def _write_nlu_to_file(
export_nlu_path: Text,
evts: List[Dict[Text, Any]]
) -> None:
"""Write the nlu data of the sender_id to the file paths."""
msgs = _collect_messages(evts)
# noinspection PyBroadException
try:
previous_examples = load_data(export_nlu_path)
except Exception as e:
logger.exception("An exception occurred while trying to load the "
"NLU data.")
export_nlu_path = questionary.text(
message="Could not load existing NLU data, please "
"specify where to store NLU data learned in "
"this session (this will overwrite any "
"existing file). {}".format(str(e)),
default=PATHS["backup"]).ask()
if export_nlu_path is None:
return
previous_examples = TrainingData()
nlu_data = previous_examples.merge(TrainingData(msgs))
with io.open(export_nlu_path, 'w', encoding="utf-8") as f:
if _guess_format(export_nlu_path) in {"md", "unk"}:
f.write(nlu_data.as_markdown())
else:
f.write(nlu_data.as_json())
def get_data(self, language):
lookup_tables = []
composite_entities = []
cmdline_args = create_argument_parser().parse_args()
files = utils.list_files(cmdline_args.data)
for file in files:
fformat = _guess_format(file)
file_content = utils.read_json_file(file)
if fformat == DIALOGFLOW_ENTITIES:
entity = file_content['name']
dialogflowReader = DialogflowReader()
examples_js = dialogflowReader._read_examples_js(fn=file, language=language, fformat=fformat)
lookup_table = self._extract_lookup_tables(entity, examples_js)
if(lookup_table):
lookup_tables.append(lookup_table)
composite_entity = self._extract_composite_entities(
entity,
examples_js)
if(composite_entity):
composite_entities.append(composite_entity)
if fformat == RASA_NLU:
rasa_nlu_data = file_content['rasa_nlu_data']
composite_entities = rasa_nlu_data['composite_entities']
lookup_tables = rasa_nlu_data['lookup_tables']
return lookup_tables, composite_entities
def _write_nlu_to_file(
export_nlu_path: Text,
evts: List[Dict[Text, Any]]
) -> None:
"""Write the nlu data of the sender_id to the file paths."""
msgs = _collect_messages(evts)
# noinspection PyBroadException
try:
previous_examples = load_data(export_nlu_path)
except Exception:
questions = [{"name": "export nlu",
"type": "input",
"message": "Could not load existing NLU data, please "
"specify where to store NLU data learned in "
"this session (this will overwrite any "
"existing file)",
"default": PATHS["backup"]}]
answers = prompt(questions)
export_nlu_path = answers["export nlu"]
previous_examples = TrainingData()
nlu_data = previous_examples.merge(TrainingData(msgs))
with io.open(export_nlu_path, 'w', encoding="utf-8") as f:
if _guess_format(export_nlu_path) in {"md", "unk"}:
f.write(nlu_data.as_markdown())
else:
f.write(nlu_data.as_json())
def _get_train_file_cmd():
"""Get the raw train data by fetching the train file given in the
command line arguments to the train script.
"""
cmdline_args = create_argument_parser().parse_args()
files = utils.list_files(cmdline_args.data)
is_rasa_format = [_guess_format(file) == RASA_NLU for file in files]
n_rasa_format = sum(is_rasa_format)
# TODO: Support multiple training files
assert sum(is_rasa_format) == 1, 'Composite entities currently ' \
'only work with exactly one train file.'
file_index = [i for i, val in enumerate(is_rasa_format) if val][0]
return files[file_index]
