Update app.py

app.py

@@ -4,16 +4,11 @@ import gradio as gr
 tokenizer = AutoTokenizer.from_pretrained("PRAli22/arat5-base-arabic-dialects-translation" )
 model = AutoModelForSeq2SeqLM.from_pretrained("PRAli22/arat5-base-arabic-dialects-translation")
 
-class Translator:
-    def __init__(self, model:AutoModelForSeq2SeqLM, tokenizer:AutoTokenizer):
-        self.model = model
-        self.tokenizer = tokenizer
-
-    def translate(self, source:str) -> str:
+def translate(source):
        
-        encoding = self.tokenizer.encode_plus(source, pad_to_max_length=True, return_tensors="pt")
+        encoding = tokenizer.encode_plus(source, pad_to_max_length=True, return_tensors="pt")
         input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
-        outputs = self.model.generate(
+        outputs = model.generate(
             input_ids=input_ids, attention_mask=attention_masks,
             max_length=256,
             do_sample=True,
@@ -22,17 +17,16 @@ class Translator:
             early_stopping=True,
             num_return_sequences=1
         )
-        translation = self.tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
+        translation = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
         return translation
 
 
-translator = Translator(model, tokenizer)
 
 
 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=translator.translate(),
+    fn=translate,
     inputs=
         gr.Textbox(label="text", placeholder="Enter the sentence "),