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109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
from pydantic import BaseModel
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import requests
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import argparse
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def main(server: str, port: int):
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# 模拟发送的特征数据
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features_data1 = {
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"somatization": 0.5,
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"obsessive_compulsive": 0.3,
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"interpersonal_sensitivity": 0.7,
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"depression": 0.6,
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"anxiety": 0.8,
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"hostility": 0.4,
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"terror": 0.2,
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"paranoia": 0.1,
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"psychoticism": 0.9,
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"other": 0.2,
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"father_parenting_style": 2,
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"mother_parenting_style": 3,
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"self_assessed_family_economic_condition": 4,
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"history_of_psychological_counseling": 1,
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"absenteeism_above_average": True,
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"academic_warning": False,
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"label": -1
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}
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features_data2 = {
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"somatization": 4.3,
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"obsessive_compulsive": 4.1,
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"interpersonal_sensitivity": 3.8,
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"depression": 4,
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"anxiety": 4.2,
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"hostility": 4.2,
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"terror": 4,
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"paranoia": 4.2,
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"psychoticism": 3.8,
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"other": 3.6,
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"father_parenting_style": 1,
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"mother_parenting_style": 0,
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"self_assessed_family_economic_condition": 0,
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"history_of_psychological_counseling": False,
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"absenteeism_above_average": True,
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"academic_warning": True,
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"label": -1
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}
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features_data3 = {
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"somatization": 1.1,
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"obsessive_compulsive": 2.3,
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"interpersonal_sensitivity": 2.6,
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"depression": 2.2,
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"anxiety": 1.6,
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"hostility": 1.5,
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"terror": 1.6,
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"paranoia": 1.5,
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"psychoticism": 1.2,
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"other": 1.3,
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"father_parenting_style": 1,
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"mother_parenting_style": 1,
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"self_assessed_family_economic_condition": 1,
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"history_of_psychological_counseling": False,
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"absenteeism_above_average": False,
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"academic_warning": False,
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"label": -1
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}
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features_data4 = {
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"somatization": 1.5,
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"obsessive_compulsive": 2.4,
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"interpersonal_sensitivity": 2.2,
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"depression": 3.2,
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"anxiety": 1.6,
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"hostility": 1.5,
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"terror": 3.1,
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"paranoia": 1.5,
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"psychoticism": 1.9,
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"other": 2.6,
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"father_parenting_style": 1,
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"mother_parenting_style": 1,
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"self_assessed_family_economic_condition": 0,
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"history_of_psychological_counseling": False,
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"absenteeism_above_average": False,
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"academic_warning": False,
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"label": -1
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}
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# 必须是 features_list, 前面出现了只有一个样本时测不准，考虑是用了样本间归一化的原因，现在么有用了
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features_data_list = [features_data4, features_data2, features_data3, features_data1]
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# 构建请求 URL
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url = f"http://{server}:{port}/inference/"
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# 发送 POST 请求
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response = requests.post(url, json=features_data_list)
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if response.status_code == 200:
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# 获取分类结果列表
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classified_results = response.json()
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print(classified_results) # 这里的 classified_results 就是返回的列表对象
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else:
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print("请求失败:", response.text)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="发送特征数据到服务器进行推断")
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parser.add_argument("--server", type=str, default="127.0.0.1", help="服务器地址")
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parser.add_argument("--port", type=int, default=8088, help="服务器端口")
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args = parser.parse_args()
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main(args.server, args.port)
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input("任意键结束！")
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