Update app.py

app.py

@@ -1,17 +1,33 @@
 import gradio as gr
+from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import snapshot_download
 
-# Data for Table 1 - Average Performance Metrics
+# Placeholder imports - replace with your own or modify as needed
+from src.about import (
+    CITATION_BUTTON_LABEL,
+    CITATION_BUTTON_TEXT,
+    EVALUATION_QUEUE_TEXT,
+    INTRODUCTION_TEXT,
+    TITLE,
+)
+from src.display.css_html_js import custom_css
+from src.display.utils import (
+    AutoEvalColumn,
+    fields
+)
+
+# Deepfake detector data
 data_avg_performance = {
     "Detector": ["NPR", "UCF", "CAMO"],
     "Accuracy": [0.7169, 0.7229, 0.7555],
     "Precision": [0.9193, 0.9436, 0.9442],
     "Recall": [0.5996, 0.592, 0.647],
-    "F-1": [0.7258, 0.7275, 0.7679],
+    "F1-Score": [0.7258, 0.7275, 0.7679],
     "MCC": [0.5044, 0.5285, 0.5707],
 }
 
-# Data for Table 2 - Dataset-specific Accuracy
 data_dataset_accuracy = {
     "Detector": ["NPR", "UCF", "CAMO"],
     "CelebA-HQ": [0.987, 0.995, 0.999],
@@ -24,26 +40,58 @@ data_dataset_accuracy = {
     "MS-COCO-Flux": [0.588, 0.576, 0.59],
 }
 
-# Convert to DataFrames
+# Convert data to DataFrames
 df_avg_performance = pd.DataFrame(data_avg_performance)
 df_dataset_accuracy = pd.DataFrame(data_dataset_accuracy)
 
-# Create Gradio interface to display the tables
-def leaderboard_view():
-    return df_avg_performance, df_dataset_accuracy
-
-# Gradio Interface
-demo = gr.Interface(
-    fn=leaderboard_view,
-    inputs=[],
-    outputs=[
-        gr.DataFrame(label="Average Performance Metrics"),
-        gr.DataFrame(label="Dataset-specific Accuracy"),
-    ],
-    title="Deepfake Detector Arena Leaderboard",
-    description="This leaderboard shows detectors and their average performance metrics as well as dataset-specific accuracy for various datasets.",
-)
+def init_leaderboard():
+    if df_avg_performance.empty or df_dataset_accuracy.empty:
+        raise ValueError("Leaderboard DataFrames are empty.")
+    
+    # Combine the two dataframes into a single leaderboard
+    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
+
+demo = gr.Blocks(css=custom_css)
+with demo:
+    gr.HTML(TITLE)
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 Deepfake Detector Arena", elem_id="dfd-leaderboard-tab", id=0):
+            leaderboard = init_leaderboard()
+            gr.Markdown("## Detector Average Performance", elem_classes="markdown-text")
+            gr.DataFrame(value=df_avg_performance)
+
+            gr.Markdown("## Dataset-specific Accuracy", elem_classes="markdown-text")
+            gr.DataFrame(value=df_dataset_accuracy)
+
+        with gr.TabItem("📝 About", elem_id="about-tab", id=2):
+            gr.Markdown("This leaderboard evaluates deepfake detection algorithms on various metrics and datasets.")
+
+        with gr.TabItem("🚀 Submit Detector Results", elem_id="submit-tab", id=3):
+            gr.Markdown("Submit your detector results for evaluation.")
+            # Add submission form elements as needed here (e.g., textboxes, file upload)
+
+    with gr.Row():
+        with gr.Accordion("📙 Citation", open=False):
+            gr.Textbox(value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=20, show_copy_button=True)
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(lambda: print("Scheduled Task"), "interval", seconds=1800)
+scheduler.start()
 
-# Launch Gradio app
-demo.launch(share=True)
-#demo.launch(server_name="0.0.0.0", server_port=7860)
+demo.queue(default_concurrency_limit=40).launch()