Upload app.py

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+from PIL import Image, ImageOps
+
+
+def run_model(
+    image: Image.Image,
+    log_relative_depth: bool,
+    export_glb: bool,
+    export_ply: bool,
+):
+    if image is None:
+        return "Please upload an image.", None
+
+    # Dummy processing: convert to grayscale (replace with your real model)
+    processed = ImageOps.grayscale(image)
+
+    log_lines = [
+        "✔ Received image",
+        f"  - size: {image.size[0]} x {image.size[1]}",
+        f"  - log_relative_depth: {log_relative_depth}",
+        f"  - export_glb_meshes: {export_glb}",
+        f"  - export_ply_meshes: {export_ply}",
+        "",
+        "This is a placeholder. Replace run_model() with your real pipeline.",
+    ]
+
+    return "\n".join(log_lines), [processed]
+
+
+with gr.Blocks(theme="gradio/soft") as demo:
+    gr.Markdown("# SAM3D‑style Demo\nA playground layout similar to advanced Spaces.")
+
+    with gr.Tab("Inputs"):
+        with gr.Row():
+            # LEFT: image + options
+            with gr.Column(scale=1):
+                image_in = gr.Image(
+                    label="Image",
+                    type="pil",
+                    height=360,
+                )
+                gr.Markdown("### Options")
+                log_depth = gr.Checkbox(label="Log relative depth", value=False)
+                export_glb = gr.Checkbox(label="Export GLB meshes", value=False)
+                export_ply = gr.Checkbox(label="Export PLY meshes", value=False)
+                run_btn = gr.Button("Run", variant="primary", size="sm")
+
+            # RIGHT: viewer / logs (placeholder)
+            with gr.Column(scale=2):
+                gr.Markdown("### Viewer / Logs")
+                logs_out = gr.Textbox(
+                    label="Logs",
+                    lines=18,
+                    max_lines=30,
+                    show_label=False,
+                )
+
+    with gr.Tab("Outputs"):
+        gr.Markdown("### Results")
+        gallery_out = gr.Gallery(
+            label="Output",
+            show_label=False,
+            columns=2,
+            height=360,
+        )
+
+    run_btn.click(
+        fn=run_model,
+        inputs=[image_in, log_depth, export_glb, export_ply],
+        outputs=[logs_out, gallery_out],
+    )
+
+if __name__ == "__main__":
+    demo.launch()