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inference.py

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+import torch
+from model import SimpleCNN
+from PIL import Image
+from torchvision import transforms
+
+# Load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = SimpleCNN()
+state = torch.load("pytorch_model.bin", map_location="cpu")
+model.load_state_dict(state)
+model.eval()
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+labels = ["no_crack", "crack"]
+
+def predict(image: Image.Image):
+    img = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        logits = model(img)
+        probs = torch.softmax(logits, dim=1)[0]
+        idx = probs.argmax().item()
+
+    return {
+        "label": labels[idx],
+        "score": float(probs[idx])
+    }