update column filter

app.py

@@ -131,16 +131,20 @@ def filter_llm_provider_func(df: pd.DataFrame, llm_provider_query: list) -> pd.D
     return df[df["LLM Provider"].isin(llm_provider_query)]
 
 
-def filter_metric_area_func(df: pd.DataFrame, metric_area_query: list) -> pd.DataFrame:
-    return df[df["Metric Area"].isin(metric_area_query)]
-
-
 def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
-    always_here_cols = [
-        AutoEvalColumn.model.name,
+    # always_here_cols = [
+    #     AutoEvalColumn.model.name,
+    # ]
+    model_provider_col = [AutoEvalColumn.model_provider.name] if AutoEvalColumn.model_provider.name in columns else []
+    # We use COLS to maintain sortingx
+    filtered_df = df[
+        (
+            [AutoEvalColumn.model.name]
+            + model_provider_col
+            + [AutoEvalColumn.use_case_name.name]
+            + [c for c in COLS if c in df.columns and c in columns and c != AutoEvalColumn.model_provider.name]
+        )
     ]
-    # We use COLS to maintain sorting
-    filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns]]
     return filtered_df
 
 

src/display/utils.py

@@ -26,7 +26,9 @@ auto_eval_column_dict.append(
     ["model", ColumnContent, ColumnContent("Model Name", "markdown", True, never_hidden=True)]
 )
 auto_eval_column_dict.append(["model_provider", ColumnContent, ColumnContent("LLM Provider", "markdown", True)])
-auto_eval_column_dict.append(["use_case_name", ColumnContent, ColumnContent("Use Case Name", "markdown", True)])
+auto_eval_column_dict.append(
+    ["use_case_name", ColumnContent, ColumnContent("Use Case Name", "markdown", True, never_hidden=True)]
+)
 auto_eval_column_dict.append(["use_case_type", ColumnContent, ColumnContent("Use Case Type", "markdown", False)])
 auto_eval_column_dict.append(
     ["accuracy_method", ColumnContent, ColumnContent("Accuracy Method", "markdown", False, never_hidden=True)]