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(
    gpu_id=1,
    use_angle_cls=False,
    show_log=False,
    det_db_thresh=0.1,
    det_db_box_thresh=0.3,
    det_limit_side_len=1248,
    drop_score=0.3,
    rec_model_dir='model/ocr/openatom_rec_repsvtr_ch_infer',
    rec_algorithm='SVTR_LCNet',
)