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app.py

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+import torch
+from transformers import (
+  AutoTokenizer, 
+  AutoModelForSeq2SeqLM,
+  AdamW,
+  get_linear_schedule_with_warmup
+)
+
+model = AutoModelForSeq2SeqLM.from_pretrained("ADEL/ANQG")
+tokenizer = AutoTokenizer.from_pretrained("ADEL/ANQG")  
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print("device",device)
+model=model.to(device)
+
+import gradio as gr
+def generate__questions(cnt,ans):
+  text="context: " +cnt + " " + "answer: " + ans + " </s>"
+  text_encoding = tokenizer.encode_plus(
+      text,max_length=512,padding=True,return_tensors="pt"
+  )
+  model.eval()
+  generated_ids =  model.generate(
+    input_ids=text_encoding['input_ids'].to(device),
+    attention_mask=text_encoding['attention_mask'].to(device),
+    max_length=72,
+    early_stopping=True,
+    num_beams=5,
+    num_return_sequences=1
+  )
+
+  preds = [
+            tokenizer.decode(gen_id,skip_special_tokens=True,clean_up_tokenization_spaces=True)
+            for gen_id in generated_ids
+    ]
+  
+  t=' '.join(preds)
+  st=t.replace('question: ',' ')
+  return(st)
+
+demo = gr.Interface(fn=generate__questions, inputs=[gr.Textbox(label='Context'),
+                                                    gr.Textbox(label='Answer')] ,
+                                                    outputs=gr.Textbox(label='Question'),
+                                                    title="Arabic Question Generation",
+                                                    description="Get the Question from given Context and a Answer")
+demo.launch()