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# import necessary libraries
import streamlit as st
from transformers import BartForConditionalGeneration, BartTokenizer
# Load BART model and tokenizer
model_name = "facebook/bart-large-cnn"
model = BartForConditionalGeneration.from_pretrained(model_name)
tokenizer = BartTokenizer.from_pretrained(model_name)
# Streamlit app
st.title("ANavya Text Summarizer")
# Create a text input box
text_input = st.text_area("Enter Text Here:")
# Function to summarize text
def summarize_text(text):
input_ids = tokenizer.encode(text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
# Create a submit button
if st.button("Submit", key="ANavya"):
if text_input:
st.subheader("Summary:")
summary = summarize_text(text_input)
st.write(summary)
# Display a footer or additional information
st.sidebar.markdown("Created by [Team Anavya]")
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