def test_load_from_path_all_sccenario(self):
processor = MongoProcessor()
processor.save_from_path("tests/testing_data/all", "all", "testUser")
training_data = processor.load_nlu("all")
assert isinstance(training_data, TrainingData)
assert training_data.training_examples.__len__() == 283
assert training_data.entity_synonyms.__len__() == 3
assert training_data.regex_features.__len__() == 5
assert training_data.lookup_tables.__len__() == 1
story_graph = processor.load_stories("all")
assert isinstance(story_graph, StoryGraph) == True
assert story_graph.story_steps.__len__() == 13
domain = processor.load_domain("all")
assert isinstance(domain, Domain)
assert domain.slots.__len__() == 8
assert domain.templates.keys().__len__() == 21
assert domain.entities.__len__() == 7
assert domain.form_names.__len__() == 2
assert domain.user_actions.__len__() == 32
assert domain.intents.__len__() == 22
assert not Utility.check_empty_string(
domain.templates["utter_cheer_up"][0]["image"]
)
assert domain.templates["utter_did_that_help"][0]["buttons"].__len__() == 2
assert domain.templates["utter_offer_help"][0]["custom"]
assert domain.slots[0].type_name == "unfeaturized"
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
def test_load_nlu(self):
processor = MongoProcessor()
training_data = processor.load_nlu("tests")
assert isinstance(training_data, TrainingData)
assert training_data.training_examples.__len__() == 43
assert training_data.entity_synonyms.__len__() == 0
assert training_data.regex_features.__len__() == 0
assert training_data.lookup_tables.__len__() == 0
