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30 lines
790 B
Python
30 lines
790 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 = "<div style='max-width:100%; max-height:360px; overflow:auto'>" + html + "</div>"
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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_count["token_count"] += 1
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pos_count[token.pos_] = pos_count.get(token.pos_, 0) + 1
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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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gr.Interface(
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text_analysis,
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gr.inputs.Textbox(placeholder="Enter sentence here..."),
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[
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"highlight", "key_values", "html"
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]
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).launch() |