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import streamlit as st | |
from transformers import pipeline | |
# Initialize the summarizer pipeline | |
summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
def summarize_text(text): | |
summary = summarizer(text, max_length=150, min_length=50, do_sample=False) | |
return summary[0]['summary_text'] | |
# Streamlit app layout | |
st.title("Text Summarizer") | |
st.write("This app uses Hugging Face's transformers to summarize any text you provide.") | |
# User input | |
input_text = st.text_area("Enter Text to Summarize", height=200) | |
if st.button("Summarize"): | |
if input_text: | |
with st.spinner("Summarizing..."): | |
summary = summarize_text(input_text) | |
st.subheader("Summary:") | |
st.write(summary) | |
else: | |
st.warning("Please enter some text to summarize.") | |