"""套图 Prompt 引擎。

借鉴 ecommerce-image-suite 的动态 Prompt 架构，浓缩为：
  - 7 种图类型 × 5 套视觉风格模板
  - 公共组件：QUALITY（画质）/ PRODUCT_REF_LOCK（商品一致性锁）/ TEXT_RENDER（图内文案规范）
  - 卖点从采集的参数表/卖点文本自动提炼

核心原则：所有图严格保持商品一致性（same silhouette, same print, same color），
只允许改变背景 / 角度 / 光线 / 排版。
"""
from __future__ import annotations

import re

# ── 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）───
# 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。

STYLE_SETS: dict[int, dict] = {
    1: {
        "name": "北欧极简",
        "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",
        "bg": "",
    },
    2: {
        "name": "清新明亮",
        "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",
        "bg": "",
    },
    3: {
        "name": "高级感深色",
        "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",
        "bg": "",
    },
    4: {
        "name": "暖调生活",
        "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",
        "bg": "",
    },
    5: {
        "name": "纯净棚拍",
        "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",
        "bg": "",
    },
}

# ── 图类型中文名（导出文件名用）───────────────────────────────────────────

TYPE_NAMES_ZH: dict[str, str] = {
    "white_bg": "白底主图",
    "key_features": "核心卖点图",
    "selling_pt": "卖点图",
    "material": "材质图",
    "lifestyle": "场景展示图",
    "multi_scene": "多场景拼图",
    "ecommerce_detail": "电商详情图",
    "size_chart": "尺寸标注图",
    "sku_collection": "SKU合集图",
    "custom": "创意图",
}

# ── 公共组件 ──────────────────────────────────────────────────────────────

QUALITY = (
    "Shot on Sony A7R V with 85mm lens at f/2.0, ultra-detailed, photorealistic, "
    "8K commercial image quality, professional retouching."
)

PRODUCT_REF_LOCK = (
    "CRITICAL: The product must look EXACTLY the same as in the reference image — "
    "identical silhouette, proportions, colors, print pattern, stitching and every design detail. "
    "Only the background, camera angle, lighting and styling may change. "
    "Do not redesign, add or remove any element of the product."
)

TEXT_RENDER = {
    "zh": (
        "Render concise Chinese marketing text inside the image: main headline max 8 Chinese characters, "
        "sub-lines max 12 characters each, font is modern clean sans-serif (Source Han Sans style), "
        "high legibility, tasteful typography layout, colors harmonized with the composition. "
        "No spelling errors, no garbled characters."
    ),
    "en": (
        "Render concise English marketing text inside the image: headline max 5 words, "
        "sub-lines max 8 words each, Helvetica Neue style sans-serif, high legibility, "
        "tasteful typography layout, colors harmonized with the composition. No spelling errors."
    ),
    "ru": (
        "Render concise Russian marketing text inside the image: headline max 4 words, "
        "sub-lines max 6 words each, modern clean sans-serif (Inter / PT Sans style), "
        "proper Cyrillic typography, high legibility, tasteful layout, colors harmonized with the composition. "
        "No spelling errors, no mixed latin/cyrillic gibberish."
    ),
}

DEFAULT_NEGATIVE_INTENT = (
    "no AI-generated look, no CGI quality, no plastic appearance, no watermark, "
    "no distorted text, no deformed product, no extra limbs, no blurry areas"
)

# ── gpt-image（/v1/images/edits 语义）专用包装 ─────────────────────────────
# 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
# "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：
# 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。

def gpt_edits_contract(ctx: dict) -> str:
    """gpt-image edits 语义契约（放开头，指令权重最高处）。

    除通用锁定条款外，注入商品文字锚定（标题 + 关键参数 + 描述）：
    官逆通道（gpt-image-2-vip 等）会把参考图当对话附件弱化处理，
    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。
    """
    anchor = f" The product is: \"{ctx['title']}\""
    if ctx.get("params_line"):
        anchor += f" (key specs: {ctx['params_line']})"
    if ctx.get("desc"):
        anchor += f". {ctx['desc']}"
    return (
        "TASK: Edit the attached product photos — re-photograph THE SAME physical product "
        "in a new setting. This is an edit of the input images, NOT a new product design.\n"
        "INPUT IMAGES: Image 1 = product front view (PRIMARY source of truth for the product's "
        f"true appearance); Image 2 (if present) = product back / detail view.{anchor}\n"
        "PRODUCT LOCK (highest priority, overrides everything else in this prompt): exactly "
        "preserve the product's shape, silhouette, proportions, colors, label text, logos, "
        "print/pattern, materials and texture. Do not redesign, restyle, recolor, re-pattern "
        "or substitute the product with a similar one. The output must show the very product "
        "from the input images AND match the product description above; if the generated "
        "product differs from either in any design detail, the image is rejected.\n"
        "MAY CHANGE: background, scene, props, camera angle, lighting, composition "
        "and in-image marketing typography only. You may relight the product so it sits "
        "naturally in the new scene (matched shadows and color temperature).\n"
        "STYLE SCOPE: all style, mood, color-palette and decoration instructions below describe "
        "the SCENE AND BACKGROUND ONLY — never apply them to the product. "
        "When any style instruction conflicts with product fidelity, product fidelity always wins."
    )


