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app.py

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+from transformers import AutoModelWithLMHead, AutoTokenizer
+import gradio as grad
+
+text2text_tkn = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+mdl = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+
+def text2text(context,answer):
+    input_text = "answer: %s  context: %s </s>" % (answer, context)
+    features = text2text_tkn ([input_text], return_tensors='pt')
+
+    output = mdl.generate(input_ids=features['input_ids'], 
+               attention_mask=features['attention_mask'],
+               max_length=64)
+
+    response=text2text_tkn.decode(output[0])    
+    return response
+
+context=grad.Textbox(lines=10, label="English", placeholder="Context")
+ans=grad.Textbox(lines=1, label="Answer")
+out=grad.Textbox(lines=1, label="Genereated Question")
+grad.Interface(text2text, inputs=[context,ans], outputs=out).launch()

requirements.txt

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+gradio
+transformers
+torch
+transformers[sentencepiece]