from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import gradio as gr tokenizer = AutoTokenizer.from_pretrained("PRAli22/arat5-arabic-dialects-translation" ) model = AutoModelForSeq2SeqLM.from_pretrained("PRAli22/arat5-arabic-dialects-translation") def translate(source): encoding = tokenizer.encode_plus(source, pad_to_max_length=True, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"] outputs = model.generate( input_ids=input_ids, attention_mask=attention_masks, max_length=256, do_sample=True, top_k=120, top_p=0.95, early_stopping=True, num_return_sequences=1 ) translation = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True) return translation css_code='body{background-image:url("https://media.istockphoto.com/id/1256252051/vector/people-using-online-translation-app.jpg?s=612x612&w=0&k=20&c=aa6ykHXnSwqKu31fFR6r6Y1bYMS5FMAU9yHqwwylA94=");}' demo = gr.Interface( fn=translate, inputs= gr.Textbox(label="text", placeholder="Enter the sentence "), outputs=gr.Textbox(label="Translation"), title="Arabic Dialects Translator", description= "This is Arabic dialects machine translation, it takes an arabian dialect sentence as input and returns it's MSA translation", css = css_code ) demo.launch()