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import streamlit as st |
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from transformers import ( |
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AutoModelForSeq2SeqLM, |
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AutoModelForTokenClassification, |
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AutoTokenizer) |
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tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") |
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extractive_summary = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn") |
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tag_model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english") |
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def summarize(text): |
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input_ids = tokenizer.encode(text, return_tensors="pt") |
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output = extractive_summary.generate(input_ids, max_new_tokens=4096) |
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return tokenizer.decode(output[0]) |
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def tag(text): |
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input_ids = tokenizer.encode(text, return_tensors="pt") |
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output = tag_model(input_ids)[0] |
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return [(output.words[i], output.labels[i]) for i in range(len(output.words))] |
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def optimize(text): |
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return optimized_text |
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st.title("NLP Demo") |
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text = st.text_area("Input text:", "Enter text here") |
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if st.button("Summarize"): |
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summary = summarize(text) |
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st.write(summary) |
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if st.button("Tag"): |
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tags = tag(text) |
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st.write(tags) |
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if st.button("Optimize"): |
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optimized_text = optimize(text) |
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st.write(optimized_text) |
