Update app.py

app.py

@@ -13,21 +13,21 @@ from src.bin.PROBE import run_probe
 
 global data_component, filter_component
 
-def get_baseline_df():
+def get_baseline_df(selected_methods, selected_metrics):
     df = pd.read_csv(CSV_RESULT_PATH)
-    present_columns = ["method_name"] + checkbox_group.value
-    df = df[present_columns]
+    present_columns = ["Method"] + selected_metrics
+    df = df[df['Method'].isin(selected_methods)][present_columns]
     return df
 
 # Function to create the plot
 def create_plot(methods_selected, x_metric, y_metric):
     df = pd.read_csv(CSV_RESULT_PATH)
-    filtered_df = df[df['method_name'].isin(methods_selected)]
+    filtered_df = df[df['Method'].isin(methods_selected)]
     
     # Create a larger plot
     plt.figure(figsize=(10, 8))  # Increase the figure size
     for method in methods_selected:
-        method_data = filtered_df[filtered_df['method_name'] == method]
+        method_data = filtered_df[filtered_df['Method'] == method]
         plt.plot(method_data[x_metric], method_data[y_metric], label=method, marker='o')
     
     plt.xlabel(x_metric)
@@ -58,6 +58,10 @@ def add_new_eval(
     results = run_probe(benchmark_type, representation_name, human_file, skempi_file, similarity_tasks, function_prediction_aspect, function_prediction_dataset, family_prediction_dataset)
     return None
 
+# Function to update leaderboard dynamically based on user selection
+def update_leaderboard(selected_methods, selected_metrics):
+    return get_baseline_df(selected_methods, selected_metrics)
+
 block = gr.Blocks()
 
 with block:
@@ -67,44 +71,60 @@ with block:
         # table jmmmu bench
         with gr.TabItem("🏅 PROBE Benchmark", elem_id="probe-benchmark-tab-table", id=1):
             
+            # 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 Methods 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"] + 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
+            )
+
             # Add the visualizer components (Dropdown, Checkbox, Button, Image)
             with gr.Row():
-                method_names = pd.read_csv(CSV_RESULT_PATH)['method_name'].unique().tolist()
+                method_names = pd.read_csv(CSV_RESULT_PATH)['Method'].unique().tolist()
                 metric_names = pd.read_csv(CSV_RESULT_PATH).columns.tolist()
-                metric_names.remove('method_name')  # Remove Method from the metric options
+                metric_names.remove('Method')  # Remove Method from the metric options
 
                 # Visualizer Controls: Smaller and underneath each other
                 with gr.Column(scale=1):
-                    method_selector = gr.CheckboxGroup(choices=method_names, label="Select Methods", interactive=True)
+                    method_selector = gr.CheckboxGroup(choices=method_names, label="Select Methods", interactive=True, value=method_names)
                     x_metric_selector = gr.Dropdown(choices=metric_names, label="Select X-axis Metric", interactive=True)
                     y_metric_selector = gr.Dropdown(choices=metric_names, label="Select Y-axis Metric", interactive=True)
                     plot_button = gr.Button("Plot")
                 
                 # Larger plot display
                 with gr.Column(scale=3):
-                    output_plot = gr.Image(label="Plot", height=480)  # Set larger height for the plot
+                    output_plot = gr.Image(label="Plot", height=600)  # Set larger height for the plot
                 
             plot_button.click(create_plot, inputs=[method_selector, x_metric_selector, y_metric_selector], outputs=output_plot)
-            
-            # Now the rest of the UI elements as they were before
-            checkbox_group = gr.CheckboxGroup(
-                choices=TASK_INFO,
-                label="Benchmark Type",
-                interactive=True,
-            )  # User can select the evaluation dimension
-            
-            baseline_value = get_baseline_df()
-            baseline_header = ["method_name"] + checkbox_group.value
-            baseline_datatype = ['markdown'] + ['number'] * len(checkbox_group.value)
-            
-            data_component = gr.components.Dataframe(
-                value=baseline_value,
-                headers=baseline_header,
-                type="pandas",
-                datatype=baseline_datatype,
-                interactive=False,
-                visible=True,
-            )
 
         # table 5
         with gr.TabItem("📝 About", elem_id="probe-benchmark-tab-table", id=2):
@@ -178,7 +198,7 @@ with block:
                 )
 
     def refresh_data():
-        value = get_baseline_df()
+        value = get_baseline_df(method_names, metric_names)
         return value
 
     with gr.Row():