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 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")
