fix: 修改一下生图的 bug
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+154
-15
@@ -27,6 +27,16 @@ ALLOWED_KINDS = [
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# 「生图要求」明确写了「N 张」时按用户指定的来，上限放宽到 24。
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PLAN_SAFETY_MAX = 15
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PLAN_REQUIREMENTS_MAX = 24
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MAX_VARIANT_MAIN_IMAGES = 2
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# 规划器的图类归并。不同模型会把“细节图”写成材质图、卖点图或详情图，
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# 因此均视作同一类覆盖，而不是强迫模型只使用 material。
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SCENE_KINDS = {"lifestyle", "multi_scene"}
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DETAIL_KINDS = {"material", "key_features", "selling_pt", "ecommerce_detail"}
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_DIMENSION_PATTERN = re.compile(
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r"尺寸|长宽高|直径|厚度|(?:长|宽|高)\s*[:：×x*]?\s*\d+|\d+(?:\.\d+)?\s*(?:厘米|毫米|cm|mm|inch|in)\b",
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re.IGNORECASE,
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)
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def _total_budget(requirements: str | None) -> int:
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@@ -47,12 +57,12 @@ SYSTEM_PROMPT = """你是一名资深电商视觉策划。根据商品信息规
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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. 先保证 SKU 主图（规则 2），其余类型（场景/细节/尺寸/合集等）按商品特点自行取舍并分配张数，追求信息效率，不为凑数出图。
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2. 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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3. 场景图（kind=lifestyle）：按核心使用场景出 1-3 张，每张聚焦一个场景。
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4. 细节图（kind=material 或 custom）：按关键细节/材质/结构出 1-2 张，每张聚焦一个卖点。
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5. 尺寸标注图（kind=size_chart）：参数含长宽高/尺寸时出 1 张。
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6. SKU 合集图（kind=sku_collection）：SKU >1 时出 1 张。
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1. 套图必须均衡覆盖：白底主图、场景图、细节图；参数含可靠的长宽高/尺寸时还必须有尺寸标注图。按这个顺序先预留张数，再安排其他类型。
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2. 白底主图（kind=white_bg）通常只出 1 张；有多个视觉差异明显的 SKU 时最多选 2 个代表款各出 1 张，variant_name 填对应规格名。**禁止因为 SKU 多而为每个 SKU 都规划主图**。仅当「生图要求」明确要求每个/全部 SKU 分别出图时才可例外。
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3. 场景图（kind=lifestyle）：至少 1 张，按核心使用场景出 1-2 张，每张聚焦一个场景。
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4. 细节图（kind=material / key_features / selling_pt / ecommerce_detail）：至少 1 张，展示关键细节、材质或结构；通常 1-2 张，每张聚焦一个卖点。
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5. 尺寸标注图（kind=size_chart）：参数含长宽高/尺寸时必须出 1 张；无可靠尺寸参数时不要虚构尺寸。
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6. SKU 合集图（kind=sku_collection）：仅在 SKU >1 且确有必要展示差异时出 1 张，优先级低于上述四类。
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7. kind 枚举：white_bg / key_features / selling_pt / material / lifestyle / multi_scene / ecommerce_detail / size_chart / sku_collection / custom。
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8. 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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@@ -114,6 +124,141 @@ def _normalize_items(raw_items: list, sku_variants: list[str]) -> list[dict]:
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return items
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def _has_dimensions(product_info: dict) -> bool:
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"""仅在已采集到尺寸信息时规划尺寸图，避免模型编造数值。"""
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for param in product_info.get("params") or []:
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if not isinstance(param, dict):
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continue
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text = f"{param.get('key') or ''} {param.get('value') or ''}"
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if _DIMENSION_PATTERN.search(text):
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return True
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return False
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def _requires_every_sku(requirements: str | None) -> bool:
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"""用户显式要求逐 SKU 出图时，保留其优先级，不施加代表款上限。"""
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if not requirements:
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return False
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return bool(re.search(
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r"(?:每个|全部|所有|逐个|分别).{0,10}(?:sku|SKU|规格|款式|颜色|变体)",
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requirements,
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))
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def _coverage_group(kind: str) -> str:
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if kind == "white_bg":
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return "main"
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if kind in SCENE_KINDS:
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return "scene"
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if kind in DETAIL_KINDS:
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return "detail"
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if kind == "size_chart":
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return "size"
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return "other"
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def _total(items: list[dict]) -> int:
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return sum(item["count"] for item in items)
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def _group_total(items: list[dict], group: str) -> int:
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return sum(item["count"] for item in items if _coverage_group(item["kind"]) == group)
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def _remove_one(items: list[dict], predicate) -> bool:
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"""从后往前删除一个生成槽位，优先保留模型较早规划的构图。"""
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for index in range(len(items) - 1, -1, -1):
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if not predicate(items[index]):
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continue
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if items[index]["count"] > 1:
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items[index]["count"] -= 1
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else:
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items.pop(index)
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return True
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return False
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def _make_room(items: list[dict], budget: int, minimums: dict[str, int]) -> bool:
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"""为必需图类腾出一张，先去除非核心图或某类的冗余张数。"""
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candidates: list[tuple[int, int]] = []
