File size: 3,589 Bytes
d4b5af9
 
5f9120d
 
d4b5af9
 
 
 
 
 
64a1aa6
 
d4b5af9
2f7d4d8
d4b5af9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
64a1aa6
 
d4b5af9
9da0b35
d4b5af9
 
 
 
7827790
64a1aa6
 
 
d4b5af9
0fab62c
 
 
 
64a1aa6
 
 
 
 
0fab62c
 
d4b5af9
64a1aa6
 
d4b5af9
 
 
64a1aa6
0fab62c
d4b5af9
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
import transformers
import sentencepiece
import torch
import numpy as np

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):
  prompt = "context: {} answer: {}".format(sentence,answer)
  print (prompt)
  max_len = 256
  encoding = tknizer.encode_plus(prompt,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


Text = "Elon Musk said that Tesla will not accept payments in Bitcoin because of environmental concerns."
Answer = "Elon Musk"

ques = get_question(Text,Answer,question_model,question_tokenizer)
print ("question: ",ques)

import gradio as gr

title = "Question Generator Three"
description = "Paste or write a text. You may also paste or write a short answer, preferably a noun or noun phrase. Submit and the machine will attempt to generate a coherent question."
Text = 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")
examples = [
                                ["""Fears of a new Covid-19 cluster linked to a hotpot restaurant have surfaced amid Hong Kong’s Omicron-fuelled fifth wave, while infections tied to an investment bank continued to expand, triggering the evacuation of residents in a building after vertical transmission of the virus was detected.
On Wednesday, hundreds thronged Covid-19 testing stations in Tuen Mun, with some residents complaining of long waiting times and chaotic arrangements. Authorities have deemed the district a high-risk area because of a higher number of infections.
Health officials said sewage testing would be conducted in Tuen Mun to monitor the spread of the coronavirus, but a string of preliminary-positive cases detected across the city suggested a wider, more worrying situation.
""", "a higher number of infections"],
                                ["""Squid Game made history on Wednesday as the first non-English-language television series and the first Korean series to score a nomination for a Screen Actors Guild Award.
The hit Netflix show, created by Hwang Dong-hyuk, is nominated for ensemble in a drama series alongside The Handmaid’s Tale, The Morning Show, Succession and Yellowstone.
Squid Game stars Lee Jung-jae and Jung Ho-yeon also landed individual nominations for actor and actress in a drama series, respectively.
""", "Yellowstone"]
                               
]

def generate_question(Text,Answer):
  return get_question(Text,Answer,question_model,question_tokenizer)

iface = gr.Interface(
  fn=generate_question, 
  inputs=[Text,Answer], 
  outputs=question, title=title, description=description, examples=examples)
iface.launch(debug=False)