Create app.py

app.py

@@ -0,0 +1,47 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+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:
+       
+        encoding = self.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(
+            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 = self.tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
+        return translation
+
+
+translator = Translator(model, tokenizer)
+
+translation = translator.translate()
+
+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=translation,
+    inputs=
+        gr.Textbox(label="text", placeholder="Enter the text "),
+    
+    outputs=gr.Textbox(label="summary"),
+    title="Text Summarizer",
+    description= "This is  Text Summarizer System, it takes a text  in English as inputs and returns it's summary",
+    css = css_code
+)
+
+demo.launch()
+