app.py

import numpy as np
import pickle
import streamlit as st
from PIL import Image
image = Image.open('woven.png')

st.image(image, caption='')
st.header("Book recommender Sytem Using Colaborative Function")
model = pickle.load(open("artifacts/model.pkl",'rb'))
books_name=pickle.load(open('artifacts/book_name.pkl','rb'))
final_rating=pickle.load( open('artifacts/final_rating.pkl','rb'))
book_pivot=pickle.load(open('artifacts/book_pivot.pkl','rb'))
def fetch_poster(suggestion):
   book_name=[]
   ids_index=[]
   poster_url=[]
   for book_id in suggestion:
      book_name.append(book_pivot.index[book_id])
   for name in book_name[0]:
      ids=np.where(final_rating['title'] == name)[0][0]
      ids_index.append(ids)
   for id in ids_index:
      url = final_rating.iloc[id]["img_url"]
      poster_url.append(url) 
   return poster_url          
def recommend_book(book_name):
  book_list=[]
  book_id=np.where(book_pivot.index==book_name)[0][0]
  distance, suggestion = model.kneighbors(book_pivot.iloc[book_id,:].values.reshape(1,-1),n_neighbors=6)
  poster_url=fetch_poster(suggestion)
  for i in range(len(suggestion)):
    books=book_pivot.index[suggestion[i]]
    for j in books:
      book_list.append(j)
  return book_list,poster_url         
selected_books = st.selectbox(
    "Type or Select a Book",
    books_name
)

if st.button("Show Recommendation"):
    recommendation_books, poster_url= recommend_book(selected_books)
    col1, col2, col3, col4, col5=st.columns(5)

    with col1:
       st.text(recommendation_books[1])
       st.image(poster_url[1])
    with col2:
       st.text(recommendation_books[2])
       st.image(poster_url[2])
    with col3:
       st.text(recommendation_books[3])
       st.image(poster_url[3])
    with col4:
       st.text(recommendation_books[4])
       st.image(poster_url[4])
    with col5:
       st.text(recommendation_books[5])
       st.image(poster_url[5])