def logout(self):
session = get_current_session()
if session.get(constants.VAR_NAME_EMAIL) is None:
return
sessionid = session.get(constants.SESSION_ID)
sessionData = SessionData.get_session(sessionid)
if sessionData is not None:
sessionData.delete()
session.terminate()
logging.info('User logged out: ' + self.email)
def __init__(self, session, corpus_dir, result_dir, result_file):
"""
Args:
session: The TensorFlow session.
corpus_dir: Name of the folder storing corpus files and vocab information.
knbase_dir: Name of the folder storing data files for the knowledge base.
result_dir: The folder containing the trained result files.
result_file: The file name of the trained model.
"""
self.session = session
# Prepare data and hyper parameters
print("# Prepare dataset placeholder and hyper parameters ...")
tokenized_data = TokenizedData(corpus_dir=corpus_dir, training=False)
# self.knowledge_base = KnowledgeBase()
# self.knowledge_base.load_knbase(knbase_dir)
self.session_data = SessionData()
self.hparams = tokenized_data.hparams
self.src_placeholder = tf.placeholder(shape=[None],
dtype=tf.string,
name="src")
self.cont_placeholder = tf.placeholder(shape=[None],
dtype=tf.string,
name="context")
src_dataset = tf.data.Dataset.from_tensor_slices(
(self.src_placeholder, self.cont_placeholder))
# cont_dataset = tf.data.Dataset.from_tensor_slices(self.cont_placeholder)
self.infer_batch = tokenized_data.get_inference_batch(src_dataset)
# Create model
print("# Creating inference model ...")
self.model = ModelCreator(training=False,
tokenized_data=tokenized_data,
batch_input=self.infer_batch)
# Restore model weights
print("# Restoring model weights ...")
self.model.saver.restore(session, os.path.join(result_dir,
result_file))
self.session.run(tf.tables_initializer())
def login(self, ip):
sessionid = SessionData.generate_id()
sessionData = SessionData(key_name=sessionid)
sessionData.sessionid = sessionid
sessionData.email = self.email
sessionData.ip = ip
sessionData.store()
session = get_current_session()
session[constants.SESSION_ID] = sessionid
session[constants.VAR_NAME_EMAIL] = self.email
LoginInfo.update(self)
class BotPredictor(object):
def __init__(self, session, corpus_dir, knbase_dir, result_dir,
result_file):
"""
Args:
session: The TensorFlow session.
corpus_dir: Name of the folder storing corpus files and vocab information.
knbase_dir: Name of the folder storing data files for the knowledge base.
result_dir: The folder containing the trained result files.
result_file: The file name of the trained model.
"""
self.session = session
# Prepare data and hyper parameters
print("# Prepare dataset placeholder and hyper parameters ...")
tokenized_data = TokenizedData(corpus_dir=corpus_dir, training=False)
self.knowledge_base = KnowledgeBase()
self.knowledge_base.load_knbase(knbase_dir)
self.session_data = SessionData()
self.hparams = tokenized_data.hparams
self.src_placeholder = tf.placeholder(shape=[None], dtype=tf.string)
src_dataset = tf.data.Dataset.from_tensor_slices(self.src_placeholder)
self.infer_batch = tokenized_data.get_inference_batch(src_dataset)
# Create model
print("# Creating inference model ...")
self.model = ModelCreator(training=False,
tokenized_data=tokenized_data,
batch_input=self.infer_batch)
# Restore model weights
print("# Restoring model weights ...")
self.model.saver.restore(session, os.path.join(result_dir,
result_file))
self.session.run(tf.tables_initializer())
def predict(self, session_id, question, html_format=False):
chat_session = self.session_data.get_session(session_id)
chat_session.before_prediction() # Reset before each prediction
if question.strip() == '':
answer = "Don't you want to say something to me?"
chat_session.after_prediction(question, answer)
return answer
pat_matched, new_sentence, para_list = check_patterns_and_replace(
question)
for pre_time in range(2):
tokens = nltk.word_tokenize(new_sentence.lower())
tmp_sentence = [' '.join(tokens[:]).strip()]
self.session.run(self.infer_batch.initializer,
feed_dict={self.src_placeholder: tmp_sentence})
outputs, _ = self.model.infer(self.session)
if self.hparams.beam_width > 0:
outputs = outputs[0]
eos_token = self.hparams.eos_token.encode("utf-8")
outputs = outputs.tolist()[0]
if eos_token in outputs:
outputs = outputs[:outputs.index(eos_token)]
if pat_matched and pre_time == 0:
out_sentence, if_func_val = self._get_final_output(
outputs,
chat_session,
para_list=para_list,
html_format=html_format)
if if_func_val:
chat_session.after_prediction(question, out_sentence)
return out_sentence
else:
new_sentence = question
else:
out_sentence, _ = self._get_final_output(
outputs, chat_session, html_format=html_format)
chat_session.after_prediction(question, out_sentence)
return out_sentence
def _get_final_output(self,
sentence,
chat_session,
para_list=None,
html_format=False):
sentence = b' '.join(sentence).decode('utf-8')
if sentence == '':
return "I don't know what to say.", False
if_func_val = False
last_word = None
word_list = []
for word in sentence.split(' '):
word = word.strip()
if not word:
continue
if word.startswith('_func_val_'):
if_func_val = True
word = call_function(word[10:],
knowledge_base=self.knowledge_base,
chat_session=chat_session,
para_list=para_list,
html_format=html_format)
if word is None or word == '':
continue
else:
if word in self.knowledge_base.upper_words:
word = self.knowledge_base.upper_words[word]
if (last_word is None or last_word
in ['.', '!', '?']) and not word[0].isupper():
word = word.capitalize()
if not word.startswith('\'') and word != 'n\'t' \
and (word[0] not in string.punctuation or word in ['(', '[', '{', '``', '$']) \
and last_word not in ['(', '[', '{', '``', '$']:
word = ' ' + word
word_list.append(word)
last_word = word
return ''.join(word_list).strip(), if_func_val
