Upload 5 files

book details.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b4889ef8a8a856d8a658614206d6c2e39299823f685e150ae8b3db401c8ed23
+size 125363

main.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from itertools import cycle
+
+pt_table = pd.read_pickle('user book data.pkl')
+book_details = pd.read_pickle('book details.pkl')
+with open('similarity score.npy', 'rb') as f:
+    similarity_score = np.load(f)
+book_names = pt_table.index.values.tolist()
+
+
+def recommend(book_name):
+    book_index = np.where(pt_table.index == book_name)[0][0]
+    distances = similarity_score[book_index]
+    similar_items = sorted(list(enumerate(distances)),
+                           key=lambda x: x[1], reverse=True)[1:7]
+    suggestion = []
+    for i in similar_items:
+        suggestion.append(pt_table.index[i[0]])
+    return suggestion
+
+
+st.set_page_config(page_title="Book recommender system")
+st.title("Book recommender system")
+option = st.selectbox("Enter or select a book name", book_names, index=0)
+if st.button("Recommend"):
+    recommendation = recommend(option)
+    cols = cycle(st.columns(3))
+    for recom in recommendation:
+        next(cols).image(book_details[book_details['Book-Title'] == recom]['Image-URL-M'].values[0],
+                         f"{book_details[book_details['Book-Title']==recom]['Book-Title'].values[0]} by {book_details[book_details['Book-Title']==recom]['Book-Author'].values[0]}", width=150)
+        print(book_details[book_details['Book-Title']
+              == recom]['Image-URL-M'].values[0])

requirements.txt

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+numpy==1.23.5
+pandas==2.0.1
+streamlit==1.29.0

similarity score.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:102e6dec5bf8a642474b87b031a9bfc18e2ae73e760ca0382349d3decd666531
+size 3987616

user book data.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:baa62baed1a5e620cb55a28ff6e171764266569bd34781a9f8de52e9788a0972
+size 4601015