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from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import gradio as gr
model_path = "jb2k/bert-base-multilingual-cased-language-detection"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
language_dict = {0: 'Arabic',
1: 'Basque',
2: 'Breton',
3: 'Catalan',
4: 'Chinese_China',
5: 'Chinese_Hongkong',
6: 'Chinese_Taiwan',
7: 'Chuvash',
8: 'Czech',
9: 'Dhivehi',
10: 'Dutch',
11: 'English',
12: 'Esperanto',
13: 'Estonian',
14: 'French',
15: 'Frisian',
16: 'Georgian',
17: 'German',
18: 'Greek',
19: 'Hakha_Chin',
20: 'Indonesian',
21: 'Interlingua',
22: 'Italian',
23: 'Japanese',
24: 'Kabyle',
25: 'Kinyarwanda',
26: 'Kyrgyz',
27: 'Latvian',
28: 'Maltese',
29: 'Mongolian',
30: 'Persian',
31: 'Polish',
32: 'Portuguese',
33: 'Romanian',
34: 'Romansh_Sursilvan',
35: 'Russian',
36: 'Sakha',
37: 'Slovenian',
38: 'Spanish',
39: 'Swedish',
40: 'Tamil',
41: 'Tatar',
42: 'Turkish',
43: 'Ukranian',
44: 'Welsh'}
examples = ['Transformers are really cool!', 'Трансформеры действительно классные!', '¡Los transformadores son realmente geniales!']
def inference(sentence):
tokenized_sentence = tokenizer(sentence, return_tensors='pt')
output = model(**tokenized_sentence)
predictions = torch.nn.functional.softmax(output.logits, dim=-1)
certainy, highest_value = torch.max(predictions, dim=-1, keepdim=False, out=None)
highest_value_int = highest_value.item()
language = language_dict[highest_value_int]
return language
if __name__ == '__main__':
interFace = gr.Interface(fn=inference,
inputs=gr.inputs.Textbox(placeholder="Enter text here", label="Text content", lines=5),
outputs=gr.outputs.Label(num_top_classes=6, label="Language of this text is "),
verbose=True,
examples = examples,
title="Language Detector",
description="Language detector with support for 45 languages. Created as part of the huggingface course community event.",
theme="grass")
interFace.launch()
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