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Add initial app.py
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app.py
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import pickle
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import streamlit as st
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import numpy as np
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st.header("Book Recommender System")
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# model = pickle.load(open("artifacts/model.pkl", "rb"))
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# book_names = pickle.load(open("artifacts/book_names.pkl", "rb"))
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# final_ratings = pickle.load(open("artifacts/final_ratings.pkl", "rb"))
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# book_pivot = pickle.load(open("artifacts/book_pivot.pkl", "rb"))
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# def fetch_poster(suggestion):
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# bookNames = []
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# idsIndex = []
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# posterUrl = []
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# for bookId in suggestion[0]:
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# name = book_pivot.index[bookId]
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# bookNames.append(book_pivot.index[bookId])
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# for name in bookNames:
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# ids = np.where(final_ratings['title'] == name)[0][0]
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# idsIndex.append(ids)
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# for idx in idsIndex:
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# row = final_ratings.iloc[idx]
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# url = row['img_url']
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# posterUrl.append(url)
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# return posterUrl
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# def recommend_book(bookName):
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# bookList = []
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# book_id = np.where(book_pivot.index == bookName)[0][0]
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# distance, suggestion = model.kneighbors(book_pivot.iloc[book_id,:].values.reshape(1, -1), n_neighbors=5)
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# poster_url = fetch_poster(suggestion)
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# for i in range(len(suggestion)):
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# books = book_pivot.index[suggestion[i]]
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# for j in books:
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# bookList.append(j)
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# return bookList, poster_url
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# selected_books = st.selectbox(
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# "Select a book",
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# book_names
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# )
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# if st.button("Show Recommendations"):
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# recommendations, posterUrls = recommend_book(selected_books)
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# st.subheader("Recommendations")
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# col1, col2, col3, col4 = st.columns(4)
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# for url in posterUrls:
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# print(url)
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# with col1:
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# st.text(recommendations[1])
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# st.image(posterUrls[1])
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# with col2:
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# st.text(recommendations[2])
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# st.image(posterUrls[2])
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# with col3:
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# st.text(recommendations[3])
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# st.image(posterUrls[3])
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# with col4:
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# st.text(recommendations[4])
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# st.image(posterUrls[4])
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