Joey
354 lines
17 KiB
Python
354 lines
17 KiB
Python
"""套图 Prompt 引擎。
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借鉴 ecommerce-image-suite 的动态 Prompt 架构，浓缩为：
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- 7 种图类型 × 5 套视觉风格模板
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- 公共组件：QUALITY（画质）/ PRODUCT_REF_LOCK（商品一致性锁）/ TEXT_RENDER（图内文案规范）
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- 卖点从采集的参数表/卖点文本自动提炼
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核心原则：所有图严格保持商品一致性（same silhouette, same print, same color），
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只允许改变背景 / 角度 / 光线 / 排版。
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"""
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from __future__ import annotations
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import re
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# ── 风格模板（与插件端 STYLE_SET_OPTIONS 对应；提示词可被用户在插件里改写覆盖）───
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# 提示词用中文：生图 provider（通义万相/豆包）均为国产模型，中文理解一流，且便于用户自行改写。
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STYLE_SETS: dict[int, dict] = {
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1: {
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"name": "北欧极简",
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"tone": "北欧极简风：浅灰或米白背景，柔和漫射光，低饱和色调，画面留白充足，构图克制干净",
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"bg": "",
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},
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2: {
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"name": "清新明亮",
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"tone": "清新明亮风：明亮的白色到浅蓝渐变背景，高调光线，色彩明快通透，整体轻盈干净",
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"bg": "",
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},
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3: {
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"name": "高级感深色",
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"tone": "高级质感风：深灰或炭黑背景，戏剧性侧光打光，突出商品材质与光泽，沉稳高级",
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"bg": "",
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},
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4: {
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"name": "暖调生活",
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"tone": "温暖生活风：暖米色背景，暖色灯光氛围，温馨的家居质感，亲和力强",
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"bg": "",
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},
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5: {
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"name": "纯净棚拍",
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"tone": "标准电商棚拍：纯色浅背景，均匀的正面柔光，无杂物干扰，商品居中突出",
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"bg": "",
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},
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}
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# ── 图类型中文名（导出文件名用）───────────────────────────────────────────
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TYPE_NAMES_ZH: dict[str, str] = {
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"white_bg": "白底主图",
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"key_features": "核心卖点图",
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"selling_pt": "卖点图",
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"material": "材质图",
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"lifestyle": "场景展示图",
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"multi_scene": "多场景拼图",
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"ecommerce_detail": "电商详情图",
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"size_chart": "尺寸标注图",
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"sku_collection": "SKU合集图",
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"custom": "创意图",
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}
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# ── 公共组件 ──────────────────────────────────────────────────────────────
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QUALITY = (
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"Shot on Sony A7R V with 85mm lens at f/2.0, ultra-detailed, photorealistic, "
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"8K commercial image quality, professional retouching."
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)
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PRODUCT_REF_LOCK = (
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"CRITICAL: The product must look EXACTLY the same as in the reference image — "
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"identical silhouette, proportions, colors, print pattern, stitching and every design detail. "
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"Only the background, camera angle, lighting and styling may change. "
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"Do not redesign, add or remove any element of the product."
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)
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TEXT_RENDER = {
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"zh": (
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"Render concise Chinese marketing text inside the image: main headline max 8 Chinese characters, "
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"sub-lines max 12 characters each, font is modern clean sans-serif (Source Han Sans style), "
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"high legibility, tasteful typography layout, colors harmonized with the composition. "
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"No spelling errors, no garbled characters."
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),
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"en": (
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"Render concise English marketing text inside the image: headline max 5 words, "
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"sub-lines max 8 words each, Helvetica Neue style sans-serif, high legibility, "
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"tasteful typography layout, colors harmonized with the composition. No spelling errors."
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),
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"ru": (
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"Render concise Russian marketing text inside the image: headline max 4 words, "
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"sub-lines max 6 words each, modern clean sans-serif (Inter / PT Sans style), "
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"proper Cyrillic typography, high legibility, tasteful layout, colors harmonized with the composition. "
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"No spelling errors, no mixed latin/cyrillic gibberish."
