Delete hidden_leaderboard

app.py

@@ -190,7 +190,6 @@ def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
 
 
 def update_table(
-    hidden_df: pd.DataFrame,
     type_query: list,
     precision_query: str,
     size_query: list,
@@ -205,10 +204,9 @@ def update_table(
     print(
         f"Update table called with: type_query={type_query}, precision_query={precision_query}, size_query={size_query}"
     )
-    print(f"hidden_df shape before filtering: {hidden_df.shape}")
 
     filtered_df = filter_models(
-        hidden_df,
+        ORIGINAL_DF,
         type_query,
         size_query,
         precision_query,
@@ -306,8 +304,8 @@ def toggle_all_categories(action: str) -> list[gr.CheckboxGroup]:
     return results
 
 
-def plot_size_vs_score(df_filtered: pd.DataFrame, df_original: pd.DataFrame) -> go.Figure:
-    df = df_original[df_original[AutoEvalColumn.row_id.name].isin(df_filtered[AutoEvalColumn.row_id.name])]
+def plot_size_vs_score(df_filtered: pd.DataFrame) -> go.Figure:
+    df = ORIGINAL_DF[ORIGINAL_DF[AutoEvalColumn.row_id.name].isin(df_filtered[AutoEvalColumn.row_id.name])]
     df = df[df["#Params (B)"] > 0]
     df = df[["model_name_for_query", "#Params (B)", "AVG", "Few-shot"]]
     df["AVG"] = df["AVG"].astype(float)
@@ -333,8 +331,8 @@ TASK_AVG_NAME_MAP = {
 }
 
 
-def plot_average_scores(df_filtered: pd.DataFrame, df_original: pd.DataFrame) -> go.Figure:
-    df = df_original[df_original[AutoEvalColumn.row_id.name].isin(df_filtered[AutoEvalColumn.row_id.name])]
+def plot_average_scores(df_filtered: pd.DataFrame) -> go.Figure:
+    df = ORIGINAL_DF[ORIGINAL_DF[AutoEvalColumn.row_id.name].isin(df_filtered[AutoEvalColumn.row_id.name])]
     df = df[["model_name_for_query", "Few-shot"] + list(TASK_AVG_NAME_MAP.keys())]
     df = df.rename(columns={"model_name_for_query": "Model", "Few-shot": "n-shot"})
     df = df.rename(columns=TASK_AVG_NAME_MAP)
@@ -497,14 +495,6 @@ with gr.Blocks() as demo_leaderboard:
     graph_size_vs_score = gr.Plot(label="Model size vs. Average score")
     graph_average_scores = gr.Plot(label="Model Performance across Task Categories")
 
-    # Dummy leaderboard for handling the case when the user uses backspace key
-    hidden_leaderboard_table_for_search = gr.Dataframe(
-        value=ORIGINAL_DF[COLS],
-        headers=COLS,
-        datatype=TYPES,
-        visible=False,
-    )
-
     # Define a hidden component that will trigger a reload only if a query parameter has been set
     hidden_search_bar = gr.Textbox(value="", visible=False)
 
@@ -542,7 +532,6 @@ with gr.Blocks() as demo_leaderboard:
         + [shown_columns.change for shown_columns in shown_columns_dict.values()],
         fn=update_table,
         inputs=[
-            hidden_leaderboard_table_for_search,
             filter_columns_type,
             filter_columns_precision,
             filter_columns_size,
@@ -558,7 +547,7 @@ with gr.Blocks() as demo_leaderboard:
 
     leaderboard_table.change(
         fn=plot_size_vs_score,
-        inputs=[leaderboard_table, hidden_leaderboard_table_for_search],
+        inputs=leaderboard_table,
         outputs=graph_size_vs_score,
         api_name=False,
         queue=False,
@@ -566,7 +555,7 @@ with gr.Blocks() as demo_leaderboard:
 
     leaderboard_table.change(
         fn=plot_average_scores,
-        inputs=[leaderboard_table, hidden_leaderboard_table_for_search],
+        inputs=leaderboard_table,
         outputs=graph_average_scores,
         api_name=False,
         queue=False,