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app.py
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app.py
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from sklearn.feature_extraction.text import TfidfVectorizer
from flask import Flask,render_template,url_for,request
import numpy as np
import pandas as pd
import pickle
from sklearn.naive_bayes import MultinomialNB
import re
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict',methods=['POST'])
def predict():
'''load the bengali stopwords from pickle file'''
stopword_list = open('book_stopwords.pkl', 'rb')
stp = pickle.load(stopword_list)
'''This function is for cleaning the reviews'''
def process_reviews(review):
review = review.replace('\n', '') # removing new line
# removing unnecessary punctuation
review = re.sub('[^\u0980-\u09FF]', ' ', str(review))
result = review.split()
review = [word.strip() for word in result if word not in stp]
review = " ".join(review)
return review
'''load the pickle file of the cleaned data '''
cleaned_data = open('book_review_data.pkl','rb')
data = pickle.load(cleaned_data)
'''Extract TF-IDF for Unigram feature '''
tfidf = TfidfVectorizer(use_idf=True, tokenizer=lambda x: x.split())
X = tfidf.fit_transform(data.cleaned)
'''load the Multinomial Naive bayes model'''
model = open('book_review_mnb.pkl', 'rb')
nb = pickle.load(model)
''' Take the input text and follow the steps to get a prediction
and pass this values into the template file
'''
if request.method == 'POST':
comment = request.form['comment']
review = process_reviews(comment)
vect = tfidf.transform([review]).toarray()
my_prediction = nb.predict(vect)
prediction_score = nb.predict_proba(vect)
score = round(max(prediction_score.reshape(-1)), 2) * 100
return render_template('sent_prediction.html',value = comment,sentiment = my_prediction,prob = score )
if __name__ == '__main__':
app.run(debug=True)