import streamlit as st
import pickle
import numpy as np


st.set_page_config(
    page_title="Kitap Öneri Sistemi",
    page_icon="📚"
)

st.title("📚 Kitap Öneri Sistemi")

st.write(
    "Bu uygulama, kullanıcı puanlarına göre seçilen kitaba benzer kitaplar önerir."
)


@st.cache_resource
def load_files():
    pivot_table = pickle.load(open("src/pivot_table.pkl", "rb"))
    similarity_scores = pickle.load(open("src/similarity_scores.pkl", "rb"))
    return pivot_table, similarity_scores


pivot_table, similarity_scores = load_files()

book_list = list(pivot_table.index)

selected_book = st.selectbox(
    "Bir kitap seçin:",
    book_list
)


def recommend_book(book_name):
    if book_name not in pivot_table.index:
        return []

    book_index = np.where(pivot_table.index == book_name)[0][0]

    similar_books = sorted(
        list(enumerate(similarity_scores[book_index])),
        reverse=True,
        key=lambda x: x[1]
    )[1:6]

    recommendations = []

    for book in similar_books:
        recommendations.append(pivot_table.index[book[0]])

    return recommendations


if st.button("Öneri Getir"):
    recommendations = recommend_book(selected_book)

    st.subheader("Önerilen Kitaplar")

    for i, book in enumerate(recommendations, start=1):
        st.write(f"{i}. {book}")