Enhance logging and error handling across tools; add full tool audit and Playwright tests

- Added model mismatch warnings in colorize, enhance-faces, and upscale routes.
- Improved error handling in colorize, enhance_faces, remove_bg, restore, and upscale scripts with detailed logging.
- Updated Dockerfile to align NCCL versions for compatibility.
- Introduced a new full tool audit script to test all tools for functionality and GPU usage.
- Created Playwright E2E tests for GPU-dependent tools to ensure proper functionality and performance.

packages/ai/python/gpu.py

@@ -1,6 +1,8 @@
 """Runtime GPU/CUDA detection utility."""
+import ctypes
 import functools
 import os
+import sys
 
 
 @functools.lru_cache(maxsize=1)
@@ -11,16 +13,36 @@ def gpu_available():
     if override is not None and override.lower() in ("0", "false", "no"):
         return False
 
-    # Use torch.cuda as the source of truth. It actually probes
-    # the hardware. onnxruntime's get_available_providers() only
-    # reports compiled-in backends, not whether a GPU exists.
+    # Use torch.cuda as the source of truth when available. It actually
+    # probes the hardware. Fall back to onnxruntime provider detection
+    # when torch is not installed (e.g. CPU-only images without PyTorch).
     try:
         import torch
-        return torch.cuda.is_available()
-    except ImportError:
-        pass
+        avail = torch.cuda.is_available()
+        if avail:
+            name = torch.cuda.get_device_name(0)
+            print(f"[gpu] CUDA available via torch: {name}", file=sys.stderr, flush=True)
+        else:
+            print("[gpu] torch loaded but CUDA not available", file=sys.stderr, flush=True)
+        return avail
+    except ImportError as e:
+        print(f"[gpu] torch not importable: {e}", file=sys.stderr, flush=True)
 
-    return False
+    # Fallback: check if onnxruntime's CUDA provider can actually load.
+    # get_available_providers() only reports *compiled-in* backends, not whether
+    # the required libraries (cuDNN, etc.) are present at runtime.  We verify
+    # by trying to load the provider shared library — this transitively checks
+    # that cuDNN is installed.
+    try:
+        import onnxruntime as _ort
+        if "CUDAExecutionProvider" not in _ort.get_available_providers():
+            return False
+        ep_dir = os.path.dirname(_ort.__file__)
+        ctypes.CDLL(os.path.join(ep_dir, "capi", "libonnxruntime_providers_cuda.so"))
+        return True
+    except (ImportError, OSError) as e:
+        print(f"[gpu] ONNX CUDA provider not functional: {e}", file=sys.stderr, flush=True)
+        return False
 
 
 def onnx_providers():