app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import torch
+import random
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelWithLMHead
+from sentence_splitter import SentenceSplitter, split_text_into_sentences
+splitter = SentenceSplitter(language='en')
+
+if torch.cuda.is_available():
+  torch_device="cuda:0"
+else:
+  torch_device="cpu"
+
+ptokenizer = AutoTokenizer.from_pretrained("tuner007/pegasus_paraphrase")
+pmodel = AutoModelForSeq2SeqLM.from_pretrained("tuner007/pegasus_paraphrase").to(torch_device)
+
+def get_answer(input_text,num_return_sequences,num_beams):
+  batch = ptokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
+  translated = pmodel.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
+  tgt_text = ptokenizer.batch_decode(translated, skip_special_tokens=True)
+  return tgt_text
+
+qtokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+qmodel = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap").to(torch_device)
+
+def get_question(answer, context, max_length=64):
+  input_text = "answer: %s  context: %s </s>" % (answer, context)
+  features = qtokenizer([input_text], return_tensors='pt').to(torch_device)
+
+  output = qmodel.generate(input_ids=features['input_ids'], 
+               attention_mask=features['attention_mask'],
+               max_length=max_length)
+
+  return qtokenizer.decode(output[0])
+
+def getqna(input):
+  input=split_text_into_sentences(text=input, language='en')
+  if len(input)==0:
+    answer= get_answer(input,10,10)[random.randint(0, 9)]
+  else:
+    sentences=[get_answer(sentence,10,10)[random.randint(0, 9)] for sentence in input]
+    answer= " ".join(sentences)
+  answer= get_answer(answer,10,10)[random.randint(0, 9)]
+  question= get_question(answer, input).replace("<pad>","").replace("</s>","")
+  return "%s \n answer:%s" % (question, answer)
+
+app = gr.Interface(fn=getqna, inputs="text", outputs="text")
+app.launch()