Update app.py

app.py

@@ -219,12 +219,10 @@ def get_user_emb(embs, ys):
 def pluck_img(user_id, user_emb):
     print(user_id, 'user_id')
     not_rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, None) == None for i in prevs_df.iterrows()]]
-    rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, None) != None for i in prevs_df.iterrows()]]
     while len(not_rated_rows) == 0:
         not_rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, None) == None for i in prevs_df.iterrows()]]
-        rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, None) != None for i in prevs_df.iterrows()]]
         time.sleep(.01)
-        print('current user has rated all rows')
+        print(f'user {user_id} has rated all rows')
     # TODO optimize this lol
     best_sim = -100000
     for i in not_rated_rows.iterrows():
@@ -415,7 +413,7 @@ with gr.Blocks(css=css, head=js_head) as demo:
 
 Explore the latent space without text prompts based on your preferences. Learn more on [the write-up](https://rynmurdock.github.io/posts/2024/3/generative_recomenders/).
     ''', elem_id="description")
-    user_id = gr.State(int(torch.randint(2**6, (1,))[0]))
+    user_id = gr.State(int(random.randint(0, 999999999)))
     print('USER_ID: ',user_id)
     calibrate_prompts = gr.State([
     './first.mp4',
@@ -477,7 +475,7 @@ log = logging.getLogger('log_here')
 log.setLevel(logging.ERROR)
 
 scheduler = BackgroundScheduler()
-scheduler.add_job(func=background_next_image, trigger="interval", seconds=.01)
+scheduler.add_job(func=background_next_image, trigger="interval", seconds=.1)
 scheduler.start()
 
 def encode_space(x):