Update app.py

app.py

@@ -16,7 +16,7 @@ with open(model_path, 'wb') as file:
     for chunk in response.iter_content(chunk_size=8192):
         file.write(chunk)
 
-pth_model = torch.jit.load(model_path).to('cuda')
+pth_model = torch.jit.load(model_path)
 pth_model.eval()
 
 DICT_EMO = {0: 'Neutral', 1: 'Happiness', 2: 'Sadness', 3: 'Surprise', 4: 'Fear', 5: 'Disgust', 6: 'Anger'}
@@ -44,7 +44,7 @@ def pth_processing(fp):
         ])
         img = img.resize((224, 224), Image.Resampling.NEAREST)
         img = ttransform(img)
-        img = torch.unsqueeze(img, 0).to('cuda')
+        img = torch.unsqueeze(img, 0)
         return img
     return get_img_torch(fp)
 
@@ -89,7 +89,7 @@ def predict(inp):
                 startX, startY, endX, endY  = get_box(fl, w, h)
                 cur_face = inp[startY:endY, startX: endX]
                 cur_face_n = pth_processing(Image.fromarray(cur_face))
-                prediction = torch.nn.functional.softmax(pth_model(cur_face_n), dim=1).cpu().detach().numpy()[0]
+                prediction = torch.nn.functional.softmax(pth_model(cur_face_n), dim=1).detach().numpy()[0]
                 confidences = {DICT_EMO[i]: float(prediction[i]) for i in range(7)}
                 
     return cur_face, confidences