from pydantic import BaseModel
import requests
import argparse

def main(server: str, port: int):
    # 模拟发送的特征数据
    features_data1 = {
        "somatization": 0.5,
        "obsessive_compulsive": 0.3,
        "interpersonal_sensitivity": 0.7,
        "depression": 0.6,
        "anxiety": 0.8,
        "hostility": 0.4,
        "terror": 0.2,
        "paranoia": 0.1,
        "psychoticism": 0.9,
        "other": 0.2,
        "father_parenting_style": 2,
        "mother_parenting_style": 3,
        "self_assessed_family_economic_condition": 4,
        "history_of_psychological_counseling": 1,
        "absenteeism_above_average": True,
        "academic_warning": False,
        "label": -1
    }
    features_data2 = {
        "somatization": 4.3,
        "obsessive_compulsive": 4.1,
        "interpersonal_sensitivity": 3.8,
        "depression": 4,
        "anxiety": 4.2,
        "hostility": 4.2,
        "terror": 4,
        "paranoia": 4.2,
        "psychoticism": 3.8,
        "other": 3.6,
        "father_parenting_style": 1,
        "mother_parenting_style": 0,
        "self_assessed_family_economic_condition": 0,
        "history_of_psychological_counseling": False,
        "absenteeism_above_average": True,
        "academic_warning": True,
        "label": -1
    }
    features_data3 = {
        "somatization": 1.1,
        "obsessive_compulsive": 2.3,
        "interpersonal_sensitivity": 2.6,
        "depression": 2.2,
        "anxiety": 1.6,
        "hostility": 1.5,
        "terror": 1.6,
        "paranoia": 1.5,
        "psychoticism": 1.2,
        "other": 1.3,
        "father_parenting_style": 1,
        "mother_parenting_style": 1,
        "self_assessed_family_economic_condition": 1,
        "history_of_psychological_counseling": False,
        "absenteeism_above_average": False,
        "academic_warning": False,
        "label": -1
    }
    features_data4 = {
        "somatization": 1.5,
        "obsessive_compulsive": 2.4,
        "interpersonal_sensitivity": 2.2,
        "depression": 3.2,
        "anxiety": 1.6,
        "hostility": 1.5,
        "terror": 3.1,
        "paranoia": 1.5,
        "psychoticism": 1.9,
        "other": 2.6,
        "father_parenting_style": 1,
        "mother_parenting_style": 1,
        "self_assessed_family_economic_condition": 0,
        "history_of_psychological_counseling": False,
        "absenteeism_above_average": False,
        "academic_warning": False,
        "label": -1
    }

    # 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
    features_data_list = [features_data4, features_data2, features_data3, features_data1]

    # 构建请求 URL
    url = f"http://{server}:{port}/inference/"

    # 发送 POST 请求
    response = requests.post(url, json=features_data_list)

    if response.status_code == 200:
        # 获取分类结果列表
        classified_results = response.json()
        print(classified_results)  # 这里的 classified_results 就是返回的列表对象
    else:
        print("请求失败:", response.text)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="发送特征数据到服务器进行推断")
    parser.add_argument("--server", type=str, default="127.0.0.1", help="服务器地址")
    parser.add_argument("--port", type=int, default=8088, help="服务器端口")
    
    args = parser.parse_args()
    main(args.server, args.port)
    input("任意键结束！")