Update app.py

app.py

@@ -12,7 +12,43 @@
 # ────────────────────────────────────────────────────────
 # Standard imports
 # ────────────────────────────────────────────────────────
+# ---- TorchVision shim so basicsr can import without torchvision installed ----
 import sys, types
+try:
+    # If torchvision is present, great — use it.
+    import torchvision.transforms.functional_tensor as _ft  # noqa: F401
+except Exception:
+    # Create a minimal module that provides rgb_to_grayscale with Torch ops.
+    import torch
+
+    _mod = types.ModuleType("torchvision.transforms.functional_tensor")
+
+    def rgb_to_grayscale(img: "torch.Tensor", num_output_channels: int = 1) -> "torch.Tensor":
+        """
+        Minimal replacement for torchvision's rgb_to_grayscale.
+        Expects a Tensor with channel-last-three: (..., 3, H, W) and returns
+        (..., 1, H, W) or (..., 3, H, W) if num_output_channels == 3.
+        """
+        if not torch.is_tensor(img):
+            raise TypeError("rgb_to_grayscale expects a torch.Tensor")
+        if img.ndim < 3 or img.shape[-3] != 3:
+            raise ValueError(f"expected tensor with C=3 as the third-from-last dim, got shape {tuple(img.shape)}")
+
+        r = img[..., -3, :, :]
+        g = img[..., -2, :, :]
+        b = img[..., -1, :, :]
+
+        gray = 0.2989 * r + 0.5870 * g + 0.1140 * b  # same weights as TV
+        if num_output_channels == 3:
+            out = torch.stack([gray, gray, gray], dim=-3)
+        else:
+            out = gray.unsqueeze(-3)
+        return out
+
+    _mod.rgb_to_grayscale = rgb_to_grayscale
+    sys.modules["torchvision.transforms.functional_tensor"] = _mod
+# ---------------------------------------------------------------------------
+
 import os
 import re
 import cv2