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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").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)
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