longer calibration

app.py

@@ -194,7 +194,7 @@ def get_user_emb(embs, ys):
  print('Dropping at 20')
  
  if mini < 1:
- feature_embs = torch.stack([torch.randn(1280), torch.randn(1280)])
+ feature_embs = torch.stack([torch.randn(1024), torch.randn(1024)])
  ys_t = [0, 1]
  print('Not enough ratings.')
  else:
@@ -386,8 +386,9 @@ def choose(img, choice, calibrate_prompts, user_id, request: gr.Request):
  # if it's still in the dataframe, add the choice
  if len(prevs_df.loc[row_mask, 'user:rating']) > 0:
  prevs_df.loc[row_mask, 'user:rating'][0][user_id] = choice
+ print(row_mask, prevs_df.loc[row_mask, 'latest_user_to_rate'], [user_id])
  prevs_df.loc[row_mask, 'latest_user_to_rate'] = [user_id]
- img, calibrate_prompts, = next_image(calibrate_prompts, user_id)
+ img, calibrate_prompts = next_image(calibrate_prompts, user_id)
  return img, calibrate_prompts
 
 css = '''.gradio-container{max-width: 700px !important}
@@ -451,11 +452,16 @@ Explore the latent space without text prompts based on your preferences. Learn m
  user_id = gr.State()
  # calibration videos -- this is a misnomer now :D
  calibrate_prompts = gr.State([
- './first.png',
- './second.png',
- './sixth.png',
- './fifth.png',
- './fourth.png',
+ './5o.png',
+ './2o.png',
+ './6o.png',
+ './7o.png',
+ './1o.png',
+ './8o.png',
+ './3o.png',
+ './4o.png',
+ './10o.png',
+ './9o.png',
  ])
  def l():
  return None
@@ -543,12 +549,17 @@ def encode_space(x):
  return im_emb.detach().to('cpu').to(torch.float32)
 
 # prep our calibration videos
-for im, txt in [ # TODO more movement
- ('./first.png', 'describe the scene: a sketch'),
- ('./second.png', 'describe the scene: omens in the suburbs'),
- ('./sixth.png', 'describe the scene: geometric abstract art of a windmill'),
- ('./fifth.png', 'describe the scene: memento mori'),
- ('./fourth.png', 'describe the scene: a green plate with anespresso'),
+for im, txt in [ # DO NOT NAME THESE PNGs JUST NUMBERS! apparently we assign images by number
+ ('./1o.png', 'describe the scene: omens in the suburbs'),
+ ('./2o.png', 'describe the scene: geometric abstract art of a windmill'),
+ ('./3o.png', 'describe the scene: memento mori'),
+ ('./4o.png', 'describe the scene: a green plate with anespresso'),
+ ('./5o.png', '5 '),
+ ('./6o.png', '6 '),
+ ('./7o.png', '7 '),
+ ('./8o.png', '8 '),
+ ('./9o.png', '9 '),
+ ('./10o.png', '10 '),
  ]:
  tmp_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'text', 'gemb'])
  tmp_df['paths'] = [im]