# 0
url = 'http://172.31.17.179:8080/recommend/popup-sentence?sessionId=123&sentence=ok&'
elif source == 'en_online1':
# 1
url = 'http://172.31.28.21:8080/recommend/popup-sentence?sessionId=123&sentence=ok&'
elif source == 'en_online2':
# 2
url = 'http://172.31.18.118:8080/recommend/popup-sentence?sessionId=123&sentence=ok&'
elif source == 'kika_online':
# kika
# url = 'http://kika-en.recommend.model.intranet.com/recommend/popup-sentence?sessionId=123&sentence=ok&'
url = 'http://172.31.21.219:8080/recommend/popup-sentence?sessionId=123&sentence=ok&'
elif source == 'business':
url = 'http://172.31.31.224:8080/recommend/popup-sentence?sessionId=123&sentence=ok&'
if isinstance(url, list):
for url_ in url:
case_runner(test_case, url_)
else:
case_runner(test_case, url)
# print(url)
def run(source):
test_case = get_test_data(source)
for case in constitute_test_case(test_case):
request_test(case, source)
if __name__ == "__main__":
run(source_input())
def check(data, source, p_t_time=1):
fail = []
created_sessionId = run_predict_create_kafka(data, source, p_t_time=1)
time.sleep(30)
train_sessionId_list = get_train_sessionId()
print('Train sessionId:')
print(train_sessionId_list)
for sessionId in created_sessionId:
if sessionId['sessionId'] not in str(train_sessionId_list):
fail.append(sessionId)
if len(fail) > 0:
print('没有找到的sessionId')
print(fail)
print('失败')
if __name__ == "__main__":
data = {'2:PtBeforeBucket': {'duid': '25ad4b27c4c3410784ee7fab223f3a98', 'tag': 'eita', 'kb_lang': 'pt_BR'},
'15:EnUsBeforeNotMod2GBDTBucket': {'duid': 'bc21bc8ea7ea480d85a0b6df109a0159', 'tag': 'morning',
'kb_lang': 'en_US'},
'16:KikaEnUsBeforeBucket': {'duid': '37dfbe888ef147c8a83b60588dc2da21', 'tag': 'no', 'kb_lang': 'en_US'},
'5:EnUsBeforeMod2Bucket': {'duid': '32b4256f0c934e91aaf5f9201ccf4f54', 'tag': 'yea', 'kb_lang': 'en_US'},
'7:EnUsBeforeNotMod2GBDTMLeapBucket': {'duid': '7bfe28fb712c4b2c9873d0b53458f6a8', 'tag': 'thanks',
'kb_lang': 'en_US'},
'11:EnNotUsBeforeGBDTBucket': {'duid': '269145da9def4d52b85c7a4f2893678a', 'tag': 'haha',
'kb_lang': 'ms_MY'},
}
# print(run_predict_create_kafka(data=data, source=source_input()))
check(data=data, source=source_input())