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app.py

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+import gradio as gr
+from transformers import AutoModelWithLMHead, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+
+def get_question(context, answer, max_length=64):
+  input_text = "answer: %s  context: %s </s>" % (answer, context)
+  features = tokenizer([input_text], return_tensors='pt')
+
+  output = model.generate(input_ids=features['input_ids'], 
+               attention_mask=features['attention_mask'],
+               max_length=max_length)
+
+  return tokenizer.decode(output[0])[16:-4]
+
+examples = [["The world's first piece of software was written by a computer scientist named Tom Kilburn in 1948.", "1948"], ["The world's first piece of software was written by a computer scientist named Tom Kilburn in 1948.", "Tom Kilburn"], ["The world's first piece of software was written by a computer scientist named Tom Kilburn in 1948.", "computer scientist"]]
+
+css = """
+.footer {display:none !important}
+"""
+
+demo = gr.Interface(fn=get_question, inputs=[gr.Textbox(lines=3, placeholder="Enter text here", label="Context"), gr.Textbox(lines=1, label="Answer")], outputs=gr.Textbox(label="Generated Question"), examples=examples, css=css)
+demo.launch()

requirements.txt

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+torch
+timm
+sentencepiece
+transformers