9d2adf338d
* FEAT: Basic app completed & file uploading added * Added pdf/txt support and quiz template * FEAT: PDF/TEXT uploading and getting file contents done * Updated .gitignore * Modified quiz template * Fix UI - Added CSS to upload page * Feat Quiz UI - Added CSS to Quiz Page * Feat: Question Extraction - Extract questions from raw text * Feat: added incorrect answer generation * Feat: Tied together question extraction and incorrect anwer generation * Fix: handle word not in vocabulary error * Fix: added document as argument to get_questions_dict * Feat: Integrated uploading file and question generation; needs fixes * Feat Quiz UI - added correct answer UI * Feat: Clean text for better processing * Fix UI - added gradient and spacing * Feat UI - Added uploading animation * Fixed options issue on quiz screen * Fix UI - added fonts, favicon etc * Fix - Formatted code * Feat UI - added fork me on github ribbon * Feat: Added quiz result functionality * Fix UI - added text to landing page * Fix UI - added CSS to submit button * Feat UI - added CSS to results page Co-authored-by: user86 <kshitij.kotasthane@gmail.com> Co-authored-by: telescopic <vigneshsuresh1775@gmail.com>
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
''' This module contains the class
|
|
for generating incorrect alternative
|
|
answers for a given answer
|
|
'''
|
|
import gensim
|
|
import gensim.downloader as api
|
|
from gensim.models import Word2Vec
|
|
from nltk.tokenize import sent_tokenize, word_tokenize
|
|
import random
|
|
import numpy as np
|
|
|
|
|
|
class IncorrectAnswerGenerator:
|
|
''' This class contains the methods
|
|
for generating the incorrect answers
|
|
given an answer
|
|
'''
|
|
|
|
def __init__(self, document):
|
|
# model required to fetch similar words
|
|
self.model = api.load("glove-wiki-gigaword-100")
|
|
self.all_words = []
|
|
for sent in sent_tokenize(document):
|
|
self.all_words.extend(word_tokenize(sent))
|
|
self.all_words = list(set(self.all_words))
|
|
|
|
def get_all_options_dict(self, answer, num_options):
|
|
''' This method returns a dict
|
|
of 'num_options' options out of
|
|
which one is correct and is the answer
|
|
'''
|
|
options_dict = dict()
|
|
try:
|
|
similar_words = self.model.similar_by_word(answer, topn=15)[::-1]
|
|
|
|
for i in range(1, num_options + 1):
|
|
options_dict[i] = similar_words[i - 1][0]
|
|
|
|
except BaseException:
|
|
self.all_sim = []
|
|
for word in self.all_words:
|
|
if word not in answer:
|
|
try:
|
|
self.all_sim.append(
|
|
(self.model.similarity(answer, word), word))
|
|
except BaseException:
|
|
self.all_sim.append(
|
|
(0.0, word))
|
|
else:
|
|
self.all_sim.append((-1.0, word))
|
|
|
|
self.all_sim.sort(reverse=True)
|
|
|
|
for i in range(1, num_options + 1):
|
|
options_dict[i] = self.all_sim[i - 1][1]
|
|
|
|
replacement_idx = random.randint(1, num_options)
|
|
|
|
options_dict[replacement_idx] = answer
|
|
|
|
return options_dict
|