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app.py

@@ -28,7 +28,9 @@ def _pip(*a):
                        capture_output=True, text=True, timeout=1200)
     ok = r.returncode == 0
     if not ok:
-        print(f"  pip FAIL ({a[-1][:30]}): {r.stderr[-150:]}")
+        print(f"  pip FAIL ({a[-1][:40]}): {r.stderr[-200:]}")
+    else:
+        print(f"  pip OK: {a[-1][:40]}")
     return ok
 
 print("=== Runtime installs ===")
@@ -56,6 +58,7 @@ if not SAM3D_PATH.exists():
 subprocess.run([sys.executable, "-m", "pip", "install", "-e", str(SAM3D_PATH), "--no-deps"],
                capture_output=True, text=True)
 
+# Hydra patch
 patch = SAM3D_PATH / "patching" / "hydra"
 if patch.exists():
     subprocess.run(["bash", str(patch)], capture_output=True, cwd=str(SAM3D_PATH))
@@ -73,57 +76,92 @@ if hf_ckpt.exists() and not local_ckpt.exists():
     local_ckpt.parent.mkdir(parents=True, exist_ok=True)
     local_ckpt.symlink_to(hf_ckpt)
 CONFIG_PATH = str(local_ckpt / "pipeline.yaml")
-print(f"Config: {Path(CONFIG_PATH).exists()}")
+print(f"Config exists: {Path(CONFIG_PATH).exists()}")
 
-# Verify key imports
-for mod in ["open3d", "utils3d", "sam2", "gsplat"]:
-    try:
-        __import__(mod)
-        print(f"  {mod}: OK")
-    except Exception as e:
-        print(f"  {mod}: {e}")
-
-print("=== Setup done ===")
-
-# --- Model state ---
-SAM3D_MODEL = None
-SAM2_GEN = None
+print("=== Startup complete ===")
 
 # --- Endpoints ---
+
 @spaces.GPU(duration=60)
 def diagnose():
+    """Safe diagnostic - test each module individually."""
     import torch
     lines = [f"torch={torch.__version__}", f"cuda={torch.cuda.is_available()}"]
     if torch.cuda.is_available():
         lines.append(f"gpu={torch.cuda.get_device_name()}")
-    for mod in ["kaolin", "gsplat", "open3d", "sam2", "utils3d"]:
+
+    # Test each module safely
+    for mod_name in ["kaolin", "open3d", "utils3d", "iopath"]:
         try:
-            m = __import__(mod)
-            lines.append(f"{mod}={getattr(m, '__version__', 'ok')}")
+            __import__(mod_name)
+            lines.append(f"{mod_name}: OK")
         except Exception as e:
-            lines.append(f"{mod}: {e}")
+            lines.append(f"{mod_name}: FAIL - {e}")
+
+    # Test sam2 (careful - no CUDA init at import)
+    try:
+        from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
+        lines.append("sam2: OK")
+    except Exception as e:
+        lines.append(f"sam2: FAIL - {e}")
+
+    # Test gsplat carefully
+    try:
+        import gsplat
+        lines.append(f"gsplat: OK ({gsplat.__version__})")
+    except Exception as e:
+        lines.append(f"gsplat: FAIL - {e}")
+
+    # Test pytorch3d carefully
+    try:
+        import pytorch3d
+        lines.append("pytorch3d: OK")
+    except Exception as e:
+        lines.append(f"pytorch3d: FAIL - {e}")
+
+    # Test MoGe
+    try:
+        import moge
+        lines.append("MoGe: OK")
+    except Exception as e:
+        lines.append(f"MoGe: FAIL - {e}")
+
+    # Test SAM3D inference
+    try:
+        from inference import Inference
+        lines.append("SAM3D inference: importable")
+    except Exception as e:
+        lines.append(f"SAM3D inference: FAIL - {e}")
+
+    # Config exists?
+    lines.append(f"config: {Path(CONFIG_PATH).exists()}")
+
     return "\n".join(lines)
 
+@spaces.GPU(duration=60)
+def diagnose_minimal():
+    """Absolutely minimal GPU test."""
+    import torch
+    return f"torch={torch.__version__}, cuda={torch.cuda.is_available()}, gpu={torch.cuda.get_device_name() if torch.cuda.is_available() else 'none'}"
+
 @spaces.GPU(duration=300)
 def reconstruct_objects(image: np.ndarray):
-    global SAM3D_MODEL, SAM2_GEN
     if image is None:
         return None, None, "No image"
     try:
         import torch, trimesh, time
         t0 = time.time()
-        print(f"GPU: {torch.cuda.get_device_name() if torch.cuda.is_available() else 'no CUDA'}")
+        print(f"GPU: {torch.cuda.get_device_name()}")
 
         # Load SAM2
-        if SAM2_GEN is None:
-            from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
-            SAM2_GEN = SAM2AutomaticMaskGenerator.from_pretrained("facebook/sam2-hiera-large")
+        from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
+        sam2_gen = SAM2AutomaticMaskGenerator.from_pretrained("facebook/sam2-hiera-large")
         print(f"  SAM2 ready ({time.time()-t0:.0f}s)")
 
         image_np = np.array(image) if not isinstance(image, np.ndarray) else image
 
         # Detect objects
-        masks = SAM2_GEN.generate(image_np)
+        masks = sam2_gen.generate(image_np)
         if not masks:
             return None, image_np, "No objects detected"
         masks = sorted(masks, key=lambda x: x["area"], reverse=True)
@@ -134,13 +172,12 @@ def reconstruct_objects(image: np.ndarray):
         print(f"  {len(masks)} masks ({time.time()-t0:.0f}s)")
 
         # Load SAM3D
-        if SAM3D_MODEL is None:
-            from inference import Inference
-            SAM3D_MODEL = Inference(CONFIG_PATH, compile=False)
+        from inference import Inference
+        sam3d = Inference(CONFIG_PATH, compile=False)
         print(f"  SAM3D ready ({time.time()-t0:.0f}s)")
 
         # Reconstruct
-        result = SAM3D_MODEL(image=image_np, mask=best_mask, seed=42)
+        result = sam3d(image=image_np, mask=best_mask, seed=42)
         print(f"  Reconstructed ({time.time()-t0:.0f}s)")
 
         if result is None:
@@ -193,7 +230,7 @@ def reconstruct_objects(image: np.ndarray):
 
 # --- UI ---
 with gr.Blocks(title="SAM 3D Objects") as demo:
-    gr.Markdown("# SAM 3D Objects\nImage -> 3D (GLB). SAM2 detection + SAM3D reconstruction.")
+    gr.Markdown("# SAM 3D Objects\nImage → 3D (GLB). SAM2 detection + SAM3D reconstruction.")
     with gr.Tab("Reconstruct"):
         with gr.Row():
             with gr.Column():
@@ -208,8 +245,12 @@ with gr.Blocks(title="SAM 3D Objects") as demo:
         btn.click(reconstruct_objects, inputs=[inp], outputs=[m3d, prev, stat])
         m3d.change(lambda x: x, inputs=[m3d], outputs=[dl])
     with gr.Tab("Diagnose"):
-        dbtn = gr.Button("GPU Diagnose")
-        dout = gr.Textbox(lines=12)
+        dbtn = gr.Button("Full Diagnose")
+        dout = gr.Textbox(lines=15)
         dbtn.click(diagnose, outputs=[dout])
+        gr.Markdown("---")
+        mbtn = gr.Button("Minimal GPU Test")
+        mout = gr.Textbox(lines=3)
+        mbtn.click(diagnose_minimal, outputs=[mout])
 
 demo.launch(mcp_server=True)