feat: reorganise layout

app.py

@@ -580,21 +580,23 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             _ = gr.Markdown("## Control Parameters")
-            dataset_dropdown = gr.Dropdown(
-                [("Internal", "internal"), ("MedleyDB", "medleydb")],
-                label="Prior Distribution (Dataset)",
-                info="This parameter has no effect when using the ST-ITO and Regression methods.",
-                value="internal",
-                interactive=True,
-            )
-            embedding_dropdown = gr.Dropdown(
-                [("AFx-Rep", "afx-rep"), ("MFCC", "mfcc"), ("MIR Features", "mir")],
-                label="Embedding Model",
-                info="This parameter has no effect when using the Mean and Regression methods.",
-                value="afx-rep",
-                interactive=True,
-            )
-            with gr.Column():
+
+            with gr.Row():
+                dataset_dropdown = gr.Dropdown(
+                    [("Internal", "internal"), ("MedleyDB", "medleydb")],
+                    label="Prior Distribution (Dataset)",
+                    info="This parameter has no effect when using the ST-ITO and Regression methods.",
+                    value="internal",
+                    interactive=True,
+                )
+                embedding_dropdown = gr.Dropdown(
+                    [("AFx-Rep", "afx-rep"), ("MFCC", "mfcc"), ("MIR Features", "mir")],
+                    label="Embedding Model",
+                    info="This parameter has no effect when using the Mean and Regression methods.",
+                    value="afx-rep",
+                    interactive=True,
+                )
+                # with gr.Column():
                 remove_approx_checkbox = gr.Checkbox(
                     label="Use Real-time Effects",
                     info="Use real-time delay and reverb effects instead of approximated ones.",
@@ -610,51 +612,52 @@ with gr.Blocks() as demo:
 
         with gr.Column():
             _ = gr.Markdown("## Parameters for ST-ITO Method")
-            optimisation_steps = gr.Slider(
-                minimum=1,
-                maximum=2000,
-                value=100,
-                step=1,
-                label="Number of Optimisation Steps",
-                interactive=True,
-            )
-            prior_weight = gr.Dropdown(
-                [
-                    ("0", 0.0),
-                    ("0.001", 0.001),
-                    ("0.01", 0.01),
-                    ("0.1", 0.1),
-                    ("1", 1.0),
-                ],
-                info="Weight of the prior distribution in the loss function. A higher value means the model will try to stay closer to the prior distribution.",
-                value=0.01,
-                label="Prior Weight",
-                interactive=True,
-            )
-            optimiser_dropdown = gr.Dropdown(
-                [
-                    "Adadelta",
-                    "Adafactor",
-                    "Adagrad",
-                    "Adam",
-                    "AdamW",
-                    "Adamax",
-                    "RMSprop",
-                    "ASGD",
-                    "NAdam",
-                    "RAdam",
-                    "SGD",
-                ],
-                value="Adam",
-                label="Optimiser",
-                interactive=True,
-            )
-            lr_slider = gr.Dropdown(
-                [("0.0001", 1e-4), ("0.001", 1e-3), ("0.01", 1e-2), ("0.1", 1e-1)],
-                value=1e-2,
-                label="Learning Rate",
-                interactive=True,
-            )
+            with gr.Row():
+                optimisation_steps = gr.Slider(
+                    minimum=1,
+                    maximum=2000,
+                    value=100,
+                    step=1,
+                    label="Number of Optimisation Steps",
+                    interactive=True,
+                )
+                prior_weight = gr.Dropdown(
+                    [
+                        ("0", 0.0),
+                        ("0.001", 0.001),
+                        ("0.01", 0.01),
+                        ("0.1", 0.1),
+                        ("1", 1.0),
+                    ],
+                    info="Weight of the prior distribution in the loss function. A higher value means the model will try to stay closer to the prior distribution.",
+                    value=0.01,
+                    label="Prior Weight",
+                    interactive=True,
+                )
+                optimiser_dropdown = gr.Dropdown(
+                    [
+                        "Adadelta",
+                        "Adafactor",
+                        "Adagrad",
+                        "Adam",
+                        "AdamW",
+                        "Adamax",
+                        "RMSprop",
+                        "ASGD",
+                        "NAdam",
+                        "RAdam",
+                        "SGD",
+                    ],
+                    value="Adam",
+                    label="Optimiser",
+                    interactive=True,
+                )
+                lr_slider = gr.Dropdown(
+                    [("0.0001", 1e-4), ("0.001", 1e-3), ("0.01", 1e-2), ("0.1", 1e-1)],
+                    value=1e-2,
+                    label="Learning Rate",
+                    interactive=True,
+                )
 
     _ = gr.Markdown("## Effect Parameters Visualisation")
     with gr.Row():