Update app.py

app.py

@@ -1,47 +1,47 @@
-import streamlit as st
-import pandas as pd
-from utils import *
-
-# Assuming data is loaded and matrices are prepared as discussed
-def load_data():
-    ratings = pd.read_csv('./data/collaborative_books_df.csv', index_col=0)
-    books = pd.read_csv('./data/collaborative_book_metadata.csv', index_col=0)
-    # book_titles=pd.read_csv('./data/book_titles.csv', index_col=0)
-    # book_titles = book_titles.reset_index()
-
-    # Merge data
-    ratings = ratings.merge(books, on='book_id')
-    book_titles = dict(zip(ratings['book_id'], ratings['title_x']))
-    return ratings, books,book_titles
-
-def initialize_session_state():
-      if "ratings" not in st.session_state:
-            st.session_state.ratings, st.session_state.books, st.session_state.book_titles = load_data()
-            st.session_state.X, st.session_state.user_mapper, st.session_state.book_mapper, st.session_state.user_inv_mapper, st.session_state.book_inv_mapper = create_matrix(st.session_state.ratings)
-            st.session_state.book_id_mapping = pd.Series( st.session_state.books.book_id.values, index= st.session_state.books.title).to_dict()
-
-initialize_session_state()
-# Streamlit interface for book recommendation
-st.title('Book Recommender System')
-
-# User inputs
-title_input = st.selectbox('Select or type a book title',  st.session_state.books['title'].unique())
-k_input = st.number_input('How many recommendations do you want?', min_value=1, max_value=20, value=5)
-
-if st.button('Find Similar Books'):
-   
-        if title_input in  st.session_state.book_id_mapping:
-            book_id =  st.session_state.book_id_mapping[title_input]    
-            distances, similar_ids = find_similar_books(book_id,  st.session_state.X, k=k_input,book_mapper= st.session_state.book_mapper,book_inv_mapper= st.session_state.book_inv_mapper)
-            similar_books = pd.DataFrame({
-                'Book Title': [ st.session_state.book_titles[ids] for ids in similar_ids],
-                'Distance': distances[0][1:]
-            })
-            
-            st.write(f"Books similar to {title_input}:")
-            st.dataframe(similar_books.sort_values(by='Distance', ascending=True))
-        else:
-            st.error("Book title not found. Please check the spelling or try another title.")
-
-        
-
+import streamlit as st
+import pandas as pd
+from utils import *
+
+# Assuming data is loaded and matrices are prepared as discussed
+def load_data():
+    ratings = pd.read_csv('./collaborative_books_df.csv', index_col=0)
+    books = pd.read_csv('./collaborative_book_metadata.csv', index_col=0)
+    # book_titles=pd.read_csv('./data/book_titles.csv', index_col=0)
+    # book_titles = book_titles.reset_index()
+
+    # Merge data
+    ratings = ratings.merge(books, on='book_id')
+    book_titles = dict(zip(ratings['book_id'], ratings['title_x']))
+    return ratings, books,book_titles
+
+def initialize_session_state():
+      if "ratings" not in st.session_state:
+            st.session_state.ratings, st.session_state.books, st.session_state.book_titles = load_data()
+            st.session_state.X, st.session_state.user_mapper, st.session_state.book_mapper, st.session_state.user_inv_mapper, st.session_state.book_inv_mapper = create_matrix(st.session_state.ratings)
+            st.session_state.book_id_mapping = pd.Series( st.session_state.books.book_id.values, index= st.session_state.books.title).to_dict()
+
+initialize_session_state()
+# Streamlit interface for book recommendation
+st.title('Book Recommender System')
+
+# User inputs
+title_input = st.selectbox('Select or type a book title',  st.session_state.books['title'].unique())
+k_input = st.number_input('How many recommendations do you want?', min_value=1, max_value=20, value=5)
+
+if st.button('Find Similar Books'):
+   
+        if title_input in  st.session_state.book_id_mapping:
+            book_id =  st.session_state.book_id_mapping[title_input]    
+            distances, similar_ids = find_similar_books(book_id,  st.session_state.X, k=k_input,book_mapper= st.session_state.book_mapper,book_inv_mapper= st.session_state.book_inv_mapper)
+            similar_books = pd.DataFrame({
+                'Book Title': [ st.session_state.book_titles[ids] for ids in similar_ids],
+                'Distance': distances[0][1:]
+            })
+            
+            st.write(f"Books similar to {title_input}:")
+            st.dataframe(similar_books.sort_values(by='Distance', ascending=True))
+        else:
+            st.error("Book title not found. Please check the spelling or try another title.")
+
+        
+