import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Replace with your model name on Hugging Face Hub
model_name = "your-username/your-model-name"

# Load the tokenizer and model from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Streamlit app UI
st.title("BART Summarization Model")

input_text = st.text_area("Input Text", "Enter text here...")

if st.button("Generate Summary"):
    if not input_text.strip():
        st.warning("Please enter some text to summarize.")
    else:
        # Tokenize and generate summary
        inputs = tokenizer(input_text, return_tensors="pt")
        summary_ids = model.generate(inputs["input_ids"], max_length=150, num_beams=4, early_stopping=True)
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        
        # Display the summary
        st.subheader("Generated Summary")
        st.write(summary)

# Optionally, you can add a section to display model information or statistics
st.sidebar.title("Model Information")
st.sidebar.write("This app uses a fine-tuned BART model for summarization.")