def _rasa_service( args: argparse.Namespace, endpoints: AvailableEndpoints, rasa_x_url: Optional[Text] = None, credentials_path: Optional[Text] = None, ): """Starts the Rasa application.""" from rasa.core.run import serve_application import rasa.utils.common # needs separate logging configuration as it is started in its own process rasa.utils.common.set_log_level(args.loglevel) io_utils.configure_colored_logging(args.loglevel) if not credentials_path: credentials_path = _prepare_credentials_for_rasa_x( args.credentials, rasa_x_url=rasa_x_url) serve_application( endpoints=endpoints, port=args.port, credentials=credentials_path, cors=args.cors, auth_token=args.auth_token, enable_api=True, jwt_secret=args.jwt_secret, jwt_method=args.jwt_method, ssl_certificate=args.ssl_certificate, ssl_keyfile=args.ssl_keyfile, ssl_ca_file=args.ssl_ca_file, ssl_password=args.ssl_password, )
def _rasa_service( args: argparse.Namespace, endpoints: "AvailableEndpoints", rasa_x_url: Optional[Text] = None, ): """Starts the Rasa application.""" from rasa.core.run import serve_application # needs separate logging configuration as it is started in its own process logging.basicConfig(level=args.loglevel) io_utils.configure_colored_logging(args.loglevel) logging.getLogger("apscheduler").setLevel(logging.WARNING) credentials_path = _prepare_credentials_for_rasa_x(args.credentials, rasa_x_url=rasa_x_url) serve_application( endpoints=endpoints, port=args.port, credentials=credentials_path, cors=args.cors, auth_token=args.auth_token, enable_api=True, jwt_secret=args.jwt_secret, jwt_method=args.jwt_method, )
def main(): from rasa.utils.io import configure_colored_logging import rasa.core.cli.train from rasa.core.utils import set_default_subparser # Running as standalone python application arg_parser = create_argument_parser() set_default_subparser(arg_parser, 'default') cmdline_arguments = arg_parser.parse_args() additional_args = _additional_arguments(cmdline_arguments) configure_colored_logging(cmdline_arguments.loglevel) loop = asyncio.get_event_loop() training_stories = loop.run_until_complete( rasa.core.cli.train.stories_from_cli_args(cmdline_arguments)) if cmdline_arguments.mode == 'default': loop.run_until_complete(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': loop.run_until_complete(do_compare_training(cmdline_arguments, training_stories, additional_args))
def logging_setup(): logger = logging.getLogger(__name__) logging.getLogger("tensorflow").setLevel(logging.ERROR) logging.getLogger("absl").setLevel(logging.ERROR) logging.getLogger("transformers").setLevel(logging.ERROR) logging.getLogger("rasa").setLevel(logging.ERROR) io_utils.configure_colored_logging(logging.INFO) return logger
def _configure_logging(args): from rasa.core.utils import configure_file_logging from rasa.utils.common import set_log_level log_level = args.loglevel or DEFAULT_LOG_LEVEL_RASA_X if isinstance(log_level, str): log_level = logging.getLevelName(log_level) logging.basicConfig(level=log_level) io_utils.configure_colored_logging(args.loglevel) set_log_level(log_level) configure_file_logging(log_level, args.log_file) logging.getLogger("werkzeug").setLevel(logging.WARNING) logging.getLogger("engineio").setLevel(logging.WARNING) logging.getLogger("pika").setLevel(logging.WARNING) logging.getLogger("socketio").setLevel(logging.ERROR) if not log_level == logging.DEBUG: logging.getLogger().setLevel(logging.WARNING) logging.getLogger("py.warnings").setLevel(logging.ERROR)
argument.model = DEFAULT_MODELS_PATH argument.connector = "cmdline" argument.enable_api = False argument.endpoints = None argument.credentials = None argument.remote_storage = None run(argument) def train_nlu_core_model() -> None: train( domain=DEFAULT_DOMAIN_PATH, config=DEFAULT_CONFIG_PATH, training_files=DEFAULT_DATA_PATH, fixed_model_name="restaurant_rasa_model", force_training=False, ) if __name__ == "__main__": configure_colored_logging(loglevel="ERROR") parser = create_argument_parser() cmdline_arguments = parser.parse_args() train_nlu_core_model() if cmdline_arguments.shell: run_rasa_shell()