Create startup_selfcheck.py

tools/startup_selfcheck.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+"""
+Startup Self-Check for Hugging Face Space
+- Verifies SAM2 + MatAnyone load and run on a dummy frame
+- Prints ✅ / ❌ in logs
+"""
+
+import logging
+import numpy as np
+import torch
+
+from models.loaders.sam2_loader import SAM2Loader
+from models.loaders.matanyone_loader import MatAnyoneLoader
+
+logger = logging.getLogger("selfcheck")
+if not logger.handlers:
+    logging.basicConfig(level=logging.INFO)
+
+def run_selfcheck():
+    results = {}
+    try:
+        # tiny dummy image
+        img = np.zeros((64, 64, 3), dtype=np.uint8)
+
+        # --- SAM2 ---
+        sam_loader = SAM2Loader(device="cuda" if torch.cuda.is_available() else "cpu")
+        sam = sam_loader.load("tiny")
+        if sam:
+            sam.set_image(img)
+            out = sam.predict(point_coords=None, point_labels=None)
+            results["SAM2"] = out["masks"].shape
+            logger.info(f"✅ SAM2 OK, masks shape {out['masks'].shape}")
+        else:
+            results["SAM2"] = None
+            logger.error("❌ SAM2 failed to load")
+
+        # --- MatAnyone ---
+        mat_loader = MatAnyoneLoader(device="cuda" if torch.cuda.is_available() else "cpu")
+        session = mat_loader.load()
+        if session:
+            alpha = session(img, np.ones((64, 64), np.float32))  # coarse mask
+            results["MatAnyone"] = alpha.shape
+            logger.info(f"✅ MatAnyone OK, alpha shape {alpha.shape}")
+        else:
+            results["MatAnyone"] = None
+            logger.error("❌ MatAnyone failed to load")
+
+    except Exception as e:
+        logger.error(f"❌ Self-check error: {e}", exc_info=True)
+
+    return results
+
+if __name__ == "__main__":
+    run_selfcheck()