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Commit b9ac888b by lichenggang
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添加异常处理,添加文本识别

parent 00d16687
Inline Side-by-side
Showing with 238 additions and 82 deletions
classify_server.py
...@@ -5,8 +5,8 @@ import grpc ...@@ -5,8 +5,8 @@ import grpc
from utils.log_manager import bom_log from utils.log_manager import bom_log
from protobuf import classify_pb2 from protobuf import classify_pb2
from protobuf import classify_pb2_grpc from protobuf import classify_pb2_grpc
import traceback
from predic_fac import PredictorFac from predic_fac import PredictorFac
from utils.status import code2msg
class Classify(classify_pb2_grpc.classifyServicer): class Classify(classify_pb2_grpc.classifyServicer):
...@@ -16,32 +16,17 @@ class Classify(classify_pb2_grpc.classifyServicer): ...@@ -16,32 +16,17 @@ class Classify(classify_pb2_grpc.classifyServicer):
def Classify(self, request, context): def Classify(self, request, context):
bom_log.info(f'grpc收到数据: {request.keyword}') bom_log.info(f'grpc收到数据: {request.keyword}')
err_msg = None
try: try:
dic_data = json.loads(request.keyword) dic_data = json.loads(request.keyword)
res = self.predictorfac.predict(dic_data) code, res = self.predictorfac.predict(dic_data)
res['status'] = 1
if '数量' not in str(res):
err_msg = '未识别到数量列'
if '参数' not in str(res) and '型号' not in str(res):
err_msg = '未识别到参数列'
except json.decoder.JSONDecodeError: except json.decoder.JSONDecodeError:
res = self.predictorfac.predict(request.keyword) code, res = self.predictorfac.predict(request.keyword)
res['status'] = 1 if code != 1:
except: res = code2msg(code)
res = { bom_log.error(f'grpc识别报错: ' + res['message'])
'status': 0, else:
'err_msg': '未知' res['status'] = code
} return classify_pb2.ClassifyReply(message=json.dumps(res))
bom_log.error(f'grpc报错: \n' + traceback.format_exc())
if err_msg:
res = {
'status': 0,
'err_msg': err_msg
}
bom_log.info(f'返回结果: ' + str(res))
return classify_pb2.ClassifyReply(message='result {msg}'.format(msg=res))
def fac_test_predic(self, data): def fac_test_predic(self, data):
res = self.predictorfac.predict(data) res = self.predictorfac.predict(data)
......
http_server.py
...@@ -5,10 +5,10 @@ from urllib.parse import unquote ...@@ -5,10 +5,10 @@ from urllib.parse import unquote
import tornado.web import tornado.web
import tornado.ioloop import tornado.ioloop
import traceback
from utils.log_manager import bom_log from utils.log_manager import bom_log
from predic_fac import PredictorFac from predic_fac import PredictorFac
from utils.status import code2msg
class BaseHandler(tornado.web.RequestHandler): class BaseHandler(tornado.web.RequestHandler):
...@@ -18,53 +18,33 @@ class BaseHandler(tornado.web.RequestHandler): ...@@ -18,53 +18,33 @@ class BaseHandler(tornado.web.RequestHandler):
class KwHandler(BaseHandler): class KwHandler(BaseHandler):
async def get(self): async def get(self):
try: target = unquote(self.get_argument('keyword'))
target = unquote(self.get_argument('keyword')) predict_type = unquote(self.get_argument('type', 'all'))
predict_type = unquote(self.get_argument('type', 'all')) bom_log.info(f'http收到识别关键词: {target}, 预测类型为{predict_type}')