GPT_EDITS_FINAL_CHECK = (
    "FINAL CHECK before output: if the product in your result differs from the input product "
    "or the product description above in any design detail (shape, color, pattern, material, "
    "logo), the image is rejected. Render any listed marketing copy / headlines exactly as "
    "written (verbatim, no extra characters, no paraphrasing)."
)


def wrap_prompt_for_gpt_edits(prompt: str, ctx: dict) -> str:
    """gpt-image edits 语义适配：契约放开头（指令权重最高处），终检放结尾。"""
    return f"{gpt_edits_contract(ctx)}\n\n{prompt}\n\n{GPT_EDITS_FINAL_CHECK}"

# ── 商品上下文提炼 ────────────────────────────────────────────────────────

def _shorten(text: str, n: int) -> str:
    text = re.sub(r"\s+", " ", (text or "")).strip()
    return text[:n]

def _clean_title(title: str) -> str:
    """去掉常见堆砌词，让标题更可读。"""
    t = _shorten(title, 60)
    return re.sub(r"[【【】】\\[\\]|/]", " ", t).strip()

def build_context(raw: dict, fallback_name: str = "", fallback_desc: str = "") -> dict:
    """从采集数据提炼生图上下文：标题、描述行、卖点列表、参数行。

    raw: {title, desc, price, params: [{key, value}], sellingPoints}
    """
    title = _clean_title(raw.get("title") or fallback_name or "product")
    desc = _shorten(raw.get("desc") or fallback_desc or "", 200)

    # 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
    selling_points: list[dict] = []
    sp_text = raw.get("sellingPoints") or ""
    if sp_text:
        for chunk in re.split(r"[；;\n·]+|(?<!\d)\.(?!\d)", sp_text):
            c = _shorten(chunk, 20)
            if c and len(selling_points) < 5:
                selling_points.append({"zh": c, "en": c})
    if not selling_points:
        for p in (raw.get("params") or [])[:12]:
            k, v = _shorten(p.get("key", ""), 10), _shorten(str(p.get("value", "")), 16)
            if k and v and k.lower() not in {"货号", "sku", "isbn", "上架时间"}:
                selling_points.append({"zh": f"{k} {v}", "en": f"{k} {v}"})
            if len(selling_points) >= 5:
                break

    params_line = "; ".join(
        f"{p.get('key')}: {p.get('value')}" for p in (raw.get("params") or [])[:8]
    )
    return {
        "title": title,
        "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
        "desc": desc,
        "selling_points": selling_points[:3],
        "params_line": params_line,
        "price": raw.get("price") or "",
    }

def _sp_lines(ctx: dict, lang: str, max_n: int = 3) -> str:
    sps = ctx["selling_points"][:max_n]
    if not sps:
        return ""
    key = "zh" if lang == "zh" else "en"
    return "; ".join(s[key] for s in sps if s.get(key))

