import gradio as gr

context = gr.inputs.Textbox(lines=10, placeholder="Enter Formatted Paragraph/Content here...")
output = gr.outputs.HTML(label="Question and Answers")
radiobutton = gr.inputs.Radio(["Wordnet", "Sense2Vec"])

def generate_question(context,radiobutton):
  summary_text = summarizer(context,summary_model,summary_tokenizer)
  for wrp in wrap(summary_text, 250):
    print (wrp)
  # np = getnounphrases(summary_text,sentence_transformer_model,3)
  np =  get_keywords(context,summary_text)
  print ("\n\nNoun phrases",np)
  output=""
  for answer in np:
    ques = get_question(summary_text,answer,question_model,question_tokenizer)
    if radiobutton=="Wordnet":
      distractors = get_distractors_wordnet(answer)
    else:
      distractors = get_distractors(answer.capitalize(),ques,s2v,sentence_transformer_model,40,0.2)
    # output= output + ques + "\n" + "Ans: "+answer.capitalize() + "\n\n"
    output = output + "<b style='color:blue;'>" + ques + "</b> <br/>"
    # output = output + "<br>"
    output = output + "<b style='color:green;'>" + "Ans: " +answer.capitalize()+  "</b> <br/>"
    if len(distractors)>0:
      for distractor in distractors[:4]:
        output = output + "<b style='color:brown;'>" + distractor+  "</b> <br/>"
    output = output + "<br>"

  summary ="Summary: "+ summary_text
  for answer in np:
    summary = summary.replace(answer,"<b>"+answer+"</b> ")
    summary = summary.replace(answer.capitalize(),"<b>"+answer.capitalize()+"</b>")
  output = output + "<p>"+summary+"</p>"
  return output


iface = gr.Interface(
  fn=generate_question, 
  inputs=[context,radiobutton], 
  outputs=output,
  title="Automatic Question Generation using NLP")
iface.launch(inline = False)