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51 lines
1.8 KiB
51 lines
1.8 KiB
import concurrent.futures
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import requests
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import socket
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def dialog_line_parse(url, text):
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"""
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将数据输入模型进行分析并输出结果
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:param url: 模型url
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:param text: 进入模型的数据
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:return: 模型返回结果
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"""
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response = requests.post(
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url,
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json=text,
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timeout=1000
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)
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if response.status_code == 200:
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return response.json()
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else:
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# logger.error(
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# "【{}】 Failed to get a proper response from remote "
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# "server. Status Code: {}. Response: {}"
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# "".format(url, response.status_code, response.text)
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# )
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print("【{}】 Failed to get a proper response from remote "
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"server. Status Code: {}. Response: {}"
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"".format(url, response.status_code, response.text))
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print(text)
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return []
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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"
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nums = 10
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nums = int(nums)
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url = "http://192.168.31.74:12000/predict"
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input_data = []
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for i in range(nums):
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input_data.append([url, {"texts": text}])
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with concurrent.futures.ThreadPoolExecutor() as executor:
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# 使用submit方法将任务提交给线程池，并获取Future对象
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futures = [executor.submit(dialog_line_parse, i[0], i[1]) for i in input_data]
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# 使用as_completed获取已完成的任务，并获取返回值
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results = [future.result() for future in concurrent.futures.as_completed(futures)]
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print(results)
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