import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "gpt2"  # You can also try other models like "gpt2-medium", "gpt2-large", etc.
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_blog_post(topic):
    prompt = f"Write a detailed blog post about {topic}."
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=512, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
    blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return blog_post

# Streamlit interface
st.title("Blog Post Generator")
st.write("Enter a topic to generate a detailed blog post.")

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