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 from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity from transformers import pipeline 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 import yake 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 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) > 5] 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] print(keyword_sentences) 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]) # nlp = spacy.load("en_core_web_sm") # labels = nlp(text) # for i in (labels.ents): # out.append(str(i)) nlp = spacy.load('en_core_web_sm') doc = nlp(text) # Extract named entities using spaCy spacy_entities = [ent.text for ent in doc.ents] print(f"\n\nSpacy Entities: {spacy_entities}\n\n") # Combine both NER results and remove duplicates entities = list(set(spacy_entities)) # Extract nouns and verbs using spaCy nouns = [chunk.text for chunk in doc.noun_chunks] verbs = [token.lemma_ for token in doc if token.pos_ == 'VERB'] print(f"Spacy Nouns: {nouns}\n\n") print(f"Spacy Verbs: {verbs}\n\n") # Use YAKE for keyphrase extraction yake_extractor = yake.KeywordExtractor() yake_keywords = yake_extractor.extract_keywords(text) yake_keywords = [kw[0] for kw in yake_keywords] print(f"Yake: {yake_keywords}\n\n") # Combine all keywords and remove duplicates combined_keywords = list(set(entities + nouns + verbs + yake_keywords)) vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(combined_keywords) similarity_matrix = cosine_similarity(tfidf_matrix) clusters = [] similarity_threshold = 0.45 for idx, word in enumerate(combined_keywords): added_to_cluster = False for cluster in clusters: # Check if the word is similar to any word in the existing cluster if any(similarity_matrix[idx, other_idx] > similarity_threshold for other_idx in cluster): cluster.append(idx) added_to_cluster = True break if not added_to_cluster: clusters.append([idx]) # Step 4: Select representative words from each cluster representative_words = [combined_keywords[cluster[0]] for cluster in clusters] # Print the representative words print("Keywords after removing similar words: ", representative_words) # return combined_keywords return representative_words 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 ) total_phrases_filtered = keywords answers = [] for answer in total_phrases_filtered: if answer not in answers and MCQs_available(answer,s2v): answers.append(answer) # answers = answers[:max_keywords] # answers = keywords return answers def generate_questions_mcq(keyword_sent_mapping, device, tokenizer, model, sense2vec, normalized_levenshtein): batch_text = [] answers = list(keyword_sent_mapping.keys()) # Get all answers from the keys for answer in answers: value_list = keyword_sent_mapping[answer] # Get list of sentences for this answer for txt in value_list: text = "\t" + txt + "\t\t" + 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 = {"questions": []} for index, val in enumerate(answers): 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, "question_type": "MCQ", "answer": val, "id": index + 1, "options": [], "options_algorithm": [], "extra_options": [], "context": keyword_sent_mapping[val] # Assuming keyword_sent_mapping is a dictionary of lists } # Get options and filter them individual_question["options"], individual_question["options_algorithm"] = get_options(val, sense2vec) individual_question["options"] = filter_phrases(individual_question["options"], 10, normalized_levenshtein) # Adjusting the number of options and extra options index = 3 individual_question["extra_options"] = individual_question["options"][index:] individual_question["options"] = individual_question["options"][:index] 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('DevBM/t5-large-squad') 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