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