feat: 迁移 ISS 服务端
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Joey
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"""出图方案规划器：DeepSeek 根据采集的商品信息生成套图方案。
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方案每项 = 一类图（标题 + 说明 + 生图提示 + 张数 + 可选 SKU 绑定），
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生成时按方案逐张出图；参考图可按 variant_name 精确绑定到对应 SKU 图。
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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import httpx
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from config import get_settings
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log = logging.getLogger("suite.planner")
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# 规划器可选用的图类型（与 prompt.py 的 builder 对应）
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ALLOWED_KINDS = [
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"white_bg", "key_features", "selling_pt", "material",
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"lifestyle", "multi_scene", "ecommerce_detail",
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"size_chart", "sku_collection", "custom",
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]
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SYSTEM_PROMPT = """你是一名资深电商视觉策划。根据商品信息规划一套电商套图的出图方案。
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## 输出硬性约束（违反即失败）
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1. 输出必须是**单行紧凑 JSON**：无换行、无缩进、无空格填充、无注释、无 markdown 围栏。
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2. 顶层只有 summary 和 items 两个字段；每个 item 严格只有 kind/title/detail/prompt_hint/count/variant_name 六个字段，不得增删。
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3. 文本长度上限（中文字符/英文单词数）：summary ≤ 25 字；title ≤ 8 字；detail ≤ 20 字；prompt_hint ≤ 15 个英文词。超限必须删减，不得省略号截断。
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4. count 默认 1，仅当该类图确需多个变体时才 >1，最大 3。总张数 8-15。
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5. variant_name 只能从「SKU规格」列表原样照抄；没有绑定就输出 null。
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## 规划规则
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1. SKU 主图：每个带图 SKU 出 1 张独立主图（kind=white_bg），variant_name 填对应规格名；单 SKU 出 1 张（variant_name=null）。
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2. 场景图（kind=lifestyle）：按核心使用场景出 2-4 张，每张聚焦一个场景。
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3. 细节图（kind=material 或 custom）：按关键细节/材质/结构出 2-3 张，每张聚焦一个卖点。
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4. 尺寸标注图（kind=size_chart）：参数含长宽高/尺寸时出 1 张。
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5. SKU 合集图（kind=sku_collection）：SKU >1 时出 1 张。
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6. kind 枚举：white_bg / key_features / selling_pt / material / lifestyle / multi_scene / ecommerce_detail / size_chart / sku_collection / custom。
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7. title 用中文短语（如「主图·粉色」「浴室壁挂」）；detail 中文说明这张图展示什么；prompt_hint 用英文描述构图要点。
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## 输出示例（紧凑单行）
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{"summary":"三色收纳盒全套图","items":[{"kind":"white_bg","title":"主图·粉色","detail":"粉色SKU白底主视觉","prompt_hint":"front view on white background","count":1,"variant_name":"粉色"}]}"""
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def _system_prompt_with_requirements(requirements: str | None) -> str:
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"""把生图要求作为最高优先级约束注入 system prompt（置于规划规则之前）。
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不仅声明优先级，还明确要求把要求落地到每个方案项的 prompt_hint，
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避免模型只把要求当作背景信息而不影响输出。
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"""
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if not (requirements and requirements.strip()):
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return SYSTEM_PROMPT
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marker = "\n## 输出硬性约束"
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idx = SYSTEM_PROMPT.find(marker)
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if idx < 0:
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return SYSTEM_PROMPT
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req = requirements.strip()
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block = (
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"\n## 生图要求（最高优先级，硬性约束，覆盖下方所有规划规则与约束）\n"
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+ req
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+ "\n\n"
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+ "规划方案时，必须把上述生图要求落地到每一项：\n"
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+ "1. 每个方案项的 prompt_hint 必须融入上述要求的关键约束（如要求纯黑背景，则每个 prompt_hint 都要写明 black background）；\n"
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+ "2. title / detail 措辞不得与上述要求矛盾；\n"
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+ "3. 任何规划规则与上述要求冲突时，一律以本生图要求为准。\n"
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)
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return SYSTEM_PROMPT[:idx] + block + SYSTEM_PROMPT[idx:]
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def _normalize_items(raw_items: list, sku_variants: list[str]) -> list[dict]:
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"""清洗模型输出：kind 白名单、count 钳制、variant 必须真实存在。"""
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items: list[dict] = []
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for it in raw_items:
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if not isinstance(it, dict):
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continue
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kind = str(it.get("kind") or "custom")
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if kind not in ALLOWED_KINDS:
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kind = "custom"
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title = str(it.get("title") or "").strip()[:20]
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if not title:
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continue
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try:
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count = max(0, min(3, int(it.get("count", 1))))
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except (TypeError, ValueError):
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count = 1
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variant = str(it.get("variant_name") or "").strip() or None
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if variant and variant not in sku_variants:
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variant = None # 幻觉规格：丢弃绑定，回退主图
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items.append({
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"kind": kind,
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"title": title,
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"detail": str(it.get("detail") or "").strip()[:80],
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"prompt_hint": str(it.get("prompt_hint") or "").strip()[:300],
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"count": count,
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"variant_name": variant,
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})
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return items
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def _repair_truncated(s: str) -> dict | None:
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"""截断修复：从最后一个完整的 '}' 处截断，剥尾逗号后按括号配平补全闭合。
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适用于「items 数组中途被 max_tokens 截断」的场景——截断点在完整对象边界，
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此前的字符串必然已闭合，简单计数配平即可。
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"""
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for cut in (m.end() for m in reversed(list(re.finditer(r'\}', s)))):
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cand = s[:cut].rstrip().rstrip(',')
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opens: list[str] = []
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for ch in cand:
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if ch in '{[':
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opens.append(ch)
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elif ch == '}' and opens and opens[-1] == '{':
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opens.pop()
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elif ch == ']' and opens and opens[-1] == '[':
