app.py

import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

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

st.title("AI Article Generator")

# Input for the article title
title = st.text_input("Enter the title of the article")

# Parameters for text generation
max_length = st.slider("Maximum length of the article", min_value=50, max_value=1000, value=500, step=50)
temperature = st.slider("Temperature (creativity level)", min_value=0.7, max_value=1.5, value=1.0, step=0.1)

# Generate the article
if st.button("Generate Article"):
    if title:
        with st.spinner("Generating article..."):
            try:
                input_ids = tokenizer.encode(title, return_tensors='pt')
                output = model.generate(
                    input_ids,
                    max_length=max_length,
                    temperature=temperature,
                    num_return_sequences=1,
                    no_repeat_ngram_size=2,
                    early_stopping=True
                )
                article = tokenizer.decode(output[0], skip_special_tokens=True)
                st.success("Article generated successfully!")
                st.write(article)
            except Exception as e:
                st.error(f"An error occurred: {e}")
    else:
        st.warning("Please enter a title to generate an article")

st.markdown("""
<style>
    .reportview-container {
        flex-direction: column;
        align-items: center;
    }
    .css-1kyxreq {
        width: 100%;
        max-width: 700px;
        margin: auto;
    }
</style>
""", unsafe_allow_html=True)