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import streamlit as st
from transformers import pipeline
# Initialize summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
# Function to generate response using the summarizer
def generate_response_with_summarizer(txt):
try:
# Generate summary
summary = summarizer(txt, max_length=130, min_length=30, do_sample=False)
return summary[0]['summary_text']
except Exception as e:
st.error(f"An error occurred during summarization: {str(e)}")
return None
# Page title and layout
st.set_page_config(page_title='🦜🔗 Text Summarization App')
st.title('🦜🔗 Text Summarization App')
# Text input area for user to input text
txt_input = st.text_area('Enter your text', '', height=200)
# Form to accept the user's text input for summarization
response = None
with st.form('summarize_form', clear_on_submit=True):
submitted = st.form_submit_button('Submit')
if submitted and txt_input:
with st.spinner('Summarizing...'):
response = generate_response_with_summarizer(txt_input)
# Display the response if available
if response:
st.info(response)
# Instructions for using the summarization app
st.subheader("Hugging Face Summarization")
st.write("""
This app uses Hugging Face's `facebook/bart-large-cnn` model to summarize input text.
The model provides concise summaries by capturing the main points of the text.
""")
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