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| import streamlit as st | |
| from transformers import pipeline | |
| def summarize_model(): | |
| model = pipeline("summarization") | |
| return model | |
| summ = summarize_model() | |
| st.title("Summarize Your Text") | |
| st.subheader("Paste any article in the text area below and click on the 'Summarize Text' button to get the summarized textual data") | |
| st.subheader("This application is using HuggingFace's transformers pre-trained model for text summarization.") | |
| sentence = st.text_area('Paste your copied data here...', height=100) | |
| button = st.button("Summarize Text") | |
| max_lengthy = st.sidebar.slider('Maximum summary length (words)', min_value=30, max_value=700, value=100, step=10) | |
| num_beamer = st.sidebar.slider('Speed vs quality of Summary (1 is fastest but less accurate)', min_value=1, max_value=8, value=4, step=1) | |
| with st.spinner("Summarizing..."): | |
| if button and sentence: | |
| summary = summ(sentence, max_length = max_lengthy, min_length = 50, num_beams=num_beamer, do_sample=True,early_stopping=True, repetition_penalty=1.5, length_penalty=1.5)[0] | |
| st.write(summary['summary_text']) |