Update TumorModel.py

TumorModel.py

@@ -1,10 +1,32 @@
+import torch.nn as nn
+
+class TumorClassification(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.con1d = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.con2d = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.con3d = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(128 * 28 * 28, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.output = nn.Linear(256, 4)
+
+    def forward(self, x):
+        x = nn.functional.relu(self.pool(self.con1d(x)))
+        x = nn.functional.relu(self.pool(self.con2d(x)))
+        x = nn.functional.relu(self.pool(self.con3d(x)))
+        x = x.view(x.size(0), -1)
+        x = nn.functional.relu(self.fc1(x))
+        x = nn.functional.relu(self.fc2(x))
+        return self.output(x)
+
 class GliomaStageModel(nn.Module):
     def __init__(self):
         super().__init__()
         self.fc1 = nn.Linear(9, 100)
         self.fc2 = nn.Linear(100, 50)
         self.fc3 = nn.Linear(50, 30)
-        self.out = nn.Linear(30, 2)  # ✅ MATCHING saved model weights
+        self.out = nn.Linear(30, 2)  # ✅ Make sure this matches your trained model
 
     def forward(self, x):
         x = nn.functional.relu(self.fc1(x))