good version for 算法注册

services/image-recognition/Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+# 安装依赖
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 复制代码
+COPY . .
+
+# 暴露端口
+EXPOSE 8000
+
+# 启动服务
+CMD ["python", "main.py"]

services/image-recognition/ai_algorithm.py

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+import logging
+import base64
+from io import BytesIO
+from typing import List, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+class ImageRecognizer:
+    """图像识别器"""
+    
+    def __init__(self):
+        """初始化图像识别器"""
+        logger.info("初始化图像识别器")
+        # 这里可以加载预训练模型
+        # 示例中使用简单的规则识别
+    
+    def recognize(self, images: List[str], params: Dict[str, Any] = None) -> List[Dict[str, Any]]:
+        """识别图像
+        
+        Args:
+            images: 图像列表，每个图像为base64编码字符串
+            params: 识别参数
+            
+        Returns:
+            识别结果列表
+        """
+        if params is None:
+            params = {}
+        
+        threshold = params.get("threshold", 0.5)
+        
+        results = []
+        for image_base64 in images:
+            # 简单的规则识别示例
+            recognition = self._simple_recognize(image_base64)
+            results.append({
+                "image": image_base64[:100] + "..." if len(image_base64) > 100 else image_base64,
+                "label": recognition["label"],
+                "confidence": recognition["confidence"]
+            })
+        
+        return results
+    
+    def _simple_recognize(self, image_base64: str) -> Dict[str, Any]:
+        """简单的图像识别实现
+        
+        Args:
+            image_base64: base64编码的图像
+            
+        Returns:
+            识别结果
+        """
+        # 简单的规则识别（基于图像大小和内容特征）
+        try:
+            # 解码base64
+            image_data = base64.b64decode(image_base64)
+            
+            # 计算图像大小特征
+            image_size = len(image_data)
+            
+            # 基于大小的简单分类
+            if image_size < 10240:  # 小于10KB
+                return {"label": "小图像", "confidence": 0.8}
+            elif image_size < 102400:  # 小于100KB
+                return {"label": "中等图像", "confidence": 0.85}
+            else:  # 大于100KB
+                return {"label": "大图像", "confidence": 0.9}
+        except Exception as e:
+            logger.error(f"Image recognition error: {str(e)}")
+            return {"label": "未知", "confidence": 0.5}

services/image-recognition/config.py

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+from pydantic_settings import BaseSettings
+from typing import Optional
+
+
+class Settings(BaseSettings):
+    """服务配置"""
+    # 服务基本配置
+    HOST: str = "0.0.0.0"
+    PORT: int = 8002
+    DEBUG: bool = True
+    
+    # 服务名称
+    SERVICE_NAME: str = "image-recognition"
+    
+    # 日志配置
+    LOG_LEVEL: str = "info"
+    
+    # 算法配置
+    ALGORITHM_THRESHOLD: float = 0.5
+    
+    class Config:
+        env_file = ".env"
+        case_sensitive = True
+
+
+# 创建全局配置实例
+settings = Settings()

services/image-recognition/main.py

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+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
+    )

services/image-recognition/requirements.txt

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+fastapi==0.104.1
+uvicorn==0.24.0.post1
+pydantic==2.5.2
+pydantic-settings==2.1.0
+python-multipart==0.0.6

services/image-recognition/start.sh

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+#!/bin/bash
+
+# 启动图像识别服务
+
+# 进入服务目录
+cd "$(dirname "$0")"
+
+# 检查虚拟环境是否存在
+if [ ! -d "venv" ]; then
+    echo "创建虚拟环境..."
+    python3 -m venv venv
+fi
+
+# 激活虚拟环境
+echo "激活虚拟环境..."
+source venv/bin/activate
+
+# 安装依赖
+echo "安装依赖..."
+pip install --no-cache-dir -r requirements.txt
+
+# 启动服务
+echo "启动图像识别服务..."
+python main.py