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Karim-Gamal commited on Apr 1, 2023

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app.py +326 -0
requirements.txt +7 -0

app.py ADDED Viewed

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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()

requirements.txt ADDED Viewed

	@@ -0,0 +1,7 @@

+transformers[sentencepiece]
+argparse
+torch
+scipy
+python-csv
+urllib3
+numpy