Create app.py

app.py

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+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import gradio as gr
+from model import AlexNet
+
+CLASSES = ['airplane','automobile','bird','cat','deer',
+           'dog','frog','horse','ship','truck']
+
+# 모델 로드
+model = AlexNet()
+model.load_state_dict(torch.load('alexnet_cifar10.pth', map_location='cpu'))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=(0.5,0.5,0.5), std=(0.5,0.5,0.5))
+])
+
+def predict(image):
+    img = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        output = model(img)
+        probs = torch.softmax(output, dim=1)[0]
+    return {CLASSES[i]: float(probs[i]) for i in range(10)}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="AlexNet CIFAR-10 분류기",
+    description="이미지를 업로드하면 10개 클래스 중 하나로 분류해요."
+)
+
+demo.launch()