--- a
+++ b/helper.py
@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+
+# Example model definition (use your actual architecture)
+class UNet(nn.Module):
+    def __init__(self):
+        super(UNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2d(64, 1, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = self.relu(self.conv1(x))
+        x = self.conv2(x)
+        return x
+
+# Step 1: Create the model instance
+model = UNet()
+
+# Step 2: Load the state dictionary
+state_dict = torch.load('leukemia_cells_unet.pt', map_location=torch.device('cpu'), weights_only=True)
+
+# Step 3: Load the weights into the model instance
+model.load_state_dict(state_dict)
+
+# Step 4: Set the model to evaluation mode
+model.eval()