def test_concerts_online_example(tmpdir):
sys.path.append("examples/concertbot/")
from train_online import train_agent
from rasa_core import utils
story_path = tmpdir.join("stories.md").strpath
with utilities.cwd("examples/concertbot"):
msgs = iter(["/greet", "/greet", "/greet"])
msgs_f = functools.partial(next, msgs)
with utilities.mocked_cmd_input(
utils,
text=[
"2", # action is wrong
"5", # choose utter_goodbye action
"1" # yes, action_listen is correct.
] * 2 + [ # repeat this twice
"0", # export
story_path # file path to export to
]):
agent = train_agent()
responses = agent.handle_text("/greet", sender_id="user1")
assert responses[-1]['text'] == "hey there!"
online.serve_agent(agent, get_next_message=msgs_f)
# the model should have been retrained and the model should now
# directly respond with goodbye
responses = agent.handle_text("/greet", sender_id="user2")
assert responses[-1]['text'] == "goodbye :("
assert os.path.exists(story_path)
print(utils.read_file(story_path))
t = training.load_data(story_path,
agent.domain,
use_story_concatenation=False)
assert len(t) == 1
assert len(t[0].events) == 9
assert t[0].events[5] == ActionExecuted("utter_goodbye")
assert t[0].events[6] == ActionExecuted("action_listen")
def test_concerts_online_example(tmpdir):
sys.path.append("examples/concertbot/")
from train_online import train_agent
from rasa_core import utils
story_path = tmpdir.join("stories.md").strpath
with utilities.cwd("examples/concertbot"):
msgs = iter(["/greet", "/greet", "/greet"])
msgs_f = functools.partial(next, msgs)
with utilities.mocked_cmd_input(
utils,
text=["2", # action is wrong
"5", # choose utter_goodbye action
"1" # yes, action_listen is correct.
] * 2 + [ # repeat this twice
"0", # export
story_path # file path to export to
]):
agent = train_agent()
responses = agent.handle_text("/greet", sender_id="user1")
assert responses[-1]['text'] == "hey there!"
online.serve_agent(agent, get_next_message=msgs_f)
# the model should have been retrained and the model should now
# directly respond with goodbye
responses = agent.handle_text("/greet", sender_id="user2")
assert responses[-1]['text'] == "goodbye :("
assert os.path.exists(story_path)
print(utils.read_file(story_path))
t = training.load_data(story_path, agent.domain,
use_story_concatenation=False)
assert len(t) == 1
assert len(t[0].events) == 9
assert t[0].events[5] == ActionExecuted("utter_goodbye")
assert t[0].events[6] == ActionExecuted("action_listen")
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
from rasa_core import train, utils
from rasa_core.training import online
from rasa_core.interpreter import NaturalLanguageInterpreter
logger = logging.getLogger(__name__)
def train_agent(interpreter):
return train.train_dialogue_model(domain_file="horoscope_domain.yml",
stories_file="data/stories.md",
output_path="models/dialog",
nlu_model_path=interpreter,
endpoints="endpoints.yml",
max_history=2,
kwargs={"batch_size": 50,
"epochs": 200,
"max_training_samples": 300
})
if __name__ == '__main__':
utils.configure_colored_logging(loglevel="DEBUG")
nlu_model_path = "./models/nlu/default/horoscopebot"
interpreter = NaturalLanguageInterpreter.create(nlu_model_path)
agent = train_agent(interpreter)
online.serve_agent(agent)
# Running as standalone python application
arg_parser = create_argument_parser()
cmdline_args = arg_parser.parse_args()
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
}
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)
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)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
from rasa_core import utils, train
from rasa_core.training import online
logger = logging.getLogger(__name__)
def train_agent():
return train.train_dialogue_model(domain_file="domain.yml",
stories_file="data/stories.md",
output_path="models/dialogue",
endpoints="endpoints.yml",
max_history=2,
kwargs={"batch_size": 50,
"epochs": 200,
"max_training_samples": 300
})
if __name__ == '__main__':
utils.configure_colored_logging(loglevel="INFO")
agent = train_agent()
online.serve_agent(agent)
