Add User select option

app.py

@@ -3,53 +3,64 @@ import pandas as pd
 from scipy.sparse import csr_matrix, coo_matrix
 from sklearn.metrics.pairwise import cosine_similarity
 
-#loading the song_dataset [in cache form to minimize resource usage]
+
+# loading the song_dataset [in cache form to minimize resource usage]
 @st.cache_data
 def load_songData(path_to_file):
-    df =pd.read_csv(path_to_file)
+    df = pd.read_csv(path_to_file)
     return df
-    
-#calling load_songData function
-song_df =  load_songData("song_dataset.csv")
 
 
+# calling load_songData function
+song_df = load_songData("song_dataset.csv")
+
+# load all user IDs
+all_users = song_df["user"].unique()
+
 # create a series with song IDs as index and titles as values
 song_titles_series = song_df.drop_duplicates(subset=["song"]).set_index("song")["title"]
 
 # sparse item-item similarity: transpose sparse matrix because we want item-item similarity (songs as rows)
-interaction_matrix = song_df.pivot_table(index="user", columns="song", values="play_count", fill_value=0)
+interaction_matrix = song_df.pivot_table(
+    index="user", columns="song", values="play_count", fill_value=0
+)
 sparse_matrix = csr_matrix(interaction_matrix)
 
 item_similarity_sparse = cosine_similarity(sparse_matrix.T, dense_output=False)
 coo = coo_matrix(item_similarity_sparse)
 
-item_similarity_df = pd.DataFrame({
+item_similarity_df = pd.DataFrame(
+    {
         "item_1": interaction_matrix.columns[coo.row],
         "item_2": interaction_matrix.columns[coo.col],
         "similarity": coo.data,
-    })
+    }
+)
+
+# for song-based recommendation engine call
 
-#for song-based recommendation engine call
 
 def recommend_similar_items_sparse(selected_songs, top_n):
     scores = {}
     for song in selected_songs:
         # getting all rows where item_1 is the selected song
         similar_items = item_similarity_df[item_similarity_df["item_1"] == song]
-        
+
         for _, row in similar_items.iterrows():
             similar_song = row["item_2"]
             similarity = row["similarity"]
-            #filtering out songs already listened to by user
+            # filtering out songs already listened to by user
             if similar_song not in selected_songs:
                 scores[similar_song] = scores.get(similar_song, 0) + similarity
-                
-        recommended_songs = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:top_n]
+
+        recommended_songs = sorted(scores.items(), key=lambda x: x[1], reverse=True)[
+            :top_n
+        ]
         return [song for song, score in recommended_songs]
-        
+
 
 # Streamlit Interface
-st.title("Song Recommendation Engine[Proj_charlie]")
+st.subheader("Song Recommendation Engine[Proj_charlie]")
 
 # Geting User inputs
 st.write("**Please select a user.**")
@@ -64,6 +75,7 @@ selected_songs = st.multiselect(
     format_func=lambda x: song_titles_series[x],
 )
 
+
 # Recommendation Magic
 
 if st.button("Get Recommendations"):