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import streamlit as st
from youtube_transcript_api import YouTubeTranscriptApi
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
import nltk
import before_run
#nltk.download('wordnet')
#nltk.download('punkt')
#nltk.download('brown')
#nltk.download('stopwords')
from nltk.tokenize import sent_tokenize
from flashtext import KeywordProcessor
from nltk.corpus import stopwords
from urllib import response
import requests
import string
import traceback
import pke
link = "http://127.0.0.1:8000/question"
summary_tokenizer = AutoTokenizer.from_pretrained("t5-base")
summary_model = AutoModelWithLMHead.from_pretrained("t5-base")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
summary_model = summary_model.to(device)
question_model = AutoModelWithLMHead.from_pretrained('ramsrigouthamg/t5_squad_v1')
question_tokenizer = AutoTokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')
question_model = question_model.to(device)
def query(url, payload):
return requests.post(url, json=payload)
def fetch_transcript(url):
vid = url.split("=")[1]
transcript = YouTubeTranscriptApi.get_transcript(vid)
result = ""
for i in transcript:
result += ' ' + i['text']
return result
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
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()
stoplist = list(string.punctuation)
stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']
stoplist += stopwords.words('english')
extractor.load_document(input=content, stoplist=stoplist)
# not contain punctuation marks or stopwords as candidates.
pos = {'PROPN','NOUN'}
extractor.candidate_selection(pos=pos)
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:
out = []
traceback.print_exc()
return out
def get_keywords(originaltext,summarytext,count):
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)
return important_keywords[:int(count)]
def get_question(context,answer,model,tokenizer):
text = "context: {} answer: {}".format(context,answer)
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 all(url,count):
transcript = fetch_transcript(url)
summarized_text = summarizer(transcript, summary_model, summary_tokenizer)
keywords = get_keywords(transcript,summarized_text,count)
qna = []
for answer in keywords:
qna.append(get_question(summarized_text,answer,question_model,question_tokenizer)+' : '+answer)
return qna
def main():
if 'submitted' not in st.session_state:
st.session_state.submitted = False
if 'opt' not in st.session_state:
st.session_state.opt = []
def callback():
st.session_state.submitted = True
st.title('QnA pair Generator')
url = st.text_input('Enter the Video Link')
count = st.text_input('Enter the number of questions you want to generate')
if (st.button("Submit URL", on_click=callback) and url and count) :
st.write("Thanks for submission !")
opt = all(url, count)
st.session_state.opt = opt
if st.session_state.submitted and st.session_state.opt:
option = st.multiselect('Select the question you want to add to database ', st.session_state.opt)
if option:
if st.button("Add question"):
for i in range(len(option)):
files = {
"question": option[i].split(":")[0],
"answer": option[i].split(":")[1]
}
response = query(link, files)
st.write(response.text)
main()
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