import os
import sys
root_path = os.getcwd()
sys.path.append(root_path)

import time
import signal
import uvicorn
import pandas as pd
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
from inference import predict_with_model
from fastapi.middleware.cors import CORSMiddleware

import threading
from http.server import SimpleHTTPRequestHandler, HTTPServer

app = FastAPI()

# 允许所有域名的跨域请求
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
    allow_headers=["*"],
)

# 定义请求处理类
class MyRequestHandler(SimpleHTTPRequestHandler):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.directory = '.'  # 设置根目录

# 定义返回结构
class PredictionResult(BaseModel):
    predictions: list

# 定义请求体模型
class Features(BaseModel):
    # 10个SCL评测量（后续再处理）
    somatization: float
    obsessive_compulsive: float
    interpersonal_sensitivity: float
    depression: float
    anxiety: float
    hostility: float
    terror: float
    paranoia: float
    psychoticism: float
    other: float
    # 基本信息特征量
    father_parenting_style: int # 温暖与理解：1；其他：0
    mother_parenting_style: int # 温暖与理解：1；其他：0
    self_assessed_family_economic_condition: int # 贫困：2；较差：1；其他：0
    history_of_psychological_counseling: bool # 有：1；无：0
    # 日常行为特征量
    absenteeism_above_average: bool # 大于平均次数：1；小于等于：0
    academic_warning: bool # 有预警：1；无预警：0

# 定义接口
@app.post("/classify/")
async def classify_features(features_list: List[Features]):

    # 定义一个空的 DataFrame 用于存储所有特征
    all_features = pd.DataFrame()

    # 遍历每个特征对象，为每个对象创建一个 DataFrame，并将其添加到 all_features 中
    for features in features_list:
        
        relevant_features = {
            "somatization": features.somatization,
            "obsessive_compulsive": features.obsessive_compulsive,
            "interpersonal_sensitivity": features.interpersonal_sensitivity,
            "depression": features.depression,
            "anxiety": features.anxiety,
            "hostility": features.hostility,
            "terror": features.terror,
            "paranoia": features.paranoia,
            "psychoticism": features.psychoticism,
            "other": features.other
        }

        # 创建只有一行的 DataFrame，并直接赋值给列
        df_feature = pd.DataFrame({
            # 数字化特征--基本信息
            '父亲教养方式数字化': [(lambda x: 0.59 if x == 1 else 0.46)(features.father_parenting_style)],
            '母亲教养方式数字化': [(lambda x: 0.69 if x == 1 else 0.56)(features.mother_parenting_style)],
            '自评家庭经济条件数字化': [(lambda x: 0.54 if x in [2, 1] else 0.47)(features.self_assessed_family_economic_condition)],
            '有无心理治疗（咨询）史数字化': [(lambda x: 0.21 if x else 0.09)(features.history_of_psychological_counseling)],
            # 数字化特征--症状因子
            '强迫症状数字化': [features.obsessive_compulsive / 4],
            '人际关系敏感数字化': [features.interpersonal_sensitivity / 4],
            '抑郁数字化': [features.depression / 4],
            '多因子症状': [(lambda x: sum(1 for value in x.values() if value > 3.0) / 10)(relevant_features)],
            # 数字化特征--日常行为
            '出勤情况数字化': [0.74 if features.absenteeism_above_average else 0.67],
            '学业情况数字化': [0.59 if features.academic_warning else 0.50]
        })

        all_features = pd.concat([all_features, df_feature], ignore_index=True)

    # # 将 DataFrame 转换为字典
    # df_dict = df_feature.to_dict(orient='records')

    # print(df_dict)
    # # 返回 FastAPI 响应
    # return df_dict
    # print(all_features)

    start_time = time.time()  # 记录开始时间
    predictions = predict_with_model(all_features)
    end_time = time.time()  # 记录结束时间
    print("预测耗时:", end_time - start_time, "秒")  # 打印执行时间

    print("预测结果:", predictions)
    # 返回预测结果
    return PredictionResult(predictions=predictions)

# 信号处理函数
def signal_handler(sig, frame):
    print("Ctrl+C detected, shutting down the server...")
    # 在这里执行关闭服务器的操作
    sys.exit(0)

# 启动服务器的函数
def run_server():
    port = 8080
    server = HTTPServer(('0.0.0.0', port), MyRequestHandler)
    print(f'Web HTTP Server listening on http://0.0.0.0:{port}')
    server.serve_forever()


if __name__ == "__main__":

    # 注册信号处理函数
    signal.signal(signal.SIGINT, signal_handler)
    
    # 创建线程并启动前端
    server_thread = threading.Thread(target=run_server)
    server_thread.start()

    name_app = os.path.basename(__file__)[0:-3]  # Get the name of the script
    log_config = {
        "version": 1,
        "disable_existing_loggers": True,
        "handlers": {
            "file_handler": {
                "class": "logging.FileHandler",
                "filename": "logfile.log",
            },
        },
        "root": {
            "handlers": ["file_handler"],
            "level": "INFO",
        },
    }
    #uvicorn.run(f'{name_app}:app', host="0.0.0.0", port=3397, reload=False,log_config=log_config)
    uvicorn.run(app, host="0.0.0.0", port=3397, reload=False)