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42 lines
910 B
Python
42 lines
910 B
Python
import spacy
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from spacy import displacy
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import gradio as gr
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nlp = spacy.load("en_core_web_sm")
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def text_analysis(text):
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doc = nlp(text)
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html = displacy.render(doc, style="dep", page=True)
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html = (
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"<div style='max-width:100%; max-height:360px; overflow:auto'>"
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+ html
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+ "</div>"
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)
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pos_count = {
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"char_count": len(text),
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"token_count": 0,
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}
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pos_tokens = []
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for token in doc:
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pos_tokens.extend([(token.text, token.pos_), (" ", None)])
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return pos_tokens, pos_count, html
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iface = gr.Interface(
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text_analysis,
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gr.inputs.Textbox(placeholder="Enter sentence here..."),
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["highlight", "key_values", "html"],
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examples=[
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["What a beautiful morning for a walk!"],
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["It was the best of times, it was the worst of times."],
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],
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)
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iface.test_launch()
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if __name__ == "__main__":
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iface.launch()
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