from transformers import GPT2LMHeadModel, GPT2Tokenizer import streamlit as st st.set_page_config( page_title="GPT-2 Demo", page_icon=":robot_face:", layout="wide") st.title("GPT-2 Text Generation Demo") st.info("This is an GPT2 Text Generation Example using HuggingFace GPT2 Model") pretrained = "gpt2-large" tokenizer = GPT2Tokenizer.from_pretrained(pretrained) model = GPT2LMHeadModel.from_pretrained(pretrained, pad_token_id=tokenizer.eos_token_id) sentence = st.text_input('Input your sentence here:', value='My favorite ice cream flavor is ') st.info("Max generated sentence: 100 words") if (st.button("Generate")): input_ids = tokenizer.encode(sentence, return_tensors='pt') paragraph_generated = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) text = tokenizer.decode(paragraph_generated[0], skip_special_tokens=True) st.write(text)