bom_log.info(f'http收到识别关键词: {target}, 预测类型为{predict_type}') code, res = self.predictorfac.predict(target, predict_type)
res = self.predictorfac.predict(target, predict_type) if code != 1:
res['status'] = 1 res = code2msg(code)
except: bom_log.error(f'http关键词识别报错: ' + res['message'])
res = { else:
'status': 0, res['status'] = code
'err_msg': '未知'
}
bom_log.error(f'http关键词识别报错: \n' + traceback.format_exc())
self.write(res) self.write(res)
class DictHandler(BaseHandler): class DictHandler(BaseHandler):
async def post(self): async def post(self):
err_msg = None
try: try:
bom_log.info(f'http收到识别字典: {self.request.body}') bom_log.info(f'http收到识别体: {self.request.body}')
dic_data = json.loads(self.request.body) dic_data = json.loads(self.request.body)
res = self.predictorfac.predict(dic_data) code, res = self.predictorfac.predict(dic_data)
res['status'] = 1
if '数量' not in str(res):
err_msg = '未识别到数量列'
if '参数' not in str(res) and '型号' not in str(res):
err_msg = '未识别到参数列'
except json.decoder.JSONDecodeError: except json.decoder.JSONDecodeError:
res = self.predictorfac.predict(self.request.body) code, res = self.predictorfac.predict(self.request.body)
res['status'] = 1 if code != 1:
except: res = code2msg(code)
res = { bom_log.error(f'http识别报错: ' + res['message'])
'status': 0, else:
'err_msg': '未知' res['status'] = code
}
bom_log.error(f'http字典识别报错: \n' + traceback.format_exc())
if err_msg:
res = {
'status': 0,
'err_msg': err_msg
}
bom_log.info(f'返回结果: ' + str(res))
self.write(res) self.write(res)
def gen_app(): def gen_app():
return tornado.web.Application(handlers=[(k, v) for k, v in register_tornado_handlers.items()]) return tornado.web.Application(handlers=[(k, v) for k, v in register_tornado_handlers.items()])
......
id_server.py
...@@ -15,10 +15,12 @@ def run(http_port, grpc_port): ...@@ -15,10 +15,12 @@ def run(http_port, grpc_port):
p_http.start() p_http.start()
p_grpc.start() p_grpc.start()
def http_run(http_port):
http_server(http_port)
if __name__ == "__main__": if __name__ == "__main__":
http_port = 50052 http_port = 50052
grpc_port = 50051 grpc_port = 50051
run(http_port, grpc_port) # run(http_port, grpc_port)
http_run(http_port)
predic_fac.py
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
import json import json
from predict import dict_predict, kw_predict from predict import dict_predict, kw_predict, txt_predict
from utils.status import code2msg, Status, StatusCode
class PredictorFac: class PredictorFac:
Status = Status
def __init__(self): def __init__(self):
self.kw_predictor = kw_predict.KwPredict('single') self.kw_predictor = kw_predict.KwPredict('single')
self.dict_predictor = dict_predict.DicPredict('dict') self.dict_predictor = dict_predict.DicPredict('dict')
self.txt_predictor = txt_predict.TxtPredict('txt')
def _get_predictor(self, data): def _get_predictor(self, data):
if isinstance(data, dict): if isinstance(data, dict):
return self.dict_predictor return self.dict_predictor
elif isinstance(data, list):
return self.txt_predictor
else: else:
return self.kw_predictor return self.kw_predictor
def predict(self, dic_data, predict_type='all'): def predict(self, dic_data, predict_type='all'):
predictor = self._get_predictor(dic_data) predictor = self._get_predictor(dic_data)
res = predictor.predict(dic_data, predict_type)
