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Update app.py
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app.py
CHANGED
@@ -169,20 +169,20 @@ def main():
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class_list.append(i['word'])
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entity_list.append(i['entity_group'])
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filename = 'model.bin'
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count_vect = CountVectorizer(ngram_range=(1,3))
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tfidf_transformer=TfidfTransformer()
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#loaded_model = pickle.load(open(filename, 'rb'))
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#loaded_model =
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loaded_model = joblib.load(filename)
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#loaded_vectorizer = dill.load(open('vectorizefile_classification.pickle', 'rb'))
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from sklearn.pipeline import Pipeline
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pipeline1 = Pipeline([('count_vect',count_vect),('tfidf_transformer',tfidf_transformer)])
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pipeline_test_output = pipeline1.fit_transform(class_list)
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predicted = loaded_model.predict(pipeline_test_output)
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pred1 = predicted
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level0 = []
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class_list.append(i['word'])
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entity_list.append(i['entity_group'])
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filename = 'Checkpoint-classification.sav'
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#filename = 'model.bin'
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# count_vect = CountVectorizer(ngram_range=(1,3))
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# tfidf_transformer=TfidfTransformer()
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loaded_model = pickle.load(open(filename, 'rb'))
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#loaded_model = pickle.load(open(filename, 'rb'))
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#loaded_model = joblib.load(filename)
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#loaded_vectorizer = dill.load(open('vectorizefile_classification.pickle', 'rb'))
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loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
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# from sklearn.pipeline import Pipeline
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# pipeline1 = Pipeline([('count_vect',count_vect),('tfidf_transformer',tfidf_transformer)])
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# pipeline_test_output = pipeline1.fit_transform(class_list)
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pipeline_test_output = loaded_vectorizer.transform(class_list)
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predicted = loaded_model.predict(pipeline_test_output)
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pred1 = predicted
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level0 = []
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