Update app.py

app.py

@@ -69,7 +69,7 @@ def convert_numpy_types(data):
 def predict(image):
     model = MLP(768)  # CLIP embedding dim is 768 for CLIP ViT L 14
     pthpath = "https://huggingface.co/haor/aesthetics/resolve/main/sac%2Blogos%2Bava1-l14-linearMSE.pth"
-    device = "cpu" if torch.cuda.is_available() else "cpu"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
 
     model.load_state_dict(torch.hub.load_state_dict_from_url(pthpath, map_location=device))
     model.to(device).eval()
@@ -87,7 +87,8 @@ def predict(image):
     with torch.no_grad():
         img_emb = model2.encode_image(inputs)
         img_emb = normalized(img_emb.cpu().numpy())
-        prediction = model(torch.from_numpy(img_emb).to(device).type(torch.FloatTensor)).item()
+        img_emb_tensor = torch.from_numpy(img_emb).to(device)
+        prediction = model(img_emb_tensor).item()
 
     result = {
         "clip_aesthetic": prediction,