def train_model_for_bot(bot: str):
""" Trains the rasa model, using the data that is loaded onto
Mongo, through the bot files """
processor = MongoProcessor()
nlu = processor.load_nlu(bot)
if not nlu.training_examples:
raise AppException("Training data does not exists!")
domain = processor.load_domain(bot)
stories = processor.load_stories(bot)
config = processor.load_config(bot)
directory = Utility.save_files(
nlu.nlu_as_markdown().encode(),
domain.as_yaml().encode(),
stories.as_story_string().encode(),
yaml.dump(config).encode(),
)
output = os.path.join(DEFAULT_MODELS_PATH, bot)
model = train(domain=os.path.join(directory,DEFAULT_DOMAIN_PATH),
config=os.path.join(directory,DEFAULT_CONFIG_PATH),
training_files=os.path.join(directory,DEFAULT_DATA_PATH),
output=output)
Utility.delete_directory(directory)
return model
async def upload_and_save(
self,
nlu: bytes,
domain: bytes,
stories: bytes,
config: bytes,
bot: Text,
user: Text,
overwrite: bool = True,
):
"""Upload the training data to temporary path and then save into mongo."""
data_path = Utility.save_files(nlu, domain, stories, config)
await self.save_from_path(data_path, bot, overwrite, user)
Utility.delete_directory(data_path)
