import streamlit as st
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Define model and tokenizer
model_name = 'gpt2-large'
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

def generate_blogpost(topic):
    inputs = tokenizer.encode(topic, return_tensors='pt')
    attention_mask = tokenizer.encode_plus(topic, return_tensors='pt')['attention_mask']
    outputs = model.generate(inputs, attention_mask=attention_mask, max_length=500, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return text

# Streamlit app
st.title('Blog Post Generator')
topic = st.text_input('Enter a topic:')
if topic:
    blogpost = generate_blogpost(topic)
    st.write(blogpost)