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import spacy
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from spacy import tokens
import streamlit as st
from heapq import nlargest
import subprocess
subprocess.run("pip3 install PyPDF2".split())
subprocess.run("python3 -m spacy download en_core_web_sm".split())
import PyPDF2
from utils import (
clean_text,
fetch_article_text,
preprocess_text_for_abstractive_summarization,
read_text_from_file,
)
#---------------------Pre-Requiste------------------------#
stopwords = STOP_WORDS
punctuation = punctuation + '\n'
if __name__=="__main__":
st.title("Text Summarizer 📝")
st.subheader("Creator: Shreyas Dixit")
n = st.sidebar.slider('Summarization %',10,90,step=10)
n = n/100
type=st.selectbox('Pick one', ['PDF','Text'])
if type=="PDF":
#Upload file
uploaded_file = st.file_uploader("Choose a file",type=['pdf','txt','docx'])
text = read_text_from_file(uploaded_file)
# FileName = uploaded_file.name
# if uploaded_file is not None:
# pdfFileObj = open("{FileName}", 'rb')
# pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
# pageObj = pdfReader.getPage(0)
# text = (pageObj.extractText())
# pdfFileObj.close()
elif type=="Text":
#Text
text=st.text_area("Input text !")
if st.button('Summarize'):
nlp = spacy.load('en_core_web_sm')
doc = nlp(text)
#Word tokenization
tokens = [tokens.text for tokens in doc]
word_frquency = {}
for word in doc:
if word.text.lower() not in stopwords:
if word.text.lower() not in punctuation:
if word.text not in word_frquency.keys():
word_frquency[word.text] = 1
else:
word_frquency[word.text] += 1
#Normalize the values
max_word = max(word_frquency.values())
for word in word_frquency.keys():
word_frquency[word] = word_frquency[word]/max_word
#Sentence Tokenization
sentence_token = [sent for sent in doc.sents]
sentence_score = {}
for sent in sentence_token:
for word in sent:
if word.text.lower() in word_frquency.keys():
if sent not in sentence_score.keys():
sentence_score[sent] = word_frquency[word.text.lower()]
else:
sentence_score[sent] += word_frquency[word.text.lower()]
#Creating a Summary
select_length = int(len(sentence_token)*n)
summary = nlargest(select_length,sentence_score,key = sentence_score.get)
summary = [word.text for word in summary]
summary = ' '.join(summary)
st.markdown(summary) |