import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer

@st.cache(allow_output_mutation=True)
def load_model():
    tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
    model = GPT2LMHeadModel.from_pretrained("gpt2-large")
    return tokenizer, model

def generate_blog_post(topic, max_length=200):
    tokenizer, model = load_model()
    input_ids = tokenizer.encode(topic, return_tensors='pt')
    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2, pad_token_id=tokenizer.eos_token_id)
    blog_post = tokenizer.decode(output[0], skip_special_tokens=True)
    return blog_post

st.title("Blog Post Generator")
st.write("Enter a topic to generate a blog post using GPT-2 large.")

topic = st.text_input("Topic:", "")
length = st.slider("Post Length (in tokens):", min_value=50, max_value=500, value=200)

if st.button("Generate"):
    if topic:
        blog_post = generate_blog_post(topic, max_length=length)
        st.subheader("Generated Blog Post")
        st.write(blog_post)
    else:
        st.write("Please enter a topic to generate a blog post.")