import jieba
from paddlenlp import Taskflow
from paddleocr import PaddleOCR

"""
项目配置
"""
# 每次从数据库获取的案子数量
PHHD_BATCH_SIZE = 10
# 没有查询到案子的等待时间(分钟)
SLEEP_MINUTES = 5
# 是否发送报错邮件
SEND_ERROR_EMAIL = True
# 是否开启布局分析
LAYOUT_ANALYSIS = False

"""
信息抽取关键词配置
"""
# 患者姓名
PATIENT_NAME = ["患者姓名"]
# 入院日期
ADMISSION_DATE = ["入院日期"]
# 出院日期
DISCHARGE_DATE = ["出院日期"]
# 发生医疗费
MEDICAL_EXPENSES = ["费用总额"]
# 个人现金支付
PERSONAL_CASH_PAYMENT = ["个人现金支付"]
# 个人账户支付
PERSONAL_ACCOUNT_PAYMENT = ["个人账户支付"]
# 个人自费金额
PERSONAL_FUNDED_AMOUNT = ["自费金额", "个人自费"]
# 医保类别
MEDICAL_INSURANCE_TYPE = ["医保类型"]
# 就诊医院
HOSPITAL = ["医院"]
# 就诊科室
DEPARTMENT = ["科室"]
# 主治医生
DOCTOR = ["主治医生"]
# 住院号
ADMISSION_ID = ["住院号"]
# 医保结算单号码
SETTLEMENT_ID = ["医保结算单号码"]
# 年龄
AGE = ["年龄"]
# 大写总额
UPPERCASE_MEDICAL_EXPENSES = ["大写总额"]

SETTLEMENT_LIST_SCHEMA = \
    (PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + MEDICAL_EXPENSES + PERSONAL_CASH_PAYMENT
     + PERSONAL_ACCOUNT_PAYMENT + PERSONAL_FUNDED_AMOUNT + MEDICAL_INSURANCE_TYPE + ADMISSION_ID + SETTLEMENT_ID
     + UPPERCASE_MEDICAL_EXPENSES)

DISCHARGE_RECORD_SCHEMA = \
    HOSPITAL + DEPARTMENT + PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + DOCTOR + ADMISSION_ID + AGE

COST_LIST_SCHEMA = PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + MEDICAL_EXPENSES

"""
别名配置
"""
# 使用别名中的value替换key。考虑到效率问题，只会替换第一个匹配到的key。
HOSPITAL_ALIAS = {
    "沐阳": ["沭阳"],
    "连水": ["涟水"],
    "唯宁": ["睢宁"],  # 雕宁
    "九〇四": ["904"],
    "漂水": ["溧水"],
}
DEPARTMENT_ALIAS = {
    "耳鼻喉": ["耳鼻咽喉"],
    "急症": ["急诊"],
}

"""
搜索过滤配置
"""
# 默认会过滤单字
HOSPITAL_FILTER = ["医院", "人民", "第一", "第二", "第三", "大学", "附属"]

DEPARTMENT_FILTER = ["医", "伤", "西", "新"]

"""
分词配置
"""
jieba.suggest_freq(("肿瘤", "医院"), True)
jieba.suggest_freq(("骨", "伤"), True)
jieba.suggest_freq(("感染", "性"), True)
jieba.suggest_freq(("胆", "道"), True)
jieba.suggest_freq(("脾", "胃"), True)

"""
模型配置
"""
SETTLEMENT_IE = Taskflow("information_extraction", schema=SETTLEMENT_LIST_SCHEMA, model="uie-x-base",
                         task_path="model/settlement_list_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16")
DISCHARGE_IE = Taskflow("information_extraction", schema=DISCHARGE_RECORD_SCHEMA, model="uie-x-base",
                        task_path="model/discharge_record_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16")
COST_IE = Taskflow("information_extraction", schema=COST_LIST_SCHEMA, model="uie-x-base", device_id=1,
                   task_path="model/cost_list_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16")

OCR = PaddleOCR(
    device="gpu:0",
    ocr_version="PP-OCRv4",
    use_textline_orientation=False,
    # 检测像素阈值，输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点
    text_det_thresh=0.1,
    # 检测框阈值，检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域
    text_det_box_thresh=0.3,
    )