def __init__(self, config_path='./models/117M'):
self.config_path = config_path
self.text_enc = encoder.get_encoder(self.config_path)
self.hparams = model.default_hparams()
with open(os.path.join(self.config_path, 'hparams.json')) as f:
self.hparams.override_from_dict(json.load(f))
self.eos_id = self.text_enc.encode('\n')[0]
def __init__(self, input_num, config_path):
self.hparams = model.default_hparams()
self.config_path = config_path
with open(os.path.join(self.config_path, 'hparams.json')) as f:
self.hparams.override_from_dict(json.load(f))
self.input_num = input_num
self.text_enc = encoder.get_encoder(self.config_path)
self.sos_id = self.text_enc.encode('\t')[0]
self.eos_id = self.text_enc.encode('\n')[0]
def interact_model(box_selection, input_text,
model_name='124M',
seed=None,
nsamples=1,
batch_size=1,
length=40,
temperature=0.7,
top_k=0,
top_p=1,
models_dir='gpt/models',
):
if st.button('Generate Your Blog Post'):
st.markdown("Body Text:")
models_dir = os.path.expanduser(os.path.expandvars(models_dir))
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
enc = encoder.get_encoder(model_name, models_dir)
hparams = model.default_hparams()
with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
# Tokens created from the sample
output = sample.sample_sequence(
hparams=hparams,
length=length,
# length=1023,
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
saver.restore(sess, ckpt)
while True:
raw_text = box_selection + input_text
# print(raw_text)
while not raw_text:
print('Prompt should not be empty!')
st.markdown('Prompt should not be empty!')
raw_text = box_selection + "Title: " + input_text + " Body: "
# print(raw_text)
context_tokens = enc.encode(raw_text)
generated = 0
for _ in range(nsamples // batch_size):
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
for i in range(batch_size):
generated += 1
text = enc.decode(out[i])
text = text + "."
st.markdown(text)
st.success("Nice Blog!")
st.stop()
def interact_model(
model_name='124M',
seed=None,
nsamples=1,
batch_size=1,
length=40,
temperature=0.7,
top_k=0,
top_p=1,
models_dir='gpt/models',
):
"""
Interactively run the model
:model_name=124M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:nsamples=1 : Number of samples to return total
:batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples.
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:models_dir : path to parent folder containing model subfolders
(i.e. contains the <model_name> folder)
"""
st.title("Generate Your Own Text!")
st.markdown("Click the button down below to generate your own text.")
if st.button('Run GPT-2'):
models_dir = os.path.expanduser(os.path.expandvars(models_dir))
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
enc = encoder.get_encoder(model_name, models_dir)
hparams = model.default_hparams()
with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
# Tokens created from the sample
output = sample.sample_sequence(hparams=hparams,
length=length,
context=context,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(
os.path.join(models_dir, model_name))
saver.restore(sess, ckpt)
while True:
raw_text = input("Model prompt >>> ")
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("Model prompt >>> ")
context_tokens = enc.encode(raw_text)
generated = 0
for _ in range(nsamples // batch_size):
out = sess.run(
output,
feed_dict={
context:
[context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
for i in range(batch_size):
generated += 1
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " +
"=" * 40)
print(text)
print("=" * 80)