import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import time import torch from transformers import T5ForConditionalGeneration,T5Tokenizer import random import spacy import zipfile import os import json from sense2vec import Sense2Vec import requests from collections import OrderedDict import string import pke import nltk import numpy from nltk import FreqDist nltk.download('brown', quiet=True, force=True) nltk.download('stopwords', quiet=True, force=True) nltk.download('popular', quiet=True, force=True) from nltk.corpus import stopwords from nltk.corpus import brown from similarity.normalized_levenshtein import NormalizedLevenshtein from nltk.tokenize import sent_tokenize from flashtext import KeywordProcessor # from Questgen.encoding.encoding import beam_search_decoding # from Questgen.mcq.mcq import tokenize_sentences # from Questgen.mcq.mcq import get_keywords # from Questgen.mcq.mcq import get_sentences_for_keyword # from Questgen.mcq.mcq import generate_questions_mcq # from Questgen.mcq.mcq import generate_normal_questions import time import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import time import torch from transformers import T5ForConditionalGeneration,T5Tokenizer import random import spacy import zipfile import os import json from sense2vec import Sense2Vec import requests from collections import OrderedDict import string import pke import nltk from nltk import FreqDist nltk.download('brown') nltk.download('stopwords') nltk.download('popular') from nltk.corpus import stopwords from nltk.corpus import brown # from similarity.normalized_levenshtein import NormalizedLevenshtein from nltk.tokenize import sent_tokenize # from flashtext import KeywordProcessor def beam_search_decoding (inp_ids,attn_mask,model,tokenizer): beam_output = model.generate(input_ids=inp_ids, attention_mask=attn_mask, max_length=256, num_beams=10, num_return_sequences=3, no_repeat_ngram_size=2, early_stopping=True ) Questions = [tokenizer.decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True) for out in beam_output] return [Question.strip().capitalize() for Question in Questions] def MCQs_available(word,s2v): word = word.replace(" ", "_") sense = s2v.get_best_sense(word) if sense is not None: return True else: return False def edits(word): "All edits that are one edit away from `word`." letters = 'abcdefghijklmnopqrstuvwxyz '+string.punctuation splits = [(word[:i], word[i:]) for i in range(len(word) + 1)] deletes = [L + R[1:] for L, R in splits if R] transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1] replaces = [L + c + R[1:] for L, R in splits if R for c in letters] inserts = [L + c + R for L, R in splits for c in letters] return set(deletes + transposes + replaces + inserts) def sense2vec_get_words(word,s2v): output = [] word_preprocessed = word.translate(word.maketrans("","", string.punctuation)) word_preprocessed = word_preprocessed.lower() word_edits = edits(word_preprocessed) word = word.replace(" ", "_") sense = s2v.get_best_sense(word) most_similar = s2v.most_similar(sense, n=15) compare_list = [word_preprocessed] for each_word in most_similar: append_word = each_word[0].split("|")[0].replace("_", " ") append_word = append_word.strip() append_word_processed = append_word.lower() append_word_processed = append_word_processed.translate(append_word_processed.maketrans("","", string.punctuation)) if append_word_processed not in compare_list and word_preprocessed not in append_word_processed and append_word_processed not in word_edits: output.append(append_word.title()) compare_list.append(append_word_processed) out = list(OrderedDict.fromkeys(output)) return out def get_options(answer,s2v): distractors =[] try: distractors = sense2vec_get_words(answer,s2v) if len(distractors) > 0: print(" Sense2vec_distractors successful for word : ", answer) return distractors,"sense2vec" except: print (" Sense2vec_distractors failed for word : ",answer) return distractors,"None" def tokenize_sentences(text): sentences = [sent_tokenize(text)] sentences = [y for x in sentences for y in x] # Remove any short sentences less than 20 letters. sentences = [sentence.strip() for sentence