45 lines
1 KiB
Python
45 lines
1 KiB
Python
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from bottle import route, run, request
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from spacy.tokenizer import Tokenizer
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from spacy.pipeline import EntityRecognizer
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import spacy
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nlp = spacy.load('en')
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tokenizer = Tokenizer(nlp.vocab)
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ner = EntityRecognizer(nlp.vocab)
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@route('/load')
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def load():
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pass
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@route('/tokenize')
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def tokenize():
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tokens = tokenizer(request.query.text)
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list = []
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for token in tokens:
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print(token)
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list.append({'text': token.text, 'offset': token.idx})
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return {'tokens': list}
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@route('/featurize')
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def tokenize():
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doc = nlp(request.query.text)
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list = []
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print(doc.vector.size)
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for vec in doc.vector:
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print(vec)
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list.append(str(vec.real))
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return {'vectors': list}
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@route('/entitize')
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def entitize():
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doc = nlp(request.query.text)
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entities = ner(doc)
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print(entities)
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list = []
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print(doc.ents.size)
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for entity in entities:
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print(entity)
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list.append(entity)
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return {'entities': list}
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run(host='0.0.0.0', port=5005, debug=True)
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