Update Questgen/main2.py

Questgen/main2.py

@@ -1,531 +1,482 @@
-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
-# 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) > 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)
-    ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
-    # Extract named entities using spaCy
-    spacy_entities = [ent.text for ent in doc.ents]
-    print(f"\n\nSpacy Entities: {spacy_entities}\n\n")
-    # Extract named entities using BERT-based NER
-    bert_entities = [entity['word'] for entity in ner_pipeline(text)]
-    print(f"BERT Entities: {bert_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 = "<context>\t" + txt + "\t<answer>\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 + " </s>"
-        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
-    
-    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
+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 = "<context>\t" + txt + "\t<answer>\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 + " </s>"
+        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
+