return res try:
res = predictor.predict(dic_data, predict_type)
if isinstance(predictor, dict_predict.DicPredict):
if '数量' not in str(res):
raise self.Status.NoQtyError
if '参数' not in str(res) and '型号' not in str(res):
raise self.Status.NoParamGnError
return 1, res
except Status.StatusErrTup as e:
stat_code = e.value
return stat_code, code2msg(stat_code)
if __name__ == "__main__": if __name__ == "__main__":
def get_test_data(): def get_test_data():
import pandas as pd import pandas as pd
import json import json
df = pd.read_excel(r'C:\Users\ICHUNT\Desktop\bomlist\sky-新建 XLSX 工作表 (2).xlsx', header=None) df = pd.read_excel(r'C:\Users\ICHUNT\Desktop\bomlist\BOM_型号匹配问题.xls', header=None)
df.fillna('?', inplace=True) df.fillna('?', inplace=True)
dic_dft = df.to_dict(orient='list') dic_dft = df.to_dict(orient='list')
return json.dumps(dic_dft) return dic_dft
data = get_test_data()
p = PredictorFac() p = PredictorFac()
data = json.loads(data)
pretty_col = {'第%s列' % k: v for k, v in data.items()} # data = get_test_data()
print(pretty_col) # data = {}
res = p.predict(pretty_col, predict_type='all') # pretty_col = {'第%s列' % k: v for k, v in data.items()}
# print(pretty_col)
data=['lm358dt 3', 'C0806fwefhkds 5k']
data = json.dumps(data)
res = p.predict(data, predict_type='all')
print(res) print(res)
predict/base_handler.py
...@@ -2,6 +2,8 @@ ...@@ -2,6 +2,8 @@
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
import re, os, pickle import re, os, pickle
from collections import Counter from collections import Counter
from copy import deepcopy
from sklearn.externals import joblib from sklearn.externals import joblib
from config import model_config from config import model_config
...@@ -9,7 +11,7 @@ from utils.log_manager import get_logger ...@@ -9,7 +11,7 @@ from utils.log_manager import get_logger
from utils.robots import dd_send_msg from utils.robots import dd_send_msg
import pandas as pd import pandas as pd
from static_config import * from static_config import *
from utils.status import StatusCode, code2msg
# 数量列阈值 # 数量列阈值
RIGHT_LEVEL = 0.7 RIGHT_LEVEL = 0.7
# 序号列阈值 # 序号列阈值
...@@ -39,6 +41,7 @@ class ClassBasePredictorMeta(type): ...@@ -39,6 +41,7 @@ class ClassBasePredictorMeta(type):
model = joblib.load(modextr_path + i) model = joblib.load(modextr_path + i)
setattr(cls, i, model) setattr(cls, i, model)
def __new__(mcs, name, bases, attrs): def __new__(mcs, name, bases, attrs):
return type.__new__(mcs, name, bases, attrs) return type.__new__(mcs, name, bases, attrs)
...@@ -50,7 +53,6 @@ class BasePredictor(metaclass=ClassBasePredictorMeta): ...@@ -50,7 +53,6 @@ class BasePredictor(metaclass=ClassBasePredictorMeta):
# 占位符 # 占位符
PLACEHOLDER = '?' PLACEHOLDER = '?'
pd = pd pd = pd
def __init__(self, name): def __init__(self, name):
self.name = name self.name = name
...@@ -100,7 +102,7 @@ class BasePredictor(metaclass=ClassBasePredictorMeta): ...@@ -100,7 +102,7 @@ class BasePredictor(metaclass=ClassBasePredictorMeta):
是否是数量列辅助函数 是否是数量列辅助函数
""" """
if isinstance(kw, str): if isinstance(kw, str):
return re.match(r'(\d+|\d+(\.\d+))($|(K)|([\u4E00-\u9FA5]{1,3}))$', kw, re.M | re.I) return re.match(r'(\d+|\d+(\.\d+))($|(K)|[个十百千万])$', kw, re.M | re.I)
else: else:
