Uploaded initial app.py

app.py

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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'}
+
+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 ("This sentence is in " + language + ". Certainy: " + str(certainy.item()))
+  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,
+                              title="Language of a sequence of text",
+                              description="",
+                              theme="grass")
+  interFace.launch()
+