Create app.py

app.py

@@ -0,0 +1,53 @@
+import transformers
+import sentencepiece
+import ipython-autotime
+
+from transformers import T5ForConditionalGeneration,T5Tokenizer
+question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
+question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')
+
+def get_question(sentence,answer,mdl,tknizer):
+  text = "context: {} answer: {}".format(sentence,answer)
+  print (text)
+  max_len = 256
+  encoding = tknizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors="pt")
+
+  input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
+
+  outs = mdl.generate(input_ids=input_ids,
+                                  attention_mask=attention_mask,
+                                  early_stopping=True,
+                                  num_beams=5,
+                                  num_return_sequences=1,
+                                  no_repeat_ngram_size=2,
+                                  max_length=300)
+
+
+  dec = [tknizer.decode(ids,skip_special_tokens=True) for ids in outs]
+
+
+  Question = dec[0].replace("question:","")
+  Question= Question.strip()
+  return Question
+
+
+context = "Elon Musk said that Tesla will not accept payments in Bitcoin because of environmental concerns."
+answer = "Elon Musk"
+
+ques = get_question(context,answer,question_model,question_tokenizer)
+print ("question: ",ques)
+
+import gradio as gr
+
+context = gr.inputs.Textbox(lines=5, placeholder="Enter paragraph/context here...")
+answer = gr.inputs.Textbox(lines=3, placeholder="Enter answer/keyword here...")
+question = gr.outputs.Textbox( type="auto", label="Question")
+
+def generate_question(context,answer):
+  return get_question(context,answer,question_model,question_tokenizer)
+
+iface = gr.Interface(
+  fn=generate_question, 
+  inputs=[context,answer], 
+  outputs=question)
+iface.launch(debug=False)