return False return False
...@@ -171,6 +173,7 @@ class BasePredictor(metaclass=ClassBasePredictorMeta): ...@@ -171,6 +173,7 @@ class BasePredictor(metaclass=ClassBasePredictorMeta):
@classmethod @classmethod
def get_comprehensive_res(cls, head_result, model_result, major='head', vote_type='standard'): def get_comprehensive_res(cls, head_result, model_result, major='head', vote_type='standard'):
if vote_type == 'standard': if vote_type == 'standard':
vote_count = {k: [] for k in STD_FIELDS_MAP.keys()} vote_count = {k: [] for k in STD_FIELDS_MAP.keys()}
else: else:
...@@ -183,8 +186,11 @@ class BasePredictor(metaclass=ClassBasePredictorMeta): ...@@ -183,8 +186,11 @@ class BasePredictor(metaclass=ClassBasePredictorMeta):
for k, v in head_result.items(): for k, v in head_result.items():
vote_count[v].append(k) vote_count[v].append(k)
vote_count_copy = deepcopy(vote_count)
for k, v in model_result.items(): for k, v in model_result.items():
if len(vote_count_copy[v]) == 1:
continue
vote_count[v].append(k) vote_count[v].append(k)
# 此处并没有处理表头和模型两者预测冲突的情况, 但因为repeat_max方法是稳定的,所以冲突后会取第一个也就是表头预测的结果, 之后按照major参数决定冲突结果 # 此处并没有处理表头和模型两者预测冲突的情况, 但因为repeat_max方法是稳定的,所以冲突后会取第一个也就是表头预测的结果, 之后按照major参数决定冲突结果
...@@ -286,5 +292,6 @@ class BasePredictor(metaclass=ClassBasePredictorMeta): ...@@ -286,5 +292,6 @@ class BasePredictor(metaclass=ClassBasePredictorMeta):
return round(count / len(data), 3) >= SIMPLE_ENCAP_LEVEL return round(count / len(data), 3) >= SIMPLE_ENCAP_LEVEL
if __name__ == "__main__": if __name__ == "__main__":
print(BasePredictor.is_ref( print(BasePredictor.is_num(['1k','1k','1k','1k']))
['?', '位置', '?', '4C33', '3C5,11C8', '3C6,3C7,2C13,2C14,C283,C285,C286', '?', 'C287,C575,10C33,10C34,10C36,10C37', '4C6,4C26,4C49,4C65', '10C7,10C10,10C11,10C29', '8C4,8C5', '8C40,8C41', '1C7,5C2,5C3,10C2,1C25,1C41,2C11,2C21,2C25,2C37,2C40,4C62,8C29,8C30', '10C12', '10C26,10C35', '1C24,1C43', '10C16,10C20,10C22,10C23', '10C19,10C25', '1C1,1C6,2C1,2C3,2C4,2C6,2C7,3C1,3C2,3C4,3C8,4C2,6C6,6C8,6C9,7C6,7C9,8C7,8C8,8C9,10C4,10C5,10C6,10C9,1C10,1C13,11C4,1C15,1C16,11C6,11C7,1C19,1C22,1C28,1C37,1C44,1C57,1C59,1C63,2C15,2C17,2C29,2C33,2C36,2C38,2C39,3C13,4C12,4C13,4C14,4C15,4C16,4C18,4C19,4C20,4C22,4C23,4C24,4C25,4C28,4C30,4C32,4C34,4C41,4C44,4C45,4C46,4C48,4C51,4C54,4C55,4C57,4C58,4C59,4C60,4C61,6C14,6C16,7C13,8C11,8C17,8C21,10C14,10C17,10C18,10C21,10C28,10C30,10C32,10C42,4C111,4C118,4C121,4C122,C28,C97,C292,C293,C294,C296,C298,C299,C300,C301,C304,C571\n', '10C3,10C15', '2C2,2C9,3C3,4C4,5C6,5C7,5C8,5C9,6C7,7C3,7C7,8C3,2C16,2C18,2C22,2C23,2C26,2C27,2C31,4C17,4C47,4C56,4C63,4C93,5C10,6C46,8C10,8C16,8C18,8C23,8C25,8C31,8C32,8C47,9C21,9C28,4C100,C98,11C9,C290,C291,C295,C297,11C10', '10C8,10C24', '5C1,5C5,2C20,2C30,4C36,4C86,9C27,2C10', '2C12,8C53,9C22', '2C5,3C14,8C37,8C44,10C27', '1C9,4C1,4C7,10C1,1C11,1C14,1C17,1C18,1C20,1C21,1C53,1C58,1C60,1C62,1C64,2C34,2C35,3C15,4C11,4C29,4C31,4C42,4C64,4C82,4C83,8C15,8C42,10C31,4C157,C96,C288,C289,C302,C303,C574', '1C2,1C8,1C30,1C32,1C38,1C70', '1C4,1C5,2C8,4C3,7C4,1C12,1C23,1C26,1C29,1C31,1C34,1C42,1C48,2C32,7C11,7C14,C1,C570', '1C51,10C39,10C41', '1C66,1C71', '?', '?', '10R9,3R24,8R45,8R46', '3C10,7R6,R154,R155,2R21,3R25,7R25,9R32,9R33,9R34,9R35,9R36,9R37,9R38,9R39,9R40,9R41,9R42,9R43,9R44,9R45,9R46,9R47,9R48,9R49,9R50,9R51,9R52,9R53,10R18', 