def init_logger():
logger_settings = LoggerSettings(logtoscreen_level=LOGTOSCREEN_LEVEL,
logtoscreen_usecolor=True,
logtofile_level=LOGTOFILE_LEVEL,
logtofile_path='',
logtoscreen_allow_multiline=True,
integ_test=False,
remove_root_handlers=False)
setup_logger(logger_settings)
def main():
from chirpy.core.logging_utils import setup_logger, LoggerSettings
from chirpy.core.entity_linker.entity_groups import ENTITY_GROUPS_FOR_EXPECTED_TYPE, EntityGroup
# Setup logging
LOGTOSCREEN_LEVEL = logging.DEBUG
logger_settings = LoggerSettings(logtoscreen_level=LOGTOSCREEN_LEVEL,
logtoscreen_usecolor=True,
logtofile_level=None,
logtofile_path=None,
logtoscreen_allow_multiline=True,
integ_test=False,
remove_root_handlers=False)
setup_logger(logger_settings)
# Test
bot_utterance = ''
user_utterance = "florida"
expected_type = ENTITY_GROUPS_FOR_EXPECTED_TYPE.location_related
test_entity_tracker(user_utterance, bot_utterance, expected_type)
# response and send it over
OVERALL_TIMEOUT = 9.75 if flags.use_timeouts else flags.inf_timeout # seconds
# Timeout for final_response function. Set at 9.35 seconds to comfortably log the latencies
FINAL_RESPONSE_TIMEOUT = 9.35 if flags.use_timeouts else flags.inf_timeout #seconds
# Timeout for progressive response
PROGRESSIVE_RESPONSE_TIMEOUT = 3 if flags.use_timeouts else flags.inf_timeout #seconds
# Timeout for NLP Pipeline
NLP_PIPELINE_TIMEOUT = 3 if flags.use_timeouts else flags.inf_timeout #seconds
logger = logging.getLogger('chirpylogger')
root_logger = logging.getLogger()
if not hasattr(root_logger, 'chirpy_handlers'):
setup_logger(PROD_LOGGER_SETTINGS)
class RemoteNonPersistentAgent(LocalAgent):
def __init__(self, session_id, user_id, new_session, last_state_creation_time):
super().__init__()
self.session_id = session_id
self.user_id = user_id
self.new_session = new_session
self.last_state_creation_time = last_state_creation_time
def lambda_handler():
local_agent = RemoteNonPersistentAgent()
user_input = ""
while user_input != "bye":
user_input = input()
response, deserialized_current_state = local_agent.process_utterance(user_input)
Returns: True iff text contains an offensive phrase
"""
return OFFENSIVE_CLASSIFIER.contains_offensive(text, log_message)
if __name__ == "__main__":
# You can test the contains_offensive function with the code below
import time
# Setup logging
from chirpy.core.logging_utils import setup_logger, LoggerSettings
LOGTOSCREEN_LEVEL = logging.DEBUG
logger_settings = LoggerSettings(logtoscreen_level=LOGTOSCREEN_LEVEL,
logtoscreen_usecolor=True,
logtofile_level=None,
logtofile_path=None,
logtoscreen_allow_multiline=True,
integ_test=False,
remove_root_handlers=False)
setup_logger(logger_settings)
texts = ["my dick", "i went to dick's sporting goods"]
for text in texts:
print()
t0 = time.time()
label = contains_offensive(text)
time_taken = time.time() - t0
print(label, time_taken, text)
