app.py

import streamlit as st
from transformers import BartTokenizer, BartForConditionalGeneration
import torch
import os

# Path to the folder containing the saved model
model_path = './Bart'  # Update this path if your model is in a different folder

# Ensure the model and tokenizer are loaded from the specified folder
model = BartForConditionalGeneration.from_pretrained(model_path)
tokenizer = BartTokenizer.from_pretrained(model_path)

st.title("BART Summarization")

input_text = st.text_area("Enter text to summarize", "")

if st.button("Summarize"):
    if input_text:
        inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
        # Set the device (CPU or GPU)
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model.to(device)
        inputs = {key: value.to(device) for key, value in inputs.items()}
        
        # Generate summary
        summary_ids = model.generate(inputs['input_ids'], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        st.write("Summary:", summary)
    else:
        st.write("Please enter text to summarize.")