pop > 10 rated and stop queuing > 10 queued per user

app.py

@@ -126,11 +126,10 @@ pipe.to(device=DEVICE)
 #pipe.unet = torch.compile(pipe.unet)
 #pipe.vae = torch.compile(pipe.vae)
 
-@spaces.GPU(duration=10)
+@spaces.GPU(duration=5)
 def generate_gpu(in_im_embs):
  print('start gen')
  in_im_embs = in_im_embs.to('cuda').unsqueeze(0).unsqueeze(0)
-
  output = pipe(prompt='', guidance_scale=0, added_cond_kwargs={}, ip_adapter_image_embeds=[in_im_embs], num_inference_steps=STEPS)
  print('image is made')
  im_emb, _ = pipe.encode_image(
@@ -209,7 +208,7 @@ def pluck_img(user_id, user_emb):
  not_rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, 'gone') == 'gone' for i in prevs_df.iterrows()]]
  while len(not_rated_rows) == 0:
  not_rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, 'gone') == 'gone' for i in prevs_df.iterrows()]]
- time.sleep(1)
+ time.sleep(.001)
  # TODO optimize this lol
  best_sim = -100000
  for i in not_rated_rows.iterrows():
@@ -241,11 +240,16 @@ def background_next_image():
  # we need to intersect not_rated_rows from this user's embed > 7. Just add a new column on which user_id spawned the 
  # media. 
  
- from_user = prevs_df[[i[1]['from_user_id'] == uid for i in prevs_df.iterrows()]]
- if len(from_user) >= 10:
- oldest = from_user.iloc[-1]['paths']
- print(f'User has {len(from_user)} rows. Popping oldest: {oldest}')
+ unrated_from_user = not_rated_rows[[i[1]['from_user_id'] == uid for i in not_rated_rows.iterrows()]]
+ rated_from_user = rated_rows[[i[1]['from_user_id'] == uid for i in rated_rows.iterrows()]]
+
+ # we pop previous ratings if there are > 10
+ if len(rated_from_user) >= 10:
+ oldest = unrated_from_user.iloc[-1]['paths']
  prevs_df = prevs_df[prevs_df['paths'] != oldest]
+ # we don't compute more after 10 are in the queue for them
+ if len(unrated_from_user) >= 10:
+ continue
  
  if len(rated_rows) < 4:
  print(f'latest user {uid} has < 4 rows') # or > 7 unrated rows')