app.py

import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

# Load model and tokenizer
model_name = 'gpt2-large'
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)

# Streamlit UI
st.title("Blog Post Generator")
text = st.text_area("Enter the starting text for your blog post:")

# Configuration for generation
generation_config = GenerationConfig(max_new_tokens=200, do_sample=True, temperature=0.7)

if text:
    try:
        # Encode input
        inputs_encoded = tokenizer(text, return_tensors='pt')

        # Generate output
        with torch.no_grad():
            model_output = model.generate(inputs_encoded["input_ids"], generation_config=generation_config)[0]

        # Decode output
        output = tokenizer.decode(model_output, skip_special_tokens=True)

        # Display result
        st.write("Generated Blog Post:")
        st.write(output)

    except Exception as e:
        st.error(f"An error occurred: {e}")