Update app.py

app.py

@@ -117,38 +117,46 @@ def create_leaderboard_component(dataframe, displayed_cols, hidden_cols=None, ca
     if cant_deselect_cols is None:
         cant_deselect_cols = [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name]
 
+    # Filtrar dataframe para conter apenas as colunas a serem exibidas (ou ocultas/não deselecionáveis)
     all_required_cols = set(displayed_cols) | set(hidden_cols) | set(cant_deselect_cols) | {AutoEvalColumn.model_type.name, AutoEvalColumn.precision.name, AutoEvalColumn.params.name, AutoEvalColumn.still_on_hub.name}
     available_cols = [col for col in all_required_cols if col in dataframe.columns]
-    filtered_df = dataframe[available_cols].copy()
+    filtered_df = dataframe[available_cols].copy() # Usar cópia para evitar SettingWithCopyWarning
 
+    # Garantir que as colunas 'always visible' estejam presentes
     for col in cant_deselect_cols:
         if col not in filtered_df.columns:
-            filtered_df[col] = np.nan
+            filtered_df[col] = np.nan # Ou algum valor padrão apropriado
+
+    # Construir lista de filtros, incluindo None para colunas ausentes
+    raw_filter_columns=[
+        ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Tipos de Modelo") if AutoEvalColumn.model_type.name in filtered_df.columns else None,
+        ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precisão") if AutoEvalColumn.precision.name in filtered_df.columns else None,
+        ColumnFilter(
+            AutoEvalColumn.params.name,
+            type="slider",
+            min=0.01,
+            max=max(150, filtered_df[AutoEvalColumn.params.name].max(skipna=True) if AutoEvalColumn.params.name in filtered_df.columns and not filtered_df[AutoEvalColumn.params.name].dropna().empty else 150), # Ajustar max dinamicamente e ignorar NaN
+            label="Selecionar número de parâmetros (B)",
+        ) if AutoEvalColumn.params.name in filtered_df.columns else None,
+        ColumnFilter(
+            AutoEvalColumn.still_on_hub.name, type="boolean", label="Deletado/incompleto", default=True
+        ) if AutoEvalColumn.still_on_hub.name in filtered_df.columns else None,
+    ]
+    
+    # Filtrar Nones da lista de filtros
+    final_filter_columns = [f for f in raw_filter_columns if f is not None]
 
     return Leaderboard(
         value=filtered_df,
-        datatype=[c.type for c in fields(AutoEvalColumn) if c.name in filtered_df.columns],
+        datatype=[c.type for c in fields(AutoEvalColumn) if c.name in filtered_df.columns], # Ajustar datatype às colunas presentes
         select_columns=SelectColumns(
             default_selection=displayed_cols,
             cant_deselect=cant_deselect_cols,
             label="Selecionar Colunas para Exibir:",
         ),
         search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name] if AutoEvalColumn.license.name in filtered_df.columns else [AutoEvalColumn.model.name],
-        hide_columns=[c for c in hidden_cols if c in filtered_df.columns],
-        filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Tipos de Modelo") if AutoEvalColumn.model_type.name in filtered_df.columns else None,
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precisão") if AutoEvalColumn.precision.name in filtered_df.columns else None,
-            ColumnFilter(
-                AutoEvalColumn.params.name,
-                type="slider",
-                min=0.01,
-                max=max(150, filtered_df[AutoEvalColumn.params.name].max() if AutoEvalColumn.params.name in filtered_df.columns and not filtered_df[AutoEvalColumn.params.name].empty else 150),
-                label="Selecionar número de parâmetros (B)",
-            ) if AutoEvalColumn.params.name in filtered_df.columns else None,
-            ColumnFilter(
-                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deletado/incompleto", default=True
-            ) if AutoEvalColumn.still_on_hub.name in filtered_df.columns else None,
-        ],
+        hide_columns=[c for c in hidden_cols if c in filtered_df.columns], # Ocultar apenas colunas existentes
+        filter_columns=final_filter_columns, # Usar a lista filtrada
         bool_checkboxgroup_label="Ocultar modelos",
         interactive=False,
     )