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 _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())
