import uvicorn import time import threading import argparse from fastapi import FastAPI, BackgroundTasks from pydantic import BaseModel from typing import Dict, Optional, List import os from core.router import ParserFactory # 初始化FastAPI应用 app = FastAPI(title="Python解析服务", version="1.0") # 存储任务状态:key=task_id, value={status, progress, result, error} task_status: Dict[str, dict] = {} # 存储服务状态 service_status = { "status": "running", "pid": os.getpid(), "start_time": time.time() } # 锁机制,保证多线程安全 task_lock = threading.Lock() # ------------------------------ # 接口定义 # ------------------------------ @app.post("/execute", summary="接收解析任务并执行") async def execute_task(file_path: str): """ 接收Java端下发的解析任务,后台异步执行 """ factory = ParserFactory() # 解析文件 result = await factory.parse(file_path) print(result.content) # 生成并显示性能报告 report = factory.generate_performance_report() print("\n" + "="*80) print(report) print("="*80) return { "code": 200, "msg": "任务执行成功", "data": result.content } @app.get("/status", summary="状态接口") async def health_check(): """ 返回实例健康状态、资源使用情况 """ return { "code": 200, "msg": "success", "data": { "status": 0, "cpu_usage": 0.2, "gpu_usage": 0.0, "memory_usage": 0.2, "gpu_memory": 0.0 } } @app.get("/shutdown", summary="优雅关闭服务(供Java端终结实例调用)") async def shutdown_service(): """ 优雅关闭服务,清理资源 """ service_status["status"] = "stopping" # 可添加清理逻辑:如保存未完成任务、关闭解析器连接等 return { "code": 200, "msg": "服务开始优雅关闭", "data": None } # ------------------------------ # 启动入口 # ------------------------------ def main(): parser = argparse.ArgumentParser(description="Python解析服务启动参数") parser.add_argument("--host", default="0.0.0.0", help="服务监听地址") parser.add_argument("--port", type=int, default=8000, help="服务监听端口") args = parser.parse_args() # 启动FastAPI服务 uvicorn.run( "parse_service:app", host=args.host, port=args.port, reload=False, # 生产环境关闭热重载 workers=1 # 单进程运行,避免任务状态共享问题 ) if __name__ == "__main__": main()