feat: AI炫图接入gpt-image-2.5云端生图(结构化提示词工程+照片参考合成)
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"""AI 炫图路由：gpt-image 生图代理（Key 仅存后端），提示词由 drawing_data 结构化构建。
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- quick 模式: /v1/images/generations 纯提示词生成
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- photo 模式: /v1/images/edits 以用户照片为参考图，把窗型合成进照片
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"""
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import base64
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import json
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import httpx
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from fastapi import APIRouter
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from pydantic import BaseModel, Field
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from huamenchuang.core.config import settings
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from huamenchuang.core.deps import CurrentUser, DbSession
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from huamenchuang.core.response import fail, ok
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from huamenchuang.services.ai_prompt import build_prompt, describe_window, pick_size
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router = APIRouter(prefix="/ai-render", tags=["AI 炫图"])
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class AiRenderBody(BaseModel):
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template_id: int | None = None
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drawing_data: str | dict | None = None
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mode: str = Field(default="quick", pattern="^(quick|photo)$")
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style: str = Field(default="modern", pattern="^(modern|chinese|european|nordic)$")
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time: str = Field(default="day", pattern="^(day|dusk|night)$")
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# quick: indoor/outdoor/studio; photo: render/install/erase/indoor/villa/fence/blank
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scene: str = "indoor"
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photo_b64: str | None = Field(default=None, description="照片模式参考图(base64, 不带 dataURL 前缀)")
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async def _resolve_drawing(db, body: AiRenderBody) -> str | dict | None:
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if body.drawing_data:
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return body.drawing_data
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if body.template_id:
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from sqlalchemy import select
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from huamenchuang.models.windoor_template import WindoorTemplate
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t = (await db.execute(select(WindoorTemplate).where(
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WindoorTemplate.id == body.template_id))).scalar_one_or_none()
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return t.drawing_data if t else None
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return None
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@router.post("/prompt")
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async def preview_prompt(body: AiRenderBody, user: CurrentUser, db: DbSession):
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"""提示词预览（前端可展开查看优化后的完整提示词）。"""
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drawing = await _resolve_drawing(db, body)
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if drawing is None:
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return fail("未找到图纸数据")
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try:
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info = describe_window(drawing)
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prompt = build_prompt(drawing, body.style, body.time, body.scene)
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except Exception:
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return fail("图纸数据解析失败")
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return ok(data={"features": info, "prompt": prompt,
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"size": pick_size(info["width_mm"], info["height_mm"])})
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@router.post("/generate")
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async def generate(body: AiRenderBody, user: CurrentUser, db: DbSession):
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"""云端 AI 生图（gpt-image）：quick=生成 / photo=参考图编辑。"""
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if not settings.ai_api_key:
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return fail("AI 生图服务未配置(HMC_AI_API_KEY)")
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drawing = await _resolve_drawing(db, body)
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if drawing is None:
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return fail("未找到图纸数据")
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if body.mode == "photo" and not body.photo_b64:
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return fail("照片模式需要上传参考照片")
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try:
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info = describe_window(drawing)
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prompt = build_prompt(drawing, body.style, body.time, body.scene)
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size = pick_size(info["width_mm"], info["height_mm"])
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except Exception:
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return fail("图纸数据解析失败")
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base = settings.ai_base_url.rstrip("/")
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headers = {"Authorization": f"Bearer {settings.ai_api_key}"}
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timeout = httpx.Timeout(settings.ai_timeout, connect=15)
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try:
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async with httpx.AsyncClient(timeout=timeout) as client:
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if body.mode == "photo":
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img_bytes = base64.b64decode(body.photo_b64.split(",")[-1])
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resp = await client.post(
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f"{base}/v1/images/edits", headers=headers,
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data={"model": settings.ai_model, "prompt": prompt, "size": size, "n": "1"},
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files={"image[]": ("photo.png", img_bytes, "image/png")})
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else:
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resp = await client.post(
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f"{base}/v1/images/generations", headers=headers,
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json={"model": settings.ai_model, "prompt": prompt, "n": 1, "size": size})
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resp.raise_for_status()
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data = resp.json()
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item = (data.get("data") or [{}])[0]
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b64 = item.get("b64_json")
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if not b64:
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return fail(f"生图服务返回异常: {str(data)[:200]}")
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except httpx.TimeoutException:
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return fail("AI 生图超时，请稍后重试")
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except httpx.HTTPStatusError as e:
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detail = e.response.text[:200] if e.response is not None else ""
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return fail(f"生图服务错误({e.response.status_code if e.response else '?'}): {detail}")
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except Exception as e:
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return fail(f"生图失败: {str(e)[:150]}")
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return ok(data={"image_b64": b64, "prompt": prompt, "size": size,
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"features": {"width_mm": info["width_mm"], "height_mm": info["height_mm"],
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"summary": info["summary"]}},
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message="生成成功")
