async def convert_and_write(cls, source_path: Path, output_path: Path) -> None:
"""Converts the given training data file and saves it to the output directory.
Args:
source_path: Path to the training data file.
output_path: Path to the output directory.
"""
reader = NLGMarkdownReader()
writer = RasaYAMLWriter()
output_nlg_path = cls.generate_path_for_converted_training_data_file(
source_path, output_path
)
training_data = reader.read(source_path)
converted_responses = {}
for response_name, examples in training_data.responses.items():
new_response_name = cls._normalize_response_name(response_name)
converted_responses[new_response_name] = examples
converted_training_data = TrainingData(responses=converted_responses)
writer.dump(output_nlg_path, converted_training_data)
print_success(f"Converted NLG file: '{source_path}' >> '{output_nlg_path}'.")
def convert_and_write(cls, source_path: Path, output_path: Path) -> None:
"""Converts the given training data file and saves it to the output directory.
Args:
source_path: Path to the training data file.
output_path: Path to the output directory.
"""
reader = NLGMarkdownReader()
writer = RasaYAMLWriter()
output_nlg_path = cls.generate_path_for_converted_training_data_file(
source_path, output_path)
yaml_training_data = reader.read(source_path)
writer.dump(output_nlg_path, yaml_training_data)
print_success(
f"Converted NLG file: '{source_path}' >> '{output_nlg_path}'.")