in sentences if len(sentence) > 20] return sentences def get_sentences_for_keyword(keywords, sentences): keyword_processor = KeywordProcessor() keyword_sentences = {} for word in keywords: word = word.strip() keyword_sentences[word] = [] keyword_processor.add_keyword(word) for sentence in sentences: keywords_found = keyword_processor.extract_keywords(sentence) for key in keywords_found: keyword_sentences[key].append(sentence) for key in keyword_sentences.keys(): values = keyword_sentences[key] values = sorted(values, key=len, reverse=True) keyword_sentences[key] = values delete_keys = [] for k in keyword_sentences.keys(): if len(keyword_sentences[k]) == 0: delete_keys.append(k) for del_key in delete_keys: del keyword_sentences[del_key] return keyword_sentences def is_far(words_list,currentword,thresh,normalized_levenshtein): threshold = thresh score_list =[] for word in words_list: score_list.append(normalized_levenshtein.distance(word.lower(),currentword.lower())) if min(score_list)>=threshold: return True else: return False def filter_phrases(phrase_keys,max,normalized_levenshtein ): filtered_phrases =[] if len(phrase_keys)>0: filtered_phrases.append(phrase_keys[0]) for ph in phrase_keys[1:]: if is_far(filtered_phrases,ph,0.7,normalized_levenshtein ): filtered_phrases.append(ph) if len(filtered_phrases)>=max: break return filtered_phrases def get_nouns_multipartite(text): out = [] extractor = pke.unsupervised.MultipartiteRank() extractor.load_document(input=text, language='en') pos = {'PROPN', 'NOUN'} stoplist = list(string.punctuation) stoplist += stopwords.words('english') extractor.candidate_selection(pos=pos) # 4. build the Multipartite graph and rank candidates using random walk, # alpha controls the weight adjustment mechanism, see TopicRank for # threshold/method parameters. try: extractor.candidate_weighting(alpha=1.1, threshold=0.75, method='average') except: return out keyphrases = extractor.get_n_best(n=10) for key in keyphrases: out.append(key[0]) return out def get_phrases(doc): phrases={} for np in doc.noun_chunks: phrase =np.text len_phrase = len(phrase.split()) if len_phrase > 1: if phrase not in phrases: phrases[phrase]=1 else: phrases[phrase]=phrases[phrase]+1 phrase_keys=list(phrases.keys()) phrase_keys = sorted(phrase_keys, key= lambda x: len(x),reverse=True) phrase_keys=phrase_keys[:50] return phrase_keys def get_keywords(nlp,text,max_keywords,s2v,fdist,normalized_levenshtein,no_of_sentences): doc = nlp(text) max_keywords = int(max_keywords) keywords = get_nouns_multipartite(text) keywords = sorted(keywords, key=lambda x: fdist[x]) keywords = filter_phrases(keywords, max_keywords,normalized_levenshtein ) phrase_keys = get_phrases(doc) filtered_phrases = filter_phrases(phrase_keys, max_keywords,normalized_levenshtein ) total_phrases = keywords + filtered_phrases total_phrases_filtered = filter_phrases(total_phrases, min(max_keywords, 2*no_of_sentences),normalized_levenshtein ) answers = [] for answer in total_phrases_filtered: if answer not in answers and MCQs_available(answer,s2v): answers.append(answer) answers = answers[:max_keywords] return answers def generate_questions_mcq(keyword_sent_mapping,device,tokenizer,model,sense2vec,normalized_levenshtein): batch_text = [] answers = keyword_sent_mapping.keys() for answer in answers: txt = keyword_sent_mapping[answer] context = "context: " + txt text = context + " " + "answer: " + answer + " " batch_text.append(text) encoding = tokenizer.batch_encode_plus(batch_text, pad_to_max_length=True, return_tensors="pt") print ("Running model for generation") input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) with torch.no_grad(): outs = model.generate(input_ids=input_ids, attention_mask=attention_masks, max_length=150) output_array ={} output_array["questions"] =[] # print(outs) for index, val in enumerate(answers): individual_question ={} out = outs[index, :] dec = tokenizer.decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True) Question = dec.replace("question:", "") Question = Question.strip() individual_question["question_statement"] = Question individual_question["question_type"] = "MCQ" individual_question["answer"] = val