def stories_from_cli_args(cmdline_arguments):
from rasa_core import utils
if cmdline_arguments.url:
return utils.download_file_from_url(cmdline_arguments.url)
else:
return cmdline_arguments.stories
finetune=cmdline_args.finetune,
skip_visualization=cmdline_args.skip_visualization)
if __name__ == '__main__':
# Running as standalone python application
arg_parser = create_argument_parser()
set_default_subparser(arg_parser, 'default')
cmdline_args = arg_parser.parse_args()
if not cmdline_args.mode:
raise ValueError("You must specify the mode you want training to run "
"in. The options are: (default|compare|interactive)")
additional_arguments = _additional_arguments(cmdline_args)
utils.configure_colored_logging(cmdline_args.loglevel)
if cmdline_args.url:
stories = utils.download_file_from_url(cmdline_args.url)
else:
stories = cmdline_args.stories
if cmdline_args.mode == 'default':
do_default_training(cmdline_args, stories, additional_arguments)
elif cmdline_args.mode == 'interactive':
do_interactive_learning(cmdline_args, stories, additional_arguments)
elif cmdline_args.mode == 'compare':
do_compare_training(cmdline_args, stories, additional_arguments)
def stories_from_cli_args(cmdline_arguments):
if cmdline_arguments.url:
return utils.download_file_from_url(cmdline_arguments.url)
else:
return cmdline_arguments.stories
utils.configure_colored_logging(cmdline_args.loglevel)
additional_arguments = {
"epochs": cmdline_args.epochs,
"batch_size": cmdline_args.batch_size,
"validation_split": cmdline_args.validation_split,
"augmentation_factor": cmdline_args.augmentation,
"debug_plots": cmdline_args.debug_plots,
"nlu_threshold": cmdline_args.nlu_threshold,
"core_threshold": cmdline_args.core_threshold,
"fallback_action_name": cmdline_args.fallback_action_name
}
if cmdline_args.url:
stories = utils.download_file_from_url(cmdline_args.url)
else:
stories = cmdline_args.stories
a = train_dialogue_model(cmdline_args.domain,
stories,
cmdline_args.out,
cmdline_args.nlu,
cmdline_args.endpoints,
cmdline_args.history,
cmdline_args.dump_stories,
additional_arguments)
if cmdline_args.online:
online.serve_agent(a)
if __name__ == '__main__':
# Running as standalone python application
arg_parser = create_argument_parser()
set_default_subparser(arg_parser, 'default')
cmdline_arguments = arg_parser.parse_args()
if not cmdline_arguments.mode:
raise ValueError("You must specify the mode you want training to run "
"in. The options are: (default|compare|interactive)")
additional_args = _additional_arguments(cmdline_arguments)
utils.configure_colored_logging(cmdline_arguments.loglevel)
if cmdline_arguments.url:
training_stories = utils.download_file_from_url(cmdline_arguments.url)
else:
training_stories = cmdline_arguments.stories
if cmdline_arguments.mode == 'default':
do_default_training(cmdline_arguments, training_stories,
additional_args)
elif cmdline_arguments.mode == 'interactive':
do_interactive_learning(cmdline_arguments, training_stories,
additional_args)
elif cmdline_arguments.mode == 'compare':
do_compare_training(cmdline_arguments, training_stories,
additional_args)
