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]