fix: resolve all audit findings — e2e coverage, feature system hardening, visual baselines

- Add 8 new E2E specs for AI tools (upscale, enhance-faces, colorize,
  restore-photo, erase-object, smart-crop, passport-photo, red-eye-removal)
  closing all HIGH/MEDIUM coverage gaps from the test matrix audit
- Fix ensureAiDirs() crash on non-Docker environments by gating on
  isDockerEnvironment() — prevents ENOENT when /data doesn't exist
- Bump torch 2.6.0→2.7.0 and torchvision 0.21.0→0.22.0 in feature
  manifest for broader Python version compatibility
- Add Python 3.14 version guard warning in install_feature.py
- Remove duplicate torchvision shims from upscale.py and enhance_faces.py
  (dispatcher.py already handles this at startup)
- Remove orphaned tools.batch i18n key and dead pipeline-builder filter
- Regenerate 4 visual regression baselines for current UI state
- Add data-testid to passport-photo generate button for E2E testability

packages/ai/python/enhance_faces.py

@@ -3,34 +3,6 @@ import sys
 import json
 import os
 
-# Patch for basicsr compatibility with torchvision >= 0.17.
-# torchvision removed transforms.functional_tensor, merging everything
-# into transforms.functional. basicsr 1.4.2 still imports the old path
-# (e.g. rgb_to_grayscale), so we create a proxy module that forwards
-# ALL attribute lookups to the new location.
-try:
-    import torchvision.transforms.functional_tensor  # noqa: F401
-except (ImportError, ModuleNotFoundError):
-    try:
-        import types
-        import torchvision.transforms.functional as _F
-
-        import torchvision.transforms
-
-        _shim = types.ModuleType("torchvision.transforms.functional_tensor")
-        _shim.__getattr__ = lambda name: getattr(_F, name)
-        # Pre-populate the attribute basicsr actually imports so that
-        # `from torchvision.transforms.functional_tensor import rgb_to_grayscale`
-        # works (from-import checks __dict__ before __getattr__).
-        _shim.rgb_to_grayscale = _F.rgb_to_grayscale
-        sys.modules["torchvision.transforms.functional_tensor"] = _shim
-        # The parent package must also reference the submodule for
-        # `from torchvision.transforms.functional_tensor import ...` to
-        # resolve correctly in all Python versions.
-        torchvision.transforms.functional_tensor = _shim
-    except (ImportError, AttributeError) as e:
-        print(f"[enhance-faces] torchvision shim failed: {e}", file=sys.stderr, flush=True)
-
 
 def emit_progress(percent, stage):
     """Emit structured progress to stderr for bridge.ts to capture."""

packages/ai/python/install_feature.py

@@ -72,7 +72,7 @@ def cpu_fallback_packages(packages: list[str]) -> list[str]:
     result = []
     for pkg in packages:
         # Handle multi-package CUDA torch entries like:
-        # "torch==2.6.0+cu126 torchvision==0.21.0+cu126 --index-url ..."
+        # "torch==2.7.0+cu126 torchvision==0.22.0+cu126 --index-url ..."
         first_token = pkg.split()[0] if pkg.strip() else ""
         if first_token.startswith("torch==") and "+cu" in first_token:
             # Extract torch and torchvision versions, strip CUDA suffix
@@ -423,6 +423,11 @@ def write_installed_atomic(ai_dir: str, data: dict) -> None:
 
 
 def main() -> None:
+    if sys.version_info >= (3, 14):
+        print(f"[WARN] Python {sys.version_info.major}.{sys.version_info.minor} detected. "
+              f"Some packages may not have pre-built wheels. Build from source may be attempted.",
+              file=sys.stderr, flush=True)
+
     if len(sys.argv) < 4:
         fail(
             f"Usage: {sys.argv[0]} <bundleId> <manifestPath> <modelsDir>\n"

packages/ai/python/upscale.py

@@ -3,34 +3,6 @@ import sys
 import json
 import os
 
-# Patch for basicsr compatibility with torchvision >= 0.17.
-# torchvision removed transforms.functional_tensor, merging everything
-# into transforms.functional. basicsr 1.4.2 still imports the old path
-# (e.g. rgb_to_grayscale), so we create a proxy module that forwards
-# ALL attribute lookups to the new location.
-try:
-    import torchvision.transforms.functional_tensor  # noqa: F401
-except (ImportError, ModuleNotFoundError):
-    try:
-        import types
-        import torchvision.transforms.functional as _F
-
-        import torchvision.transforms
-
-        _shim = types.ModuleType("torchvision.transforms.functional_tensor")
-        _shim.__getattr__ = lambda name: getattr(_F, name)
-        # Pre-populate the attribute basicsr actually imports so that
-        # `from torchvision.transforms.functional_tensor import rgb_to_grayscale`
-        # works (from-import checks __dict__ before __getattr__).
-        _shim.rgb_to_grayscale = _F.rgb_to_grayscale
-        sys.modules["torchvision.transforms.functional_tensor"] = _shim
-        # The parent package must also reference the submodule for
-        # `from torchvision.transforms.functional_tensor import ...` to
-        # resolve correctly in all Python versions.
-        torchvision.transforms.functional_tensor = _shim
-    except (ImportError, AttributeError) as e:
-        print(f"[upscale] torchvision shim failed: {e}", file=sys.stderr, flush=True)
-
 
 def emit_progress(percent, stage):
     """Emit structured progress to stderr for bridge.ts to capture."""

packages/shared/src/i18n/en.ts

@@ -165,7 +165,6 @@ export const en = {
       description: "Convert images to base64 strings for embedding in HTML, CSS, and more",
     },
     pipeline: { name: "Pipeline Builder", description: "Chain multiple tools into a workflow" },
-    batch: { name: "Batch Processing", description: "Apply any tool to multiple images" },
   },
   settings: {
     title: "Settings",