Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
 
@@ -29,27 +28,19 @@ data_dataset_accuracy = {
 df_avg_performance = pd.DataFrame(data_avg_performance)
 df_dataset_accuracy = pd.DataFrame(data_dataset_accuracy)
 
-def init_leaderboard():
-    if df_avg_performance.empty:
-        raise ValueError("Leaderboard DataFrame is empty.")
-    
-    # Display average performance leaderboard only
-    leaderboard = Leaderboard(
-        value=df_avg_performance,
-        datatype=['str', 'number', 'number', 'number', 'number', 'number'],
-        select_columns=SelectColumns(
-            default_selection=["Detector", "Accuracy", "Precision", "Recall", "F1-Score", "MCC"],
-            label="Select Columns to Display:"
-        ),
-        search_columns=["Detector"],
-        filter_columns=[
-            ColumnFilter("Detector", type="checkboxgroup", label="Detectors"),
-        ],
-        bool_checkboxgroup_label="Hide detectors",
-        interactive=False,
-    )
-    return leaderboard
+# Function to highlight the maximum values in bold and color them
+def highlight_max(s):
+    is_max = s == s.max()
+    return ['font-weight: bold; color: red;' if v else '' for v in is_max]
 
+# Style the dataframe
+def style_dataframe(df):
+    return df.style.apply(highlight_max, subset=["Accuracy", "Precision", "Recall", "F1-Score", "MCC"])
+
+def style_dataset_accuracy(df):
+    return df.style.apply(highlight_max, subset=df.columns[1:])
+
+# Gradio demo with the styled dataframes
 demo = gr.Blocks()
 
 with demo:
@@ -58,12 +49,17 @@ with demo:
 
     with gr.Tabs():
         with gr.TabItem("🏅 Deepfake Detector Arena", elem_id="dfd-leaderboard-tab"):
-            # Only show the leaderboard here
-            leaderboard = init_leaderboard()
+            # Add text for Average Performance Metrics
+            gr.Markdown("## Average Performance Metrics")
+            
+            # Display the average performance metrics with highlighted max values
+            styled_avg_performance = style_dataframe(df_avg_performance)
+            gr.DataFrame(styled_avg_performance.render(), label="Average Performance Metrics", interactive=False)
 
-            # Add a separate dataframe for dataset-specific accuracy
+            # Add a separate dataframe for dataset-specific accuracy with highlighted max values
             gr.Markdown("## Dataset-specific Accuracy")
-            gr.DataFrame(value=df_dataset_accuracy)
+            styled_dataset_accuracy = style_dataset_accuracy(df_dataset_accuracy)
+            gr.DataFrame(styled_dataset_accuracy.render(), label="Dataset-specific Accuracy", interactive=False)
 
         with gr.TabItem("📝 About"):
             gr.Markdown("This leaderboard evaluates deepfake detection algorithms on various metrics and datasets.")
@@ -72,4 +68,4 @@ with demo:
             gr.Markdown("Submit your detector results for evaluation.")
             # Add submission form elements as needed here
 
-demo.queue(default_concurrency_limit=40).launch()
+demo.queue(default_concurrency_limit=40).launch()