Update app.py

app.py

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