import streamlit as st
import numpy as np
from joblib import load   

st.title("Book Recommendation System")

# Load models and data
model = load("model.pkl")
books_name = load("book_names.pkl")
final_rating = load("final_rating.pkl")
book_pivot_tale = load("book_pivot_tale.pkl")

# Book selection
select_book = st.selectbox("Select a book", options=books_name)

# Recommendation function
def recommended_book(book_name):
    book_id = np.where(book_pivot_tale.index == book_name)[0][0]
    distances, suggestions = model.kneighbors(
        book_pivot_tale.iloc[book_id, :].values.reshape(1, -1), n_neighbors=6
    )
    recommended_books = []
    for i in suggestions[0][1:]:  # Skipping the first as it's the input book itself
        recommended_books.append(book_pivot_tale.index[i])
    return recommended_books

# On button click
if st.button("Recommend"):
    books_list = recommended_book(select_book)
    st.success("Recommended books are:")
    for i, book in enumerate(books_list):
        st.write(f"{i+1}. {book}")