Generation function for the GPT-2 model

interactive_conditional_samples.py

# Here, we have added a `raw_text` parameter
# From our Flask API we'll pass in the incoming text into this function
def interact_model(raw_text, model_name='117M', seed=None, nsamples=1, batch_size=1, length=None, temperature=1, top_k=40, models_dir='models/'):
    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)
        output = sample.sample_sequence(
            hparams=hparams, length=length,
            context=context,
            batch_size=batch_size,
            temperature=temperature, top_k=top_k
        )

        saver = tf.train.Saver()
        ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
        saver.restore(sess, ckpt)
		
		# We have created a new variable that stores the final text
        final_text = ""
		
		# We have removed the `raw_text` model prompt
		# We'll directly parse the `raw_text` into the model
        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])
				
				"""
				Instead of printing the text, we'll concatenate 
				it to the `final_string` variable
				"""
                final_text += text

		# Finally return the `final_text` variable as the response
        return final_text