text: # -*- coding: utf-8 -*-
  
  """ Use torchMoji to predict emojis from a single text input
  """
  
  from __future__ import print_function, division, unicode_literals
  import example_helper
  import json
  import csv
  import argparse
  
  import numpy as np
  import emoji
  
  from torchmoji.sentence_tokenizer import SentenceTokenizer
  from torchmoji.model_def import torchmoji_emojis
  from torchmoji.global_variables import PRETRAINED_PATH, VOCAB_PATH
  
  # Emoji map in emoji_overview.png
  EMOJIS = ":joy: :unamused: :weary: :sob: :heart_eyes: \
  :pensive: :ok_hand: :blush: :heart: :smirk: \
  :grin: :notes: :flushed: :100: :sleeping: \
  :relieved: :relaxed: :raised_hands: :two_hearts: :expressionless: \
  :sweat_smile: :pray: :confused: :kissing_heart: :heartbeat: \
  :neutral_face: :information_desk_person: :disappointed: :see_no_evil: :tired_face: \
  :v: :sunglasses: :rage: :thumbsup: :cry: \
  :sleepy: :yum: :triumph: :hand: :mask: \
  :clap: :eyes: :gun: :persevere: :smiling_imp: \
  :sweat: :broken_heart: :yellow_heart: :musical_note: :speak_no_evil: \
  :wink: :skull: :confounded: :smile: :stuck_out_tongue_winking_eye: \
  :angry: :no_good: :muscle: :facepunch: :purple_heart: \
  :sparkling_heart: :blue_heart: :grimacing: :sparkles:".split(' ')
  text = "I love you"
  
  def top_elements(array, k):
      ind = np.argpartition(array, -k)[-k:]
      return ind[np.argsort(array[ind])][::-1]
  
  if __name__ == "__main__":
      #argparser = argparse.ArgumentParser()
      #argparser.add_argument('--text', type=str, required=True, help="Input text to emojize")
      #argparser.add_argument('--maxlen', type=int, default=30, help="Max length of input text")
      #args = argparser.parse_args()
  
      # Tokenizing using dictionary
      with open(VOCAB_PATH, 'r') as f:
          vocabulary = json.load(f)
  
      st = SentenceTokenizer(vocabulary, 30)
  
      # Loading model
      model = torchmoji_emojis(PRETRAINED_PATH)
      # Running predictions
      tokenized, _, _ = st.tokenize_sentences([text])
      # Get sentence probability
      prob = model(tokenized)[0]
  
      # Top emoji id
      emoji_ids = top_elements(prob, 5)
  
      # map to emojis
      emojis = map(lambda x: EMOJIS[x], emoji_ids)
  
      print(emoji.emojize("{} {}".format(text,' '.join(emojis)), use_aliases=True))
  