# coding:utf-8
from docs.config import dictionaryPath
from docs.config import dictionaryUrl
from machine_models.databases import File
from machine_models.train_model import train
from machine_models.databases.mysql_helper import Model
from machine_models.databases import session
from machine_models.databases.mysql_helper import Project
from machine_models.predict_model import predict
from util.file_operations import generate_directory, del_directory
from docs.config import baseDir
import os
import joblib
import uuid
import datetime

# 词典文件加载，只加载一次
if not os.path.exists(dictionaryPath):
    status = File.download_file(dictionaryUrl, dictionaryPath)
    if not status:
        raise ValueError("词典文件下载失败")
tfidf_vec = joblib.load(dictionaryPath)

def train_fail(project_id, user_id):
    '''
    记录失败日志
    :param project_id:
    :param user_id:
    :return:
    '''
    fail_model = Model(state=2, projectId=project_id, createperson=user_id,
                       createTime=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
    session.add(fail_model)
    session.commit()
    return False


def train_model(request_params: dict):
    # 清空数据库链接对象缓存
    session.expire_all()
    session.commit()

    # 获取训练项目数据
    project_id = request_params.get("id")
    user_id = request_params.get("userId", "")
    label_type = request_params.get("type", 1)
    fields = request_params.get("fields", "")
    model_dir = ""
    try:
        # 不存在项目Id
        if not project_id:
            return train_fail(project_id, user_id)
        model_dir = os.path.join(baseDir, str(uuid.uuid4()))
        dir_status = generate_directory(model_dir)
        # 文件夹生成错误
        if not dir_status:
            return train_fail(project_id, user_id)
        # 开始训练
        model_detail = train(project_id, fields.split(","), tfidf_vec, label_type, model_dir)
        # 训练失败
        if not model_detail:
            return train_fail(project_id, user_id)
        # 训练成功记录
        model_detail.projectId = project_id
        model_detail.state = 0
        model_detail.createperson = user_id
        model_detail.createTime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        session.add(model_detail)
        session.commit()
        # 清空本次训练生成文件
        del_directory(model_dir)
        return True
    except Exception as e:
        train_fail(project_id, user_id)
        # 清空本次训练生成文件
        if model_dir and os.path.exists(model_dir):
            del_directory(model_dir)
        return train_fail(project_id, user_id)


def predict_model(request_params):
    # 清空本地数据库缓存
    session.expire_all()
    session.commit()
    # 获取预测参数
    project_id = request_params.get("id", -1)
    id_list = request_params.get("id_list", [])
    model_id = request_params.get("model_id", -1)
    project_info = session.query(Project).filter_by(id=project_id).first()

    # 查询项目信息
    if not project_info:
        return {"error_code": 0, "error_message": f"项目信息不存在--> {project_id}"}
    focus_field, target_label, label_type = project_info.focusField.split(
        ","), project_info.labels.split(","), project_info.type

    # 查询模型信息
    model_info = session.query(Model).filter_by(id=model_id).first()
    print(type(model_id), "-->", model_id)
    if not model_info:
        return {"error_code": 0, "error_message": f"模型信息不存在--> {model_id}"}

    # 加载模型
    model_url = model_info.modelFile
    model_dir = os.path.join(baseDir, model_url)
    model_path = os.path.join(model_dir, "model.model")
    if not os.path.exists(model_path):
        dir_status = generate_directory(model_dir)
        if not dir_status:
            return {"error_code": 0, "error_message": f"文件夹创建失败，请检查存储设备-->"}
        status = File.download_file(model_url, model_path)
        if not status:
            return {"error_code": 0, "error_message": f"oss储存模型加载失败--> {model_id}"}
    try:
        data = predict(id_list, tfidf_vec, label_type, focus_field, target_label, model_path)
    except Exception as e:
        print(e)
        return {"error_code": 0, "error_message": "预测过程出错"}
    # 清空缓存
    if os.path.exists(model_dir):
        del_directory(model_dir)
    return {"error_code": 1, "data": data}