async def train(
background_tasks: BackgroundTasks,
current_user: User = Depends(auth.get_current_user),
):
"""
Trains the chatbot
"""
ModelProcessor.is_training_inprogress(current_user.get_bot())
ModelProcessor.is_daily_training_limit_exceeded(current_user.get_bot())
background_tasks.add_task(start_training, current_user.get_bot(),
current_user.get_user())
return {"message": "Model training started."}
async def get_model_training_history(
current_user: User = Depends(auth.get_current_user), ):
"""
Fetches model training history, when and who trained the bot
"""
training_history = list(
ModelProcessor.get_training_history(current_user.get_bot()))
return {"data": {"training_history": training_history}}
def start_training(bot: str, user: str, token: str = None, reload=True):
"""
prevents training of the bot,
if the training session is in progress otherwise start training
:param reload: whether to reload model in the cache
:param bot: bot id
:param token: JWT token for remote model reload
:param user: user id
:return: model path
"""
exception = None
model_file = None
training_status = None
if Utility.environment.get('model') and Utility.environment['model'][
'train'].get('event_url'):
Utility.train_model_event(bot, user, token)
else:
try:
model_file = train_model_for_bot(bot)
training_status = MODEL_TRAINING_STATUS.DONE.value
agent_url = Utility.environment['model']['train'].get('agent_url')
if agent_url:
Utility.http_request(
'get', urljoin(agent_url, "/api/bot/model/reload"), token,
user)
else:
if reload:
AgentProcessor.reload(bot)
except Exception as e:
logging.exception(e)
training_status = MODEL_TRAINING_STATUS.FAIL.value
exception = str(e)
finally:
ModelProcessor.set_training_status(
bot=bot,
user=user,
status=training_status,
model_path=model_file,
exception=exception,
)
return model_file
def start_training(bot: str, user: str, reload=True):
"""
prevents training of the bot,
if the training session is in progress otherwise start training
:param reload: whether to reload model in the cache
:param bot: bot id
:param user: user id
:return: model path
"""
exception = None
model_file = None
training_status = None
ModelProcessor.set_training_status(
bot=bot, user=user, status=MODEL_TRAINING_STATUS.INPROGRESS.value,
)
try:
model_file = train_model_for_bot(bot)
training_status = MODEL_TRAINING_STATUS.DONE.value
except Exception as e:
logging.exception(e)
training_status = MODEL_TRAINING_STATUS.FAIL.value
exception = str(e)
raise AppException(exception)
finally:
ModelProcessor.set_training_status(
bot=bot,
user=user,
status=training_status,
model_path=model_file,
exception=exception,
)
if reload:
AgentProcessor.reload(bot)
return model_file
async def train(
background_tasks: BackgroundTasks,
current_user: User = Depends(auth.get_current_user),
):
"""
Trains the chatbot
"""
ModelProcessor.is_training_inprogress(current_user.get_bot())
ModelProcessor.is_daily_training_limit_exceeded(current_user.get_bot())
ModelProcessor.set_training_status(
bot=current_user.get_bot(), user=current_user.get_user(), status=MODEL_TRAINING_STATUS.INPROGRESS.value,
)
token = auth.create_access_token(data={"sub": current_user.email})
background_tasks.add_task(
start_training, current_user.get_bot(), current_user.get_user(), token.decode('utf8')
)
return {"message": "Model training started."}