app.py

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_score=pickle.load((open('similarity_score.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'].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 = np.where(pt.index == user_input)[0][0]
        similar_items = sorted(list(enumerate(similarity_score[index])), key=lambda x: x[1], reverse=True)[1:6]
        data = []
        for i in similar_items:
            item = []
            # print(pt.index[i[0]])
            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'].values))
            data.append(item)
        print(data)
        return render_template('recommend.html',data=data)

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