def main():
import rasa.utils
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)
rasa.utils.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 main():
from rasa_core.agent import Agent
from rasa_core.interpreter import NaturalLanguageInterpreter
from rasa_core.utils import (AvailableEndpoints, set_default_subparser)
import rasa_nlu.utils as nlu_utils
import rasa_core.cli
from rasa_core import utils
loop = asyncio.get_event_loop()
# Running as standalone python application
arg_parser = create_argument_parser()
set_default_subparser(arg_parser, 'default')
cmdline_arguments = arg_parser.parse_args()
logging.basicConfig(level=cmdline_arguments.loglevel)
_endpoints = AvailableEndpoints.read_endpoints(cmdline_arguments.endpoints)
if cmdline_arguments.output:
nlu_utils.create_dir(cmdline_arguments.output)
if not cmdline_arguments.core:
raise ValueError("you must provide a core model directory to evaluate "
"using -d / --core")
if cmdline_arguments.mode == 'default':
_interpreter = NaturalLanguageInterpreter.create(
cmdline_arguments.nlu, _endpoints.nlu)
_agent = Agent.load(cmdline_arguments.core, interpreter=_interpreter)
stories = loop.run_until_complete(
rasa_core.cli.train.stories_from_cli_args(cmdline_arguments))
loop.run_until_complete(
test(stories, _agent, cmdline_arguments.max_stories,
cmdline_arguments.output,
cmdline_arguments.fail_on_prediction_errors,
cmdline_arguments.e2e))
elif cmdline_arguments.mode == 'compare':
compare(cmdline_arguments.core, cmdline_arguments.stories,
cmdline_arguments.output)
story_n_path = os.path.join(cmdline_arguments.core, 'num_stories.json')
number_of_stories = utils.read_json_file(story_n_path)
plot_curve(cmdline_arguments.output, number_of_stories)
logger.info("Finished evaluation")
cmdline_args.out, _interpreter,
_endpoints, cmdline_args.dump_stories,
cmdline_args.config[0], None,
additional_arguments)
interactive.run_interactive_learning(
_agent,
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)
