Update app.py

app.py

@@ -1,24 +1,57 @@
 import gradio as gr
 import pandas as pd
 
-# Sample dataframe - replace with your results
+# Expanded dataframe with hidden columns
 leaderboard_df = pd.DataFrame({
-    'Name': ['Alice', 'Bob', 'Charlie'],
-    'Score': [95, 88, 92],
-    'Rank': [1, 3, 2]
+    'Name': ['Alice', 'Bob', 'Charlie', 'Dana'],
+    'Score': [95, 88, 92, 85],
+    'Rank': [1, 3, 2, 4],
+    'Dataset': ['Mendelian traits', 'Complex traits', 'Mendelian traits', 'Complex traits'],
+    'Approach': ['Zero-shot', 'Linear probing', 'Linear probing', 'Zero-shot']
 })
 
-def get_leaderboard():
-    return leaderboard_df.sort_values('Rank')
+def filter_leaderboard(dataset_choice, approach_choice):
+    """Filter dataframe based on selections and drop filter columns"""
+    filtered_df = leaderboard_df[
+        (leaderboard_df['Dataset'] == dataset_choice) &
+        (leaderboard_df['Approach'] == approach_choice)
+    ]
+    return filtered_df.drop(columns=['Dataset', 'Approach']).sort_values('Rank')
 
-with gr.Blocks(title="Leaderboard") as demo:
-    gr.Markdown("# 🏆 Leaderboard")
-    gr.Dataframe(
-        value=get_leaderboard,
-        #headers=["Name", "Score", "Rank"],
-        #datatype=["str", "number", "number"],
-        #row_count=5,
-        #col_count=(3, "fixed"),
+with gr.Blocks(title="Advanced Leaderboard") as demo:
+    gr.Markdown("## 🏆 Interactive Leaderboard")
+    
+    with gr.Row():
+        dataset_filter = gr.Radio(
+            choices=["Mendelian traits", "Complex traits"],
+            value="Mendelian traits",
+            label="Select Dataset"
+        )
+        approach_filter = gr.Radio(
+            choices=["Zero-shot", "Linear probing"],
+            value="Zero-shot",
+            label="Select Approach"
+        )
+    
+    leaderboard = gr.Dataframe(
+        headers=["Name", "Score", "Rank"],
+        datatype=["str", "number", "number"],
+        interactive=False,
+    )
+    
+    # Connect filters to update the table
+    for filter in [dataset_filter, approach_filter]:
+        filter.change(
+            fn=filter_leaderboard,
+            inputs=[dataset_filter, approach_filter],
+            outputs=leaderboard
+        )
+    
+    # Initial load
+    demo.load(
+        fn=filter_leaderboard,
+        inputs=[dataset_filter, approach_filter],
+        outputs=leaderboard
     )
 
 demo.launch()