app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import gdown
import os

# Set the title of the Streamlit app
st.title("Text Summarization with Fine-Tuned BART")

# Function to download the model from Google Drive
def download_model_from_drive(file_id, dest_path):
    url = f'https://drive.google.com/uc?id={file_id}'
    try:
        gdown.download(url, dest_path, quiet=False)
        st.success(f"Downloaded {dest_path}")
    except Exception as e:
        st.error(f"Error downloading {dest_path}: {e}")

# Ensure the model directory exists
model_dir = 'model'
if not os.path.exists(model_dir):
    os.makedirs(model_dir)

# File IDs for your model components
file_ids = {
    'model': '1-V2bEtPR9Y3iBXK9zOR-qM5y9hKiQUnF',
    'config': '1-T2etSP_k_3j5LzunWq8viKGQCQ5RMr_',
    'tokenizer': '1-cRYNPWqlNNGRxeztympRRfVuy3hWuMY',
    'vocab': '1-t9AhomeH7YIIpAqCGTok8wjvl0tml0F',
    'merges': '1-l77_KEdK7GBFjMX_6UXGE-ZTGDraaDm'
}

# Download the model files
with st.spinner("Downloading model..."):
    download_model_from_drive(file_ids['model'], os.path.join(model_dir, 'pytorch_model.bin'))
    download_model_from_drive(file_ids['config'], os.path.join(model_dir, 'config.json'))
    download_model_from_drive(file_ids['tokenizer'], os.path.join(model_dir, 'tokenizer.json'))
    download_model_from_drive(file_ids['vocab'], os.path.join(model_dir, 'vocab.json'))
    download_model_from_drive(file_ids['merges'], os.path.join(model_dir, 'merges.txt'))

# Load the model and tokenizer
@st.cache(allow_output_mutation=True)
def load_model_and_tokenizer():
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_dir)
        model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
        return tokenizer, model
    except Exception as e:
        st.error(f"Error loading model or tokenizer: {e}")
        return None, None

tokenizer, model = load_model_and_tokenizer()

# Input text from user
input_text = st.text_area("Enter the text to summarize:")

if st.button("Summarize"):
    if input_text:
        if tokenizer and model:
            try:
                # Tokenize the input text
                inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
                
                # Perform summarization
                with torch.no_grad():
                    summary_ids = model.generate(inputs['input_ids'], max_length=150, num_beams=4, early_stopping=True)
                
                # Decode the summary
                summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
                
                st.write(f"Summary: {summary}")
            except Exception as e:
                st.error(f"Error during summarization: {e}")
        else:
            st.error("Model or tokenizer not loaded.")
    else:
        st.write("Please enter some text to summarize.")