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opens.pop()
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suffix = ''.join('}' if o == '{' else ']' for o in reversed(opens))
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try:
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data = json.loads(cand + suffix)
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if isinstance(data, dict):
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return data
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except json.JSONDecodeError:
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continue
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return None
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def _extract_json(text: str) -> dict:
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"""从模型输出提取 JSON：剥离思考块/markdown 围栏，截断时尝试修复。"""
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s = (text or '').strip()
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# 剥离思考块（思考型模型会把推理过程放进 <think>）
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s = re.sub(r'<think>.*?</think>', '', s, flags=re.S).strip()
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# 剥离 markdown 代码围栏
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m = re.search(r'```(?:json)?\s*(.*?)```', s, flags=re.S)
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if m:
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s = m.group(1).strip()
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try:
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return json.loads(s)
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except json.JSONDecodeError:
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pass
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start = s.find('{')
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if start >= 0:
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repaired = _repair_truncated(s[start:])
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if repaired is not None:
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log.warning("规划器输出疑似被截断，已自动截断修复（可能丢失末尾部分方案项）")
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return repaired
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raise ValueError("模型输出无法解析为 JSON")
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async def generate_plan(
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product_info: dict,
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sku_variants: list[str],
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image_stats: dict,
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platform: str,
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requirements: str | None = None,
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) -> dict:
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"""调用 DeepSeek 生成方案。返回 {summary, items}。
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requirements：生图要求，最高优先级注入 system prompt，规划方案必须遵循。
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"""
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s = get_settings()
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if not s.deepseek_api_key:
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raise RuntimeError("未配置 DEEPSEEK_API_KEY（.env）")
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user_payload: dict = {
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"商品信息": product_info, # {title, desc, params:[{key,value}], sellingPoints, price}
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"SKU规格": sku_variants, # 带图的 SKU 规格名（variant_name 只能从中选）
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"图片统计": image_stats, # {main: n, sku: n, detail: n}
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"目标平台": platform, # ozon/wb/cn（决定图内文案语言）
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}
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# 生图要求同时在 user 侧强调（与 system prompt 双重约束），确保模型真正遵循
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if requirements and requirements.strip():
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user_payload["生图要求（最高优先级，必须体现在每个方案项中）"] = requirements.strip()
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user_content = json.dumps(user_payload, ensure_ascii=False)
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async with httpx.AsyncClient(timeout=90, verify=False) as client:
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resp = await client.post(
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f"{s.deepseek_base_url.rstrip('/')}/chat/completions",
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headers={"Authorization": f"Bearer {s.deepseek_api_key}", "Content-Type": "application/json"},
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json={
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"model": s.deepseek_model,
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"messages": [
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{"role": "system", "content": _system_prompt_with_requirements(requirements)},
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{"role": "user", "content": user_content},
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],
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"response_format": {"type": "json_object"},
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"temperature": 0.3,
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"max_tokens": 8000,
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},
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)
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resp.raise_for_status()
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body = resp.json()
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message = body["choices"][0]["message"]
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finish_reason = body["choices"][0].get("finish_reason", "")
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usage = body.get("usage") or {}
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log.info(
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"规划器 token 用量: prompt=%s completion=%s finish=%s",
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usage.get("prompt_tokens", "?"), usage.get("completion_tokens", "?"), finish_reason,
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)
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content = message.get("content") or ""
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# 思考型输出：content 为空时从 reasoning_content 里捞
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if not content.strip() and message.get("reasoning_content"):
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content = message["reasoning_content"]
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try:
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data = _extract_json(content)
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except ValueError as exc:
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log.error(
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"规划器输出解析失败 finish_reason=%s content[:200]=%s",
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finish_reason, content[:200],
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)
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raise RuntimeError("规划器输出解析失败") from exc
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items = _normalize_items(data.get("items") or [], sku_variants)
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if not items:
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raise RuntimeError("规划器未返回有效方案项")
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# 总量保护：超过 18 张时按比例截断
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total = sum(i["count"] for i in items)
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while total > 18 and items:
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last = items[-1]
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if last["count"] > 1:
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last["count"] -= 1
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else:
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items.pop()
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total = sum(i["count"] for i in items)
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return {"summary": str(data.get("summary") or "").strip()[:100], "items": items}
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