individual_question["id"] = index+1 individual_question["options"], individual_question["options_algorithm"] = get_options(val, sense2vec) individual_question["options"] = filter_phrases(individual_question["options"], 10,normalized_levenshtein) index = 3 individual_question["extra_options"]= individual_question["options"][index:] individual_question["options"] = individual_question["options"][:index] individual_question["context"] = keyword_sent_mapping[val] if len(individual_question["options"])>0: output_array["questions"].append(individual_question) return output_array def generate_normal_questions(keyword_sent_mapping,device,tokenizer,model): #for normal one word questions batch_text = [] answers = keyword_sent_mapping.keys() for answer in answers: txt = keyword_sent_mapping[answer] context = "context: " + txt text = context + " " + "answer: " + answer + " " batch_text.append(text) encoding = tokenizer.batch_encode_plus(batch_text, pad_to_max_length=True, return_tensors="pt") print ("Running model for generation") input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) with torch.no_grad(): outs = model.generate(input_ids=input_ids, attention_mask=attention_masks, max_length=150) output_array ={} output_array["questions"] =[] for index, val in enumerate(answers): individual_quest= {} out = outs[index, :] dec = tokenizer.decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True) Question= dec.replace('question:', '') Question= Question.strip() individual_quest['Question']= Question individual_quest['Answer']= val individual_quest["id"] = index+1 individual_quest["context"] = keyword_sent_mapping[val] output_array["questions"].append(individual_quest) return output_array def random_choice(): a = random.choice([0,1]) return bool(a) class QGen: def __init__(self): self.tokenizer = T5Tokenizer.from_pretrained('t5-large') model = T5ForConditionalGeneration.from_pretrained('Parth/result') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) # model.eval() self.device = device self.model = model self.nlp = spacy.load('en_core_web_sm') self.s2v = Sense2Vec().from_disk('s2v_old') self.fdist = FreqDist(brown.words()) self.normalized_levenshtein = NormalizedLevenshtein() self.set_seed(42) def set_seed(self,seed): numpy.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) def predict_mcq(self, payload): start = time.time() inp = { "input_text": payload.get("input_text"), "max_questions": payload.get("max_questions", 4) } text = inp['input_text'] sentences = tokenize_sentences(text) joiner = " " modified_text = joiner.join(sentences) keywords = get_keywords(self.nlp,modified_text,inp['max_questions'],self.s2v,self.fdist,self.normalized_levenshtein,len(sentences) ) keyword_sentence_mapping = get_sentences_for_keyword(keywords, sentences) for k in keyword_sentence_mapping.keys(): text_snippet = " ".join(keyword_sentence_mapping[k][:3]) keyword_sentence_mapping[k] = text_snippet final_output = {} if len(keyword_sentence_mapping.keys()) == 0: return final_output else: try: generated_questions = generate_questions_mcq(keyword_sentence_mapping,self.device,self.tokenizer,self.model,self.s2v,self.normalized_levenshtein) except: return final_output end = time.time() final_output["statement"] = modified_text final_output["questions"] = generated_questions["questions"] final_output["time_taken"] = end-start if torch.device=='cuda': torch.cuda.empty_cache() return final_output def predict_shortq(self, payload): inp = { "input_text": payload.get("input_text"), "max_questions": payload.get("max_questions", 4) } text = inp['input_text'] sentences = tokenize_sentences(text) joiner = " " modified_text = joiner.join(sentences) keywords = get_keywords(self.nlp,modified_text,inp['max_questions'],self.s2v,self.fdist,self.normalized_levenshtein,len(sentences) ) keyword_sentence_mapping = get_sentences_for_keyword(keywords, sentences) for k in keyword_sentence_mapping.keys(): text_snippet = " ".join(keyword_sentence_mapping[k][:3]) keyword_sentence_mapping[k] = text_snippet final_output = {} if len(keyword_sentence_mapping.keys()) == 0: print('ZERO') return final_output else: generated_questions = generate_normal_questions(keyword_sentence_mapping,self.device,self.tokenizer,self.model) print(generated_questions) final_output["statement"] = modified_text final_output["questions"] = generated_questions["questions"] if torch.device=='cuda': torch.cuda.empty_cache() return final_output def paraphrase(self,payload): start = time.time() inp = { "input_text": payload.get("input_text"), "max_questions": payload.get("max_questions", 3) } text = inp['input_text'] num = inp['max_questions'] self.sentence= text self.text= "paraphrase: " + self.sentence + " " encoding = self.tokenizer.encode_plus(self.text,pad_to_max_length=True, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"].to(self.device), encoding["attention_mask"].to(self.device) beam_outputs = self.model.generate( input_ids=input_ids, attention_mask=attention_masks, max_length= 50, num_beams=50, num_return_sequences=num, no_repeat_ngram_size=2, early_stopping=True ) # print ("\nOriginal Question ::") # print (text) # print ("\n") # print ("Paraphrased Questions :: ") final_outputs =[] for beam_output in beam_outputs: sent = self.tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True) if sent.lower() != self.sentence.lower() and sent not in final_outputs: final_outputs.append(sent) output= {} output['Question']= text output['Count']= num output['Paraphrased Questions']= final_outputs for i, final_output in enumerate(final_outputs): print("{}: {}".format(i, final_output)) if torch.device=='cuda': torch.cuda.empty_cache() return output class BoolQGen: def __init__(self): self.tokenizer = T5Tokenizer.from_pretrained('t5-base') model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_boolean_questions') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) # model.eval() self.device = device self.model = model self.set_seed(42) def set_seed(self,seed): numpy.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) def random_choice(self): a = random.choice([0,1]) return bool(a) def predict_boolq(self,payload): start = time.time() inp = { "input_text": payload.get("input_text"), "max_questions": payload.get("max_questions", 4) } text = inp['input_text'] num= inp['max_questions'] sentences = tokenize_sentences(text) joiner = " " modified_text = joiner.join(sentences) answer = self.random_choice() form = "truefalse: %s passage: %s " % (modified_text, answer) encoding = self.tokenizer.encode_plus(form, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"].to(self.device), encoding["attention_mask"].to(self.device) output = beam_search_decoding(input_ids, attention_masks,self.model,self.tokenizer) if torch.device=='cuda': torch.cuda.empty_cache() final= {} final['Text']= text final['Count']= num final['Boolean Questions']= output return final class AnswerPredictor: def __init__(self): self.tokenizer = T5Tokenizer.from_pretrained('t5-large', model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('Parth/boolean') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) # model.eval() self.device = device self.model = model self.set_seed(42) def set_seed(self,seed): numpy.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) def greedy_decoding (inp_ids,attn_mask,model,tokenizer): greedy_output = model.generate(input_ids=inp_ids, attention_mask=attn_mask, max_length=256) Question = tokenizer.decode(greedy_output[0], skip_special_tokens=True,clean_up_tokenization_spaces=True) return Question.strip().capitalize() def predict_answer(self,payload): answers = [] inp = { "input_text": payload.get("input_text"), "input_question" : payload.get("input_question") } for ques in payload.get("input_question"): context = inp["input_text"] question = ques input = "question: %s context: %s " % (question,context) encoding = self.tokenizer.encode_plus(input, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"].to(self.device), encoding["attention_mask"].to(self.device) greedy_output = self.model.generate(input_ids=input_ids, attention_mask=attention_masks, max_length=256) Question = self.tokenizer.decode(greedy_output[0], skip_special_tokens=True,clean_up_tokenization_spaces=True) answers.append(Question.strip().capitalize()) return answers