Fix: Make app startup more resilient to prevent SSE errors

- Don't raise exception if HF_TOKEN is missing (show warning instead)
- Don't raise exception if SAM 3 model fails to load (allow app to start)
- Add graceful import handling for Sam3Processor/Sam3Model
- Add startup verification messages
- This prevents app crashes that cause 'SSE is not enabled' errors

app.py

@@ -15,7 +15,17 @@ import torch
 import pydicom
 import numpy as np
 from PIL import Image, ImageEnhance, ImageDraw
-from transformers import Sam3Processor, Sam3Model
+try:
+    from transformers import Sam3Processor, Sam3Model
+    SAM3_AVAILABLE = True
+except ImportError:
+    print("⚠️ Warning: Sam3Processor/Sam3Model not found in transformers.")
+    print("⚠️ Please ensure transformers>=4.45.0 is installed.")
+    print("⚠️ You may need: pip install transformers>=4.45.0")
+    SAM3_AVAILABLE = False
+    # Create dummy classes to prevent import errors
+    Sam3Processor = None
+    Sam3Model = None
 import matplotlib.pyplot as plt
 from matplotlib.patches import Rectangle
 from scipy import ndimage
@@ -31,7 +41,9 @@ except ImportError:
 # Hugging Face Token (must be set as HF_TOKEN environment variable in Space settings)
 hf_token = os.getenv("HF_TOKEN")
 if not hf_token:
-    raise ValueError("HF_TOKEN environment variable is required. Please set it in Space settings.")
+    print("⚠️ WARNING: HF_TOKEN environment variable not set!")
+    print("⚠️ Some features may not work. Please set HF_TOKEN in Space settings.")
+    hf_token = None  # Allow app to start, but model loading will fail gracefully
 
 # Login to Hugging Face Hub
 try:
@@ -46,26 +58,39 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 model = None
 processor = None
 
-# SAM 3 model identifier - matching official implementation
-SAM_MODEL_ID = "facebook/sam3"
-
-try:
-    # Load model with proper dtype (float16 for GPU, float32 for CPU) - matching official implementation
-    model = Sam3Model.from_pretrained(
-        SAM_MODEL_ID, 
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        token=hf_token
-    ).to(device)
-    processor = Sam3Processor.from_pretrained(SAM_MODEL_ID, token=hf_token)
-    model.eval()
-    print(f"✅ SAM 3 Model Loaded Successfully! ({SAM_MODEL_ID})")
-except Exception as e:
-    print(f"❌ Failed to load SAM 3 model: {e}")
-    print("Ensure you have:")
-    print("  1. transformers>=4.45.0 for SAM 3 support")
-    print("  2. Valid Hugging Face token with access to SAM 3")
-    print("  3. Sufficient memory for the model")
-    raise
+if not SAM3_AVAILABLE:
+    print("❌ SAM 3 classes not available in transformers library.")
+    print("❌ Please install: pip install transformers>=4.45.0")
+    print("⚠️ App will start but segmentation features will be disabled.")
+else:
+    # SAM 3 model identifier - matching official implementation
+    SAM_MODEL_ID = "facebook/sam3"
+    
+    if hf_token is None:
+        print("⚠️ Cannot load model: HF_TOKEN not set")
+        model = None
+        processor = None
+    else:
+        try:
+            # Load model with proper dtype (float16 for GPU, float32 for CPU) - matching official implementation
+            model = Sam3Model.from_pretrained(
+                SAM_MODEL_ID, 
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                token=hf_token
+            ).to(device)
+            processor = Sam3Processor.from_pretrained(SAM_MODEL_ID, token=hf_token)
+            model.eval()
+            print(f"✅ SAM 3 Model Loaded Successfully! ({SAM_MODEL_ID})")
+        except Exception as e:
+            print(f"⚠️ Failed to load SAM 3 model: {e}")
+            print("Ensure you have:")
+            print("  1. transformers>=4.45.0 for SAM 3 support")
+            print("  2. Valid Hugging Face token with access to SAM 3")
+            print("  3. Sufficient memory for the model")
+            print("⚠️ App will start but segmentation features will be disabled until model loads.")
+            # Don't raise - allow app to start and show error in UI
+            model = None
+            processor = None
 
 def run_sam3_inference(pil_image, prompt_text, threshold=0.5, mask_threshold=0.5):
     """
@@ -81,8 +106,8 @@ def run_sam3_inference(pil_image, prompt_text, threshold=0.5, mask_threshold=0.5
         results dict with 'masks' and 'scores' keys, or None if failed
     """
     if model is None or processor is None:
-        print("❌ Model not loaded")
-        return None
+        print("❌ Model not loaded - please check HF_TOKEN and model availability")
+        raise ValueError("SAM 3 model not loaded. Please check that HF_TOKEN is set correctly and the model is accessible.")
     
     try:
         # Prepare inputs - matching official implementation
@@ -3214,4 +3239,15 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    # Verify model is loaded before launching
+    if model is None or processor is None:
+        print("⚠️ WARNING: SAM 3 model failed to load!")
+        print("⚠️ The app will start but segmentation features will not work.")
+        print("⚠️ Please check:")
+        print("   1. HF_TOKEN environment variable is set correctly")
+        print("   2. transformers>=4.45.0 is installed")
+        print("   3. Sufficient memory/GPU available")
+    else:
+        print("✅ SAM 3 model ready - app starting...")
+    
+    demo.launch(server_name="0.0.0.0", server_port=7860)