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),
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}
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DEFAULT_NEGATIVE_INTENT = (
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"no AI-generated look, no CGI quality, no plastic appearance, no watermark, "
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"no distorted text, no deformed product, no extra limbs, no blurry areas"
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)
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# ── gpt-image（/v1/images/edits 语义）专用包装 ─────────────────────────────
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# gpt-image 的 edits 端点把输入图当"被编辑的底图"、prompt 当"编辑指令"（豆包/通义则是
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# "主体参考"），风格词会被字面执行到商品上。按 OpenAI 官方提示词指南的编辑模式：
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# 按序号说明输入图、PRESERVE/MAY CHANGE 分列、首尾重申不变量、文案逐字渲染。
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def gpt_edits_contract(ctx: dict) -> str:
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"""gpt-image edits 语义契约（放开头，指令权重最高处）。
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除通用锁定条款外，注入商品文字锚定（标题 + 关键参数 + 描述）：
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官逆通道（gpt-image-2-vip 等）会把参考图当对话附件弱化处理，
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input_fidelity 类 API 参数不生效，此时商品文字描述是保真的唯一兜底。
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"""
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anchor = f" The product is: \"{ctx['title']}\""
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if ctx.get("params_line"):
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anchor += f" (key specs: {ctx['params_line']})"
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if ctx.get("desc"):
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anchor += f". {ctx['desc']}"
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return (
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"TASK: Edit the attached product photos — re-photograph THE SAME physical product "
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"in a new setting. This is an edit of the input images, NOT a new product design.\n"
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"INPUT IMAGES: Image 1 = product front view (PRIMARY source of truth for the product's "
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f"true appearance); Image 2 (if present) = product back / detail view.{anchor}\n"
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"PRODUCT LOCK (highest priority, overrides everything else in this prompt): exactly "
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"preserve the product's shape, silhouette, proportions, colors, label text, logos, "
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"print/pattern, materials and texture. Do not redesign, restyle, recolor, re-pattern "
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"or substitute the product with a similar one. The output must show the very product "
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"from the input images AND match the product description above; if the generated "
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"product differs from either in any design detail, the image is rejected.\n"
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"MAY CHANGE: background, scene, props, camera angle, lighting, composition "
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"and in-image marketing typography only. You may relight the product so it sits "
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"naturally in the new scene (matched shadows and color temperature).\n"
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"STYLE SCOPE: all style, mood, color-palette and decoration instructions below describe "
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"the SCENE AND BACKGROUND ONLY — never apply them to the product. "
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"When any style instruction conflicts with product fidelity, product fidelity always wins."
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)
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GPT_EDITS_FINAL_CHECK = (
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"FINAL CHECK before output: if the product in your result differs from the input product "
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"or the product description above in any design detail (shape, color, pattern, material, "
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"logo), the image is rejected. Render any listed marketing copy / headlines exactly as "
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"written (verbatim, no extra characters, no paraphrasing)."
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)
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def wrap_prompt_for_gpt_edits(prompt: str, ctx: dict) -> str:
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"""gpt-image edits 语义适配：契约放开头（指令权重最高处），终检放结尾。"""
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return f"{gpt_edits_contract(ctx)}\n\n{prompt}\n\n{GPT_EDITS_FINAL_CHECK}"
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# ── 商品上下文提炼 ────────────────────────────────────────────────────────
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def _shorten(text: str, n: int) -> str:
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text = re.sub(r"\s+", " ", (text or "")).strip()
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return text[:n]
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def _clean_title(title: str) -> str:
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"""去掉常见堆砌词，让标题更可读。"""
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t = _shorten(title, 60)
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return re.sub(r"[【【】】\\[\\]|/]", " ", t).strip()
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def build_context(raw: dict, fallback_name: str = "", fallback_desc: str = "") -> dict:
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"""从采集数据提炼生图上下文：标题、描述行、卖点列表、参数行。
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raw: {title, desc, price, params: [{key, value}], sellingPoints}
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"""
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title = _clean_title(raw.get("title") or fallback_name or "product")
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desc = _shorten(raw.get("desc") or fallback_desc or "", 200)
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# 卖点：优先显式卖点文本；否则从参数表里挑短而有信息量的键值对
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selling_points: list[dict] = []
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sp_text = raw.get("sellingPoints") or ""
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if sp_text:
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for chunk in re.split(r"[；;\n·]+|(?<!\d)\.(?!\d)", sp_text):
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c = _shorten(chunk, 20)
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if c and len(selling_points) < 5:
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selling_points.append({"zh": c, "en": c})
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if not selling_points:
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for p in (raw.get("params") or [])[:12]:
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k, v = _shorten(p.get("key", ""), 10), _shorten(str(p.get("value", "")), 16)
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if k and v and k.lower() not in {"货号", "sku", "isbn", "上架时间"}:
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selling_points.append({"zh": f"{k} {v}", "en": f"{k} {v}"})
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if len(selling_points) >= 5:
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break
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params_line = "; ".join(
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f"{p.get('key')}: {p.get('value')}" for p in (raw.get("params") or [])[:8]
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)
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return {
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"title": title,
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"title_en": title, # 采集源多为中文标题，英文场景直接用原词避免乱翻译
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"desc": desc,
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"selling_points": selling_points[:3],
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"params_line": params_line,
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"price": raw.get("price") or "",
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}
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def _sp_lines(ctx: dict, lang: str, max_n: int = 3) -> str:
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sps = ctx["selling_points"][:max_n]
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if not sps:
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return ""
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key = "zh" if lang == "zh" else "en"
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return "; ".join(s[key] for s in sps if s.get(key))
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# ── 各图类型 Prompt ───────────────────────────────────────────────────────
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def _prompt_white_bg(ctx: dict, style: dict, lang: str) -> str:
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return (
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f"E-commerce main product image on pure white background (RGB 255,255,255), "
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f"product \"{ctx['title']}\" centered and filling about 85% of the frame, "
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f"front view, even shadowless studio lighting with a faint natural contact shadow, "
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f"{style['tone']}. No text, no props, no background elements. {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_key_features(ctx: dict, style: dict, lang: str) -> str:
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sp = _sp_lines(ctx, lang) or ctx["title"]
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return (
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f"E-commerce key-features infographic for product \"{ctx['title']}\", square layout: "
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f"product on the left two-thirds ({style['bg']}), right column lists 3 feature callouts "
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f"with minimal line icons, thin leader lines pointing to product details. "
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f"Feature callouts: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_selling_pt(ctx: dict, style: dict, lang: str) -> str:
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sp = _sp_lines(ctx, lang, 1) or ctx["title"]
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return (
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f"Single-selling-point e-commerce poster for product \"{ctx['title']}\": "
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f"hero product close-up at dynamic angle ({style['bg']}), one large bold headline "
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f"about \"{sp}\", generous negative space, one small magnified detail circle "
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f"highlighting material or craft. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_material(ctx: dict, style: dict, lang: str) -> str:
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return (
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f"Macro material close-up of product \"{ctx['title']}\": extreme detail shot revealing "
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f"fabric weave / surface texture / stitching / finish, shallow depth of field, "
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f"raking light across the surface, {style['tone']}. Small caption label in corner. "
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f"{TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_lifestyle(ctx: dict, style: dict, lang: str) -> str:
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bg = f" ({style['bg']})" if style.get("bg") else ""
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return (
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f"Lifestyle in-context scene for product \"{ctx['title']}\": the product is naturally "
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f"used / placed in a real environment{bg}, realistic human-scale surroundings, "
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f"soft daylight, authentic candid mood, product remains the clear visual focus. "
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f"{style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_multi_scene(ctx: dict, style: dict, lang: str) -> str:
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sp = _sp_lines(ctx, lang)
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return (
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f"Triptych multi-scene e-commerce image for product \"{ctx['title']}\": three vertical panels "
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f"separated by thin gutters, each panel shows the SAME product in a different usage scene "
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f"(e.g. home interior / outdoor street / office desk), consistent color grading across panels. "
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f"Panel captions: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_ecommerce_detail(ctx: dict, style: dict, lang: str) -> str:
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sp = _sp_lines(ctx, lang) or ctx["title"]
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params = ctx["params_line"]
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return (
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f"E-commerce detail-page hero section for product \"{ctx['title']}\", square layout: "
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f"top half is a hero banner with the product at a 3/4 angle ({style['bg']}); "
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f"bottom half is a clean spec card listing 3 feature rows with line icons"
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+ (f" (specs: {params})" if params else "")