# ── 各图类型 Prompt ───────────────────────────────────────────────────────

def _prompt_white_bg(ctx: dict, style: dict, lang: str) -> str:
    return (
        f"E-commerce main product image on pure white background (RGB 255,255,255), "
        f"product \"{ctx['title']}\" centered and filling about 85% of the frame, "
        f"front view, even shadowless studio lighting with a faint natural contact shadow, "
        f"{style['tone']}. No text, no props, no background elements. {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_key_features(ctx: dict, style: dict, lang: str) -> str:
    sp = _sp_lines(ctx, lang) or ctx["title"]
    return (
        f"E-commerce key-features infographic for product \"{ctx['title']}\", square layout: "
        f"product on the left two-thirds ({style['bg']}), right column lists 3 feature callouts "
        f"with minimal line icons, thin leader lines pointing to product details. "
        f"Feature callouts: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_selling_pt(ctx: dict, style: dict, lang: str) -> str:
    sp = _sp_lines(ctx, lang, 1) or ctx["title"]
    return (
        f"Single-selling-point e-commerce poster for product \"{ctx['title']}\": "
        f"hero product close-up at dynamic angle ({style['bg']}), one large bold headline "
        f"about \"{sp}\", generous negative space, one small magnified detail circle "
        f"highlighting material or craft. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_material(ctx: dict, style: dict, lang: str) -> str:
    return (
        f"Macro material close-up of product \"{ctx['title']}\": extreme detail shot revealing "
        f"fabric weave / surface texture / stitching / finish, shallow depth of field, "
        f"raking light across the surface, {style['tone']}. Small caption label in corner. "
        f"{TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_lifestyle(ctx: dict, style: dict, lang: str) -> str:
    bg = f" ({style['bg']})" if style.get("bg") else ""
    return (
        f"Lifestyle in-context scene for product \"{ctx['title']}\": the product is naturally "
        f"used / placed in a real environment{bg}, realistic human-scale surroundings, "
        f"soft daylight, authentic candid mood, product remains the clear visual focus. "
        f"{style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_multi_scene(ctx: dict, style: dict, lang: str) -> str:
    sp = _sp_lines(ctx, lang)
    return (
        f"Triptych multi-scene e-commerce image for product \"{ctx['title']}\": three vertical panels "
        f"separated by thin gutters, each panel shows the SAME product in a different usage scene "
        f"(e.g. home interior / outdoor street / office desk), consistent color grading across panels. "
        f"Panel captions: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_ecommerce_detail(ctx: dict, style: dict, lang: str) -> str:
    sp = _sp_lines(ctx, lang) or ctx["title"]
    params = ctx["params_line"]
    return (
        f"E-commerce detail-page hero section for product \"{ctx['title']}\", square layout: "
        f"top half is a hero banner with the product at a 3/4 angle ({style['bg']}); "
        f"bottom half is a clean spec card listing 3 feature rows with line icons"
        + (f" (specs: {params})" if params else "")
        + f" and one highlighted row: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_size_chart(ctx: dict, style: dict, lang: str) -> str:
    dims = ctx["params_line"]
    return (
        f"Product size chart infographic for \"{ctx['title']}\": product shown in clean front and side views "
        f"on light background, with thin measurement annotation lines (arrows) marking length, width and height, "
        f"measurement values rendered next to each line"
        + (f" (known specs: {dims})" if dims else "")
        + f", small caption row, precise technical drawing aesthetic. {style['tone']}. "
        f"{TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_sku_collection(ctx: dict, style: dict, lang: str) -> str:
    return (
        f"Colorway collection image for product \"{ctx['title']}\": the SAME product in all its color/variant "
        f"options arranged in a neat equal grid (2-4 items per row), each colorway with a small label chip below it, "
        f"consistent lighting and scale across all items, clean e-commerce presentation. "
        f"{style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
    )

def _prompt_custom(ctx: dict, style: dict, lang: str, extra: dict) -> str:
    hint = (extra.get("prompt_hint") or "").strip()
    purpose = extra.get("title") or ""
    detail = extra.get("detail") or ""
    bg = f" {style['bg']} as environment." if style.get("bg") else ""
    composed = (
        f"E-commerce marketing image for product \"{ctx['title']}\""
        + (f" — {purpose}" if purpose else "")
        + (f": {detail}" if detail else "")
        + "."
    )
    if hint:
        composed += f" Composition: {hint}."
    return f"{composed}{bg} {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"

_PROMPT_BUILDERS = {
    "white_bg": _prompt_white_bg,
    "key_features": _prompt_key_features,
    "selling_pt": _prompt_selling_pt,
    "material": _prompt_material,
    "lifestyle": _prompt_lifestyle,
    "multi_scene": _prompt_multi_scene,
    "ecommerce_detail": _prompt_ecommerce_detail,
    "size_chart": _prompt_size_chart,
    "sku_collection": _prompt_sku_collection,
}


def build_prompt(type_id: str, ctx: dict, style_set: int, lang: str, extra: dict | None = None,
                 style_prompt: str | None = None, requirements: str | None = None) -> str:
    """构造指定图类型的完整生图 prompt。

    extra: 方案项信息 {title, detail, prompt_hint}——custom 类型必需，
    预设类型也会把 prompt_hint 作为构图补充注入。
    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）。
    requirements: 生图要求（最高优先级，强制约束），置于 prompt 最前面，
    声明覆盖一切冲突指令，用户可在此输入强制要求。
    """
    if style_prompt and style_prompt.strip():
        style = {"name": "custom", "tone": style_prompt.strip(), "bg": ""}
    else:
        style = STYLE_SETS.get(style_set, STYLE_SETS[1])
    extra = extra or {}
    if type_id == "custom":
        prompt = _prompt_custom(ctx, style, lang, extra)
    else:
        builder = _PROMPT_BUILDERS.get(type_id)
        if builder is None:
            raise ValueError(f"未知图类型: {type_id}")
        prompt = builder(ctx, style, lang)
        hint = (extra.get("prompt_hint") or "").strip()
        if hint:
            prompt = prompt.rstrip(".") + f". Additional composition guidance: {hint}."
    # 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）
    if requirements and requirements.strip():
        prompt = (
            "STRICT REQUIREMENTS (highest priority, must be followed exactly, "
            "override any conflicting instruction): "
            + requirements.strip().rstrip(".")
            + ". "
            + prompt
        )
    return prompt + ". " + DEFAULT_NEGATIVE_INTENT


def type_name(type_id: str) -> str:
    return TYPE_NAMES_ZH.get(type_id, type_id)