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# -*- 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(' ')
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, args.maxlen)
# Loading model
model = torchmoji_emojis(PRETRAINED_PATH)
# Running predictions
tokenized, _, _ = st.tokenize_sentences([args.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(args.text,' '.join(emojis)), use_aliases=True))
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