app.py

import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-2 model and tokenizer
model_name = 'gpt2'
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Function to generate blog post for a given topic
def generate_blog_post(topic, max_length=300):
    # Encode the input topic into tokens
    input_ids = tokenizer.encode(topic, return_tensors='pt')

    # Generate text
    output = model.generate(
        input_ids,
        max_length=max_length,
        num_return_sequences=1,
        no_repeat_ngram_size=2,
        early_stopping=True
    )

    # Decode the output tokens into a string
    blog_post = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return blog_post

# Streamlit app
st.title("Blog Post Generator")

topic = st.text_input("Enter a topic for the blog post:")
max_length = st.slider("Maximum length of the blog post:", min_value=50, max_value=1000, value=300)

if st.button("Generate Blog Post"):
    if topic:
        with st.spinner('Generating blog post...'):
            blog_post = generate_blog_post(topic, max_length)
        st.write(blog_post)
    else:
        st.warning("Please enter a topic.")