'R1,R2,R3,R4', '7R3,7R4,7R7,7R8,7R23,7R24,8R39,8R41,8R42,8R44', '1R1,1R2,1R16,1R19,1R26,1R49', '10R1,10R8,10R10,10R13', '2R7,2R8,3R3,3R4,3R5,3R6,3R7,2R10,3R20,3R33,3R34,3R35,3R36,3R37', '8R3,8R29,8R30', '6R3', '8R33,8R34,8R35,8R36', '?', '8R2,3R16', '8R37,8R38,8R40,8R43', '?', '2R4,2R5,10R2,10R3,3R17,5R29,5R30,5R32,5R33,5R38,9R13,9R14', '?', '3R15', '5R42', '4R10,4R11,4R12,4R28,4R35', '?', '10R4,10R14', '6R2', '9R5', '1R50,1R53,1R54,1R55,1R56,2R19,5R14,5R19,5R24,5R25,11R11,11R12', '5R40', '1R51', '8R1,4R15,4R16,4R23,4R24,4R33,4R34,10R22,10R30,10R40,10R41', '8R5,8R7', 'R156,R161,1R6,2R2,10R5,8R24,9R15,9R16,11R13,11R15,11R16', '5R28,5R41', '8R18', 'R158', '1R7', '8R16', '2R9', '7R1,8R4,8R6,9R1,10R6,1R21,1R42,1R57,1R58,R160,2R26,3R19,3R23,8R20,8R22,10R11,R167,9R54', '7R5,1R22,1R27,5R16', '1R43', '8R21,8R28', '10R17', '2R12', '1R31', '1R28,10R12', '1R3,5R4,5R37', '1R32', '1R5', '1R4,1R13,1R17,1R23,1R33', '1R14,1R39', '1R15', '5R2,1R12,1R20,1R44,5R11,5R12,5R13,5R17,5R18,5R35', '8R26,8R31', 'R164', '1R29', '2R3', '1R34', '5R3,1R10,1R41,1R60,5R36,11R14', 'R163', '1R18', 'R162,R165', 'R159', '5RN3,5RN4,5RN5,5RN6', '?', '?', '7FB5', '8FB1,10FB1,11FB1,FB4,FB6,FB8,8L2', '?', '10FB2,10FB3', '1L3,1L4', '?', '1L2', 'L2,3L1', 'L1', '1L5', '1L1', '1L6', '5L1,5L2', '10L1,10L2,10L3,10L4', '?', '?', 'D45', 'Q23', '1Q1,1Q8,3Q1,5Q3,5Q4,1Q5,1Q7', '1Q3', '?', '1Q6', '5Q2', '5Q5', '5D3,5D4,5D5,5D6', '5D7,5D8,5D9,7D1,7D2,7D3,7D4,7D5,8D3,8D4,11D1,11D2,5D10,5D11,5D12,5D20,5D22,5D23,5D24', '8D1', '8D2', 'T4', 'U1', 'U7,U18', '?', 'U9', 'U17', '1U2,1U3,1U10', '1U4,1U6,1U7', '2U1', '4U1,4U2,4U3,4U4', '?', '1U1,1U9', '1U5', '8U1', '2U2', '1U11', '3U2', '10U1', '3Y2', '?', '2Y1', 'Y1', '3J1', '8T1', '8JA5', 'CN13', 'J45', '?', '5JA2,5JA3', '1CN2', 'J2,J3', 'J53,J56,J58', '2CN1', 'J4', 'J1', 'J52', 'P2', 'J43']))
print(re.match(r'(\d+|\d+(\.\d+))($|(K)|[个十百千万])$', '1k', re.M | re.I))
\ No newline at end of file
predict/txt_predict.py 0 → 100644
#!/usr/bin/env python
# -*- coding:utf-8 -*-
from predict.base_handler import BasePredictor
from static_config import EN_TO_ZH_MAP
import re
class TxtPredict(BasePredictor):
@classmethod
def isnum(cls, kw):
"""
是否是数字串
"""
if str(kw).startswith('0'):
return False
return re.match(r'(\d+|\d+(\.\d+))($|(K)|[个十百千万])$', kw, re.M | re.I)
def pre_deal(self, li: list):
data = []
resp = {}
for row_index, i in enumerate(li):
raw_items = re.split(r'(?:，|,|;|\s)\s*', i)
no_null_raw_items = [j.strip() for j in raw_items if j.strip()]
resp_data = {
'qty': None,
'brand': None,
'gn_or_param': None,
'statu_code': 1
}
for item in no_null_raw_items:
if self.isnum(item):
resp_data['qty'] = item
no_null_raw_items.remove(item)
break
else:
resp_data = {'message': '未检测到数量', 'statu_code': 0}
resp[row_index] = resp_data
data.append((row_index, resp_data, no_null_raw_items))
continue
if not no_null_raw_items:
resp_data = {'message': '未检测到参数/型号', 'statu_code': 0}
resp[row_index] = resp_data
data.append((row_index, resp_data, no_null_raw_items))
return data
def predict(self, li: list, predict_type='all'):
self.info.info('预测类型为: %s, 接收数据: %s' % (predict_type, li))
data = self.pre_deal(li)
resp = {}
for row_index, resp_data, nonull_nonum_items in data:
