app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import tensorflow as tf

#for reproducability
SEED = 64

#maximum number of words in output text
# MAX_LEN = 30

title = st.text_input('Enter the seed words', ' ')
input_sequence = title

number = st.number_input('Insert how many words', 1)
MAX_LEN = number

if st.button('Submit'):

    #get transformers
    from transformers import TFGPT2LMHeadModel, GPT2Tokenizer

    tokenizer = AutoTokenizer.from_pretrained("ml6team/gpt-2-medium-conditional-quote-generator")
    GPT2 = model = AutoModelForCausalLM.from_pretrained("ml6team/gpt-2-medium-conditional-quote-generator")
    tf.random.set_seed(SEED)
    
    input_ids = tokenizer.encode(input_sequence, return_tensors='tf')

# generate text until the output length (which includes the context length) reaches 50
    #greedy_output = GPT2.generate(input_ids, max_length = MAX_LEN)
    outputs = model(**inputs)

    print("Output:\n" + 100 * '-')
    print(tokenizer.decode(outputs[0], skip_special_tokens = True))
else:
    st.write(' ')


# print("Output:\n" + 100 * '-')
# print(tokenizer.decode(sample_output[0], skip_special_tokens = True), '...')