创建一个队列系统来按照请求的资历一个接一个地处理请求？

如何解决创建一个队列系统来按照请求的资历一个接一个地处理请求？

我有一个非常简单的 API，可以预测是否存在不安全驾驶行为。除了识别行为的不规律性之外，API 还允许表征所犯的违规行为。

如何设置排队系统（例如使用RabbitMQ）按照请求的资历顺序依次处理图像？

main.py

from model import ClassificationModel
import argparse
import os


def predict_img(img_path,with_heatmap=False):
    model = ClassificationModel() <--- Aqui descargo el modelo cada vez que llamo a predict_img

    row = model.predict(img_path=img_path,row=None,img=None,load=True,original_image_path=img_path,print_heatmap=with_heatmap)

    results = row.to_json()
    print(results)

    return results


if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument("--path","-p",help="path of the image",required=True)
    parser.add_argument("--heatmap","-heat",help="generate heatmap or not",required=True)

    args = parser.parse_args()

    if not(os.path.exists(args.path)):
        raise FileNotFoundError("{} does not exist.".format(args.path))

    elif args.path.split(".")[-1] not in ["png","jpg","jpeg"]:
        raise TypeError("The file is not a valid image file.")

    if args.heatmap.upper() == "TRUE":
        args.heatmap = True
    else:
        args.heatmap = False

    result = predict_img(img_path=args.path,with_heatmap=args.heatmap)

model.py

import numpy as np

np.random.seed(40)
from keras_preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import load_model

import settings as st

from predict import Predict


class ClassificationModel(Predict):
    def __init__(self):
        self.list_pos = [
            "safe driving","texting - right","talking to the phone - right","texting - left","talking on the phone - left","operating the radio","drinking","reaching behind","hair and makeup","talking to passenger"
        ]

        self.image_size = 384
        self.generator = ImageDataGenerator(brightness_range=[0.7,1.5],rotation_range=35,fill_mode='nearest',zoom_range=0.125,rescale=1. / 255)

        self.model = load_model(st.model_path)
        self.threshold = 0.5
        self.n_step = 5
        self.normal_index = 0