# -*- coding: UTF-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import logging
import warnings
from rasa_core.actions import Action
from rasa_core.agent import Agent
from rasa_core.channels.console import ConsoleInputChannel
from rasa_core.events import SlotSet
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
logger = logging.getLogger(__name__)
support_search = ["话费", "流量"]
def extract_item(item):
"""
check if item supported, this func just for lack of train data.
:param item: item in track, eg: "流量"、"查流量"
:return:
"""
if item is None:
return None
for name in support_search:
if name in item:
return name
return None
class ActionSearchConsume(Action):
def name(self):
return 'action_search_consume'
def run(self, dispatcher, tracker, domain):
item = tracker.get_slot("item")
item = extract_item(item)
if item is None:
dispatcher.utter_message("您好，我现在只会查话费和流量")
dispatcher.utter_message("你可以这样问我：“帮我查话费”")
return []
time = tracker.get_slot("time")
if time is None:
dispatcher.utter_message("您想查询哪个月的话费？")
return []
# query database here using item and time as key. but you may normalize time format first.
dispatcher.utter_message("好，请稍等")
if item == "流量":
dispatcher.utter_message("您好，您{}共使用{}二百八十兆，剩余三十兆。".format(time, item))
else:
dispatcher.utter_message("您好，您{}共消费二十八元。".format(time))
return []
class MobilePolicy(KerasPolicy):
def model_architecture(self, num_features, num_actions, max_history_len):
"""Build a Keras model and return a compiled model."""
from keras.layers import LSTM, Activation, Masking, Dense
from keras.models import Sequential
n_hidden = 32 # size of hidden layer in LSTM
# Build Model
batch_shape = (None, max_history_len, num_features)
model = Sequential()
model.add(Masking(-1, batch_input_shape=batch_shape))
model.add(LSTM(n_hidden, batch_input_shape=batch_shape))
model.add(Dense(input_dim=n_hidden, output_dim=num_actions))
model.add(Activation("softmax"))
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
logger.debug(model.summary())
return model
def train_nlu():
from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.model import Trainer
from rasa_nlu import config
training_data = load_data("data/nlu.json")
trainer = Trainer(config.load("data/nlu_model_config.json"))
trainer.train(training_data)
model_directory = trainer.persist("models/", project_name="ivr", fixed_model_name="demo")
return model_directory
def train_dialogue(domain_file="data/domain.yml",
model_path="models/dialogue",
training_data_file="data/stories.md"):
from rasa_core.featurizers import (MaxHistoryTrackerFeaturizer,
BinarySingleStateFeaturizer)
featurizer = MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(), max_history=5)
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=5), KerasPolicy(featurizer)])
agent.train(
training_data_file,
epochs=200,
batch_size=16,
augmentation_factor=50,
validation_split=0.2
)
agent.persist(model_path)
return agent
def run_ivrbot_online(input_channel=ConsoleInputChannel(),
interpreter=RasaNLUInterpreter("models/ivr/demo"),
domain_file="data/domain.yml",
training_data_file="data/stories.md"):
from rasa_core.featurizers import (MaxHistoryTrackerFeaturizer,
BinarySingleStateFeaturizer)
featurizer = MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(), max_history=5)
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=5), KerasPolicy(featurizer)],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
def run(serve_forever=True):
agent = Agent.load("models/dialogue",
interpreter=RasaNLUInterpreter("models/ivr/demo"))
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
if __name__ == "__main__":
logging.basicConfig(level="INFO")
parser = argparse.ArgumentParser(
description="starts the bot")
parser.add_argument(
"task",
choices=["train-nlu", "train-dialogue", "run", "online-train"],
help="what the bot should do - e.g. run or train?")
task = parser.parse_args().task
# decide what to do based on first parameter of the script
if task == "train-nlu":
train_nlu()
elif task == "train-dialogue":
train_dialogue()
elif task == "run":
run()
elif task == "online-train":
run_ivrbot_online()
else:
warnings.warn("Need to pass either 'train-nlu', 'train-dialogue', 'run' or 'online-train' to use the script.")
exit(1)
