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| import nltk, pke, string, torch, requests, random | |
| from nltk.tokenize import sent_tokenize | |
| from nltk.corpus import stopwords | |
| from flashtext import KeywordProcessor | |
| def postprocesstext(content): | |
| final="" | |
| for sent in sent_tokenize(content): | |
| sent = sent.capitalize() | |
| final = final +" "+sent | |
| return final | |
| def summarizer(text,model,tokenizer): | |
| text = text.strip().replace("\n"," ") | |
| text = "summarize: "+text | |
| # print (text) | |
| max_len = 512 | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| encoding = tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,\ | |
| truncation=True, return_tensors="pt").to(device) | |
| input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"] | |
| outs = model.generate(input_ids=input_ids, | |
| attention_mask=attention_mask, | |
| early_stopping=True, | |
| num_beams=3, | |
| num_return_sequences=1, | |
| no_repeat_ngram_size=2, | |
| min_length = 75, | |
| max_length=300) | |
| dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs] | |
| summary = dec[0] | |
| summary = postprocesstext(summary) | |
| summary= summary.strip() | |
| return summary | |
| def get_nouns_multipartite(content): | |
| out=[] | |
| try: | |
| extractor = pke.unsupervised.MultipartiteRank() | |
| extractor.load_document(input=content) | |
| # not contain punctuation marks or stopwords as candidates. | |
| pos = {'PROPN','NOUN'} | |
| #pos = {'PROPN','NOUN'} | |
| stoplist = list(string.punctuation) | |
| stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] | |
| stoplist += stopwords.words('english') | |
| # extractor.candidate_selection(pos=pos, stoplist=stoplist) | |
| 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. | |
| extractor.candidate_weighting(alpha=1.1, | |
| threshold=0.75, | |
| method='average') | |
| keyphrases = extractor.get_n_best(n=15) | |
| for val in keyphrases: | |
| out.append(val[0]) | |
| except Exception as e: | |
| out = [] | |
| #traceback.print_exc() | |
| print("EXCEPTION: {}".format(e)) | |
| return out | |
| def filter_overlap_words(l): | |
| nl = [] | |
| for i in range(len(l)): | |
| temp_l = l[0:i]+l[i+1:] | |
| inside=False | |
| for j in temp_l: | |
| if l[i] not in j: | |
| if l[i] not in nl: | |
| nl.append(l[i]) | |
| inside = True | |
| else: | |
| if inside: | |
| nl.remove(l[i]) | |
| break | |
| return nl | |
| def get_keywords(originaltext,summarytext): | |
| keywords = get_nouns_multipartite(originaltext) | |
| #print ("keywords unsummarized: ",keywords) | |
| keyword_processor = KeywordProcessor() | |
| for keyword in keywords: | |
| keyword_processor.add_keyword(keyword) | |
| keywords_found = keyword_processor.extract_keywords(summarytext) | |
| keywords_found = list(set(keywords_found)) | |
| #print("keywords_found in summarized: ",keywords_found) | |
| important_keywords =[] | |
| for keyword in keywords: | |
| if keyword in keywords_found: | |
| important_keywords.append(keyword) | |
| ## find keywords which don't have common word .. | |
| from copy import deepcopy | |
| imp_words = deepcopy(important_keywords) | |
| #imp_words = filter_overlap_words(important_keywords) | |
| imp_words = [str(i).title() for i in imp_words] | |
| return imp_words[:5] | |
| def get_question(context,answer,model,tokenizer): | |
| text = "context: {} answer: {}".format(context,answer) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| encoding = tokenizer.encode_plus(text,max_length=384, pad_to_max_length=False,\ | |
| truncation=True, return_tensors="pt").to(device) | |
| input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"] | |
| outs = model.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=72) | |
| dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs] | |
| Question = dec[0].replace("question:","") | |
| Question= Question.strip() | |
| return Question | |
| def get_related_word(word): | |
| url = "https://api.datamuse.com/words" | |
| querystring = {"ml":word} | |
| responses = requests.request("GET", url, params=querystring) | |
| related_words = [] | |
| count = 0 | |
| responses = responses.json() | |
| for res in responses: | |
| if count >= 4: | |
| break | |
| if res["word"]!=word and res["word"]!="": | |
| related_words.append(res["word"]) | |
| count += 1 | |
| related_words = [str(i).title() for i in related_words] | |
| return related_words | |
| def get_final_option_list(ans,other_options): | |
| option1 = ans | |
| option2,option3,option4 = "dummy","dummy","dummy" | |
| try: | |
| option2 = other_options[0] | |
| except: | |
| pass | |
| try: | |
| option3 = other_options[1] | |
| except: | |
| pass | |
| try: | |
| option4 = other_options[2] | |
| except: | |
| pass | |
| final_options = [option1,option2,option3,option4] | |
| random.shuffle(final_options) | |
| final_options = tuple(final_options) | |
| ans_index= 0 | |
| for i in range(4): | |
| if final_options[i] == ans: | |
| ans_index = i | |
| return final_options, ans_index | |
| def load_raw_text(): | |
| return "Billy and Ron are brothers. Billy is 5 years old. Ron is 7 years old. One day their mom took them to the zoo. Billy wore his red cap, and Ron wore his blue cap. They had fun watching all the animals. Ron liked the monkeys the best. He wanted to stay and watch them some more, but Billy wanted to go see the elephants. Elephants were Billy’s favorite. Their mom said it was time to go see the elephants, and Ron was sad. But their mom said they could come back and see the monkeys again before they left the zoo. Billy and Ron had a great day at the zoo." | |