test

app.py

@@ -8,7 +8,8 @@ model, preprocess = clip.load("ViT-B/32", device=device)
 
 
 def predict(image):
-    labels = "Japanese, Chinese, Roman, Greek, Etruscan, Scandinavian, Celtic, Medieval, Victorian, Neoclassic, Romanticism, Art Nouveau, Art deco"
+    labels = "Early American Art,19th and 20th–Century Art,Contemporary Art,Modern Folk,African American Art,Latino Art,Mesoamerican,Egyptian,British Art,Celtic Art,German Art,Medieval European,Gothic,Native American,African Art,Asia pacific Art,Oceanía,Classical,Byzantine,Medieval,Gothic,Renaissance,Baroque,Rococo,Neoclassical,Modernism,Postmodern ,Irish,German,French,Italian,Spanish,Portuguese,Greek,Chinese,Japanese,Korean,Thai,Australian,Middle Eastern,Mesopotamian,Prehistoric,Mexican,Popart,Scottish,Netherlands"
+    # labels = "Japanese, Chinese, Roman, Greek, Etruscan, Scandinavian, Celtic, Medieval, Victorian, Neoclassic, Romanticism, Art Nouveau, Art deco"
     labels = labels.split(',')
 
     converter = ImageEnhance.Color(image)