Adjust layout

app.py

@@ -89,7 +89,7 @@ def load_inferencer(checkpoint_path=None, device=None):
         kwargs['pose2d_weights'] = checkpoint_path
     else:
         # default to rtmo-s
-        kwargs['pose2d'] = 'rtmo-s'
+        kwargs['pose2d'] = 'rtmo'
     return MMPoseInferencer(**kwargs)
 
 # ——— Gradio prediction function ———
@@ -132,31 +132,30 @@ def predict(image: Image.Image,
     vis_img = Image.open(os.path.join(vis_dir, out_files[0])) if out_files else None
     return vis_img, active
 
-# ——— Build Gradio Interface ———
-inputs = [
-    gr.Image(type="pil",
-              label="Upload Image"),
-    gr.Dropdown(label="Select Remote Checkpoint",
-                choices=list(REMOTE_CHECKPOINTS.keys()),
-                value=list(REMOTE_CHECKPOINTS.keys())[0]),
-    gr.File(file_types=['.pth'], label="Or Upload Your Own Checkpoint (optional)"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.1, label="Bounding Box Threshold (bbox_thr)"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.65, label="NMS Threshold (nms_thr)"),
-]
-outputs = [
-    gr.Image(type="pil", label="Annotated Image"),
-    gr.Textbox(label="Active Checkpoint", interactive=False),
-]
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=inputs,
-    outputs=outputs,
-    title="RTMO Pose Demo",
-    description="Upload an image and select or upload a RTMO .pth checkpoint to get 2D pose annotation.",
-)
+# Build Gradio UI with Blocks for improved layout
 
 def main():
+    with gr.Blocks() as demo:
+        gr.Markdown("## RTMO Pose Demo")
+        with gr.Row():
+            with gr.Column(scale=1, min_width=300):
+                img_input = gr.Image(type="pil", label="Upload Image")
+                remote_dd = gr.Dropdown(label="Select Remote Checkpoint",
+                                      choices=list(REMOTE_CHECKPOINTS.keys()),
+                                      value=list(REMOTE_CHECKPOINTS.keys())[0])
+                upload_ckpt = gr.File(file_types=['.pth'], label="Or Upload Your Own Checkpoint (optional)")
+                bbox_thr = gr.Slider(minimum=0.0, maximum=1.0, step=0.01,
+                                     value=0.1, label="Bounding Box Threshold")
+                nms_thr = gr.Slider(minimum=0.0, maximum=1.0, step=0.01,
+                                    value=0.65, label="NMS Threshold")
+                run_btn = gr.Button("Run Inference")
+            with gr.Column(scale=2):
+                output_img = gr.Image(type="pil", label="Annotated Image",
+                                      elem_id="output_image", interactive=False)
+                active_tb = gr.Textbox(label="Active Checkpoint", interactive=False)
+        run_btn.click(predict,
+                      inputs=[img_input, remote_dd, upload_ckpt, bbox_thr, nms_thr],
+                      outputs=[output_img, active_tb])
     demo.launch()
 
 if __name__ == "__main__":