Update app.py

app.py

@@ -51,22 +51,25 @@ def inference():
     model.eval()
     all_loss = 0
     length = 0
-    with torch.no_grad():
-        data = torch.from_numpy(data)
-        image = torch.unsqueeze(data, 0)
-        patch_data = torch.unsqueeze(image, 0).to(device).float()  # (N, C_{in}, D_{in}, H_{in}, W_{in})
+    try:
+        with torch.no_grad():
+            data = torch.from_numpy(data)
+            image = torch.unsqueeze(data, 0)
+            patch_data = torch.unsqueeze(image, 0).to(device).float()  # (N, C_{in}, D_{in}, H_{in}, W_{in})
 
-        # Pre : Prediction Result
-        pre_probs = model(patch_data)
+            # Pre : Prediction Result
+            pre_probs = model(patch_data)
 
-        # pre_probs = F.sigmoid(pre_probs)#todo
-        pre_flat = pre_probs.view(-1)
-        np.round(pre_flat.numpy()[0], decimals=2)
-        #(1-pre_flat.numpy()[0]).astype(np.float32)
-        #pre_flat.numpy()[0].astype(np.float32)
-        p = float(np.round(pre_flat.numpy()[0], decimals=2))
-        n = float(np.round(1-p, decimals=2))
-        return {'急性期': n, '亚急性期': p}
+            # pre_probs = F.sigmoid(pre_probs)#todo
+            pre_flat = pre_probs.view(-1)
+            np.round(pre_flat.numpy()[0], decimals=2)
+            #(1-pre_flat.numpy()[0]).astype(np.float32)
+            #pre_flat.numpy()[0].astype(np.float32)
+            p = float(np.round(pre_flat.numpy()[0], decimals=2))
+            n = float(np.round(1-p, decimals=2))
+            return {'急性期': n, '亚急性期': p}
+    except:
+        return {'': ''}
 
 
 
@@ -131,7 +134,7 @@ def clear_all():
     global Current_name
     Current_name = None
     Image_3D = None
-    return np.ones((10,10)),np.ones((10,10)),np.ones((10,10)),None,None,None
+    return np.ones((10,10)),np.ones((10,10)),np.ones((10,10)),'','',{'': ''}
 
 class App:
     def __init__(self):