Update app.py

app.py

@@ -106,44 +106,9 @@ def get_songs_from_spotify(playlist_link, songs_df, previews_df):
     previews_df = pd.concat([previews_df,new_df])
     new_df = new_df[["elo_score",	"artist",	"song_title"]]
     songs_df = pd.concat([songs_df,new_df])
-    return songs_df, previews_df
-
-def get_songs_from_spotify(playlist_link, songs_df, previews_df):
-    if match := re.match(r"https://open.spotify.com/playlist/(.*)\?", playlist_link):
-        playlist_uri = match.groups()[0]
-    else:
-        raise ValueError("Expected format: https://open.spotify.com/playlist/...")
-    
-    # Get playlist track information
-    tracks = sp.playlist_tracks(playlist_uri)["items"]
-    
-    # For efficiency, collect new rows in a list before converting it to a DataFrame
-    new_rows = []
-    for track in tracks:
-        track_info = {
-            'artist': track['track']['artists'][0]['name'], 
-            'song_title': track['track']['name'], 
-            'preview_url': track['track']['preview_url'],
-            'elo_score': 1000
-        }
-        new_rows.append(track_info)
-    
-    new_df = pd.DataFrame(new_rows)
-    
-    # Merge original DataFrames to ensure we don't have duplicates across them
-    consolidated_df = pd.concat([songs_df[['artist', 'song_title']], previews_df[['artist', 'song_title']]]).drop_duplicates()
-    
-    # Identify new, unique songs not in the original datasets
-    unique_songs = pd.merge(new_df, consolidated_df, on=['artist', 'song_title'], how='left', indicator=True).query('_merge=="left_only"').drop('_merge', axis=1)
-    
-    # Update previews_df if there is a 'preview_url' column in the new unique songs DataFrame
-    if 'preview_url' in unique_songs:
-        previews_df = pd.concat([previews_df, unique_songs])
-    
-    # Drop the 'preview_url' for songs_df update
-    unique_songs_simplified = unique_songs.drop(['preview_url'], axis=1, errors='ignore')
-    songs_df = pd.concat([songs_df, unique_songs_simplified])
     
+    songs_df = songs_df.drop_duplicates(subset=['artist', 'song_title'])
+    previews_df = previews_df.drop_duplicates(subset=['artist', 'song_title'])
     return songs_df, previews_df
 
 def update_scores(winner, loser, k_factor=100):
@@ -297,38 +262,11 @@ def import_csv(file, songs_df, previews_df):
         previews_df = pd.concat([previews_df,new_df])
         new_df = new_df[["elo_score",	"artist",	"song_title"]]
         songs_df = pd.concat([songs_df,new_df])
-    return songs_df, previews_df
 
-def import_csv(file, songs_df, previews_df):
-    if file is not None:
-        new_df = pd.read_csv(file)
-        
-        # Ensure the input DataFrames have the correct structure, creating them if they don't exist
-        if 'elo_score' not in songs_df.columns or 'artist' not in songs_df.columns or 'song_title' not in songs_df.columns:
-            songs_df = pd.DataFrame(columns=['elo_score', 'artist', 'song_title'])
-        if 'preview_url' not in previews_df.columns:
-            previews_df = pd.DataFrame(columns=["elo_score", "artist", "song_title", "preview_url"])
-        
-        # Ensure the new DataFrame has the correct columns
-        if all(column in new_df.columns for column in ["elo_score", "artist", "song_title", "preview_url"]):
-            new_df = new_df[["elo_score", "artist", "song_title", "preview_url"]]
-        else:
-            raise ValueError("New CSV file does not contain required columns.")
-        
-        # Merge original DataFrames to ensure we don't have duplicates across them
-        consolidated_df = pd.concat([songs_df[['artist', 'song_title']], previews_df[['artist', 'song_title']]]).drop_duplicates()
-        
-        # Identify new, unique songs not in the original datasets
-        unique_songs = pd.merge(new_df, consolidated_df, on=['artist', 'song_title'], how='left', indicator=True).query('_merge=="left_only"').drop('_merge', axis=1)
-        
-        # Update previews_df with new unique songs including their preview URLs
-        previews_df = pd.concat([previews_df, unique_songs])
-        
-        # Separate the unique songs data without the 'preview_url' for songs_df
-        unique_songs_simplified = unique_songs.drop(['preview_url'], axis=1, errors='ignore')
-        songs_df = pd.concat([songs_df, unique_songs_simplified])
-        
+    songs_df = songs_df.drop_duplicates(subset=['artist', 'song_title'])
+    previews_df = previews_df.drop_duplicates(subset=['artist', 'song_title'])
     return songs_df, previews_df
+
     
 # Function to remove a song
 def remove_song(artist, song_title, songs_df):