async def chat(request_data: TextData, current_user: User = Depends(auth.get_current_user)): """ Fetches a bot response for a given text/query. It is basically used to test the chat functionality of the agent """ model = AgentProcessor.get_agent(current_user.get_bot()) response = await model.handle_text(request_data.data, sender_id=current_user.get_user()) return {"data": {"response": response}}
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
async def predict_intent(request_data: TextData, current_user: User = Depends(auth.get_current_user)): """ Fetches the predicted intent of the entered text form the loaded agent """ model = AgentProcessor.get_agent(current_user.get_bot()) response = await model.parse_message_using_nlu_interpreter( request_data.data) intent = response.get("intent").get("name") if response else None confidence = response.get("intent").get("confidence") if response else None return {"data": {"intent": intent, "confidence": confidence}}
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 chat( request_data: TextData, current_user: User = Depends(auth.get_current_user) ): """ Fetches a bot response for a given text/query. It is basically used to test the chat functionality of the agent """ if Utility.environment.get('model') and Utility.environment['model']['train'].get('agent_url'): agent_url = Utility.environment['model']['train'].get('agent_url') token = auth.create_access_token(data={"sub": current_user.email}) response = Utility.http_request('post', urljoin(agent_url, "/api/bot/chat"), token.decode('utf8'), current_user.get_user(), json={'data': request_data.data}) else: model = AgentProcessor.get_agent(current_user.get_bot()) response = await model.handle_text( request_data.data, sender_id=current_user.get_user() ) response = {"data": {"response": response}} return response
async def download_model( background_tasks: BackgroundTasks, current_user: User = Depends(auth.get_current_user), ): """ Downloads latest trained model file """ try: model_path = AgentProcessor.get_latest_model(current_user.get_bot()) response = FileResponse( model_path, filename=os.path.basename(model_path), background=background_tasks, ) response.headers[ "Content-Disposition"] = "attachment; filename=" + os.path.basename( model_path) return response except Exception as e: raise AppException(str(e))