non_block?

app.py

@@ -363,10 +363,39 @@ def next_image(calibrate_prompts, user_id):
 
 
 
-
+done_init = False
 
 
 def start(_, calibrate_prompts, user_id, request: gr.Request):
+    global done_init
+
+    if not done_init:
+        # prep our calibration videos
+        for im in [
+            './first.mp4',
+            './second.mp4',
+            './third.mp4',
+            './fourth.mp4',
+            './fifth.mp4',
+            './sixth.mp4',
+            './seventh.mp4',
+            './eigth.mp4',
+            './ninth.mp4',
+            './tenth.mp4',
+            ]:
+            tmp_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'text', 'gemb'])
+            tmp_df['paths'] = [im]
+            image = list(imageio.imiter(im))
+            image = image[len(image)//2]
+            im_emb, gemb = encode_space(image)
+
+            tmp_df['embeddings'] = [im_emb.detach().to('cpu')]
+            tmp_df['gemb'] = [gemb.detach().to('cpu')]
+            tmp_df['user:rating'] = [{' ': ' '}]
+            prevs_df = pd.concat((prevs_df, tmp_df))
+
+
+
     user_id = int(str(time.time())[-7:].replace('.', ''))
     image, calibrate_prompts, text = next_image(calibrate_prompts, user_id)
     return [
@@ -548,31 +577,6 @@ def encode_space(x):
             
     return im_emb.detach().to('cpu').to(torch.float32), gemb
 
-# prep our calibration videos
-for im in [
-    './first.mp4',
-    './second.mp4',
-    './third.mp4',
-    './fourth.mp4',
-    './fifth.mp4',
-    './sixth.mp4',
-    './seventh.mp4',
-    './eigth.mp4',
-    './ninth.mp4',
-    './tenth.mp4',
-    ]:
-    tmp_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'text', 'gemb'])
-    tmp_df['paths'] = [im]
-    image = list(imageio.imiter(im))
-    image = image[len(image)//2]
-    im_emb, gemb = encode_space(image)
-
-    tmp_df['embeddings'] = [im_emb.detach().to('cpu')]
-    tmp_df['gemb'] = [gemb.detach().to('cpu')]
-    tmp_df['user:rating'] = [{' ': ' '}]
-    prevs_df = pd.concat((prevs_df, tmp_df))
-
-
 demo.launch(share=True, server_port=8443)