Update newapi.py

newapi.py

@@ -28,7 +28,7 @@ transform = transforms.Compose([
     transforms.Resize((224, 224)),
     transforms.Grayscale(num_output_channels=1),  # Ensure grayscale
     transforms.ToTensor(),
-    transforms.Normalize(mean=[0.5], std=[0.5]),
+    transforms.Normalize(mean=[0.286], std=[0.229]),  # Adjust mean/std if needed
 ])
 
 # Define the exact same model used during training
@@ -41,7 +41,7 @@ class BrainTumorModel(nn.Module):
         self.con2d = nn.Conv2d(32, 64, kernel_size=3)
         self.con3d = nn.Conv2d(64, 128, kernel_size=3)
         self.pool = nn.MaxPool2d(2)
-        self.fc1 = nn.Linear(128 * 25 * 25, 512)      # Matches your saved model
+        self.fc1 = nn.Linear(128 * 28 * 28, 512)      # Match the saved model's input size
         self.fc2 = nn.Linear(512, 256)
         self.output = nn.Linear(256, 4)               # 4 classes expected
 
@@ -49,7 +49,7 @@ class BrainTumorModel(nn.Module):
         x = self.pool(torch.relu(self.con1d(x)))
         x = self.pool(torch.relu(self.con2d(x)))
         x = self.pool(torch.relu(self.con3d(x)))
-        x = x.view(-1, 128 * 25 * 25)
+        x = x.view(-1, 128 * 28 * 28)  # Flatten the feature maps
         x = torch.relu(self.fc1(x))
         x = torch.relu(self.fc2(x))
         x = self.output(x)