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"""Demo for NER4OPT.""" |
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import warnings |
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warnings.filterwarnings("ignore", category=DeprecationWarning) |
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import streamlit as st |
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import ner4opt |
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from ner4opt.utils import preprocess, spacy_tokenize_sentence |
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from spacy import displacy |
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HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>""" |
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@st.cache_resource |
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def load_models(): |
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"""Load and cache NER4OPT models.""" |
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lexical_ner_model = ner4opt.Ner4Opt("lexical") |
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lexical_plus_ner_model = ner4opt.Ner4Opt("lexical_plus") |
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semantic_ner_model = ner4opt.Ner4Opt("semantic") |
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hybrid_ner_model = ner4opt.Ner4Opt("hybrid") |
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return lexical_ner_model, lexical_plus_ner_model, semantic_ner_model, hybrid_ner_model |
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def main(): |
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st.title("""Ner4Opt: Named Entity Recognition for Optimization""") |
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st.markdown("""Given an optimization problem in natural language, Ner4Opt extracts optimization related entities from free-form text. The source code for Ner4Opt is available at https://github.com/skadio/ner4opt""") |
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option = st.sidebar.selectbox( |
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'Select a lexical, semantic, or hybrid model for extracting entities', |
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('Lexical', 'Lexical Plus', 'Semantic', 'Hybrid'), index=3) |
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text = st.text_area( |
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"Text", |
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"Cautious Asset Investment has a total of $150,000 to manage and decides to invest it in money market fund, which yields a 2% return as well as in foreign bonds, which gives and average rate of return of 10.2%. Internal policies require PAI to diversify the asset allocation so that the minimum investment in money market fund is 40% of the total investment. Due to the risk of default of foreign countries, no more than 40% of the total investment should be allocated to foreign bonds. How much should the Cautious Asset Investment allocate in each asset so as to maximize its average return?" |
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) |
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text = text.strip() |
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if text == "": |
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st.write("Please write a valid sentence.") |
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lexical_ner_model, lexical_plus_ner_model, semantic_ner_model, hybrid_ner_model = load_models( |
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) |
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if option == "Lexical": |
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predicted_entities = lexical_ner_model.get_entities(text) |
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elif option == "Lexical Plus": |
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predicted_entities = lexical_plus_ner_model.get_entities(text) |
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elif option == "Semantic": |
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predicted_entities = semantic_ner_model.get_entities(text) |
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elif option == "Hybrid": |
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predicted_entities = hybrid_ner_model.get_entities(text) |
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entities_formatted = [] |
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for tag in predicted_entities: |
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entities_formatted.append({ |
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"start": tag['start'], |
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"end": tag['end'], |
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"label": tag['entity_group'], |
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"score": tag['score'] |
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}) |
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ner_for_display = [{ |
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"text": spacy_tokenize_sentence(preprocess(text)), |
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"ents": entities_formatted, |
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"title": "Named Entities" |
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}] |
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html_ner = displacy.render(ner_for_display, style="ent", manual=True) |
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html_ner = html_ner.replace("\n", " ") |
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st.write(HTML_WRAPPER.format(html_ner), unsafe_allow_html=True) |
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if __name__ == '__main__': |
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main() |
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