old_contents: # Copyright 2016 Mario Graff (https://github.com/mgraffg)
  
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  
  #     http://www.apache.org/licenses/LICENSE-2.0
  
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import pickle
  
  __version__ = "1.2.3"
  
  
  class WorkerTC:
      def __init__(self, filename):
          with open(filename, 'rb') as f:
              t, c, le = pickle.load(f)
  
          self.model = t
          self.svc = c
          self.le = le
  
      def predict_dict(self, tweet):
          tweet['klass'] = self.predict(tweet['text'])
          return tweet
  
      def predict(self, text):
          vec = self.model[text]
          return self.le.inverse_transform(self.svc.predict([vec]))[0]
  
new_contents: # Copyright 2016 Mario Graff (https://github.com/mgraffg)
  
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  
  #     http://www.apache.org/licenses/LICENSE-2.0
  
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import pickle
  
  __version__ = "1.3.3"
  
  
  class WorkerTC:
      def __init__(self, filename):
          with open(filename, 'rb') as f:
              t, c, le = pickle.load(f)
  
          self.model = t
          self.svc = c
          self.le = le
  
      def predict_dict(self, tweet):
          tweet['klass'] = self.predict(tweet['text'])
          return tweet
  
      def predict(self, text):
          vec = self.model[text]
          return self.le.inverse_transform(self.svc.predict([vec]))[0]
  
subject: Update __init__.py