Upload app.py

app.py

@@ -0,0 +1,34 @@
+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])