Update app.py

app.py

@@ -3,18 +3,19 @@ import pandas as pd
 
 # Expanded dataframe with hidden columns
 leaderboard_df = (
-    pd.read_csv("results.csv", usecols=["dataset", "modality", "model", "AUPRC", "se"])
-    .rename(columns={"dataset": "Dataset", "modality": "Approach", "model": "Model"})
+    pd.read_csv("results.csv", usecols=["dataset", "modality", "model", "score", "se", "metric"])
+    .rename(columns={"dataset": "Dataset", "modality": "Approach", "model": "Model", "metric": "Metric"})
     .round(3)
 )
 
-def filter_leaderboard(dataset_choice, approach_choice):
+def filter_leaderboard(dataset_choice, approach_choice, metric_choice):
     """Filter dataframe based on selections and drop filter columns"""
     filtered_df = leaderboard_df[
         (leaderboard_df['Dataset'] == dataset_choice) &
-        (leaderboard_df['Approach'] == approach_choice)
+        (leaderboard_df['Approach'] == approach_choice) &
+        (leaderboard_df['Metric'] == metric_choice)
     ]
-    return filtered_df.drop(columns=['Dataset', 'Approach']).sort_values('AUPRC', ascending=False)
+    return filtered_df.drop(columns=['Dataset', 'Approach', 'Metric']).sort_values('score', ascending=False)
 
 with gr.Blocks(title="TraitGym Leaderboard") as demo:
     gr.Markdown("## 🏆 TraitGym Leaderboard")
@@ -30,25 +31,30 @@ with gr.Blocks(title="TraitGym Leaderboard") as demo:
             value="Zero-shot",
             label="Select Approach"
         )
+        metric_filter = gr.Radio(
+            choices=["AUPRC", "AUPRC_by_chrom_weighted_average"],
+            value="AUPRC",
+            label="Select Metric"
+        )
     
     leaderboard = gr.Dataframe(
-        headers=["Model", "AUPRC", "se"],
+        headers=["Model", "score", "se"],
         datatype=["str", "number", "number"],
         interactive=False,
     )
     
     # Connect filters to update the table
-    for filter in [dataset_filter, approach_filter]:
+    for filter in [dataset_filter, approach_filter, metric_filter]:
         filter.change(
             fn=filter_leaderboard,
-            inputs=[dataset_filter, approach_filter],
+            inputs=[dataset_filter, approach_filter, metric_filter],
             outputs=leaderboard
         )
     
     # Initial load
     demo.load(
         fn=filter_leaderboard,
-        inputs=[dataset_filter, approach_filter],
+        inputs=[dataset_filter, approach_filter, metric_filter],
         outputs=leaderboard
     )