import pickle
import streamlit as st
import numpy as np

st.header("Book Recommender System")

model = pickle.load(open("artifacts/model.pkl", "rb"))
book_names = pickle.load(open("artifacts/book_names.pkl", "rb"))
final_ratings = pickle.load(open("artifacts/final_ratings.pkl", "rb"))
book_pivot = pickle.load(open("artifacts/book_pivot.pkl", "rb"))

def fetch_poster(suggestion):
    bookNames = []
    idsIndex = []
    posterUrl = []

    for bookId in suggestion[0]:        
        name = book_pivot.index[bookId]
        
        bookNames.append(book_pivot.index[bookId])
        
    for name in bookNames:        
        ids = np.where(final_ratings['title'] == name)[0][0]
        idsIndex.append(ids)

    for idx in idsIndex:
        row = final_ratings.iloc[idx]
        url = row['img_url']
        posterUrl.append(url)

    return posterUrl

def recommend_book(bookName):
    bookList = []

    book_id = np.where(book_pivot.index == bookName)[0][0]
    
    distance, suggestion = model.kneighbors(book_pivot.iloc[book_id,:].values.reshape(1, -1), n_neighbors=5)

    poster_url = fetch_poster(suggestion)

    for i in range(len(suggestion)):
        books = book_pivot.index[suggestion[i]]

        for j in books:
            bookList.append(j)

    return bookList, poster_url

selected_books = st.selectbox(
    "Select a book", 
    book_names
)

if st.button("Show Recommendations"):
    recommendations, posterUrls = recommend_book(selected_books)
    
    st.subheader("Recommendations")

    col1, col2, col3, col4 = st.columns(4)

    for url in posterUrls:
        print(url)

    with col1:
        st.text(recommendations[1])
        st.image(posterUrls[1])

    with col2:
        st.text(recommendations[2])
        st.image(posterUrls[2])

    with col3:
        st.text(recommendations[3])
        st.image(posterUrls[3])

    with col4:
        st.text(recommendations[4])
        st.image(posterUrls[4])