app.py

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from flask import Flask, render_template, request
import pickle

cv = pickle.load(open('word_list.pkl', 'rb'))
word_list = cv.get_feature_names()
clf = pickle.load(open('model.pkl', 'rb'))

app = Flask(__name__)


@app.route('/')
def index():
    return render_template('index.html')


@app.route('/predict', methods=['POST'])
def predict():
    review = request.form.get('review')

    input_review = transform_review(review)

    sentiment = clf.predict(input_review)[0]
    print("The sentiment is", sentiment)

    if sentiment == 1:
        return render_template('index.html', sentiment=1)
    else:
        return render_template('index.html', sentiment=-1)


def transform_review(review):
    count_words = []
    for i in word_list:
        count_words.append(review.count(i))

    input_review = np.array(count_words).reshape(1,3000)

    return input_review


if __name__ == "__main__":
    app.run(debug=True)