modify app

app.py

@@ -95,7 +95,7 @@ def perform_ito(input_audio, reference_audio, ito_reference_audio, num_steps, op
     ):
         ito_log += log_entry
         ito_param_output = mastering_transfer.get_param_output_string(current_params)
-        loss_values.append({"step": step, "loss": loss})
+        loss_values.append({"step": int(step), "loss": loss})
         
         # Convert current_output to numpy array if it's a tensor
         if isinstance(current_output, torch.Tensor):
@@ -155,8 +155,6 @@ with gr.Blocks() as demo:
             ito_output_audio = gr.Audio(label="ITO Output Audio")
             ito_param_output = gr.Textbox(label="ITO Predicted Parameters", lines=15)
         with gr.Column():
-            ito_steps_taken = gr.Number(label="ITO Steps Taken")
-            ito_log = gr.Textbox(label="ITO Log", lines=10)
             ito_loss_plot = gr.LinePlot(
                 x="step",
                 y="loss",
@@ -166,6 +164,7 @@ with gr.Blocks() as demo:
                 height=400,
                 width=600,
             )
+            ito_log = gr.Textbox(label="ITO Log", lines=10)
 
     def run_ito(input_audio, reference_audio, ito_reference_audio, num_steps, optimizer, learning_rate, af_weights):
         af_weights = [float(w.strip()) for w in af_weights.split(',')]

inference.py

@@ -273,13 +273,9 @@ class MasteringStyleTransfer:
 
         top_diffs = sorted(all_diffs, key=lambda x: x[4], reverse=True)[:top_n]
         
-        output = ["Top 10 parameter differences (sorted by normalized difference):"]
+        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}:")
-            output.append(f"  Initial: {initial_value:.4f}")
-            output.append(f"  ITO:     {ito_value:.4f}")
-            output.append(f"  Normalized Diff: {normalized_diff:.4f}")
-            output.append("")
+            output.append(f"{fx_name.upper()} - {param_name}: {initial_value:.3f} / {ito_value:.3f} / {normalized_diff:.3f}")
         
         return "\n".join(output)