app.py

import streamlit as st
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load the GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Set the maximum length of generated text
max_length = 200

# Define a function to generate text
def generate_text(prompt):
    # Encode the prompt
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    # Generate text
    output = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        num_beams=5,
        no_repeat_ngram_size=2,
        early_stopping=True
    )

    # Decode the generated text
    text = tokenizer.decode(output[0], skip_special_tokens=True)

    return text

# Set up the Streamlit app
st.title("GPT-2 Text Generator")

# Add a text input widget for the user to enter a prompt
prompt = st.text_input("Enter a prompt:")

# When the user clicks the "Generate" button, generate text
if st.button("Generate"):
    with st.spinner("Generating text..."):
        text = generate_text(prompt)
        st.write(text)