Update app.py

app.py

@@ -10,6 +10,7 @@ from transformers import GLPNForDepthEstimation, GLPNImageProcessor
 
 import open3d as o3d
 
+
 # ------------------------------
 # Model setup (loaded once)
 # ------------------------------
@@ -18,6 +19,7 @@ FE = GLPNImageProcessor.from_pretrained("vinvino02/glpn-nyu")
 MODEL = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu").to(DEVICE)
 MODEL.eval()
 
+
 # ------------------------------
 # Utilities
 # ------------------------------
@@ -30,6 +32,7 @@ def _resize_to_mult32(img: Image.Image, max_h=480):
     new_w = new_w - diff if diff < 16 else new_w + (32 - diff)
     return img.resize((new_w, new_h), Image.BICUBIC)
 
+
 def predict_depth(image_pil: Image.Image):
     """Run GLPN and return cropped RGB (as PIL) + raw depth (float32 numpy)."""
     img = _resize_to_mult32(image_pil.convert("RGB"))
@@ -47,11 +50,13 @@ def predict_depth(image_pil: Image.Image):
     rgb = img.crop((pad, pad, img.width - pad, img.height - pad))
     return rgb, depth
 
+
 def depth_to_colormap(depth: np.ndarray):
     """Return a PIL image (plasma colormap) from depth for preview."""
     import matplotlib
     matplotlib.use("Agg")
     import matplotlib.pyplot as plt
+    import matplotlib.cm as cm
 
     d = depth.copy()
     d -= d.min()
@@ -59,12 +64,10 @@ def depth_to_colormap(depth: np.ndarray):
         d /= d.max()
     d8 = (d * 255).astype(np.uint8)
 
-    # Make a small PNG buffer
-    import matplotlib.cm as cm
-    cmap = cm.get_cmap("plasma")
-    colored = (cmap(d8)[:, :, :3] * 255).astype(np.uint8)
+    colored = (cm.get_cmap("plasma")(d8)[:, :, :3] * 255).astype(np.uint8)
     return Image.fromarray(colored)
 
+
 def rgbd_to_pointcloud(rgb_pil: Image.Image, depth: np.ndarray):
     """Create an Open3D point cloud from RGB + relative depth."""
     # Normalize depth to 0..1 then to 0..255 uint8 for Open3D RGBD convenience
@@ -98,6 +101,7 @@ def rgbd_to_pointcloud(rgb_pil: Image.Image, depth: np.ndarray):
             pcd.orient_normals_to_align_with_direction()
     return pcd
 
+
 def pointcloud_to_mesh(pcd: o3d.geometry.PointCloud, depth=10):
     if len(pcd.points) == 0:
         return None
@@ -110,6 +114,7 @@ def pointcloud_to_mesh(pcd: o3d.geometry.PointCloud, depth=10):
     mesh.compute_vertex_normals()
     return mesh
 
+
 def save_o3d(obj, path):
     ext = os.path.splitext(path)[1].lower()
     if isinstance(obj, o3d.geometry.PointCloud):
@@ -118,15 +123,14 @@ def save_o3d(obj, path):
         else:
             raise ValueError("Point cloud: please save as .ply")
     elif isinstance(obj, o3d.geometry.TriangleMesh):
-        if ext == ".obj":
-            o3d.io.write_triangle_mesh(path, obj)
-        elif ext == ".ply":
+        if ext in {".obj", ".ply"}:
             o3d.io.write_triangle_mesh(path, obj)
         else:
             raise ValueError("Mesh: use .obj or .ply")
     else:
         raise ValueError("Unsupported type for saving")
 
+
 def render_mesh_image(mesh: o3d.geometry.TriangleMesh, width=640, height=480):
     """
     Try offscreen render for a preview PNG. If it fails (e.g., no EGL/OSMesa),
@@ -135,7 +139,7 @@ def render_mesh_image(mesh: o3d.geometry.TriangleMesh, width=640, height=480):
     try:
         from open3d.visualization import rendering
 
-        # Make sure mesh has vertex colors or a default material
+        # Ensure it has some color
         if not mesh.has_vertex_colors():
             mesh.paint_uniform_color([0.8, 0.8, 0.85])
 
@@ -152,7 +156,6 @@ def render_mesh_image(mesh: o3d.geometry.TriangleMesh, width=640, height=480):
         extent = bbox.get_extent()
         radius = np.linalg.norm(extent) * 0.8 + 1e-6
 
-        # Camera looking at center from +z
         cam = scene.camera
         cam.look_at(center, center + [0, 0, radius], [0, 1, 0])
 
@@ -162,6 +165,7 @@ def render_mesh_image(mesh: o3d.geometry.TriangleMesh, width=640, height=480):
     except Exception:
         return None
 
+
 # ------------------------------
 # Gradio pipeline
 # ------------------------------
@@ -206,14 +210,15 @@ def run_pipeline(image: Image.Image, poisson_depth: int = 10):
 
     return depth_vis, preview, pcd_path, mesh_obj_path
 
+
 # ------------------------------
 # Interface
 # ------------------------------
 TITLE = "Monocular Depth → Point Cloud → Poisson Mesh (GLPN + Open3D)"
 DESC = """
 Upload an image. We estimate relative depth (GLPN), build a point cloud, and reconstruct
-a mesh (Poisson). Outputs: depth preview, mesh preview (if renderer available), 
-and downloads for .ply (point cloud) and .obj (mesh).  
+a mesh (Poisson). Outputs: depth preview, mesh preview (if renderer available),
+and downloads for .ply (point cloud) and .obj (mesh).
 **Note:** monocular depth lacks absolute scale; this is for visualization/demo purposes.
 """
 
@@ -223,7 +228,12 @@ with gr.Blocks(title="2D → 3D Reconstruction") as demo:
 
     with gr.Row():
         with gr.Column():
-            in_img = gr.Image(type="pil", label="Input Image")
+            in_img = gr.Image(
+                type="pil",
+                sources=["upload", "clipboard"],
+                label="Input Image",
+                image_mode="RGB"
+            )
             poisson_depth = gr.Slider(5, 12, value=10, step=1, label="Poisson depth (mesh detail)")
             run_btn = gr.Button("Reconstruct 3D", variant="primary")
 
@@ -245,4 +255,5 @@ with gr.Blocks(title="2D → 3D Reconstruction") as demo:
     mesh_obj_view.change(lambda p: p, inputs=mesh_obj_view, outputs=mesh_obj_file)
 
 if __name__ == "__main__":
-    demo.launch()
+    # share=True creates a public link (useful on Spaces/Colab/local)
+    demo.launch(share=True)