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+ f" and one highlighted row: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_size_chart(ctx: dict, style: dict, lang: str) -> str:
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dims = ctx["params_line"]
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return (
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f"Product size chart infographic for \"{ctx['title']}\": product shown in clean front and side views "
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f"on light background, with thin measurement annotation lines (arrows) marking length, width and height, "
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f"measurement values rendered next to each line"
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+ (f" (known specs: {dims})" if dims else "")
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+ f", small caption row, precise technical drawing aesthetic. {style['tone']}. "
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f"{TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_sku_collection(ctx: dict, style: dict, lang: str) -> str:
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return (
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f"Colorway collection image for product \"{ctx['title']}\": the SAME product in all its color/variant "
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f"options arranged in a neat equal grid (2-4 items per row), each colorway with a small label chip below it, "
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f"consistent lighting and scale across all items, clean e-commerce presentation. "
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f"{style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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)
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def _prompt_custom(ctx: dict, style: dict, lang: str, extra: dict) -> str:
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hint = (extra.get("prompt_hint") or "").strip()
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purpose = extra.get("title") or ""
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detail = extra.get("detail") or ""
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bg = f" {style['bg']} as environment." if style.get("bg") else ""
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composed = (
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f"E-commerce marketing image for product \"{ctx['title']}\""
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+ (f" — {purpose}" if purpose else "")
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+ (f": {detail}" if detail else "")
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+ "."
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)
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if hint:
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composed += f" Composition: {hint}."
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return f"{composed}{bg} {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
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_PROMPT_BUILDERS = {
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"white_bg": _prompt_white_bg,
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"key_features": _prompt_key_features,
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"selling_pt": _prompt_selling_pt,
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"material": _prompt_material,
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"lifestyle": _prompt_lifestyle,
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"multi_scene": _prompt_multi_scene,
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"ecommerce_detail": _prompt_ecommerce_detail,
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"size_chart": _prompt_size_chart,
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"sku_collection": _prompt_sku_collection,
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}
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def build_prompt(type_id: str, ctx: dict, style_set: int, lang: str, extra: dict | None = None,
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style_prompt: str | None = None, requirements: str | None = None) -> str:
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"""构造指定图类型的完整生图 prompt。
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extra: 方案项信息 {title, detail, prompt_hint}——custom 类型必需，
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预设类型也会把 prompt_hint 作为构图补充注入。
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style_prompt: 用户改写的风格提示词，覆盖 style_set 内置模板（tone/bg 整体替换）。
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requirements: 生图要求（最高优先级，强制约束），置于 prompt 最前面，
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声明覆盖一切冲突指令，用户可在此输入强制要求。
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"""
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if style_prompt and style_prompt.strip():
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style = {"name": "custom", "tone": style_prompt.strip(), "bg": ""}
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else:
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style = STYLE_SETS.get(style_set, STYLE_SETS[1])
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extra = extra or {}
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if type_id == "custom":
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prompt = _prompt_custom(ctx, style, lang, extra)
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else:
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builder = _PROMPT_BUILDERS.get(type_id)
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if builder is None:
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raise ValueError(f"未知图类型: {type_id}")
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prompt = builder(ctx, style, lang)
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hint = (extra.get("prompt_hint") or "").strip()
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if hint:
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prompt = prompt.rstrip(".") + f". Additional composition guidance: {hint}."
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# 生图要求：最高优先级，置于最前并声明覆盖冲突指令（用户输入原样保留，不翻译）
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if requirements and requirements.strip():
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prompt = (
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"STRICT REQUIREMENTS (highest priority, must be followed exactly, "
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"override any conflicting instruction): "
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+ requirements.strip().rstrip(".")
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+ ". "
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+ prompt
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)
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return prompt + ". " + DEFAULT_NEGATIVE_INTENT
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def type_name(type_id: str) -> str:
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return TYPE_NAMES_ZH.get(type_id, type_id)
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