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| from __future__ import print_function, division, unicode_literals | |
| import gradio as gr | |
| import sys | |
| from os.path import abspath, dirname | |
| import json | |
| import numpy as np | |
| from torchmoji.sentence_tokenizer import SentenceTokenizer | |
| from torchmoji.model_def import torchmoji_emojis | |
| from emoji import emojize | |
| from huggingface_hub import hf_hub_download | |
| model_name = "Uberduck/torchmoji" | |
| model_path = hf_hub_download(repo_id=model_name, filename="pytorch_model.bin") | |
| vocab_path = hf_hub_download(repo_id=model_name, filename="vocabulary.json") | |
| emoji_codes = [] | |
| with open('./data/emoji_codes.json', 'r') as f: | |
| emoji_codes = json.load(f) | |
| def top_elements(array, k): | |
| ind = np.argpartition(array, -k)[-k:] | |
| return ind[np.argsort(array[ind])][::-1] | |
| maxlen = 30 | |
| with open(vocab_path, 'r') as f: | |
| vocabulary = json.load(f) | |
| st = SentenceTokenizer(vocabulary, maxlen) | |
| model = torchmoji_emojis(model_path) | |
| def predict(deepmoji_analysis, emoji_count): | |
| return_label = {} | |
| tokenized, _, _ = st.tokenize_sentences([deepmoji_analysis]) | |
| prob = model(tokenized) | |
| for prob in [prob]: | |
| # Find top emojis for each sentence. Emoji ids (0-63) | |
| # correspond to the mapping in emoji_overview.png | |
| # at the root of the torchMoji repo. | |
| scores = [] | |
| for i, t in enumerate([deepmoji_analysis]): | |
| t_prob = prob[i] | |
| # sort top | |
| ind_top_ids = top_elements(t_prob, emoji_count) | |
| for ind in ind_top_ids: | |
| # unicode emoji + :alias: | |
| label_emoji = emojize(emoji_codes[str(ind)], language="alias") | |
| label_name = label_emoji + emoji_codes[str(ind)] | |
| # propability | |
| label_prob = t_prob[ind] | |
| return_label[label_name] = label_prob | |
| return return_label | |
| input_textbox = gr.Textbox( | |
| label="English Text", | |
| lines=1, | |
| value="" | |
| ) | |
| slider = gr.Slider(1, 64, value=5, step=1, label="Top # Emoji", info="Choose between 1 and 64 top emojis to show") | |
| gradio_app = gr.Interface( | |
| predict, | |
| [ | |
| input_textbox, | |
| slider, | |
| ], | |
| outputs="label", | |
| examples=[ | |
| ["You love hurting me, huh?", 5], | |
| ["I know good movies, this ain't one", 5], | |
| ["It was fun, but I'm not going to miss you", 5], | |
| ["My flight is delayed.. amazing.", 5], | |
| ["What is happening to me??", 5], | |
| ["This is the shit!", 5], | |
| ["This is shit!", 5], | |
| ], | |
| live=True | |
| ) | |
| if __name__ == "__main__": | |
| gradio_app.launch() |