app.py

import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained model and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def generate_text(prompt, max_length=50):
    # Encode the input prompt
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    
    # Generate text
    outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1)
    
    # Decode the generated text
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return text

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

prompt = st.text_area("Input", "Once upon a time...")
max_length = st.slider("Max Length", min_value=10, max_value=100, value=50)

if st.button("Generate"):
    generated_text = generate_text(prompt, max_length)
    st.subheader("Generated Text")
    st.write(generated_text)