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@@ -2,6 +2,7 @@
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from fastapi import APIRouter
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from huamenchuang.api import (
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ai_render,
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auth,
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business,
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contracts,
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@@ -29,6 +30,7 @@ api_router.include_router(quote.router)
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api_router.include_router(script.router)
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api_router.include_router(order_format.router)
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api_router.include_router(finance.router)
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api_router.include_router(ai_render.router)
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api_router.include_router(warehouse.router)
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api_router.include_router(business.router)
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api_router.include_router(platform.router)
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@@ -5,6 +5,7 @@ from pydantic_settings import BaseSettings
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_PROJECT_ROOT = Path(__file__).resolve().parents[3]
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_DEFAULT_DB = f"sqlite+aiosqlite:///{(_PROJECT_ROOT / 'huamenchuang.db').as_posix()}"
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_ENV_FILE = _PROJECT_ROOT / ".env"
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class Settings(BaseSettings):
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@@ -19,8 +20,15 @@ class Settings(BaseSettings):
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"template", "customer", "contract", "warehouse", "order",
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"quote", "optimize", "render",
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]
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# AI 炫图（OpenAI Images 兼容中转站；未配置时前端降级本地程序化渲染）
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ai_base_url: str = "https://geminiapi.ai"
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ai_api_key: str = ""
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ai_model: str = "gpt-image-2.5-1k"
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ai_timeout: int = 180
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model_config = {"env_prefix": "HMC_", "env_file": ".env"}
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# .env 锚定项目根(与进程 cwd 无关); 不存在时不加载
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model_config = {"env_prefix": "HMC_",
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"env_file": str(_ENV_FILE) if _ENV_FILE.exists() else None}
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settings = Settings()
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"""AI 炫图提示词构建器：从 drawing_data 提取窗型结构特征，生成专业英文生图提示词。
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设计原则（生图模型工程实践）:
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- 主体先行: 窗户的几何/材质/分格描述置前，场景氛围居后
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- 结构量化: 宽高 mm、分格数、扇数、开向全部显式给出，抑制模型自由发挥
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- 负面约束: 明确"保持窗型几何不变"（edits 模式关键）
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- 光影专项: 时间(日/黄昏/夜)与室内外视角分别给出摄影级光照词
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"""
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from huamenchuang.core.drawing import parse_drawing, replay_shape
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# 风格 → 型材材质/颜色描述
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STYLE_FINISH = {
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"modern": ("matte anthracite grey aluminum frames, minimalist slim profiles", "modern minimalist"),
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"chinese": ("dark bronze aluminum frames with wood-grain inner finish and subtle Chinese lattice grille", "new-Chinese style"),
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"european": ("cream white aluminum frames with classic decorative architrave", "classic European"),
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"nordic": ("white aluminum frames with light oak interior foil", "Scandinavian Nordic"),
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}
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# 时间 → 摄影光照
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TIME_LIGHT = {
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"day": "bright midday daylight, soft natural shadows, clear sky",
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"dusk": "warm golden-hour sunset light, long soft shadows, amber sky",
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"night": "night scene, warm interior glow shining through the glass, cool blue ambience",
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}
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# 场景 → 场景描述（照片式 7 场景对齐源站 renderV2）
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SCENE_DESC = {
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"render": "the window installed on a clean rendered exterior wall, straight-on elevation view",
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"install": "the brand-new window just mounted into the wall opening, installation foam and fixing screws still visible at the edges, home renovation context",
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"erase": "renovation scene: the old worn window has been removed from the masonry opening and this new window is being fitted in its place, dusty sill",
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"indoor": "interior view, the window as the focal point of a tastefully furnished {style_word} living room with sofa, wooden floor, curtains and green plants",
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"villa": "exterior view of a {style_word} villa facade featuring this large window, landscaped garden with lawn below, blue sky",
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"fence": "view from inside looking out through the window, clear unobstructed garden and distant hills beyond the glass, no railings or bars outside",
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"blank": "unfurnished rough-condition room with bare grey cement walls, the window as the only fitted element, pre-renovation state",
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}
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# 快速模式场景（无照片）
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QUICK_SCENE = {
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"indoor": "interior view in a tastefully furnished {style_word} room, the window as the light source",
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"outdoor": "exterior facade view on a residential building, mild urban greenery around",
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"studio": "studio product shot on a seamless neutral background, softbox lighting, slight floor reflection",
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}
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def _sash_kind(sub: dict) -> str | None:
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t = str(sub.get("type", ""))
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is_door = bool(sub.get("isDoor"))
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if "Screen" in t:
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return None # 纱扇不参与文字描述
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if "Kfc" in t:
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return "tilt-and-turn " + ("door leaf" if is_door else "sash")
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if "DoubleSash" in t:
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return "pair of French " + ("door leaves" if is_door else "casement sashes opening from the center")
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if "Slide" in t:
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return "sliding " + ("door panel" if is_door else "sash")
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if t in ("Sash",):
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return "side-hung casement " + ("door leaf" if is_door else "sash")
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return None
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def _opening_words(sub: dict) -> str:
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toward = str(sub.get("openToward") or "")
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od = sub.get("od")
