Update app.py

app.py

@@ -99,39 +99,58 @@ with block:
             # 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")
+
+            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)
+            single_metric_selector = gr.Dropdown(choices=[], label="Select Metric", visible=False)
             
-            # Dynamic metric selectors (will be updated based on benchmark type)
-            x_metric_selector = gr.Dropdown(choices=[], label="Select X-axis Metric")
-            y_metric_selector = gr.Dropdown(choices=[], label="Select Y-axis Metric")
+            # 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 metric selectors when benchmark type is chosen
             def update_metric_choices(benchmark_type):
-                if benchmark_type == 'flexible':
-                    # Show all metrics for the flexible visualizer
-                    metric_names = df.columns.tolist()
-                    return gr.update(choices=metric_names, value=metric_names[0]), gr.update(choices=metric_names, value=metric_names[1])
+                if benchmark_type == 'flexible' or benchmark_type == 'similarity':
+                    # Show x and y metric selectors for similarity and flexible
+                    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
+                    )
                 elif benchmark_type in benchmark_specific_metrics:
+                    # Show single metric selector for other benchmark types
                     metrics = benchmark_specific_metrics[benchmark_type]
-                    return gr.update(choices=metrics, value=metrics[0]), gr.update(choices=metrics)
-                return gr.update(choices=[]), gr.update(choices=[])
-
+                    return (
+                        gr.update(visible=False),  # Hide x-axis metric selector
+                        gr.update(visible=False),  # Hide y-axis metric selector
+                        gr.update(choices=metrics, value=metrics[0], visible=True)
+                    )
+                return 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")
+            
+            # Update selectors when benchmark type changes
             benchmark_type_selector.change(
-                update_metric_choices, 
-                inputs=[benchmark_type_selector], 
-                outputs=[x_metric_selector, y_metric_selector]
+                update_metric_choices,
+                inputs=[benchmark_type_selector],
+                outputs=[x_metric_selector, y_metric_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], 
+                benchmark_plot,
+                inputs=[benchmark_type_selector, method_selector, x_metric_selector, y_metric_selector, single_metric_selector],
                 outputs=plot_output
-            )
+)
             
         with gr.TabItem("📝 About", elem_id="probe-benchmark-tab-table", id=2):
             with gr.Row():
@@ -170,18 +189,13 @@ with block:
                         interactive=True,
                     )
                 
-                    function_prediction_dataset = gr.Radio(
-                        choices=function_prediction_dataset_options,
-                        label="Select Function Prediction Dataset",
-                        interactive=True,
-                    )
-                
                     family_prediction_dataset = gr.CheckboxGroup(
                         choices=family_prediction_dataset_options,
                         label="Select Family Prediction Dataset",
                         interactive=True,
                     )
 
+                    function_prediction_dataset = "All_Data_Sets"
             with gr.Column():
                 human_file = gr.components.File(label="Click to Upload the representation file (csv) for Human dataset", file_count="single", type='filepath')
                 skempi_file = gr.components.File(label="Click to Upload the representation file (csv) for SKEMPI dataset", file_count="single", type='filepath')