Update app.py

app.py

@@ -9,6 +9,64 @@ import json
 import random
 import re
 
+import numpy as np
+import emoji, json
+from torchmoji.global_variables import PRETRAINED_PATH, VOCAB_PATH
+from torchmoji.sentence_tokenizer import SentenceTokenizer
+from torchmoji.model_def import torchmoji_emojis
+import torch
+
+# 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]
+
+
+with open("vocabulary.json", 'r') as f:
+    vocabulary = json.load(f)
+
+st = SentenceTokenizer(vocabulary, 100)
+
+emojimodel = torchmoji_emojis("pytorch_model.bin")
+
+if USE_GPU:
+  emojimodel.to("cuda:0")
+
+def deepmojify(sentence, top_n=5, prob_only=False):
+    list_emojis = []
+    def top_elements(array, k):
+        ind = np.argpartition(array, -k)[-k:]
+        return ind[np.argsort(array[ind])][::-1]
+
+    tokenized, _, _ = st.tokenize_sentences([sentence])
+    tokenized = np.array(tokenized).astype(int)  # convert to float first
+    if USE_GPU:
+        tokenized = torch.tensor(tokenized).cuda()  # then convert to PyTorch tensor
+
+    prob = emojimodel.forward(tokenized)[0]
+    if not USE_GPU:
+        prob = torch.tensor(prob)
+    if prob_only:
+        return prob
+    emoji_ids = top_elements(prob.cpu().numpy(), top_n)
+    emojis = map(lambda x: EMOJIS[x], emoji_ids)
+    list_emojis.append(emoji.emojize(f"{' '.join(emojis)}", language='alias'))
+    # returning the emojis as a list named as list_emojis
+    return list_emojis, prob
 
 app = Flask(__name__)