good version for 算法注册

services/image-recognition/main.py

@@ -0,0 +1,108 @@
+from fastapi import FastAPI, HTTPException, UploadFile, File
+from pydantic import BaseModel
+import uvicorn
+import json
+import logging
+import base64
+from io import BytesIO
+from .ai_algorithm import ImageRecognizer
+from .config import settings
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# 初始化FastAPI应用
+app = FastAPI(
+    title="图像识别服务",
+    description="提供图像识别功能的AI服务",
+    version="1.0.0"
+)
+
+# 初始化识别器
+recognizer = ImageRecognizer()
+
+# 定义请求模型
+class PredictRequest(BaseModel):
+    input_data: list
+    params: dict = {}
+
+# 定义响应模型
+class PredictResponse(BaseModel):
+    predictions: list
+    status: str
+
+@app.post("/predict", response_model=PredictResponse)
+async def predict(request: PredictRequest):
+    """算法预测接口"""
+    try:
+        logger.info(f"Received prediction request for {len(request.input_data)} images")
+        predictions = recognizer.recognize(request.input_data, request.params)
+        logger.info(f"Prediction completed: {predictions}")
+        return PredictResponse(
+            predictions=predictions,
+            status="success"
+        )
+    except Exception as e:
+        logger.error(f"Prediction error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/predict/file")
+async def predict_file(file: UploadFile = File(...)):
+    """通过文件上传进行预测"""
+    try:
+        logger.info(f"Received file upload: {file.filename}")
+        
+        # 读取文件内容
+        contents = await file.read()
+        
+        # 转换为base64
+        image_base64 = base64.b64encode(contents).decode('utf-8')
+        
+        # 调用识别器
+        predictions = recognizer.recognize([image_base64])
+        
+        logger.info(f"File prediction completed: {predictions}")
+        return {
+            "predictions": predictions,
+            "status": "success",
+            "filename": file.filename
+        }
+    except Exception as e:
+        logger.error(f"File prediction error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/health")
+async def health_check():
+    """健康检查接口"""
+    return {
+        "status": "healthy",
+        "service": "image-recognition",
+        "version": "1.0.0"
+    }
+
+@app.get("/info")
+async def service_info():
+    """服务信息接口"""
+    return {
+        "name": "图像识别服务",
+        "description": "提供图像识别功能的AI服务",
+        "version": "1.0.0",
+        "endpoints": {
+            "/predict": "POST - 图像识别预测",
+            "/predict/file": "POST - 通过文件上传进行预测",
+            "/health": "GET - 健康检查",
+            "/info": "GET - 服务信息"
+        }
+    }
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "main:app",
+        host=settings.HOST,
+        port=settings.PORT,
+        reload=settings.DEBUG
+    )