def start_training(bot: str, user: str):
""" Prevents training of the bot if the training session is in progress otherwise start training """
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,
)
AgentProcessor.reload(bot)
return model_file
async def train(current_user: User = Depends(auth.get_current_user)):
model_file = await train_model_from_mongo(current_user.get_bot())
AgentProcessor.reload(current_user.get_bot())
return {
"data": {
"file": model_file
},
"message": "Model trained successfully"
}