def interactive_core(args: argparse.Namespace):
args.finetune = False # Don't support finetuning
zipped_model = train.train_core(args)
perform_interactive_learning(args, zipped_model)
def interactive(args: argparse.Namespace) -> None:
_set_not_required_args(args)
file_importer = TrainingDataImporter.load_from_config(
args.config, args.domain, args.data)
if args.model is None:
loop = asyncio.get_event_loop()
story_graph = loop.run_until_complete(file_importer.get_stories())
if not story_graph or story_graph.is_empty():
rasa.shared.utils.cli.print_error_and_exit(
"Could not run interactive learning without either core data or a model containing core data."
)
zipped_model = train.train_core(
args) if args.core_only else train.train(args)
if not zipped_model:
rasa.shared.utils.cli.print_error_and_exit(
"Could not train an initial model. Either pass paths "
"to the relevant training files (`--data`, `--config`, `--domain`), "
"or use 'rasa train' to train a model.")
else:
zipped_model = get_provided_model(args.model)
if not (zipped_model and os.path.exists(zipped_model)):
rasa.shared.utils.cli.print_error_and_exit(
f"Interactive learning process cannot be started as no initial model was "
f"found at path '{args.model}'. Use 'rasa train' to train a model."
)
if not args.skip_visualization:
logger.info(f"Loading visualization data from {args.data}.")
perform_interactive_learning(args, zipped_model, file_importer)
def interactive_core(args: argparse.Namespace):
_set_not_required_args(args)
if args.model is None:
zipped_model = train.train_core(args)
else:
zipped_model = get_provided_model(args.model)
perform_interactive_learning(args, zipped_model)
def interactive_core(args: argparse.Namespace):
args.fixed_model_name = None
args.store_uncompressed = False
if args.model is None:
zipped_model = train.train_core(args)
else:
zipped_model = get_provided_model(args.model)
perform_interactive_learning(args, zipped_model)