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)