model.py

import numpy as np
import pandas as pd

class ContentBasedRecommender:
    def __init__(self, train_data):
        self.train_data = train_data

    def predict(self, user_id, k=10):
        user_books = set(self.train_data[self.train_data['user_id'] == user_id]['book_id'])
        similar_books = set().union(*(self.train_data[self.train_data['book_id'] == book_id]['similar_books'].iloc[0] for book_id in user_books))
        recommended_books = list(similar_books - user_books)

        return np.random.choice(recommended_books, size=min(k, len(recommended_books)), replace=False).tolist()