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| import json | |
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| # Parameters | |
| data_dir = f'./processed' | |
| weight_dir = f'./weight' | |
| info_path = f'./processed/summary_book.csv' | |
| num = 10 | |
| lb = 0 | |
| # Load R matrix from file | |
| R = np.load(f'{data_dir}/R.npy', allow_pickle=True) | |
| # Load prediction | |
| prediction = np.load(f'{weight_dir}/predicted.npy', allow_pickle=True) | |
| # Load dictionary from JSON file | |
| with open(f'{data_dir}/user_id_map.json', 'r') as file: | |
| user2id = json.load(file) | |
| with open(f'{data_dir}/book_id_map.json', 'r') as file: | |
| book2id = json.load(file) | |
| # Define the input and output functions for Gradio | |
| def recommend_books(user_id): | |
| # Recommend | |
| user_idx = user2id[str(user_id)] | |
| predict = prediction[:, user_idx] # get prediction for user | |
| predict_dict = {book: np.round(predict[idx], 2) for book, idx in book2id.items()} | |
| # Load information about book | |
| book_df = pd.read_csv(info_path) | |
| book_df = book_df[book_df["Num-Rating"] > lb] | |
| book_df['predict'] = book_df["ISBN"].map(predict_dict) | |
| df = book_df.nlargest(num, ["predict", "Mean-Rating"]).reset_index(drop=True) | |
| df["context"] = df.apply( | |
| lambda book: f"{book['Book-Title']} ({book['Year-Of-Publication']}) - by {book['Book-Author']}", axis=1 | |
| ) | |
| return df['context'].values | |
| st.title('Book Recommender System') | |
| # Display dialogue box that contains content | |
| user_id = st.selectbox( | |
| 'Enter your ID:', | |
| user2id.keys() | |
| ) | |
| # Setting a button | |
| if st.button('Recommend'): | |
| recommendations = recommend_books(user_id) | |
| st.write('**_Your ID:_**', user_id) | |
| st.write('**_Your top 10 recommendations:_**') | |
| for num, i in enumerate(recommendations): | |
| st.write(num + 1, ':', i) | |