Update app.py

app.py

@@ -95,13 +95,13 @@ with block:
                 inputs=[leaderboard_method_selector, leaderboard_metric_selector], 
                 outputs=data_component
             )
-            
-            # Dropdown for benchmark type
-            benchmark_types = TASK_INFO + ['flexible']
-            benchmark_type_selector = gr.Dropdown(choices=benchmark_types, label="Select Benchmark Type for Visualization", value="flexible")
 
+            # 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
@@ -111,25 +111,46 @@ with block:
             plot_button = gr.Button("Plot")
             plot_output = gr.Image(label="Plot")
             
-            # Update metric selectors when benchmark type is chosen
+            # Update metric selectors based on benchmark type
             def update_metric_choices(benchmark_type):
-                if benchmark_type == 'flexible' or benchmark_type == 'similarity':
-                    # Show x and y metric selectors for similarity and flexible
+                if benchmark_type == 'similarity':
+                    # Show x and y metric selectors for similarity
                     metric_names = benchmark_specific_metrics.get(benchmark_type, [])
                     return (
                         gr.update(choices=metric_names, value=metric_names[0], visible=True),
                         gr.update(choices=metric_names, value=metric_names[1], visible=True),
-                        gr.update(visible=False)  # Hide single metric selector
+                        gr.update(visible=False), gr.update(visible=False), 
+                        gr.update(visible=False), gr.update(visible=False)
                     )
-                elif benchmark_type in benchmark_specific_metrics:
-                    # Show single metric selector for other benchmark types
-                    metrics = benchmark_specific_metrics[benchmark_type]
+                elif benchmark_type == 'function':
+                    # Show aspect and dataset type selectors for function
+                    aspect_types = benchmark_specific_metrics[benchmark_type]['aspect_types']
+                    dataset_types = benchmark_specific_metrics[benchmark_type]['dataset_types']
+                    return (
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(choices=aspect_types, value=aspect_types[0], visible=True),
+                        gr.update(choices=dataset_types, value=dataset_types[0], visible=True),
+                        gr.update(visible=False), gr.update(visible=False)
+                    )
+                elif benchmark_type == 'family':
+                    # Show dataset and metric selectors for family
+                    datasets = benchmark_specific_metrics[benchmark_type]['datasets']
+                    metrics = benchmark_specific_metrics[benchmark_type]['metrics']
                     return (
-                        gr.update(visible=False),  # Hide x-axis metric selector
-                        gr.update(visible=False),  # Hide y-axis metric selector
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(choices=datasets, value=datasets[0], visible=True),
                         gr.update(choices=metrics, value=metrics[0], visible=True)
                     )
-                return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+                elif benchmark_type == 'affinity':
+                    # Show single metric selector for affinity
+                    metrics = benchmark_specific_metrics[benchmark_type]
+                    return (
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(choices=metrics, value=metrics[0], visible=True)
+                    )
+                return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
             
             # Dropdown for benchmark type
             benchmark_type_selector = gr.Dropdown(choices=list(benchmark_specific_metrics.keys()), label="Select Benchmark Type")
@@ -138,19 +159,14 @@ with block:
             benchmark_type_selector.change(
                 update_metric_choices,
                 inputs=[benchmark_type_selector],
-                outputs=[x_metric_selector, y_metric_selector, single_metric_selector]
+                outputs=[x_metric_selector, y_metric_selector, aspect_type_selector, dataset_type_selector, dataset_selector, single_metric_selector]
             )
             
-            # Generate the plot based on user input
-            def benchmark_plot(benchmark_type, method_names, x_metric, y_metric, single_metric):
-                # Implement plot generation logic based on selected benchmark type and metrics
-                pass
-            
             plot_button.click(
                 benchmark_plot,
-                inputs=[benchmark_type_selector, method_selector, x_metric_selector, y_metric_selector, single_metric_selector],
+                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():