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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 huggingface_hub import hf_hub_download | |
| model_name = "Pendrokar/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") | |
| def top_elements(array, k): | |
| ind = np.argpartition(array, -k)[-k:] | |
| return ind[np.argsort(array[ind])][::-1] | |
| maxlen = 30 | |
| print('Tokenizing using dictionary from {}'.format(vocab_path)) | |
| with open(vocab_path, 'r') as f: | |
| vocabulary = json.load(f) | |
| st = SentenceTokenizer(vocabulary, maxlen) | |
| model = torchmoji_emojis(model_path) | |
| def predict(deepmoji_analysis): | |
| output_text = "\n" | |
| 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_tokens = tokenized[i] | |
| t_score = [t] | |
| t_prob = prob[i] | |
| ind_top = top_elements(t_prob, 5) | |
| t_score.append(sum(t_prob[ind_top])) | |
| t_score.extend(ind_top) | |
| t_score.extend([t_prob[ind] for ind in ind_top]) | |
| scores.append(t_score) | |
| output_text += str(t_score) | |
| return str(tokenized) + output_text | |
| gradio_app = gr.Interface( | |
| fn=predict, | |
| inputs="text", | |
| outputs="text", | |
| examples=[ | |
| "You love hurting me, huh?", | |
| "I know good movies, this ain't one", | |
| "It was fun, but I'm not going to miss you", | |
| "My flight is delayed.. amazing.", | |
| "What is happening to me??", | |
| "This is the shit!", | |
| "This is shit!", | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| gradio_app.launch() |