import concurrent.futures
import requests
import socket


def dialog_line_parse(url, text):
    """
    将数据输入模型进行分析并输出结果
    :param url: 模型url
    :param text: 进入模型的数据
    :return: 模型返回结果
    """

    response = requests.post(
        url,
        json=text,
        timeout=1000
    )
    if response.status_code == 200:
        return response.json()
    else:
        # logger.error(
        #     "【{}】 Failed to get a proper response from remote "
        #     "server. Status Code: {}. Response: {}"
        #     "".format(url, response.status_code, response.text)
        # )
        print("【{}】 Failed to get a proper response from remote "
            "server. Status Code: {}. Response: {}"
            "".format(url, response.status_code, response.text))
        print(text)
        return []


text = "User:生成目录#\n问：为论文题目《基于跨文化意识培养的中职英语词汇教学模式及策略行动研究》生成目录，要求只有一级标题和二级标题，一级标题使用中文数字 例如一、xxx；二级标题使用阿拉伯数字 例如1.1 xxx；一级标题不少于7个；每个一级标题至少包含3个二级标题\n答:\n\nAssistant:"  # 获取用户query中的文本 例如"I love you"
nums = 10

nums = int(nums)
url = "http://192.168.31.74:18001/predict"

input_data = []
for i in range(nums):
    input_data.append([url, {"texts": text}])

with concurrent.futures.ThreadPoolExecutor() as executor:
    # 使用submit方法将任务提交给线程池，并获取Future对象
    futures = [executor.submit(dialog_line_parse, i[0], i[1]) for i in input_data]

    # 使用as_completed获取已完成的任务，并获取返回值
    results = [future.result() for future in concurrent.futures.as_completed(futures)]

print(results)