def from_dataset(cls, dataset):
print("Start preparing training data ...")
# load pairs and initialise Voc
voc = cls('name is a useless parameter')
# filter pairs longer than MAX_LENGTH
print("Counting words...")
for pair in dataset:
voc.addSentence(standardise_sentence(pair.utterance))
voc.addSentence(standardise_sentence(pair.response))
print("Counted words:", voc.num_words)
return voc
def indexesFromSentence(voc, sentence):
sentence = standardise_sentence(sentence)
try:
return [voc.word2index[word]
for word in sentence.split(' ')] + [EOS_token]
except KeyError:
print(sentence)
def chat(policy, env):
env.reset()
env._state = []
while 1:
try:
input_sentence = input('> ')
if input_sentence == 'q':
break
input_sentence = env.sentence2tensor(
standardise_sentence(input_sentence))
env.update_state(input_sentence)
response, tensor = policy.response(env.state)
env.update_state(tensor)
print('Bot:', response)
except KeyError:
print("Error: Encountered unknown word.")
def get_response(policy, env, utterance) -> Text:
"""
:param policy:
:param env:
:param utterance: Text
:return:
"""
try:
message_tensor = env.sentence2tensor(standardise_sentence(utterance))
env.update_state(message_tensor)
response, tensor = policy.response(env.state)
env.update_state(tensor)
except KeyError:
response = "Error: Encountered unknown word."
return response