Delete app.py

app.py

@@ -1,77 +0,0 @@
-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)