Update app.py

app.py

@@ -56,77 +56,79 @@ with block:
         # table jmmmu bench
         with gr.TabItem("🏅 PROBE Leaderboard", elem_id="probe-benchmark-tab-table", id=1):
 
-            method_names = pd.read_csv(CSV_RESULT_PATH)['method_name'].unique().tolist()
-            metric_names = pd.read_csv(CSV_RESULT_PATH).columns.tolist()
-            metrics_with_method = metric_names.copy()
-            metric_names.remove('method_name')  # Remove method_name from the metric options
-        
-            # Leaderboard section with method and metric selectors
-            with gr.Row():
-                # Add method and metric selectors for leaderboard
-                leaderboard_method_selector = gr.CheckboxGroup(
-                    choices=method_names, label="Select method_names for Leaderboard", value=method_names, interactive=True
+            with gr.Row(show_progress=True):
+                method_names = pd.read_csv(CSV_RESULT_PATH)['method_name'].unique().tolist()
+                metric_names = pd.read_csv(CSV_RESULT_PATH).columns.tolist()
+                metrics_with_method = metric_names.copy()
+                metric_names.remove('method_name')  # Remove method_name from the metric options
+            
+                # Leaderboard section with method and metric selectors
+                with gr.Row():
+                    # Add method and metric selectors for leaderboard
+                    leaderboard_method_selector = gr.CheckboxGroup(
+                        choices=method_names, label="Select method_names for Leaderboard", value=method_names, interactive=True
+                    )
+                    leaderboard_metric_selector = gr.CheckboxGroup(
+                        choices=metric_names, label="Select Metrics for Leaderboard", value=metric_names, interactive=True
+                    )
+    
+                # Display the filtered leaderboard
+                baseline_value = get_baseline_df(method_names, metric_names)
+                baseline_header = ["method_name"] + metric_names
+                baseline_datatype = ['markdown'] + ['number'] * len(metric_names)
+    
+                data_component = gr.components.Dataframe(
+                    value=baseline_value,
+                    headers=baseline_header,
+                    type="pandas",
+                    datatype=baseline_datatype,
+                    interactive=False,
+                    visible=True,
+                )
+    
+                # Update leaderboard when method/metric selection changes
+                leaderboard_method_selector.change(
+                    update_leaderboard, 
+                    inputs=[leaderboard_method_selector, leaderboard_metric_selector], 
+                    outputs=data_component
                 )
-                leaderboard_metric_selector = gr.CheckboxGroup(
-                    choices=metric_names, label="Select Metrics for Leaderboard", value=metric_names, interactive=True
+                leaderboard_metric_selector.change(
+                    update_leaderboard, 
+                    inputs=[leaderboard_method_selector, leaderboard_metric_selector], 
+                    outputs=data_component
                 )
 
-            # Display the filtered leaderboard
-            baseline_value = get_baseline_df(method_names, metric_names)
-            baseline_header = ["method_name"] + metric_names
-            baseline_datatype = ['markdown'] + ['number'] * len(metric_names)
-
-            data_component = gr.components.Dataframe(
-                value=baseline_value,
-                headers=baseline_header,
-                type="pandas",
-                datatype=baseline_datatype,
-                interactive=False,
-                visible=True,
-            )
-
-            # Update leaderboard when method/metric selection changes
-            leaderboard_method_selector.change(
-                update_leaderboard, 
-                inputs=[leaderboard_method_selector, leaderboard_metric_selector], 
-                outputs=data_component
-            )
-            leaderboard_metric_selector.change(
-                update_leaderboard, 
-                inputs=[leaderboard_method_selector, leaderboard_metric_selector], 
-                outputs=data_component
-            )
-
-            # Dropdown for benchmark type
-            benchmark_type_selector = gr.Dropdown(choices=list(benchmark_specific_metrics.keys()), label="Select Benchmark Type")
-
-            # Dynamic selectors
-            x_metric_selector = gr.Dropdown(choices=[], label="Select X-axis Metric", visible=False)
-            y_metric_selector = gr.Dropdown(choices=[], label="Select Y-axis Metric", visible=False)
-            aspect_type_selector = gr.Dropdown(choices=[], label="Select Aspect Type", visible=False)
-            dataset_type_selector = gr.Dropdown(choices=[], label="Select Dataset Type", visible=False)
-            dataset_selector = gr.Dropdown(choices=[], label="Select Dataset", visible=False)
-            single_metric_selector = gr.Dropdown(choices=[], label="Select Metric", visible=False)
-            
-            # CheckboxGroup for methods
-            method_selector = gr.CheckboxGroup(choices=method_names, label="Select methods to visualize", interactive=True, value=method_names)
-            
-            # Button to draw the plot for the selected benchmark
-            plot_button = gr.Button("Plot")
-            plot_output = gr.Image(label="Plot")
-            
-            # Update selectors when benchmark type changes
-            benchmark_type_selector.change(
-                update_metric_choices,
-                inputs=[benchmark_type_selector],
-                outputs=[x_metric_selector, y_metric_selector, aspect_type_selector, dataset_type_selector, dataset_selector, single_metric_selector]
-            )
-            
-            plot_button.click(
-                benchmark_plot,
-                inputs=[benchmark_type_selector, method_selector, x_metric_selector, y_metric_selector, aspect_type_selector, dataset_type_selector, dataset_selector, single_metric_selector],
-                outputs=plot_output
-            )
+            with gr.Row(variant='panel', show_progress=True):
+                # Dropdown for benchmark type
+                benchmark_type_selector = gr.Dropdown(choices=list(benchmark_specific_metrics.keys()), label="Select Benchmark Type")
+    
+                # Dynamic selectors
+                x_metric_selector = gr.Dropdown(choices=[], label="Select X-axis Metric", visible=False)
+                y_metric_selector = gr.Dropdown(choices=[], label="Select Y-axis Metric", visible=False)
+                aspect_type_selector = gr.Dropdown(choices=[], label="Select Aspect Type", visible=False)
+                dataset_type_selector = gr.Dropdown(choices=[], label="Select Dataset Type", visible=False)
+                dataset_selector = gr.Dropdown(choices=[], label="Select Dataset", visible=False)
+                single_metric_selector = gr.Dropdown(choices=[], label="Select Metric", visible=False)
+                
+                # CheckboxGroup for methods
+                method_selector = gr.CheckboxGroup(choices=method_names, label="Select methods to visualize", interactive=True, value=method_names)
+                
+                # Button to draw the plot for the selected benchmark
+                plot_button = gr.Button("Plot")
+                plot_output = gr.Image(label="Plot")
+                
+                # Update selectors when benchmark type changes
+                benchmark_type_selector.change(
+                    update_metric_choices,
+                    inputs=[benchmark_type_selector],
+                    outputs=[x_metric_selector, y_metric_selector, aspect_type_selector, dataset_type_selector, dataset_selector, single_metric_selector]
+                )
+                
+                plot_button.click(
+                    benchmark_plot,
+                    inputs=[benchmark_type_selector, method_selector, x_metric_selector, y_metric_selector, aspect_type_selector, dataset_type_selector, dataset_selector, single_metric_selector],
+                    outputs=plot_output
+                )
             
         with gr.TabItem("📝 About", elem_id="probe-benchmark-tab-table", id=2):
             with gr.Row():