# import gdown
# import torch

# # https://drive.google.com/drive/folders/17G-ejd4scK1DYko5k0ssXZjEy7P-ClI6

# import streamlit as st
# import gdown
# import torch
# from transformers import pipeline

# # Function to download the model from Google Drive
# def download_file_from_drive(file_id, output_path):
#     url = f'https://drive.google.com/uc?id={file_id}'
#     gdown.download(url, output_path, quiet=False)

# # Replace 'YOUR_FILE_ID' with the actual file ID of your model
# file_id = '1A2B3C4D5E6F7G8H9I0J'



import streamlit as st
import requests
import torch
from transformers import pipeline
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Replace with your Hugging Face model repository path
model_repo_path = 'AbdurRehman313/T5_samsum_model_files'

# Load the model and tokenizer
model = T5ForConditionalGeneration.from_pretrained(model_repo_path)
tokenizer = T5Tokenizer.from_pretrained(model_repo_path)

# Initialize the summarization pipeline
summarizer = pipeline('summarization', model=model,tokenizer=tokenizer)

# Streamlit app layout
st.title("Text Summarization App")

# User input
text_input = st.text_area("Enter text to summarize", height=300)

# Summarize the text
if st.button("Summarize"):
    if text_input:
        with st.spinner("Generating summary..."):
            try:
                summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False)
                st.subheader("Summary")
                st.write(summary[0]['summary_text'])
            except Exception as e:
                st.error(f"Error during summarization: {e}")
    else:
        st.warning("Please enter some text to summarize.")