fix: nnUNet models disappearing from dropdown on HF Spaces

Three bugs fixed:

1. _ensure_nnunet_from_hub: final success check only verified plans.json
but not the .pth weight file. Now checks both.

2. model_registry.py: register_nnunet() failures were logged at debug
level (invisible). Changed to warning with exc_info=True so the
actual download error is visible in HF Spaces logs.

3. orchestrator.py: get_available_models() checked cfg.checkpoint_path
directly from the stale NNUNET_MODELS config (still None before
download). Now uses is_model_registered() which triggers
_ensure_models_registered() -> register_nnunet() -> HF Hub download,
so checkpoint_path is populated by the time we check.

seg_app/config/settings.py

@@ -235,9 +235,17 @@ def _ensure_nnunet_from_hub(
                     shutil.copy2(str(downloaded_path), str(dest))
             logger.info(f"  -> {dest}")
 
-        if (local_dir / "plans.json").is_file():
+        if (
+            (local_dir / "plans.json").is_file()
+            and (local_dir / "fold_0" / checkpoint_name).is_file()
+        ):
             logger.info(f"nnUNet checkpoint ready: {local_dir}")
             return str(local_dir)
+        else:
+            logger.warning(
+                f"Download appeared to succeed but required files are missing "
+                f"in {local_dir} (plans.json or fold_0/{checkpoint_name})"
+            )
 
     except Exception as e:
         logger.warning(f"Failed to download nnUNet weights from {hf_hub_repo}: {e}")

seg_app/inference/model_registry.py

@@ -230,7 +230,7 @@ def _ensure_models_registered() -> None:
         register_nnunet()
     except Exception as e:
         import logging
-        logging.getLogger(__name__).debug(f"nnUNet registration skipped: {e}")
+        logging.getLogger(__name__).warning(f"nnUNet registration failed: {e}", exc_info=True)
 
     # Register Medical SAM 3D for volumetric interactive refinement
     from seg_app.models.medical_sam_3d import register_medical_sam_3d

seg_app/inference/orchestrator.py

@@ -191,15 +191,20 @@ def get_available_models() -> List[Dict[str, str]]:
 
     Returns:
         List of dicts with 'id' and 'display_name' for each model.
-        Only models with real checkpoints on disk are included.
+        Only models that are registered (checkpoint available or
+        downloadable from HF Hub) are included.
     """
     from seg_app.config.settings import NNUNET_MODELS
+    from seg_app.inference.model_registry import is_model_registered
 
     models: List[Dict[str, str]] = []
 
-    # Add every nnUNet variant that has a checkpoint on disk
+    # Add every nnUNet variant that was successfully registered.
+    # is_model_registered() triggers _ensure_models_registered() which
+    # downloads missing weights from HF Hub, so checkpoint_path in the
+    # config object is updated by the time we check here.
     for cfg in NNUNET_MODELS:
-        if cfg.checkpoint_path is not None:
+        if is_model_registered(cfg.model_id):
             models.append({"id": cfg.model_id, "display_name": cfg.display_name})
 
     # Medical SAM 3D is always available (weights on HF Hub or local)
@@ -253,7 +258,7 @@ def _resolve_task_to_model(task_name: str) -> str:
     """
     # Stub - will delegate to config/tasks.py
     task_to_model = {
-        "brain_lesion": "nnunet-isles",
+        "brain_lesion": "nnunet-isles-dwi",
     }
     
     if task_name not in task_to_model: