Remove the error when the DataFrame is empty

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 from apscheduler.schedulers.background import BackgroundScheduler
 from huggingface_hub import snapshot_download
+import pandas as pd
 
 from src.about import (
     CITATION_BUTTON_LABEL,
@@ -56,36 +57,24 @@ LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS,
     pending_eval_queue_df,
 ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
 
-def init_leaderboard(dataframe):
-    if dataframe is None or dataframe.empty:
-        raise ValueError("Leaderboard DataFrame is empty or None.")
-    return Leaderboard(
-        value=dataframe,
-        datatype=[c.type for c in fields(AutoEvalColumn)],
-        select_columns=SelectColumns(
-            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
-            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
-            label="Select Columns to Display:",
-        ),
-        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
-        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
-        filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-            ColumnFilter(AutoEvalColumn.weight_type.name, type="checkboxgroup", label="Weight Format"),
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
-            ColumnFilter(
-                AutoEvalColumn.params.name,
-                type="slider",
-                min=0.01,
-                max=150,
-                label="Select the number of parameters (B)",
-            ),
-            ColumnFilter(
-                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
-            ),
-        ],
-        bool_checkboxgroup_label="Hide models",
-        interactive=False,
+def init_leaderboard(df):
+    """Initialize the leaderboard with the given DataFrame."""
+    if df is None or df.empty:
+        # Create an empty DataFrame with the required columns
+        df = pd.DataFrame(columns=COLS)
+        print("Creating empty leaderboard - no evaluations completed yet")
+    
+    # Create the leaderboard
+    return gr.Dataframe(
+        headers=COLS,
+        datatype=["str"] * len(COLS),
+        row_count=10,
+        col_count=(len(COLS), "fixed"),
+        value=df,
+        wrap=True,
+        column_widths=[50] + [None] * (len(COLS) - 1),
+        max_rows=50,
+        type="pandas",
     )
 
 

app_local.py

@@ -42,9 +42,7 @@ print("\nGetting evaluation queue DataFrames...")
 
 def init_leaderboard(dataframe):
     print(f"Initializing leaderboard with DataFrame shape: {dataframe.shape}")
-    if dataframe is None or len(dataframe) == 0:
-        raise ValueError("Leaderboard DataFrame is empty or None.")
-        
+    
     # Get all fields from AutoEvalColumn
     auto_eval_fields = fields(AutoEvalColumn)
     print(f"AutoEvalColumn fields: {[f.name for f in auto_eval_fields]}")
@@ -53,6 +51,12 @@ def init_leaderboard(dataframe):
     field_mapping = {f.name: f for f in auto_eval_fields}
     print(f"Field mapping: {field_mapping}")
     
+    if dataframe is None or len(dataframe) == 0:
+        # Create an empty DataFrame with the correct columns
+        import pandas as pd
+        dataframe = pd.DataFrame(columns=[f.name for f in auto_eval_fields])
+        print("Created empty DataFrame with correct columns")
+    
     # Verify all required columns are present
     for col in dataframe.columns:
         if col not in field_mapping: