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Federated-Learning-Based-Multilingual-Emoji-Prediction-Demo / app.py
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# !pip install argparse
import gradio as gr
import requests
import argparse
args_dict = dict(
EX_LIST = [["This is wonderful!"],
["Nice car"],
["La France est la meilleure รฉquipe du monde"],
["Visca Barca"],
["Hala Madrid"],
["Buongiorno"],
# ["Auf einigen deutschen StraรŸen gibt es kein Radar"],
["Tempo soleggiato in Italia"],
["Bonjour"],
["ุตุจุงุญ ุงู„ุฎูŠุฑ"],
["ุงูƒู„ ุฒูˆุฌุชูŠ ุฌู…ูŠู„"],
],
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)
list_interface = []
list_title = []
# 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)
# the MMiniLM model
API_URL_MMiniLM = "https://api-inference.huggingface.co/models/Karim-Gamal/MMiniLM-L12-finetuned-emojis-IID-Fed"
headers_MMiniLM = {"Authorization": "Bearer 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"}
def query_MMiniLM(payload):
response = requests.post(API_URL_MMiniLM, headers=headers_MMiniLM, json=payload)
return response.json()
query_MMiniLM({ "inputs": 'test',})
def _method(text):
text = preprocess(text)
output_temp = query_MMiniLM({
"inputs": text,
})
# output_dict = {d['label']: d['score'] for d in output_temp[0]}
if output_temp:
try:
output_dict = {d['label']: d['score'] for d in output_temp[0]}
except:
pass
else:
# handle the case where output_temp is empty
output_dict = {}
input_list = list(output_dict.items())[:3]
output_dict = {key: value for key, value in input_list}
return output_dict
# 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')
# the XLM model
API_URL_XLM = "https://api-inference.huggingface.co/models/Karim-Gamal/XLM-Roberta-finetuned-emojis-IID-Fed"
headers_XLM = {"Authorization": "Bearer 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"}
def query_XLM(payload):
response = requests.post(API_URL_XLM, headers=headers_XLM, json=payload)
return response.json()
query_XLM({ "inputs": 'test',})
def _method(text):
text = preprocess(text)
output_temp = query_XLM({
"inputs": text,
})
# output_dict = {d['label']: d['score'] for d in output_temp[0]}
if output_temp:
output_dict = {d['label']: d['score'] for d in output_temp[0]}
else:
output_dict = {}
try:
input_list = list(output_dict.items())[:3]
except:
pass
output_dict = {key: value for key, value in input_list}
return output_dict
# 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')
# the bert model
API_URL_BERT = "https://api-inference.huggingface.co/models/Karim-Gamal/BERT-base-finetuned-emojis-IID-Fed"
headers_BERT = {"Authorization": "Bearer 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"}
def query_BERT(payload):
response = requests.post(API_URL_BERT, headers=headers_BERT, json=payload)
return response.json()
query_BERT({ "inputs": 'test',})
def _method(text):
text = preprocess(text)
output_temp = query_BERT({
"inputs": text,
})
# output_dict = {d['label']: d['score'] for d in output_temp[0]}
if output_temp:
try:
output_dict = {d['label']: d['score'] for d in output_temp[0]}
except:
pass
else:
# handle the case where output_temp is empty
output_dict = {}
try:
input_list = list(output_dict.items())[:3]
except:
pass
output_dict = {key: value for key, value in input_list}
return output_dict
# 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')
# the 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' : 'โค', 'face': '๐Ÿ˜', 'joy':'๐Ÿ˜‚', 'love':'๐Ÿ’•', 'fire':'๐Ÿ”ฅ', 'smile':'๐Ÿ˜Š', 'sunglasses':'๐Ÿ˜Ž', 'sparkle':'โœจ', 'blue':'๐Ÿ’™', 'kiss':'๐Ÿ˜˜', 'camera':'๐Ÿ“ท', 'USA':'๐Ÿ‡บ๐Ÿ‡ธ', 'sun':'โ˜€' , 'purple':'๐Ÿ’œ', 'blink':'๐Ÿ˜‰', 'hundred':'๐Ÿ’ฏ', 'beam':'๐Ÿ˜', 'tree':'๐ŸŽ„', 'flash':'๐Ÿ“ธ', 'tongue':'๐Ÿ˜œ'}
# Extract the dictionary from the list
try:
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')
import time
# delay of 40 seconds
time.sleep(40)
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 {'๐Ÿ”ฅ': 0.6, 'โœจ': 0.3, '๐Ÿ’ฏ': 0.1}
elif input_rating <= 4 and input_rating >2:
return {'โœจ': 0.6, '๐Ÿ˜‰': 0.3, '๐Ÿ’ฏ': 0.1}
elif input_rating >4:
return {'๐Ÿ˜': 0.6, '๐Ÿ’ฏ': 0.3, '๐Ÿ’•': 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. ๐Ÿ˜‚',
# 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 donโ€™t change the basic structure! --> <div class="div_table_"> <table class="table"> <tr> <td><aside class="profile-card"> <div class="mask-shadow"></div> <header> <!-- hereโ€™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> <!-- hereโ€™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> <!-- hereโ€™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='Federated-Learning-Based-Multilingual-Emoji-Prediction',
css='.gradio-container {color : orange}',)
# css='.gradio-container {background-color: white; color : orange}',)
demo.launch()