Handle SAM3 gated model with HF_TOKEN and trust_remote_code

app.py

@@ -37,7 +37,15 @@ def load_sam3(prompt: str, device: str):
             "This Space installs transformers from GitHub; if you still see this, restart the Space "
             "to rebuild with the latest image."
         )
-    return SAM3Segmenter(text_prompt=prompt, device=device)
+    try:
+        return SAM3Segmenter(text_prompt=prompt, device=device)
+    except OSError as e:
+        # Common for gated models when HF_TOKEN is not set
+        raise gr.Error(
+            "Failed to load SAM3. The model is gated; please add your HF token as a Space secret "
+            "named HF_TOKEN (Settings → Secrets) and restart the Space.\n\n"
+            f"Loader error: {e}"
+        )
 
 
 def _make_overlay(rgb_image: np.ndarray, anomaly_map: np.ndarray) -> Image.Image:

segmenters/sam3.py

@@ -3,7 +3,8 @@ from __future__ import annotations
 import numpy as np
 import torch
 from PIL import Image
-from transformers import Sam3Processor, Sam3Model
+import os
+from transformers import Sam3Processor, Sam3Model
 
 from segmenters import BaseSegmenter
 
@@ -41,9 +42,19 @@ class SAM3Segmenter(BaseSegmenter):
         self.mask_threshold = mask_threshold
 
         # Loading model model + defining processor
-        self.model = Sam3Model.from_pretrained(model_name).to(self.device)
-        self.model.eval()
-        self.processor = Sam3Processor.from_pretrained(model_name)
+        token = os.getenv("HF_TOKEN")
+        # facebook/sam3 is a gated model; token required on Spaces without pre-approval
+        self.model = Sam3Model.from_pretrained(
+            model_name,
+            token=token,
+            trust_remote_code=True,
+        ).to(self.device)
+        self.model.eval()
+        self.processor = Sam3Processor.from_pretrained(
+            model_name,
+            token=token,
+            trust_remote_code=True,
+        )
 
     def get_object_mask(self, image: np.ndarray) -> np.ndarray:
         """