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

# Replace with your Hugging Face model repository path
model_repo_path = 'ASaboor/Bart_samsum'

# Load the model and tokenizer
model = BartForConditionalGeneration.from_pretrained(model_repo_path)
tokenizer = BartTokenizer.from_pretrained(model_repo_path)

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

# Streamlit app layout
st.set_page_config(page_title="Text Summarization App", page_icon=":memo:", layout="wide")

st.title("Text Summarization App")
st.write("""
    This app uses a fine-tuned BART model to generate summaries of your input text.
    Enter the text you want to summarize in the box below and click "Summarize" to see the result.
""")

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

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

# Optional: Add a footer or additional information
st.markdown("""
    ---
    Made with ❤️ using [Streamlit](https://streamlit.io) and [Hugging Face Transformers](https://huggingface.co/transformers/).
""")