def run_bank_bot(serve_forever=True):
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.channels.console import CmdlineInput
interpreter = RasaNLUInterpreter('models/nlu/default/bank_nlu')
agent = Agent.load('models/dialogue', interpreter=interpreter)
if serve_forever:
agent.handle_channels([CmdlineInput()])
return agent
def run_bot(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/current/nlu/')
agent = Agent.load('./models/dialogue/', interpreter=interpreter)
if serve_forever:
agent.handle_channels([CmdlineInput()])
return agent
policies=[MemoizationPolicy(),
KerasPolicy()],
interpreter=interpreter,
generator=None)
agent.train(training_data_file,
input_channel=input_channel,
max_history=2,
batch_size=50,
epochs=200,
max_training_samples=300,
validation_split=0.2)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
directory = './models'
for filename in os.listdir(directory):
if filename.endswith(".tar.gz"):
#print('FILE NAME== ',filename)
tar = tarfile.open(os.path.join(directory, filename))
tar.extractall()
tar.close()
continue
else:
continue
nlu_interpreter = RasaNLUInterpreter('./nlu')
run_restaurant_online(CmdlineInput(), nlu_interpreter)
from rasa_core.agent import Agent
from rasa_core.channels.console import CmdlineInput
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.training import interactive
from rasa_core.utils import EndpointConfig
def train_agent(input_channel, nlu_interpreter,
domain_file="domain.yml",
training_data_file='./data/dialogue/stories.md'):
#endpoints = "endpoints.yml"
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=2), KerasPolicy()],
interpreter=nlu_interpreter)
data = agent.load_data(training_data_file)
agent.train(data, input_channel=input_channel, batch_size=50, epochs=200, max_training_samples=300)
agent = Agent.load('models/dialogue/default/dialogue_model', interpreter = nlu_interpreter, action_endpoint=EndpointConfig(url = "http://localhost:5055/webhook"))
interactive.run_interactive_learning(agent, training_data_file)
return agent
if __name__ == '__main__':
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/nlu_model')
train_agent(CmdlineInput(), nlu_interpreter)
from rasa_core.utils import EndpointConfig
logger = logging.getLogger(__name__)
def run_weather_online(input_channel,
interpreter,
domain_file="weather_domain.yml",
training_data_file='data/stories.md'):
#policies2 = policy_config.load("config.yml")
action_endpoints = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent(
"weather_domain.yml",
interpreter=interpreter,
policies=[MemoizationPolicy(),
KerasPolicy(epochs=200, batch_size=50)],
action_endpoint=action_endpoints)
#data = asyncio.run(agent.load_data(training_data_file))
data = agent.load_data(training_data_file)
agent.train(data)
interactive.run_interactive_learning(agent, training_data_file)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = NaturalLanguageInterpreter.create(
'./models/nlu/default/weathernlu')
run_weather_online(CmdlineInput(), nlu_interpreter)
import logging
from rasa_core.agent import Agent
from rasa_core.channels.console import CmdlineInput
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def train_online(input_channel , interpreter,
domain_file = 'domain.yml',
training_data = './data/stories.md'):
agent = Agent(domain_file, policies = [MemoizationPolicy(max_history=2),
KerasPolicy(epochs = 500, batch_size=10)], interpreter = interpreter)
data = agent.load_data(training_data)
agent.continue_training(data,
input_channel = input_channel,
augmentation_factor = 50,
validation_split = 0.2)
agent.persist(model_path)
return agent
if __name__ =="__main__":
logging.basicConfig(level = 'INFO')
nlu_interpreter = RasaNLUInterpreter('./models/current/nlu/')
train_online(CmdlineInput(),nlu_interpreter)
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.channels.slack import SlackInput
from rasa_core.channels.console import CmdlineInput;
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/restaurantnlu')
agent = Agent.load('./models/dialogue', interpreter = nlu_interpreter)
input_channel = SlackInput('xoxp-359418684578-360311923670-373479391381-faf4e29bddac4cd131221b8cf0ffa627', #app verification token
#'xoxb-359418684578-372796513233-gHxeV6CHQFl8MPkZVrPHBsnQ', # bot verification token
#'7uAoXNmNANaqCa9WpALV9EhR', # slack verification token
True)
cmd_channel = CmdlineInput()
agent.handle_channels(channels=[input_channel, cmd_channel], http_port=5004, serve_forever=True)