code: def predict_text(stoi, model, text):
      
  
      # prefix text with tokens:
      #   xbos: beginning of sentence
      #   xfld 1: we are using a single field here
      input_str = 'xbos xfld 1 ' + text
  
      # predictions are done on arrays of input.
      # We only have a single input, so turn it into a 1x1 array
      texts = [input_str]
  
      # tokenize using the fastai wrapper around spacy
      tok = Tokenizer().proc_all_mp(partition_by_cores(texts))
  
      # turn into integers for each word
      encoded = [stoi[p] for p in tok[0]]
  
      # we want a [x,1] array where x is the number
      #  of words inputted (including the prefix tokens)
      ary = np.reshape(np.array(encoded),(-1,1))
  
      # turn this array into a tensor
      tensor = torch.from_numpy(ary)
  
      # wrap in a torch Variable
      variable = Variable(tensor)
  
      # do the predictions
      predictions = model(variable)
  
      # convert back to numpy
      numpy_preds = predictions[0].data.numpy()
  
      return softmax(numpy_preds[0])[0]
docs: Do the actual prediction on the text using the
          model and mapping files passed