good version for 算法注册

services/text-classification/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/text-classification/ai_algorithm.py

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+import logging
+from typing import List, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+class TextClassifier:
+    """文本分类器"""
+    
+    def __init__(self):
+        """初始化文本分类器"""
+        logger.info("初始化文本分类器")
+        # 这里可以加载预训练模型
+        # 示例中使用简单的规则分类
+    
+    def classify(self, texts: List[str], params: Dict[str, Any] = None) -> List[Dict[str, Any]]:
+        """分类文本
+        
+        Args:
+            texts: 文本列表
+            params: 分类参数
+            
+        Returns:
+            分类结果列表
+        """
+        if params is None:
+            params = {}
+        
+        threshold = params.get("threshold", 0.5)
+        
+        results = []
+        for text in texts:
+            # 简单的规则分类示例
+            classification = self._simple_classify(text)
+            results.append({
+                "text": text,
+                "label": classification["label"],
+                "confidence": classification["confidence"]
+            })
+        
+        return results
+    
+    def _simple_classify(self, text: str) -> Dict[str, Any]:
+        """简单的文本分类实现
+        
+        Args:
+            text: 待分类的文本
+            
+        Returns:
+            分类结果
+        """
+        # 简单的规则分类
+        text_lower = text.lower()
+        
+        if any(keyword in text_lower for keyword in ["技术", "科技", "编程", "代码"]):
+            return {"label": "技术", "confidence": 0.9}
+        elif any(keyword in text_lower for keyword in ["体育", "足球", "篮球", "运动"]):
+            return {"label": "体育", "confidence": 0.85}
+        elif any(keyword in text_lower for keyword in ["电影", "音乐", "娱乐", "游戏"]):
+            return {"label": "娱乐", "confidence": 0.8}
+        elif any(keyword in text_lower for keyword in ["美食", "餐厅", "烹饪", "食物"]):
+            return {"label": "美食", "confidence": 0.85}
+        elif any(keyword in text_lower for keyword in ["政治", "新闻", "政府", "政策"]):
+            return {"label": "政治", "confidence": 0.9}
+        else:
+            return {"label": "其他", "confidence": 0.7}

services/text-classification/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 = 8001
+    DEBUG: bool = True
+    
+    # 服务名称
+    SERVICE_NAME: str = "text-classification"
+    
+    # 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/text-classification/main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import uvicorn
+import json
+import logging
+from .ai_algorithm import TextClassifier
+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="文本分类服务",
+    description="提供文本分类功能的AI服务",
+    version="1.0.0"
+)
+
+# 初始化分类器
+classifier = TextClassifier()
+
+# 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: {request.input_data}")
+        predictions = classifier.classify(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.get("/health")
+async def health_check():
+    """健康检查接口"""
+    return {
+        "status": "healthy",
+        "service": "text-classification",
+        "version": "1.0.0"
+    }
+
+@app.get("/info")
+async def service_info():
+    """服务信息接口"""
+    return {
+        "name": "文本分类服务",
+        "description": "提供文本分类功能的AI服务",
+        "version": "1.0.0",
+        "endpoints": {
+            "/predict": "POST - 文本分类预测",
+            "/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/text-classification/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/text-classification/start.sh

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+#!/bin/bash
+
+# 启动文本分类服务
+
+# 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 "启动文本分类服务..."
+python main.py