def chat():
with tf.Session() as sess:
# Create model and load parameters.
model = create_model(sess, forward_only=True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
vocab_path = os.path.join(FLAGS.data_dir, "vocab%d.in" % FLAGS.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
# Decode from standard input.
# sys.stdout.write("> ")
sys.stdout.flush()
# sentence = sys.stdin.readline()
past = collections.deque(maxlen=3)
# past.extendleft([sentence])
s = socket.socket()
s.connect((cfg.HOST, cfg.PORT))
s.send("PASS {}\r\n".format(cfg.PASS).encode("utf-8"))
s.send("NICK {}\r\n".format(cfg.NICK).encode("utf-8"))
s.send("JOIN {}\r\n".format(cfg.CHAN).encode("utf-8"))
s.setblocking(1)
last_sentence = ""
while True:
try:
response = s.recv(1024).decode("utf-8")
if response == "PING :tmi.twitch.tv\r\n":
s.send("PONG :tmi.twitch.tv\r\n".encode("utf-8",'ignore'))
else:
text = re.findall('PRIVMSG.+?(?=\:)\:(.+?(?=\r))',response)
for msg in text:
past.extendleft([msg.encode('utf-8')])
# print past
except:
pass
new=[]
for t in past:
new.append(t.decode("utf-8",'ignore'))
# new.append( t.encode('ascii','ignore') )
sentence= u' '.join(new)
# print sentence
predicted_sentence = _get_predicted_sentence(sentence.encode('utf-8','replace'), vocab, rev_vocab, model, sess)
i = 0
while ("_UNK" in predicted_sentence or predicted_sentence == "") and i<10:
i+=1
predicted_sentence = _get_predicted_sentence(sentence.encode('utf-8','replace'), vocab, rev_vocab, model, sess)
# predicted_sentence ='Kappa'
print(predicted_sentence)
send_chat(s,predicted_sentence)
sys.stdout.flush()
past.extendleft([predicted_sentence])
# print sentence
sleep(2)
def predict():
def _get_test_dataset():
with open(TEST_DATASET_PATH) as test_fh:
test_sentences = [s.strip() for s in test_fh.readlines()]
return test_sentences
results_filename = '_'.join([
'results',
str(FLAGS.num_layers),
str(FLAGS.size),
str(FLAGS.vocab_size)
])
results_path = os.path.join(FLAGS.results_dir, results_filename)
with tf.Session() as sess, open(results_path, 'w') as results_fh:
# Create model and load parameters.
model = create_model(sess, forward_only=True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
vocab_path = os.path.join(FLAGS.data_dir,
"vocab%d.in" % FLAGS.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
test_dataset = _get_test_dataset()
for sentence in test_dataset:
# Get token-ids for the input sentence.
predicted_sentence = _get_predicted_sentence(
sentence, vocab, rev_vocab, model, sess)
print(sentence, ' -> ', predicted_sentence)
results_fh.write(predicted_sentence + '\n')
def predict():
def _get_test_dataset():
with open(TEST_DATASET_PATH) as test_fh:
test_sentences = [s.strip() for s in test_fh.readlines()]
return test_sentences
results_filename = '_'.join(['results', str(FLAGS.num_layers), str(FLAGS.size), str(FLAGS.vocab_size)])
results_path = os.path.join(FLAGS.results_dir, results_filename)
with tf.Session() as sess, open(results_path, 'w') as results_fh:
# Create model and load parameters.
model = create_model(sess, forward_only=True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
vocab_path = os.path.join(FLAGS.data_dir, "vocab%d.in" % FLAGS.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
test_dataset = _get_test_dataset()
for sentence in test_dataset:
# Get token-ids for the input sentence.
predicted_sentence = _get_predicted_sentence(sentence, vocab, rev_vocab, model, sess)
print(sentence, ' -> ', predicted_sentence)
results_fh.write(predicted_sentence + '\n')
def chat():
with tf.Session() as sess:
# Create model and load parameters.
model = create_model(sess, forward_only=True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
vocab_path = os.path.join(FLAGS.data_dir, "vocab%d.in" % FLAGS.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
# Decode from standard input.
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
predicted_sentence = _get_predicted_sentence(sentence, vocab, rev_vocab, model, sess)
print(predicted_sentence)
print("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
