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）
195 lines
9.3 KiB
Python
195 lines
9.3 KiB
Python
"""Mock LLM 后端：启发式兜底（无 key 也能端到端跑通）。
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逻辑：黑名单硬拦 -> 常识风险词标 review -> 动态风格/配色推导 -> 分类。
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这是生产环境 LLM 不可用时的安全降级路径，保证流水线永远能产出可用结果。
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"""
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from typing import Any, Dict, List
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from ..classify import classify, prompt_suggestion
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from ..style_rules import derive_style_palette, derive_composition
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def _dedup_limit(items: List[str], limit: int) -> List[str]:
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"""去重（大小写不敏感）并限量，保留首次出现顺序。"""
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seen = set()
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out: List[str] = []
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for it in items:
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it = (it or "").strip()
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if not it:
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continue
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low = it.lower()
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if low in seen:
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continue
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seen.add(low)
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out.append(it)
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if len(out) >= limit:
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break
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return out
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# 常识风险词（兜底用；真实判定交给 LLM）。同时被 seed_node 复用为「种子护栏」，
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# 避免真人/IP/平台词作为 Google Trends 相关查询种子浪费抓取。
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COMMON_RISK_WORDS = [
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"disney", "marvel", "nike", "adidas", "apple", "iphone", "mcdonalds",
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"mcdonald", "starbucks", "coca", "pepsi", "pokemon", "mario", "hello kitty",
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"sanrio", "sonic", "minions", "barbie", "harry potter", "batman", "spiderman",
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"star wars", "fortnite", "roblox", "minecraft", "tiktok", "netflix", "pearl jam",
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"nirvana", "taylor swift", "trump", "biden", "kardashian", "lebron", "kanye",
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"kick", "gta", "ufc", "westmeath", "lottery", "prison break", "margot robbie",
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"dana white", "euro", "spotify", "youtube", "instagram", "xbox", "playstation",
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"noah kahan", "gina carano", "camry", "hurricanes", "eras tour",
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"springsteen", "reiner", "eliza lopes", "camilla", "h&m", "truck accident attorney",
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]
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# 原创短标语池（mock 模式的 slogan；纯原创、无版权无品牌）
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# 原创短标语池（mock 模式的 slogan；纯原创、无版权无品牌）
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# 英语通用 + 各国语言（JP=日语短标语），按国家动态注入
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_STABLE_SLOGANS_EN = [
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"good vibes", "stay cozy", "happy place", "be kind", "dream big",
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"keep smiling", "sunshine", "peace love", "stay wild", "pet the cat",
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"coffee first", "tiny paws", "warm hugs", "soft life", "grow slowly",
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"lucky charm", "sweet dreams", "go outside", "mindful", "lazy days",
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]
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_STABLE_SLOGANS_JP = [
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"ゆめいっぱい", "やさしい気持ち", "ずっと元気", "おだやかな日", "きょうもハッピー",
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"ねこが好き", "いっしょにね", "ぽかぽか", "はるの風", "なつのおもいで",
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"きらきら", "わくわく", "のんびり", "しあわせ", "えがお",
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]
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def _stable_slogan(topic: str, country: str = "") -> str:
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"""按主题哈希稳定选一条原创标语（同一主题缓存一致；mock 兜底用）。
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country=JP → 日语短标语；其他国家 → 英语。"""
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import hashlib
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pool = _STABLE_SLOGANS_JP if str(country).upper() == "JP" else _STABLE_SLOGANS_EN
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h = int(hashlib.md5((topic or "").encode("utf-8")).hexdigest(), 16)
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return pool[h % len(pool)]
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class MockBackend:
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name = "mock"
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def screen(
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self,
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topics: List[str],
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country: str,
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aesthetic_hint: str,
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system_prompt: str,
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blacklist: List[str],
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batch_size: int = 12,
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) -> List[Dict[str, Any]]:
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bl = [b.lower() for b in (blacklist or [])]
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out: List[Dict[str, Any]] = []
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for t in topics:
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tl = t.lower()
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hits = [b for b in bl if b and b in tl]
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blocked = bool(hits)
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soft_hits = [w for w in COMMON_RISK_WORDS if w in tl]
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if blocked:
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risk_level = "blocked"
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elif soft_hits:
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risk_level = "review"
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else:
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risk_level = "safe"
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cat = classify(t)
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art_style, palette = derive_style_palette(t, country, category=cat)
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# motif：从分类模板取核心描述，去掉配色/白底尾巴，保持干净可复用
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motif = prompt_suggestion(t, cat).split(" --no ")[0].split(",")[0].strip()
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composition = derive_composition(t, cat)
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negative = ("no real people, no likeness of any person, no copyrighted characters, "
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"no brand logos, no trademarks, no celebrity, no readable text unless safe")
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slogan = _stable_slogan(t, country) # 按国家语言（JP→日语短标语，其余英语）
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out.append({
