Python FastApi结合异步执行方式

from fastapi import FastAPI
app = FastAPI()


@app.get("/info")
async def info():
    return {"key": "HelloWorld"}

import uvicorn
from threading import Thread
from time import sleep


def my_async(f):
    def wrapper(*args, **kwargs):
        thr = Thread(target=f, args=args, kwargs=kwargs)
        thr.start()
    return wrapper


@my_async
def print_info():
    sleep(5)
    print("############test")


if __name__ == '__main__':
    # 放在uvicorn.run前面，不然走不到此方法；
    print_info()
    uvicorn.run(app='Web:app', host='127.0.0.1', port=8000, reload=True, debug=True)

from fastapi import FastAPI
from concurrent.futures import ThreadPoolExecutor
import asyncio
 
app = FastAPI()
executor = ThreadPoolExecutor(max_workers=10)  # 创建线程池，可以根据需求调整线程数
 
# 模拟一个阻塞的图片识别函数
def blocking_image_recognition(image_data):
    # 模拟耗时操作，实际中会调用模型进行识别
    asyncio.sleep(5)
    return f"Recognized: {image_data}"
 
@app.post("/recognize-image")
async def recognize_image(image_data: str):
    # 使用线程池异步执行阻塞操作
    loop = asyncio.get_event_loop()
    result = await loop.run_in_executor(executor, blocking_image_recognition, image_data)
 
    # 此处可以将结果保存到数据库或进行其他操作
    # 使用异步方式进行数据库操作
    # ...
 
    return {"result": result}