modify app

app.py

@@ -234,8 +234,8 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             ito_output_audio = gr.Audio(label="ITO Output Audio")
-            ito_param_output = gr.Textbox(label="ITO Predicted Parameters", lines=15)
             ito_step_slider = gr.Slider(minimum=1, maximum=100, step=1, label="ITO Step", interactive=True)
+            ito_param_output = gr.Textbox(label="ITO Predicted Parameters", lines=15)
         with gr.Column():
             ito_loss_plot = gr.LinePlot(
                 x="step",
@@ -249,7 +249,7 @@ with gr.Blocks() as demo:
             ito_log = gr.Textbox(label="ITO Log", lines=10)
 
     all_results = gr.State([])
-    
+
     ito_button.click(
         perform_ito,
         inputs=[input_audio, reference_audio, ito_reference_audio, num_steps, optimizer, learning_rate, af_weights],

inference.py

@@ -245,11 +245,11 @@ class MasteringStyleTransfer:
                 for param_name, param_value in fx_params.items():
                     if isinstance(param_value, torch.Tensor):
                         param_value = param_value.item()
-                    output.append(f"  {param_name}: {param_value:.4f}")
+                    output.append(f"  {param_name}: {param_value:.2f}")
             elif isinstance(fx_params, torch.Tensor):
-                output.append(f"  {fx_params.item():.4f}")
+                output.append(f"  {fx_params.item():.2f}")
             else:
-                output.append(f"  {fx_params:.4f}")
+                output.append(f"  {fx_params:.2f}")
         
         return "\n".join(output)
 
@@ -278,7 +278,7 @@ class MasteringStyleTransfer:
         
         output = [f"   Top {top_n} parameter differences (initial / ITO / normalized diff):"]
         for fx_name, param_name, initial_value, ito_value, normalized_diff in top_diffs:
-            output.append(f"      {fx_name.upper()} - {param_name}: {initial_value:.3f} / {ito_value:.3f} / {normalized_diff:.3f}")
+            output.append(f"      {fx_name.upper()} - {param_name}: {initial_value:.2f} / {ito_value:.2f} / {normalized_diff:.2f}")
         
         return "\n".join(output)