app.py

# from crypt import methods
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-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 = 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=[]
        temp_df = books[books['Book-Title']==pt.index[i[0]]]
        item.extend(list(temp_df.drop_duplicates('Book-Title')['Book-Title']))
        item.extend(list(temp_df.drop_duplicates('Book-Title')['Book-Author']))
        item.extend(list(temp_df.drop_duplicates('Book-Title')['Image-URL-M']))
        
        data.append(item)
    return render_template('recommend.html',data=data)

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