def test_agent_and_persist():
policies = config.load("policies.yml")
policies[0] = KerasPolicy(epochs=2) # Keep training times low
agent = Agent("domain.yml", policies=policies)
training_data = agent.load_data("data/stories.md")
agent.train(training_data, validation_split=0.0)
agent.persist("./tests/models/dialogue")
loaded = Agent.load("./tests/models/dialogue")
assert agent.handle_text("/greet") is not None
assert loaded.domain.action_names == agent.domain.action_names
assert loaded.domain.intents == agent.domain.intents
assert loaded.domain.entities == agent.domain.entities
assert loaded.domain.templates == agent.domain.templates
from rasa_core.agent import Agent
# this will catch predictions the model isn't very certain about
# there is a threshold for the NLU predictions as well as the action predictions
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
core_threshold=0.2,
nlu_threshold=0.6)
agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy()])
# loading our neatly defined training dialogues
training_data = agent.load_data('stories.md')
agent.train(
training_data,
validation_split=0.2,
epochs=400
)
agent.persist('models/dialogue')
agent = Agent.load('models/dialogue', interpreter=model_directory)
print("Your bot is ready to talk! Type your messages here or send 'stop'")
while True:
a = input()
if a == 'stop':
break
responses = agent.handle_text(a)
for response in responses:
print(response["text"])