++

main.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+"""FastAPI 入口：AI 图片生成服务。"""
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel, Field, model_validator
+
+from utils.mm_api_t2i import OUTPUT_DIR, PROVIDERS, generate_images
+from utils.agnes_image import VALID_RATIOS, generate_images as agnes_generate_images
+
+app = FastAPI(title="AI Image T2I API", version="1.0.0")
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+app.mount("/output", StaticFiles(directory=OUTPUT_DIR), name="output")
+
+
+class ImageGenerationRequest(BaseModel):
+    prompt: str = Field(min_length=1, description="图片提示词")
+    provider: str = Field(default="grok", pattern="^(grok|gpt)$", description="模型提供方")
+    model: str | None = Field(default=None, description="覆盖提供方默认模型")
+    size: str = Field(default="1024x1024", description="图片尺寸")
+    n: int = Field(default=1, ge=1, le=10, description="生成数量")
+    profile_name: str = Field(pattern=r"^[\w-]+$", description="调用方标识（必填），图片会保存到 output/<profile_name>/ 下")
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_profile_name(cls, data):
+        if isinstance(data, dict) and not data.get("profile_name"):
+            raise ValueError("缺少 profile_name 参数，请在请求中带上你的 agent/profile 名称")
+        return data
+
+
+class GeneratedImage(BaseModel):
+    filename: str
+    url: str
+    bytes: int
+
+
+class ImageGenerationResponse(BaseModel):
+    provider: str
+    model: str
+    images: list[GeneratedImage]
+
+
+@app.get("/health")
+def health_check():
+    return {"status": "ok"}
+
+
+@app.post("/images/generations", response_model=ImageGenerationResponse)
+def generate_images_endpoint(request: ImageGenerationRequest):
+    result = generate_images(
+        provider=request.provider,
+        model=request.model,
+        prompt=request.prompt,
+        size=request.size,
+        n=request.n,
+        profile_name=request.profile_name,
+    )
+    return ImageGenerationResponse(**result)
+
+
+class AgnesImageGenerationRequest(BaseModel):
+    prompt: str = Field(min_length=1, description="图片提示词")
+    size: str = Field(default="1K", description="输出尺寸档位: 1K / 2K / 3K / 4K，也支持精确尺寸如 1024x1024")
+    ratio: str | None = Field(default=None, description=f"宽高比，支持 {sorted(VALID_RATIOS)}；与 size 档位配合使用")
+    images: list[str] | None = Field(default=None, description="图生图 / 多图合成的输入图像 URL 或 Data URI Base64")
+    profile_name: str = Field(pattern=r"^[\w-]+$", description="调用方标识（必填），图片会保存到 output/<profile_name>/ 下")
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_profile_name(cls, data):
+        if isinstance(data, dict) and not data.get("profile_name"):
+            raise ValueError("缺少 profile_name 参数，请在请求中带上你的 agent/profile 名称")
+        return data
+
+
+class AgnesImageGenerationResponse(BaseModel):
+    model: str
+    size: str
+    ratio: str
+    images: list[GeneratedImage]
+
+
+@app.post("/agnes/images/generations", response_model=AgnesImageGenerationResponse)
+def agnes_generate_images_endpoint(request: AgnesImageGenerationRequest):
+    result = agnes_generate_images(
+        prompt=request.prompt,
+        size=request.size,
+        ratio=request.ratio,
+        images=request.images,
+        profile_name=request.profile_name,
+    )
+    return AgnesImageGenerationResponse(**result)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)