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import streamlit as st |
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from spacy import displacy |
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import span_marker |
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import spacy |
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import spacy_udpipe |
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spacy_udpipe.download("ar") |
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nlp = spacy_udpipe.load("ar") |
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nlp.add_pipe("span_marker", |
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config={"model": "iahlt/span-marker-xlm-roberta-base-ar"}) |
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def get_html(html: str): |
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"""Convert HTML so it can be rendered.""" |
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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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html = html.replace("\n", " ") |
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return WRAPPER.format(html) |
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def page_init(): |
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st.header("Named Entity Recognition Demo") |
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if __name__ == '__main__': |
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page_init() |
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displacy_options = {} |
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sample_text = "تعلم في جامعة أوكسفورد، جامعة برنستون، جامعة كولومبيا." |
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text = st.text_area("Text", sample_text, height=200, max_chars=1000) |
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btn = st.button("Annotate") |
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if text and btn: |
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doc = nlp(text) |
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html = displacy.render( |
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doc, |
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style="ent", |
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options=displacy_options, |
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manual=False, |
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) |
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style = "<style>mark.entity { display: inline-block }</style>" |
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st.write(f"{style}{get_html(html)}", unsafe_allow_html=True) |
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else: |
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st.write("") |
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st.markdown( |
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""" |
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<style> |
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textarea { |
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direction: rtl; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True, |
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) |
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