30 length df; remove with most dislikes.

app.py

@@ -269,17 +269,23 @@ def background_next_image():
                 tmp_df['user:rating'] = [{' ': ' '}]
                 tmp_df['from_user_id'] = [uid]
                 prevs_df = pd.concat((prevs_df, tmp_df))
+                
                 # we can free up storage by deleting the image
-                if len(prevs_df) > 50:
-                    oldest_path = prevs_df.iloc[6]['paths']
-                    if os.path.isfile(oldest_path):
-                        os.remove(oldest_path)
+                if len(prevs_df) > 30:
+                    cands = prevs_df.iloc[6:]
+                    cands['sum_bad_ratings'] = [sum([int(t==0) for t in i.values()]) for i in cands['user:rating']]
+                    worst_row = cands.loc[cands['sum_bad_ratings']==cands['sum_bad_ratings'].max()].iloc[0]
+                    worst_path = worst_row['paths']
+                    print('Removing worst row:', worst_row, 'from prevs_df of len', len(prevs_df))
+                    if os.path.isfile(worst_path):
+                        os.remove(worst_path)
                     else:
                         # If it fails, inform the user.
-                        print("Error: %s file not found" % oldest_path)
-                    # only keep 50 images & embeddings & ips, then remove oldest besides calibrating
-                    prevs_df = pd.concat((prevs_df.iloc[:6], prevs_df.iloc[7:]))
-    
+                        print("Error: %s file not found" % worst_path)
+
+                    # only keep x images & embeddings & ips, then remove the most often disliked besides calibrating
+                    prevs_df = prevs_df[prevs_df['paths'] != worst_path]
+                    print('prevs_df is now length:', len(prevs_df))
 
 def pluck_embs_ys(user_id):
     rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, None) != None for i in prevs_df.iterrows()]]