Update app.py

app.py

@@ -206,7 +206,7 @@ def get_user_emb(embs, ys):
     
     print('Gathering coefficients')
     #lin_class = Ridge(fit_intercept=False).fit(feature_embs, chosen_y)
-    lin_class = SVC(max_iter=50000, kernel='linear', C=.1, class_weight='balanced').fit(feature_embs, chosen_y)
+    lin_class = SVC(max_iter=500, kernel='linear', C=.1, class_weight='balanced').fit(feature_embs, chosen_y)
     coef_ = torch.tensor(lin_class.coef_, dtype=torch.double).detach().to('cpu')
     coef_ = coef_ / coef_.abs().max() * 3
     print('Gathered')
@@ -468,13 +468,13 @@ Explore the latent space without text prompts based on your preferences. Learn m
 <div style='text-align:center; font-size:14px'>Thanks to @multimodalart for their contributions to the demo, esp. the interface and @maxbittker for feedback.
 </ div>''')
 
-scheduler = BackgroundScheduler()
-scheduler.add_job(func=background_next_image, trigger="interval", seconds=1)
-scheduler.start()
+
 log = logging.getLogger('log_here')
 log.setLevel(logging.ERROR)
 
-
+scheduler = BackgroundScheduler()
+scheduler.add_job(func=background_next_image, trigger="interval", seconds=.01)
+scheduler.start()
 
 def encode_space(x):
     im_emb, _ = pipe.encode_image(