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import streamlit as st |
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from transformers import pipeline |
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model_name = AutoModelForSeq2SeqLM.from_pretrained("Safna/abstractive") |
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tokenizer_name = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-12-3") |
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summarizer = pipeline("summarization", model= model_name, tokenizer= tokenizer_name) |
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st.title("Text Summarization App") |
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input_text = st.text_area("Enter the text you want to summarize:") |
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if st.button("Generate Summary"): |
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if input_text: |
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with st.spinner("Generating summary..."): |
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summary = summarizer(input_text, max_length=64, min_length=10, length_penalty=2.0, num_beams=4, early_stopping=True) |
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st.subheader("Generated Summary:") |
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st.write(summary[0]["summary_text"]) |
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st.sidebar.header("Instructions") |
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st.sidebar.markdown("1. Enter the text you want to summarize.") |
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st.sidebar.markdown("2. Click the 'Generate Summary' button.") |
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