app.py

# test
###
from flask import Flask, render_template, request
import pickle
import numpy as np

popular_df = pickle.load(open('./popular.pkl', 'rb'))
pt = pickle.load(open('./pt.pkl', 'rb'))
books = pickle.load(open('./books.pkl', 'rb'))
similarity_scores = pickle.load(open('./similarity_scores.pkl', 'rb'))
app = Flask(__name__)


@app.route('/')
def index():
    return render_template("index.html",
                           book_name=list(popular_df['Book-Title'].values),
                           author=list(popular_df['Book-Author'].values),
                           image=list(popular_df['Image-URL-M'].values),
                           votes=list(popular_df['num_ratings'].values),
                           rating=list(popular_df['avg_ratings'].values), )


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


@app.route('/recommend_books', methods=['post'])
def recommend():
    user_input = request.form.get('user_input')
    # index fetch for that book
    index = np.where(pt.index == user_input)[0][0]
    # get the most similar book to bookname in sorted way
    similar_items = sorted(list(enumerate(similarity_scores[index])), key=lambda x: x[1], reverse=True)[1:5]
    data = []
    for i in similar_items:
        item = []
        temp_df = books[books['Book-Title'] == pt.index[i[0]]]
        item.extend(list(temp_df.drop_duplicates('Book-Title')['Book-Title'].values))
        item.extend(list(temp_df.drop_duplicates('Book-Title')['Book-Author'].values))
        item.extend(list(temp_df.drop_duplicates('Book-Title')['Image-URL-M'].values))
        data.append(item)
    # print(data)
    return render_template('recommend.html', data=data)


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