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if "up" in str(sub.get("openDirection") or "").lower() or (isinstance(od, (int, float)) and 60 <= od <= 180 and "up" in str(sub.get("openDirection", "")).lower()):
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return "top-hung"
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return "inward-opening" if toward == "inward" else "outward-opening"
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def describe_window(drawing: str | dict) -> dict:
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"""从 drawing_data 提取窗型结构特征（供提示词与前端展示）。"""
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dd = parse_drawing(drawing)
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shapes = [s for s in dd.get("sm", []) if isinstance(s, dict)]
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if not shapes:
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return {"count": 0, "summary": "", "width_mm": 0, "height_mm": 0}
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xs, ys = [], []
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parts: list[str] = []
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sash_total = 0
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has_arc = has_screen = has_deco = False
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for shape in shapes:
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regions = replay_shape(shape)
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pts = [[(seg["ps"]["x"], seg["ps"]["y"]) for seg in r] for r in regions]
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if not pts:
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continue
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flat = [p for r in pts for p in r]
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x0, y0 = min(p[0] for p in flat), min(p[1] for p in flat)
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x1, y1 = max(p[0] for p in flat), max(p[1] for p in flat)
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xs += [x0, x1]; ys += [y0, y1]
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w, h = round(x1 - x0), round(y1 - y0)
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n_region = len(regions)
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# 扇描述
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sashes = [sub for sub in (shape.get("sm") or []) if isinstance(sub, dict)]
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kind_words: list[str] = []
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for sub in sashes:
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k = _sash_kind(sub)
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if k is None:
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if "Screen" in str(sub.get("type", "")):
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has_screen = True
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continue
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sash_total += 1
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kind_words.append(f"{_opening_words(sub)} {k}")
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if shape.get("rt"):
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has_arc = True
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if shape.get("decs"):
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has_deco = True
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part = f"{w}×{h}mm unit with {n_region} glass panes"
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if kind_words:
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part += ", " + " and ".join(dict.fromkeys(kind_words))
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parts.append(part)
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if not xs:
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return {"count": 0, "summary": "", "width_mm": 0, "height_mm": 0}
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width_mm, height_mm = round(max(xs) - min(xs)), round(max(ys) - min(ys))
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multi = len(parts) > 1
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summary = "; ".join(parts)
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if multi:
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summary = f"a combination of {len(parts)} window units joined together: " + summary
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else:
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summary = parts[0]
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if has_arc:
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summary += ", arched/curved top (heritage style)"
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if has_screen:
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summary += ", with retractable fly screen"
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if has_deco:
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summary += ", with decorative gliding bars (grilles) between the glass"
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return {"count": len(parts), "summary": summary, "width_mm": width_mm,
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"height_mm": height_mm, "sashes": sash_total}
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def build_prompt(drawing: str | dict, style: str = "modern", time: str = "day",
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scene: str = "render", ratio: str = "landscape") -> str:
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"""构建优化后的完整英文提示词。
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ratio: portrait/landscape/square → 依据窗型宽高比的构图引导。
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"""
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info = describe_window(drawing)
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finish, style_word = STYLE_FINISH.get(style, STYLE_FINISH["modern"])
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light = TIME_LIGHT.get(time, TIME_LIGHT["day"])
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scene_tpl = SCENE_DESC.get(scene) or QUICK_SCENE.get(scene) or SCENE_DESC["render"]
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scene_text = scene_tpl.replace("{style_word}", style_word)
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window_desc = info["summary"] or "a modern aluminum window"
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composition = {
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"portrait": "vertical composition, window fills most of the frame height",
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"landscape": "horizontal composition, generous wall context around the window",
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"square": "balanced square composition, window centered",
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}.get(ratio, "")
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prompt = (
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f"Photorealistic architectural photograph of {scene_text}. "
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f"The window: {window_desc}; {finish}. "
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f"Lighting: {light}. {composition} "
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"Keep the window geometry, pane division and frame profiles exactly as described — do not add or remove sashes. "
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"Realistic glass with soft sky reflection, physically correct shadows and contact shadows, "
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"true-to-material aluminum texture, high dynamic range, ultra-detailed, 8k, professional architectural photography, no people, no text, no watermark."
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)
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return prompt
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def pick_size(width_mm: int, height_mm: int) -> str:
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"""按窗型宽高比选择生成尺寸（gpt-image 支持 1024x1024 / 1536x1024 / 1024x1536）。"""
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if not width_mm or not height_mm:
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return "1024x1024"
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r = width_mm / height_mm
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if r > 1.2:
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return "1536x1024"
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if r < 0.83:
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return "1024x1536"
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return "1024x1024"
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