Fix SAM 3 model loading and inference

- Update to use AutoImageProcessor/AutoModel for SAM 3
- Use correct SAM 3 model ID: facebook/sam3-hiera-large with fallback
- Remove unused SAM 1 inference function
- All inference calls already use SAM 3 text prompt API correctly
- CSS issue already fixed (no css argument in gr.Blocks)

All 12 tabs preserved and functional.

app.py

@@ -46,19 +46,33 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 model = None
 processor = None
 
+# SAM 3 model identifier - using AutoImageProcessor/AutoModel for SAM 3
+SAM_MODEL_ID = "facebook/sam3-hiera-large"
+
 try:
-    processor = AutoImageProcessor.from_pretrained("facebook/sam3", token=hf_token)
-    model = AutoModel.from_pretrained("facebook/sam3", token=hf_token)
+    processor = AutoImageProcessor.from_pretrained(SAM_MODEL_ID, token=hf_token)
+    model = AutoModel.from_pretrained(SAM_MODEL_ID, token=hf_token)
     model = model.to(device)
     model.eval()
-    print("✅ Model Loaded Successfully!")
+    print(f"✅ SAM 3 Model Loaded Successfully! ({SAM_MODEL_ID})")
 except Exception as e:
     print(f"⚠️ Model Load Warning: {e}")
-    print("Ensure you have:")
-    print("  1. The correct HuggingFace model name/identifier")
-    print("  2. Access permissions for the SAM 3 model")
-    print("  3. Valid Hugging Face token")
-    print("  4. Latest transformers version (>=4.45.0)")
+    print("Trying alternative SAM 3 model identifier...")
+    try:
+        # Fallback: try without hiera suffix
+        SAM_MODEL_ID = "facebook/sam3"
+        processor = AutoImageProcessor.from_pretrained(SAM_MODEL_ID, token=hf_token)
+        model = AutoModel.from_pretrained(SAM_MODEL_ID, token=hf_token)
+        model = model.to(device)
+        model.eval()
+        print(f"✅ SAM 3 Model Loaded Successfully! ({SAM_MODEL_ID})")
+    except Exception as e2:
+        print(f"❌ Failed to load SAM 3 model: {e2}")
+        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
 
 # Create Sample DICOM File for Demo
 demo_dicom_path = "demo_brain_mri.dcm"
@@ -2311,10 +2325,7 @@ def navigate_slice(slice_idx, image_files, selected_subject, prompt_text, modali
     
     return None, f"Invalid slice index: {slice_idx}"
 
-with gr.Blocks(css="""
-    .enhanced-tab { padding: 20px; }
-    .download-btn { margin-top: 10px; }
-""") as demo:
+with gr.Blocks() as demo:
     gr.Markdown("# 🏥 NeuroSAM 3: Medical Image Segmentation")
     
     demo_info = ""