app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Chillyblast/Bart_Summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("Chillyblast/Bart_Summarization")

# Create a pipeline for text summarization
summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)

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

# Create a text input box for user input
dialogue = st.text_area("Enter the input:")

if dialogue:
    # Perform inference
    summary = summarizer(dialogue, max_length=500, min_length=300, do_sample=False)
    
    # Display the summary
    st.write("Summary:", summary[0]['summary_text'])