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for index, item in enumerate(items):
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group = _coverage_group(item["kind"])
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if _group_total(items, group) <= minimums.get(group, 0):
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continue
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# other 最低优先；同类冗余时从后面的项开始删。
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priority = 0 if group == "other" else 1
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candidates.append((priority, -index))
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if not candidates:
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return False
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_, neg_index = min(candidates)
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index = -neg_index
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item = items[index]
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if item["count"] > 1:
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item["count"] -= 1
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else:
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items.pop(index)
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return True
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def _balanced_items(
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items: list[dict],
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sku_variants: list[str],
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product_info: dict,
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requirements: str | None,
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budget: int,
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) -> list[dict]:
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"""将 LLM 方案约束为信息均衡的商品套图。
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提示词能改善规划倾向，但不能保证每次输出。这里把 SKU 主图总量限制为
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代表款数量，并在预算允许时补齐核心图类，避免 SKU 多时挤掉场景/细节/尺寸图。
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"""
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balanced = [dict(item) for item in items if item["count"] > 0]
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if not _requires_every_sku(requirements):
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max_mains = 1 if len(sku_variants) <= 1 else MAX_VARIANT_MAIN_IMAGES
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while sum(item["count"] for item in balanced if item["kind"] == "white_bg") > max_mains:
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_remove_one(balanced, lambda item: item["kind"] == "white_bg")
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required: list[tuple[str, dict]] = [
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("main", {
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"kind": "white_bg", "title": "白底主图", "detail": "商品白底主视觉",
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"prompt_hint": "front view on pure white background", "count": 1,
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"variant_name": sku_variants[0] if sku_variants else None,
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}),
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("scene", {
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"kind": "lifestyle", "title": "使用场景", "detail": "展示商品实际使用场景",
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"prompt_hint": "product in a natural everyday use setting", "count": 1,
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"variant_name": None,
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}),
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("detail", {
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"kind": "material", "title": "细节展示", "detail": "突出材质与结构细节",
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"prompt_hint": "close-up of authentic material and construction details", "count": 1,
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"variant_name": None,
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}),
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]
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if _has_dimensions(product_info):
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required.append(("size", {
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"kind": "size_chart", "title": "尺寸标注", "detail": "展示已采集的商品尺寸",
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"prompt_hint": "clear size chart using known product dimensions", "count": 1,
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"variant_name": None,
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}))
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# 用户明确要求较小张数时，优先保留前面的核心图类；不突破其指定预算。
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required = required[:budget]
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minimums = {group: 1 for group, _ in required}
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for group, fallback in required:
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if _group_total(balanced, group) > 0:
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continue
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while _total(balanced) >= budget and _make_room(balanced, budget, minimums):
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pass
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if _total(balanced) < budget:
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balanced.append(fallback)
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while _total(balanced) > budget and _make_room(balanced, budget, minimums):
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pass
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return balanced
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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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@@ -234,16 +379,10 @@ async def generate_plan(
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items = _normalize_items(data.get("items") or [], sku_variants)
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if items:
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# 总量保护：只防跑飞不干预正常规划。生图要求指定「N 张」时按 N 放宽上限
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# 先压缩多 SKU 的重复主图、补齐核心视觉类别，再执行总量保护。
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# 生图要求指定「N 张」时按 N 放宽上限，并始终不突破该预算。
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budget = _total_budget(requirements)
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total = sum(i["count"] for i in items)
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while total > budget 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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items = _balanced_items(items, sku_variants, product_info, requirements, budget)
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return {"summary": str(data.get("summary") or "").strip()[:100], "items": items}
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last_error = RuntimeError("规划器未返回有效方案项")
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