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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."
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