if resp_data['statu_code'] == 0:
resp[row_index] = resp_data
continue
if len(nonull_nonum_items) == 1:
resp_data['gn_or_param'] = nonull_nonum_items[0]
elif len(nonull_nonum_items) >= 2:
result_1, prab = self.get_single_predict(nonull_nonum_items[-1], BasePredictor.model, BasePredictor.extractor)
result0, prab = self.get_single_predict(nonull_nonum_items[0], BasePredictor.model, BasePredictor.extractor)
if EN_TO_ZH_MAP[result_1] == '品牌':
resp_data['brand'] = nonull_nonum_items[-1]
resp_data['gn_or_param'] = ' '.join(nonull_nonum_items[:-1])
elif EN_TO_ZH_MAP[result0] == '品牌':
resp_data['brand'] = nonull_nonum_items[0]
resp_data['gn_or_param'] = ' '.join(nonull_nonum_items[1:])
else:
resp_data['gn_or_param'] = ' '.join(nonull_nonum_items)
resp[row_index] = resp_data
return {'results': resp}
if __name__ == "__main__":
print(TxtPredict.is_num('1K'))
utils/status.py 0 → 100644
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import inspect
def code2msg(code):
if code in StatusCode.StatusDict:
return {"status": code, "message": StatusCode.StatusDict[code][1]}
else:
return {"status": "0", "message": StatusCode.StatusDict["0"][1]}
class StatusCode:
Succeed = "1"
Unknown = "0"
ParamError = "100001"
MissingParam = "100002"
NoQtyError = "100003"
NoParamGnError = "100004"
StatusDict = {
"1": ("succeed", "正常"),
"0": ("unknown", "未知错误"),
"100001": ("param error", "输入参数错误"),
"100002": ("missing param", "缺少输入的参数"),
"100003": ("no qty", "未检测到数量"),
"100004": ("no param or gn", "未检测到参数/型号列"),
}
class BaseStatusError(Exception):
def __init__(self, *args, **kwargs):
super().__init__(*args)
class_name = kwargs["class_name"] if "class_name" in kwargs else self.__class__.__name__
self.value = kwargs["value"] if "value" in kwargs else getattr(StatusCode, class_name)
if "msg" in kwargs:
self.msg = kwargs["msg"]
elif args:
self.msg = args[0]
else:
self.msg = StatusCode.StatusDict[self.value][0]
error_logging = False if "off_error_logging" in kwargs else True
self.error_logging = bool(kwargs["error_logging"]) if "error_logging" in kwargs else error_logging
self.frames = inspect.getouterframes(inspect.currentframe())[1] if self.error_logging else None
def __str__(self):
return str(self.msg)
class Status:
class Succeed(BaseStatusError):
class NotFound(BaseStatusError):
def __init__(self, *args, **kwargs):
super().__init__(off_error_logging=True, *args, **kwargs)
def __init__(self, *args, **kwargs):
super().__init__(off_error_logging=True, *args, **kwargs)
self.result = args[0]
class MissingParam(BaseStatusError):
def __init__(self, *args, **kwargs):
super().__init__(off_error_logging=True, *args, **kwargs)
class ParamError(BaseStatusError):
def __init__(self, *args, **kwargs):
super().__init__(off_error_logging=True, *args, **kwargs)
class NoQtyError(BaseStatusError):
def __init__(self, *args, **kwargs):
super().__init__(off_error_logging=True, *args, **kwargs)
class NoParamGnError(BaseStatusError):
def __init__(self, *args, **kwargs):
super().__init__(off_error_logging=True, *args, **kwargs)
class Unknown(BaseStatusError):
pass
StatusErrTup = (Succeed.NotFound, Unknown,
ParamError, MissingParam, ParamError, NoQtyError, NoParamGnError)
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