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| from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration, AutoModelForSequenceClassification | |
| import torch | |
| import gradio as gr | |
| import argparse | |
| from scipy.special import softmax | |
| import csv | |
| import urllib.request | |
| import numpy as np | |
| import requests | |
| args_dict = dict( | |
| EX_LIST = [["This is wonderful!"], | |
| ["Nice car"], | |
| ["La France est la meilleure équipe du monde"], | |
| ["Visca Barca"], | |
| ["Hala Madrid"], | |
| ["Buongiorno"], | |
| # ["Auf einigen deutschen Straßen gibt es kein Radar"], | |
| ["Tempo soleggiato in Italia"], | |
| ["Bonjour"], | |
| ["صباح الخير"], | |
| ["اكل زوجتي جميل"], | |
| ], | |
| #MMiniLM | |
| # Load the pretrained model and tokenizer | |
| tokenizer_MMiniLM = AutoTokenizer.from_pretrained("Karim-Gamal/MMiniLM-L12-finetuned-emojis-IID-Fed"), | |
| model_MMiniLM = AutoModelForSequenceClassification.from_pretrained("Karim-Gamal/MMiniLM-L12-finetuned-emojis-IID-Fed"), | |
| #XLM | |
| # Load the pretrained model and tokenizer | |
| tokenizer_XLM = AutoTokenizer.from_pretrained("Karim-Gamal/XLM-Roberta-finetuned-emojis-IID-Fed"), | |
| model_XLM = AutoModelForSequenceClassification.from_pretrained("Karim-Gamal/XLM-Roberta-finetuned-emojis-IID-Fed"), | |
| #Bert | |
| # Load the pretrained model and tokenizer | |
| tokenizer_Bert = AutoTokenizer.from_pretrained("Karim-Gamal/BERT-base-finetuned-emojis-IID-Fed"), | |
| model_Bert = AutoModelForSequenceClassification.from_pretrained("Karim-Gamal/BERT-base-finetuned-emojis-IID-Fed"), | |
| description = 'Real-time Emoji Prediction', | |
| article = '<head><style>@import url(https://fonts.googleapis.com/css?family=Open+Sans:400italic,600italic,700italic,800italic,400,600,700,800)<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-1BmE4kWBq78iYhFldvKuhfTAU6auU8tT94WrHftjDbrCEXSU1oBoqyl2QvZ6jIW3" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/font/bootstrap-icons.css"> <link rel="stylesheet" href="https://unpkg.com/[email protected]/dist/bootstrap-table.min.css">\ | |
| .table-responsive{-sm|-md|-lg|-xl} body{ background-color: #f5f5f5; padding: 120px 0; font-family: \'Open Sans\', sans-serif; } img{ max-width:100%; } .div_table_{ position:relative; width: max-content; margin:0 auto; } .profile-card{ position:relative; width:280px; margin:0 auto; padding:40px 30px 30px; background:#fff; border: 5px solid rgba(255,255,255,.7); text-align:center; border-radius:40px; transition: all 200ms ease; } .profile-card_2{ position:relative; width:60%; // margin:0 auto; padding:40px 30px 30px; background:#fff; border: 5px solid rgba(255,255,255,.7); text-align:center; border-radius:40px; transition: all 200ms ease; } .mask-shadow{ z-index:-1 !important; width:95%; height:12px; background:#000; bottom:0; left:0; right:0; margin:0 auto; position:absolute; border-radius:4px; opacity:0; transition: all 400ms ease-in; } .mask-shadow_2{ z-index:-1 !important; width:95%; height:12px; background:#000; bottom:0; left:0; right:0; margin:0 auto; position:absolute; border-radius:4px; opacity:0; transition: all 400ms ease-in; } .profile-card:hover{ box-shadow: 0px 30px 60px -5px rgba(55,55,71,0.3); transform: translate3d(0,-5px,0); .mask-shadow{ opacity:1; box-shadow: 0px 30px 60px -5px rgba(55,55,71,0.3); position:absolute; } } .profile-card_2:hover{ box-shadow: 0px 30px 60px -5px rgba(55,55,71,0.3); transform: translate3d(0,-5px,0); .mask-shadow{ opacity:1; box-shadow: 0px 30px 60px -5px rgba(55,55,71,0.3); position:absolute; } } .profile-card header{ display:block; margin-bottom:10px; } .profile-card_2 header{ display:block; margin-bottom:10px; } .profile-card header a{ width:150px; height:150px; display:block; border-radius:100%; margin:-120px auto 0; box-shadow: 0 0 0 5px #82b541; } .profile-card_2 header a{ width:85%; height:85%; display:block; border-radius:10%; margin:-120px auto 0; box-shadow: 0 0 0 5px #82b541; } .profile-card header a img{ border-radius: 50%; width:150px; height:150px; } .profile-card_2 header a img{ border-radius: 10%; width:100%; height:100%; } .profile-card:hover header a, .profile-card header a:hover{ animation: bounceOut .4s linear; -webkit-animation: bounceOut .4s linear; } .profile-card_2:hover header a, .profile-card header a:hover{ animation: bounceOut .4s linear; -webkit-animation: bounceOut .4s linear; } .profile-card header h1{ font-size:20px; padding:20px; color:#444; text-transform:uppercase; margin-bottom:5px; } .profile-card_2 header h1{ font-size:20px; padding:20px; color:#444; text-transform:uppercase; margin-bottom:5px; } .profile-card header h2{ font-size:14px; color:#acacac; text-transform:uppercase; margin:0; } .profile-card_2 header h2{ font-size:14px; color:#acacac; text-transform:uppercase; margin:0; } /*content*/ .profile-bio{ font-size:14px; color:#a5a5a5; line-height:1.7; font-style: italic; margin-bottom:30px; } /*link social*/ .profile-social-links{ margin:0; padding:0; list-style:none; } .profile-social-links li{ display: inline-block; margin: 0 10px; } .profile-social-links li a{ width: 55px; height:55px; display:block; background:#f1f1f1; border-radius:50%; -webkit-transition: all 2.75s cubic-bezier(0,.83,.17,1); -moz-transition: all 2.75s cubic-bezier(0,.83,.17,1); -o-transition: all 2.75s cubic-bezier(0,.83,.17,1); transition: all 2.75s cubic-bezier(0,.83,.17,1); transform-style: preserve-3d; } .profile-social-links li a img{ width:35px; height:35px; margin:10px auto 0; } .profile-social-links li a:hover{ background:#ddd; transform: scale(1.2); -webkit-transform: scale(1.2); } /*animation hover effect*/ @-webkit-keyframes bounceOut { 0% { box-shadow: 0 0 0 4px #82b541; opacity: 1; } 25% { box-shadow: 0 0 0 1px #82b541; opacity: 1; } 50% { box-shadow: 0 0 0 7px #82b541; opacity: 1; } 75% { box-shadow: 0 0 0 4px #82b541; opacity: 1; } 100% { box-shadow: 0 0 0 5px #82b541; opacity: 1; } } @keyframes bounceOut { 0% { box-shadow: 0 0 0 6px #82b541; opacity: 1; } 25% { box-shadow: 0 0 0 2px #82b541; opacity: 1; } 50% { box-shadow: 0 0 0 9px #82b541; opacity: 1; } 75% { box-shadow: 0 0 0 3px #82b541; opacity: 1; } 100% { box-shadow: 0 0 0 5px #82b541; opacity: 1; } }</style></head>', | |
| ) | |
| config = argparse.Namespace(**args_dict) | |
| # Preprocess text (username and link placeholders) | |
| def preprocess(text): | |
| text = text.lower() | |
| new_text = [] | |
| for t in text.split(" "): | |
| t = '@user' if t.startswith('@') and len(t) > 1 else t | |
| t = '' if t.startswith('http') else t | |
| new_text.append(t) | |
| # print(" ".join(new_text)) | |
| return " ".join(new_text) | |
| def test_with_sentance(text ,net ,tokenizer): | |
| # text = "good morning" | |
| text = preprocess(text) | |
| # tc = TweetCleaner(remove_stop_words=True, remove_retweets=False) | |
| # print('before : ' ,text) | |
| # text = tc.get_cleaned_text(text) | |
| # print('after : ' ,text) | |
| net.eval() | |
| encoded_input = tokenizer.encode(text, padding=True, truncation=True, return_tensors='pt') | |
| net.to('cpu') | |
| # print(type()) | |
| # encoded_input = {k: v.to(DEVICE) for k, v in encoded_input.items()} | |
| output = net(encoded_input) | |
| scores = output[0][0].detach().numpy() | |
| scores = softmax(scores) | |
| # download label mapping | |
| labels=[] | |
| mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/emoji/mapping.txt" | |
| with urllib.request.urlopen(mapping_link) as f: | |
| html = f.read().decode('utf-8').split("\n") | |
| csvreader = csv.reader(html, delimiter='\t') | |
| labels = [row[1] for row in csvreader if len(row) > 1] | |
| ranking = np.argsort(scores) | |
| ranking = ranking[::-1] | |
| output_d = {} | |
| for i in range(scores.shape[0]): | |
| l = labels[ranking[i]] | |
| s = scores[ranking[i]] | |
| # print(f"{ranking[i]}) {l} {np.round(float(s), 4)}") | |
| output_d[l] = np.round(float(s), 4) | |
| if i == 2 : | |
| # break | |
| return output_d | |
| # net.to('cuda:0') | |
| list_interface = [] | |
| list_title = [] | |
| # BERT | |
| def _method(text): | |
| # tokenizer = AutoTokenizer.from_pretrained(config.CHECKPOINT_BERT) | |
| # model_loaded = torch.load('/content/NEW_MODELS_Imbalance/Bert/g_ex3_bert_multi_fed_data_epoch_2.pt', map_location=torch.device('cpu')) | |
| return test_with_sentance(text , config.model_Bert , config.tokenizer_Bert) | |
| # greet("sun") | |
| interface = gr.Interface( | |
| fn = _method, | |
| inputs=gr.Textbox(placeholder="Enter sentence here..."), | |
| outputs="label", | |
| examples=config.EX_LIST, | |
| live = True, | |
| title = 'BERT Multilingual', | |
| description=config.description, | |
| article = '', | |
| ) | |
| list_interface.append(interface) | |
| list_title.append('BERT Multilingual') | |
| # XLM | |
| def _method(text): | |
| # tokenizer = AutoTokenizer.from_pretrained(config.CHECKPOINT_BERT) | |
| # model_loaded = torch.load('/content/NEW_MODELS_Imbalance/Bert/g_ex3_bert_multi_fed_data_epoch_2.pt', map_location=torch.device('cpu')) | |
| return test_with_sentance(text , config.model_XLM , config.tokenizer_XLM) | |
| # greet("sun") | |
| interface = gr.Interface( | |
| fn = _method, | |
| inputs=gr.Textbox(placeholder="Enter sentence here..."), | |
| outputs="label", | |
| examples=config.EX_LIST, | |
| live = True, | |
| title = 'XLM Roberta Multilingual', | |
| description=config.description, | |
| article = '', | |
| ) | |
| list_interface.append(interface) | |
| list_title.append('XLM Roberta Multilingual') | |
| # MMiniLM | |
| def _method(text): | |
| # tokenizer = AutoTokenizer.from_pretrained(config.CHECKPOINT_BERT) | |
| # model_loaded = torch.load('/content/NEW_MODELS_Imbalance/Bert/g_ex3_bert_multi_fed_data_epoch_2.pt', map_location=torch.device('cpu')) | |
| return test_with_sentance(text , config.model_MMiniLM , config.tokenizer_MMiniLM) | |
| # greet("sun") | |
| interface = gr.Interface( | |
| fn = _method, | |
| inputs=gr.Textbox(placeholder="Enter sentence here..."), | |
| outputs="label", | |
| examples=config.EX_LIST, | |
| live = True, | |
| title = 'MiniLM Multilingual', | |
| description=config.description, | |
| article = '', | |
| ) | |
| list_interface.append(interface) | |
| list_title.append('MiniLM Multilingual') | |
| # Switch | |
| API_URL_Switch = "https://api-inference.huggingface.co/models/Karim-Gamal/switch-base-8-finetuned-SemEval-2018-emojis-IID-Fed" | |
| headers_Switch = {"Authorization": "Bearer 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"} | |
| def query_Switch(payload): | |
| response = requests.post(API_URL_Switch, headers=headers_Switch, json=payload) | |
| return response.json() | |
| query_Switch({ "inputs": 'test',}) | |
| def _method(text): | |
| text = preprocess(text) | |
| output_temp = query_Switch({ | |
| "inputs": text, | |
| }) | |
| text_to_emoji = {'red' : '❤', 'face': '😍', 'joy':'😂', 'love':'💕', 'fire':'🔥', 'smile':'😊', 'sunglasses':'😎', 'sparkle':'✨', 'blue':'💙', 'kiss':'😘', 'camera':'📷', 'USA':'🇺🇸', 'sun':'☀' , 'purple':'💜', 'blink':'😉', 'hundred':'💯', 'beam':'😁', 'tree':'🎄', 'flash':'📸', 'tongue':'😜'} | |
| # Extract the dictionary from the list | |
| try: | |
| # code that may raise an exception | |
| d = output_temp[0] | |
| except: | |
| pass | |
| # Extract the text from the 'generated_text' key | |
| text = d['generated_text'] | |
| # my_dict = {} | |
| # my_dict[str(text_to_emoji[text.split(' ')[0]])] = 0.99 | |
| return text_to_emoji[text.split(' ')[0]] | |
| # greet("sun") | |
| interface = gr.Interface( | |
| fn = _method, | |
| inputs=gr.Textbox(placeholder="Enter sentence here..."), | |
| outputs="text", | |
| examples=config.EX_LIST, | |
| live = True, | |
| title = 'Switch-Base-8', | |
| description=config.description, | |
| article = '', | |
| ) | |
| list_interface.append(interface) | |
| list_title.append('Switch-Base-8') | |
| # About us | |
| def _method(input_rating): | |
| # tokenizer = AutoTokenizer.from_pretrained(config.CHECKPOINT_BERT) | |
| # model_loaded = torch.load('/content/NEW_MODELS_Imbalance/Bert/g_ex3_bert_multi_fed_data_epoch_2.pt', map_location=torch.device('cpu')) | |
| if input_rating <=2: | |
| return {'🔥': 0.6, '✨': 0.3, '💯': 0.1} | |
| elif input_rating <= 4 and input_rating >2: | |
| return {'✨': 0.6, '😉': 0.3, '💯': 0.1} | |
| elif input_rating >4: | |
| return {'😍': 0.6, '💯': 0.3, '💕': 0.1} | |
| # return test_with_sentance(text , config.model_loaded_bert_multi_NONIID , config.tokenizer_bert) | |
| # greet("sun") | |
| interface = gr.Interface( | |
| fn = _method, | |
| inputs=gr.Slider(1, 5, value=4), | |
| outputs="label", | |
| # examples=config.EX_LIST, | |
| live = True, | |
| title = 'About us', | |
| description='We don\'t have sad emoji so our rating will always be great. 😂', | |
| # CSS Source : https://codepen.io/bibiangel199/pen/warevP | |
| article = config.article + '<!-- this is the markup. you can change the details (your own name, your own avatar etc.) but don’t change the basic structure! --> <div class="div_table_"> <table class="table"> <tr> <td><aside class="profile-card"> <div class="mask-shadow"></div> <header> <!-- here’s the avatar --> <a href="https://www.linkedin.com/in/hossam-amer-23b9329b/"> <img src="https://drive.google.com/uc?export=view&id=1-C_UIimeqbofJC_lldC7IQzIOX_OYRSn"> </a> <!-- the username --> <h1 style = " font-size:20px; padding:20px; color:#444; margin-bottom:5px; " >Dr. Hossam Amer</h1> <!-- and role or location --> <h2 style = " font-size:14px; color:#acacac; text- margin:0; " >Research Scientist at Microsoft</h2> </header> </aside></td> </tr> </table> </div> <div class="div_table_"> <table class="table"> <tr> <td><aside class="profile-card"> <div class="mask-shadow"></div> <header> <!-- here’s the avatar --> <a href="https://www.linkedin.com/in/ahmed-mohamed-gaber-143b25175/"> <img src="https://drive.google.com/uc?export=view&id=1OiGZwhL23PYhIJzQexYvPDFRrgUIprMj"> </a> <!-- the username --> <h1 style = " font-size:20px; padding:20px; color:#444; margin-bottom:5px; ">Ahmed Gaber</h1> <!-- and role or location --> <h2 style = " font-size:14px; color:#acacac; text- margin:0; " >Master\'s student at Queen\'s University</h2> </header> </aside></td> <td><aside class="profile-card"> <div class="mask-shadow"></div> <header> <!-- here’s the avatar --> <a href="https://www.linkedin.com/in/karim-gamal-mahmoud/"> <img src="https://drive.google.com/uc?export=view&id=1Lg2RzimITL9y__X2hycBTX10rJ4o87Ax"> </a> <!-- the username --> <h1 style=" font-size:20px; padding:20px; color:#444; margin-bottom:5px; ">Karim Gamal</h1> <!-- and role or location --> <h2 style = " font-size:14px; color:#acacac; text- margin:0; " >Master\'s student at Queen\'s University</h2> </header> </aside></td> </tr> </table> </div>', | |
| ) | |
| list_interface.append(interface) | |
| list_title.append('About us') | |
| demo = gr.TabbedInterface( | |
| list_interface, | |
| list_title, | |
| title='Multilingual Emoji Prediction Using Federated Learning', | |
| css='.gradio-container {color : orange}',) | |
| # css='.gradio-container {background-color: white; color : orange}',) | |
| demo.launch() | |