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"topic": t,
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"safe_for_print": not blocked,
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"risk_level": risk_level,
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"risk_reasons": [f"命中黑名单: {hits}"] if hits
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else (["疑似受保护实体，需人工复核"] if soft_hits else []),
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"suitable_for_print": not blocked,
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"design_category": cat,
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"concept": f"（启发式兜底）围绕「{t}」做原创{art_style}风格印花",
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"motif": motif,
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"art_style": art_style,
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"color_palette": palette,
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"composition": composition,
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"slogan": slogan,
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"negative_prompt": negative,
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"confidence": 0.55 if risk_level == "safe" else 0.4,
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})
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return out
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def generate_seeds(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""规则生成种子词（零 API 成本）：借用月份主题、临近节日、trending 派生、历史热点。"""
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month_themes = context.get("month_themes", []) or []
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upcoming = context.get("upcoming_holidays", []) or []
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trending = context.get("trending_seeds", []) or []
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history = context.get("history_hotspots", []) or []
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style: List[str] = []
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related: List[str] = []
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# 月份主题 + 临近节日 → 风格种子（带美学倾向）
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style += list(month_themes)
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style += [f"{h.lower()} aesthetic" for h in upcoming]
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style += trending[:4]
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# related：历史 safe 热点 + 剩余 trending（行业交叉验证）
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related += history[:6]
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related += trending[4:8]
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max_seeds = int(context.get("max_seeds") or 0)
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max_style = int(context.get("max_style_seeds", 10) or 10)
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max_related = int(context.get("max_related_seeds", 10) or 10)
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if max_seeds > 0:
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# 不再按类型分：总量均分到 style/related
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max_style = max_related = (max_seeds + 1) // 2
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return {
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"style_seeds": _dedup_limit(style, max_style),
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"related_seeds": _dedup_limit(related, max_related),
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}
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def generate_pinterest_terms(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""规则生成 Pinterest 搜索词（零 API 成本）：从种子词池随机取 + 两两组合增加多样性。"""
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import random
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seeds = [str(s).strip() for s in (context.get("seeds") or []) if str(s).strip()]
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used = {str(u).strip().lower() for u in (context.get("used_terms") or [])}
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count = int(context.get("count", 10))
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pool = [s for s in seeds if s.lower() not in used]
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random.shuffle(pool)
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terms = pool[:count]
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# 不足时用「种子词 + 风格词」组合补足（视觉导向，避免与已用重复）
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style_tail = ["t-shirt design", "graphic tee", "print art", "vintage tee", "flat design"]
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i = 0
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while len(terms) < count and pool:
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combo = f"{pool[i % len(pool)]} {style_tail[(i // len(pool)) % len(style_tail)]}"
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if combo.lower() not in used and combo not in terms:
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terms.append(combo)
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i += 1
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# 自动追加 " t-shirt design"：让 Pinterest 返回真正的 T 恤印花图（更适合作印花设计参考）
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terms = [f"{t} t-shirt design" if "t-shirt design" not in t.lower() else t for t in terms]
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return {"search_terms": terms}
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def analyze_pinterest_images(self, image_paths, term="", country="", on_400=None):
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"""规则生成设计简报（零 API 成本）：按搜索词启发式推导风格/配色/构图。"""
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from ..classify import classify, prompt_suggestion
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cat = classify(term)
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art_style, palette = derive_style_palette(term, country, category=cat)
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motif = prompt_suggestion(term, cat).split(" --no ")[0].split(",")[0].strip()
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composition = derive_composition(term, cat)
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negative = ("no real people, no likeness of any person, no copyrighted characters, "
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"no brand logos, no trademarks, no celebrity, no readable text unless safe")
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n = max(1, len(image_paths or []))
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paths = list(image_paths or [])
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return [{
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"topic": term,
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"concept": f"（启发式兜底）围绕「{term}」做原创{art_style}风格印花",
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"motif": motif,
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"art_style": art_style,
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"color_palette": palette,
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"composition": composition,
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"negative_prompt": negative,
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"image_prompt": (f"{motif}, {art_style}, {palette}, {composition}, "
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f"original {art_style} t-shirt print design"),
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# 生图参考：每条简报对应其来源爬取图（mock 按图逐张产出简报，顺序一一对应）
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"ref_images": [str(paths[i])] if i < len(paths) else [],
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"source": "pinterest",
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} for i in range(n)]
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