Update app.py

app.py

@@ -15,19 +15,19 @@ global data_component, filter_component
 
 def get_baseline_df(selected_methods, selected_metrics):
     df = pd.read_csv(CSV_RESULT_PATH)
-    present_columns = ["Method"] + selected_metrics
-    df = df[df['Method'].isin(selected_methods)][present_columns]
+    present_columns = ["method_name"] + selected_metrics
+    df = df[df['method_name'].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'].isin(methods_selected)]
+    filtered_df = df[df['method_name'].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'] == method]
+        method_data = filtered_df[filtered_df['method_name'] == method]
         plt.plot(method_data[x_metric], method_data[y_metric], label=method, marker='o')
     
     plt.xlabel(x_metric)
@@ -70,12 +70,17 @@ with block:
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         # table jmmmu bench
         with gr.TabItem("🏅 PROBE Benchmark", 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
+            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 Methods for Leaderboard", value=method_names, interactive=True
+                    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
@@ -83,7 +88,7 @@ with block:
 
             # Display the filtered leaderboard
             baseline_value = get_baseline_df(method_names, metric_names)
-            baseline_header = ["Method"] + metric_names
+            baseline_header = ["method_name"] + metric_names
             baseline_datatype = ['markdown'] + ['number'] * len(metric_names)
 
             data_component = gr.components.Dataframe(
@@ -109,15 +114,12 @@ with block:
 
             # Add the visualizer components (Dropdown, Checkbox, Button, Image)
             with gr.Row():
-                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')  # 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, 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)
+                    method_selector = gr.CheckboxGroup(choices=method_names, label="Select method_names", interactive=True, value=method_names)
+                    x_metric_selector = gr.Dropdown(choices=metrics_with_method, label="Select X-axis Metric", interactive=True)
+                    y_metric_selector = gr.Dropdown(choices=metrics_with_method, label="Select Y-axis Metric", interactive=True)
                     plot_button = gr.Button("Plot")
                 
                 # Larger plot display