upload model.py

model.py

@@ -0,0 +1,24 @@
+import torch
+import torchvision
+from torchvision.models import EfficientNet_B2_Weights
+from torch import nn
+
+def create_model(num_classes=7):
+    weights = EfficientNet_B2_Weights.DEFAULT
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    for param in model.parameters():
+        param.requires_grad = False  # Freeze for inference
+
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3),
+        nn.Linear(model.classifier[1].in_features, num_classes)
+    )
+
+    return model
+
+def load_model(weights_path="fine_tuned_effnetb2_dermamnist.pth"):
+    model = create_model(num_classes=7)
+    model.load_state_dict(torch.load(weights_path, map_location=torch.device("cpu")))
+    model.eval()
+    return model