79 lines
2.0 KiB
Python
79 lines
2.0 KiB
Python
from fastapi import FastAPI
|
|
from fastapi.middleware.cors import CORSMiddleware
|
|
from pydantic import BaseModel
|
|
import uvicorn
|
|
|
|
from app.services.qwen_service import get_qwen_service
|
|
|
|
app = FastAPI(title="Qwen2-VL 物体识别 API")
|
|
|
|
# CORS 配置
|
|
app.add_middleware(
|
|
CORSMiddleware,
|
|
allow_origins=["https://ai.yydd.ai", "http://localhost:3000"],
|
|
allow_credentials=True,
|
|
allow_methods=["*"],
|
|
allow_headers=["*"],
|
|
)
|
|
|
|
# 获取 Qwen 服务（全局单例）
|
|
qwen_service = get_qwen_service()
|
|
|
|
class DetectRequest(BaseModel):
|
|
imageUrl: str
|
|
clickX: int
|
|
clickY: int
|
|
imageIndex: int
|
|
imageWidth: int
|
|
imageHeight: int
|
|
|
|
class DetectResponse(BaseModel):
|
|
success: bool
|
|
data: dict | None
|
|
error: str | None
|
|
|
|
@app.on_event("startup")
|
|
async def startup_event():
|
|
"""启动时加载模型"""
|
|
print("🚀 正在加载 Qwen2-VL-2B 模型...")
|
|
await qwen_service.load_model()
|
|
print("✅ 模型加载完成！")
|
|
|
|
@app.post("/api/detect-object", response_model=DetectResponse)
|
|
async def detect_object(request: DetectRequest):
|
|
"""检测图片中点击位置的物体"""
|
|
try:
|
|
result = await qwen_service.detect_object_at_point(
|
|
image_url=request.imageUrl,
|
|
click_x=request.clickX,
|
|
click_y=request.clickY,
|
|
image_index=request.imageIndex,
|
|
image_width=request.imageWidth,
|
|
image_height=request.imageHeight
|
|
)
|
|
|
|
return DetectResponse(
|
|
success=True,
|
|
data=result,
|
|
error=None
|
|
)
|
|
|
|
except Exception as e:
|
|
print(f"❌ 检测失败: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return DetectResponse(
|
|
success=False,
|
|
data=None,
|
|
error=f"物体识别失败: {str(e)}"
|
|
)
|
|
|
|
@app.get("/health")
|
|
async def health_check():
|
|
"""健康检查"""
|
|
return {"status": "ok", "model_loaded": qwen_service.is_loaded}
|
|
|
|
if __name__ == "__main__":
|
|
uvicorn.run(app, host="0.0.0.0", port=8002)
|
|
|