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ac2ef7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import streamlit as st
import bs4 as bs
import urllib.request
import re
def main():
st.title("Wikipedia Summarizer")
url_topull= st.text_input("Enter the Wikipedia URL to pull - ")
if url_topull!='':
scraped_data = urllib.request.urlopen(url_topull)
article = scraped_data.read()
parsed_article=bs.BeautifulSoup(article,'lxml')
paragraphs = parsed_article.find_all('p')
article_text = ""
for p in paragraphs:
article_text += p.text
article_text = re.sub(r'\[[0-9]*\]', ' ', article_text)
article_text = re.sub(r'\s+', ' ', article_text)
import nltk
nltk.download('punkt')
nltk.download('stopwords')
import heapq
number=st.text_input('How many sentences long do you want your summary to be?')
if number!='':
sent_num = int(number)
formatted_article_text = re.sub('[^a-zA-Z]', ' ', article_text )
formatted_article_text = re.sub(r'\s+', ' ', formatted_article_text)
sentence_list = nltk.sent_tokenize(article_text)
stopwords = nltk.corpus.stopwords.words('english')
word_frequencies = {}
for word in nltk.word_tokenize(formatted_article_text):
if word not in stopwords:
if word not in word_frequencies.keys():
word_frequencies[word] = 1
else:
word_frequencies[word] += 1
maximum_frequncy = max(word_frequencies.values())
for word in word_frequencies.keys():
word_frequencies[word] = (word_frequencies[word]/maximum_frequncy)
sentence_scores = {}
for sent in sentence_list:
for word in nltk.word_tokenize(sent.lower()):
if word in word_frequencies.keys():
if len(sent.split(' ')) < 30:
if sent not in sentence_scores.keys():
sentence_scores[sent] = word_frequencies[word]
else:
sentence_scores[sent] += word_frequencies[word]
summary_sentences = heapq.nlargest(sent_num, sentence_scores, key=sentence_scores.get)
summary = ' '.join(summary_sentences)
st.markdown("# Summary: ")
st.write(summary)
if __name__ == '__main__':
main()
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