Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,72 @@
+"""
+EuroSAT Classifier — Gradio demo for Hugging Face Spaces.
+Upload a satellite image → get land-use class predictions.
+"""
+
+import torch
+import gradio as gr
+from torchvision import transforms
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from model import SimpleNet, CLASS_NAMES
+
+
+# ── Load model ────────────────────────────────────────────────────────
+
+def load_model():
+    """Download weights from HF Hub and load into SimpleNet."""
+    # TODO: replace with your actual HF repo id after upload
+    weights_path = hf_hub_download(
+        repo_id="yava-code/eurosat-simplenet",
+        filename="simple_net_v1.pth",
+    )
+    model = SimpleNet(num_classes=10)
+    model.load_state_dict(torch.load(weights_path, map_location="cpu"))
+    model.eval()
+    return model
+
+
+model = load_model()
+
+preprocess = transforms.Compose([
+    transforms.Resize((64, 64)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+
+# ── Inference ─────────────────────────────────────────────────────────
+
+def predict(image: Image.Image) -> dict[str, float]:
+    """Return class probabilities for a satellite image."""
+    if image is None:
+        return {}
+
+    tensor = preprocess(image).unsqueeze(0)  # [1, 3, 64, 64]
+
+    with torch.no_grad():
+        logits = model(tensor)
+        probs = torch.nn.functional.softmax(logits, dim=1)[0]
+
+    return {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))}
+
+
+# ── Gradio UI ─────────────────────────────────────────────────────────
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Satellite Image"),
+    outputs=gr.Label(num_top_classes=5, label="Predictions"),
+    title="🛰️ EuroSAT Land-Use Classifier",
+    description=(
+        "Upload a Sentinel-2 satellite image to classify its land-use type. "
+        "Custom CNN (SimpleNet, ~850K params) trained from scratch on EuroSAT."
+    ),
+    examples=[],  # add example images if you want
+    theme=gr.themes.Soft(),
+)
+
+if __name__ == "__main__":
+    demo.launch()