", "negative_prompt", "image_prompt"],
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"additionalProperties": False,
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},
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}
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@@ -375,18 +394,10 @@ PINTEREST_ANALYZE_SCHEMA = {
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}
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def build_pinterest_analyze_user_prompt(term: str, country: str, image_count: int) -> str:
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def build_pinterest_analyze_user_prompt() -> str:
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return (
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f"Country: {country}\n"
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f"Pinterest search term: {term}\n"
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f"Reference images attached: {image_count} images.\n\n"
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f"Analyze the attached images and produce {image_count} ORIGINAL design briefs "
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f"(one per image), each capturing the visual vibe as an original T-shirt print design. "
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f"Do NOT copy the images.\n"
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f"For EACH brief you MUST set image_index to the 0-based position of the input image "
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f"it was derived from (first image = 0, second = 1, ...). Every image_index from 0 to "
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f"{max(image_count - 1, 0)} must appear exactly once — this links each brief to its "
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f"source image so the design is generated from the SAME image that was analyzed."
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"Analyze the attached image and produce one ORIGINAL T-shirt print design brief "
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"that captures its visual vibe without copying it."
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)
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@@ -587,8 +598,7 @@ class OpenAICompatBackend(LLMBackend):
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on_400=None) -> List[Dict[str, Any]]:
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"""多模态分析 Pinterest 图片 → 原创设计简报列表。
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图片输入不被模型支持（纯文本模型 400）时自动降级为纯文本分析（仅用搜索词）。
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失败返回 []，由节点兜底（回退 mock 规则简报）。
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图片输入失败/无有效图片时直接放弃（返回 []，不降级纯文本），由节点跳过该产品。
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on_400: 每次 HTTP 400（且含「内容/图片」）时回调（供调用方累计放弃计数）。
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"""
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cfg = self._cfg
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@@ -605,11 +615,26 @@ class OpenAICompatBackend(LLMBackend):
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data_uris: List[str] = []
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for p in image_paths:
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try:
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pk = Path(p)
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raw = pk.read_bytes()
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# 校验图片完整性：损坏/截断的图片会被火山方舟等多模态接口直接 400 拒绝，
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# 必须滤掉后才能编码 base64（PIL 打开失败即视为损坏）。
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if not raw or len(raw) < 100:
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print(f"[pinterest_analyze] 图片文件过小/为空，跳过: {p} ({len(raw)}B)")
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continue
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try:
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from PIL import Image
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_im = Image.open(pk)
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_im.verify() # 校验文件头/结构，不完整则抛异常
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_im.close()
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except Exception as _ve: # noqa: BLE001
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print(f"[pinterest_analyze] 图片损坏/不完整，跳过: {p} ({_ve})")
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continue
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import base64 as b64
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mime = "image/png"
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if Path(p).suffix.lower() in (".jpg", ".jpeg"):
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if pk.suffix.lower() in (".jpg", ".jpeg"):
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mime = "image/jpeg"
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data_uris.append(f"data:{mime};base64,{b64.b64encode(Path(p).read_bytes()).decode()}")
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data_uris.append(f"data:{mime};base64,{b64.b64encode(raw).decode()}")
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except Exception as e: # noqa: BLE001
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print(f"[pinterest_analyze] 图片读取失败 {p}: {e}")
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@@ -623,12 +648,11 @@ class OpenAICompatBackend(LLMBackend):
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except Exception: # noqa: BLE001
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pass
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def _call(use_images: bool) -> str:
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def _call() -> str:
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user_content: List[Any] = [
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{"type": "text", "text": build_pinterest_analyze_user_prompt(term, country, len(data_uris))},
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{"type": "text", "text": build_pinterest_analyze_user_prompt()},
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]
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if use_images:
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user_content += [{"type": "image_url", "image_url": {"url": u}} for u in data_uris]
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user_content += [{"type": "image_url", "image_url": {"url": u}} for u in data_uris]
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payload = {
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"model": model,
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"messages": [
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@@ -656,19 +680,15 @@ class OpenAICompatBackend(LLMBackend):
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resp.raise_for_status()
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return str(resp.json()["choices"][0]["message"].get("content") or "")
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raw = ""
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if data_uris:
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try:
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raw = _call(use_images=True)
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except Exception as e: # noqa: BLE001 纯文本模型不支持图片 → 降级纯文本
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print(f"[pinterest_analyze] 图片输入失败，降级纯文本分析: {e}")
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raw = ""
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if not raw:
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try:
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raw = _call(use_images=False)
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except Exception as e: # noqa: BLE001
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print(f"[pinterest_analyze] 分析失败: {e}")
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return []
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# 图片输入失败/无有效图片 → 直接放弃该产品（不降级纯文本），由节点跳过后续流程
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if not data_uris:
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print("[pinterest_analyze] 无有效图片输入，放弃该产品（不降级纯文本）")
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return []
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try:
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raw = _call()
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except Exception as e: # noqa: BLE001
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print(f"[pinterest_analyze] 图片分析失败，放弃该产品（不降级纯文本）: {e}")
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return []
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try:
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parsed = _extract_json(raw)
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except Exception as e: # noqa: BLE001
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@@ -687,11 +707,7 @@ class OpenAICompatBackend(LLMBackend):
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continue
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designs.append({
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"topic": term,
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"concept": str(d.get("concept", "")).strip(),
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"motif": str(d.get("motif", "")).strip(),
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"art_style": str(d.get("art_style", "")).strip(),
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"color_palette": str(d.get("color_palette", "")).strip(),
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"composition": str(d.get("composition", "")).strip(),
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"suitable_for_print": bool(d.get("suitable_for_print", True)),
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"negative_prompt": str(d.get("negative_prompt", "")).strip(),
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"image_prompt": str(d.get("image_prompt", "")).strip(),
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# 生图参考：每条简报对应其来源爬取图（LLM 按图逐张产出简报，顺序一一对应）
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