pod_trend_agent/graph/nodes/prompt_node.py

"""节点 5/6：提示词构造（prompt_build）。

读取 prompts/<country>/ 的 extra 风格规则，用固定模板装配四种最终提示词
（image_prompt / wearable_prompt / composite_prompt / composite_negative）。
四要素缺失时用 derive_style_palette 动态兜底，保证每条提示词结构一致、有规则。
"""
from typing import Any, Dict, List
import random

from graph.style_rules import derive_style_palette, derive_composition
from graph.templates import assemble_prompts
from graph.validate import validate_brief, with_fallback

# —— Pinterest 图生图生最终设计稿时统一追加的「小印花 + 纯白底」约束段 ——
# (从热点搜集的文字生图模板里提炼：尺寸缩小、禁止自带背景/满幅)
PINTEREST_PRINT_SUFFIX = (
    " standalone pure print design on a pure white background, "
    "the print artwork is SMALL and CENTERED with clearly larger white margins around it, "
    "print area between about 15x18 cm and 26x32 cm, "
    "do NOT fill the entire canvas, do NOT force full-bleed, "
    "do NOT add any gradient, texture or background color behind the artwork, "
    "no garment, no shirt, no model, no mannequin, no watermark"
)

# Pinterest 生图提示词 4 段结构中第 3 段的引导前缀：把 LLM 产出的 negative_prompt
# 转成一条正向「Strictly avoid: ...」条款拼进 image_prompt，让防复制/防商标约束落到生成指令
NEG_LEAD = "Strictly avoid: "

# Pinterest 生图提示词 4 段结构中第 4 段（仅当设计含文字时追加）：
# 要求模型把引号内的文字按原文逐字正确拼写，避免乱码/拼错
SPELLING_RULE = (
    "Render every phrase shown in quotes exactly as written, "
    "correctly spelled."
)

# review（疑似商标/受保护主题）简报统一追加的「原创化魔改」引导段：
# 只做风格参考，禁复刻品牌/商标/角色，换名换细节，生成通用非侵权致敬式设计。
# 两个模式（热点采集 / Pinterest 参考）共用同一文本，避免不一致。
REVIEW_REBRAND_HINT = (
    "IMPORTANT: this theme is ONLY a loose stylistic reference. "
    "Do NOT reproduce any brand logo, trademark, character, mascot, copyrighted artwork or real person. "
    "Create a fully ORIGINAL design with a different name and distinct visual details and colors — "
    "a generic, non-infringing homage in the same mood, clearly distinct from the original."
)

# 图像生成策略敏感词 → 安全等效描述（生成设计稿前清洗 motif，
# 避免 gpt-image 等内容策略频繁拦截导致"生图限制多"）
_IMG_RISKY_SWAP = {
    "skull": "smiley mascot", "skeleton": "cute mascot", "blood": "red accents",
    "gore": "bold shapes", "gun": "star", "weapon": "tool", "bomb": "firework",
    "drug": "confetti", "demon": "cute monster", "devil": "mischievous imp",
    "occult": "mystic pattern", "satanic": "dark pattern", "nazi": "retro emblem",
    "hitler": "retro emblem", "zombie": "friendly ghoul", "horror": "spooky-cute",
    "vampire": "night owl", "politics": "abstract shapes", "political": "abstract",
    "president": "captain", "army": "team", "police": "officer",
}


def _safe_motif(motif: str) -> str:
    """清洗 motif 中的图像策略敏感词（替换为安全等效描述），降低生图内容政策拦截率。"""
    low = motif.lower()
    for k, v in _IMG_RISKY_SWAP.items():
        if k in low:
            # 按词边界替换（避免误伤 "letterhead" 等）
            import re
            motif = re.sub(rf"\b{re.escape(k)}\b", v, motif, flags=re.IGNORECASE)
            low = motif.lower()
    return motif


@with_fallback("prompt_build")
def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
    screened: List[Dict[str, Any]] = state.get("screened") or []
    config = state["config"]
    country = state["country"]
    cc = state["country_config"]
    extra_rules = cc.get("extra_style_rules") or []
    tpls = config.get("prompt_templates") or {}

    briefs: List[Dict[str, Any]] = []
    for r in screened:
        r = validate_brief(r)
        art, pal = derive_style_palette(
            r["topic"], country, extra_rules=extra_rules, category=r.get("design_category")
        )
        motif = (r.get("motif") or "").strip() or r.get("topic", "")
        cleaned = _safe_motif(motif)
        if cleaned != motif:
            print(f"[prompt] motif 敏感词清洗: 「{motif}」→「{cleaned}」（降低生图内容政策拦截）")
            r["motif"] = cleaned
            motif = cleaned
        art_style = (r.get("art_style") or art).strip()
        palette = (r.get("color_palette") or pal).strip()
        composition = (r.get("composition") or derive_composition(r["topic"], r.get("design_category"))).strip()

        prompts = assemble_prompts(motif, art_style, palette, composition, tpls, country)
        llm_ip = (r.get("image_prompt") or "").strip()
        llm_neg = (r.get("negative_prompt") or "").strip()
        if r.get("source") == "pinterest" and llm_ip:
            # —— Pinterest 参考模式：跳过四要素模板，按 4 段结构拼 image_prompt ——
            #   ① image_prompt（分析模型产出） + 固定输出形态后缀 PINTEREST_PRINT_SUFFIX
            #   ② 负向条款（由 LLM negative_prompt 经 NEG_LEAD 引导，转化进正向指令）
            #   ③ 拼写锁定句 SPELLING_RULE（仅当 LLM image_prompt 已含引号文字段时）
            # 是否含文字、拼写与否均由分析模型产出决定，本模式不注入 slogan。
            seg: List[str] = [llm_ip, PINTEREST_PRINT_SUFFIX.strip()]
            if llm_neg:
                seg.append(NEG_LEAD + llm_neg)
            if '"' in llm_ip:
                seg.append(SPELLING_RULE)
            prompts["image_prompt"] = ", ".join(seg)
            print(f"[prompt] Pinterest 简报按 4 段结构拼 image_prompt（跳过四要素模板）: 「{r['topic']}」")
        else:
            # —— 热点采集模式：四要素模板装配 + 文字印花（约 30% 概率注入 slogan）——
            slogan = (r.get("slogan") or "").strip()
            if slogan and random.random() < float(config.get("prompt_templates", {}).get("text_ratio", 0.3)):
                text_seg = (f', with the text "{slogan}" rendered as bold retro typography, '
                            f'lettering clean and correctly spelled, high contrast, as the focal text of the print')
                prompts["image_prompt"] = prompts["image_prompt"] + text_seg
                r["used_slogan"] = slogan
        # review（疑似商标/受保护主题）→ 追加「原创化魔改」引导（两个模式通用）
        if str(r.get("risk_level", "")).strip().lower() == "review":
            prompts["image_prompt"] = prompts["image_prompt"] + " " + REVIEW_REBRAND_HINT
            print(f"[prompt] review 简报注入原创化魔改引导: 「{r['topic']}」")
        r.update(prompts)
        r["motif"] = motif
        r["art_style"] = art_style
        r["color_palette"] = palette
        r["composition"] = composition
        briefs.append(r)

    stats = dict(state.get("stats") or {})
    stats["prompt"] = {"briefs": len(briefs)}
    return {"briefs": briefs, "stats": stats}