Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from torch.utils.data import DataLoader
+from PIL import Image
+import gradio as gr
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load class names (make sure this file is in the Space)
+with open("cifar10_classes.txt") as f:
+    CLASSES = [line.strip() for line in f.readlines()]
+
+def build_model(num_classes: int, device: str = "cpu"):
+    try:
+        weights = models.ResNet18_Weights.DEFAULT
+        model = models.resnet18(weights=weights)
+    except AttributeError:
+        model = models.resnet18(weights="IMAGENET1K_V1")
+    model.fc = nn.Linear(model.fc.in_features, num_classes)
+    model = model.to(device)
+    return model
+
+num_classes = len(CLASSES)
+model = build_model(num_classes, device=DEVICE)
+
+state_dict = torch.load("ast_cifar10_resnet18.pth", map_location=DEVICE)
+model.load_state_dict(state_dict)
+model.eval()
+
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+def predict(image: Image.Image):
+    if image is None:
+        return {}
+    x = preprocess(image).unsqueeze(0).to(DEVICE)
+    with torch.no_grad():
+        logits = model(x)
+        probs = torch.softmax(logits, dim=1)[0]
+    return {CLASSES[i]: float(probs[i]) for i in range(len(CLASSES))}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload CIFAR-like image"),
+    outputs=gr.Label(num_top_classes=3, label="Top-3 Predictions"),
+    title="AST CIFAR-10 Classifier",
+    description="ResNet18 fine-tuned with Adaptive Sparse Training (AST) on CIFAR-10.",
+)
+
+if __name__ == "__main__":
+    demo.launch()