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| import gradio as gr | |
| import asyncio | |
| import torch.nn.functional as F | |
| from torch import nn | |
| import os | |
| os.environ['CURL_CA_BUNDLE'] = '' | |
| from sentence_transformers import SentenceTransformer | |
| sentencemodel = SentenceTransformer('johnpaulbin/toxic-gte-small-3') | |
| USE_GPU = False | |
| """ Use torchMoji to predict emojis from a single text input | |
| """ | |
| 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 | |
| model = nn.Sequential( | |
| nn.Linear(448, 300), # Increase the number of neurons | |
| nn.ReLU(), | |
| nn.BatchNorm1d(300), # Batch normalization | |
| nn.Linear(300, 300), # Increase the number of neurons | |
| nn.ReLU(), | |
| nn.BatchNorm1d(300), # Batch normalization | |
| nn.Linear(300, 200), # Increase the number of neurons | |
| nn.ReLU(), | |
| nn.BatchNorm1d(200), # Batch normalization | |
| nn.Linear(200, 125), # Increase the number of neurons | |
| nn.ReLU(), | |
| nn.BatchNorm1d(125), # Batch normalization | |
| nn.Linear(125, 2), | |
| nn.Dropout(0.05) # Dropout | |
| ) | |
| model.load_state_dict(torch.load("large-tuned.pth", map_location=torch.device('cpu')), strict=False) | |
| model.eval() | |
| def inf(inpt): | |
| TEXT = inpt.lower() | |
| probs = deepmojify(TEXT, prob_only=True) | |
| embedding = sentencemodel.encode(TEXT, convert_to_tensor=True) | |
| INPUT = torch.cat((probs, embedding)) | |
| output = F.softmax(model(INPUT.view(1, -1)), dim=1) | |
| if not output[0][1] > 0.4: | |
| return "Not toxic " + str(output[0][0]) | |
| else: | |
| return "Toxic! " + str(output[0][1]) | |
| iface = gr.Interface(fn=inf, inputs="text", outputs="text") | |
| iface.queue(concurrency_count=500).launch() |