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from flask import Flask, request, render_template
import pickle5 as pkl
from preprocessing import clean_text, clean_stopword, preprocess_text
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import re
import string
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
nltk.download('stopwords')
nltk.download('punkt_tab')
# Initialize Flask app
app = Flask(__name__)
def process_input(text):
prep_result = preprocess_text(text)
kalimat = nltk.sent_tokenize(prep_result)
tfidf_vectorizer = pkl.load(open('./tfidf_vectorizer.pkl', 'rb'))
tfidf_preprocessing = pkl.load(open('./tfidf_preprocessing.pkl', 'rb'))
terms = tfidf_vectorizer.get_feature_names_out()
tfidf = pd.DataFrame(data=tfidf_preprocessing.toarray(), columns=terms)
cossim = cosine_similarity(tfidf, tfidf)
similarity_matrix = pd.DataFrame(cossim,
index=range(len(kalimat)),
columns=range(len(kalimat)))
G = nx.DiGraph()
for i in range(len(cossim)):
G.add_node(i)
for i in range(len(cossim)):
for j in range(len(cossim)):
similarity = cossim[i][j]
if similarity > 0.1 and i != j:
G.add_edge(i, j)
closeness = nx.closeness_centrality(G)
sorted_closeness = sorted(closeness.items(), key=lambda x: x[1], reverse=True)
print(sorted_closeness)
ringkasan_closeness = ""
for node, closeness_preprocessing in sorted_closeness[:3]:
top_sentence = kalimat[node]
ringkasan_closeness += top_sentence + " "
print(ringkasan_closeness)
return top_sentence
@app.route('/', methods=['GET', 'POST'])
def summarize():
result = None
if request.method == 'POST':
text = request.form['text']
if text:
result = process_input(text)
return render_template('index.html', result=result)
if __name__ == '__main__':
app.run(debug=True)
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