fix filtering

app.py

@@ -130,10 +130,12 @@ def filter_models(
     df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
 ) -> pd.DataFrame:
     # Show all models
-    if show_deleted:
-        filtered_df = df
-    else:  # Show only still on the hub models
-        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
+    # if show_deleted:
+    #     filtered_df = df
+    # else:  # Show only still on the hub models
+    #     filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
+    
+    filtered_df = df
 
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]

src/display/utils.py

@@ -32,14 +32,14 @@ auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "ma
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
 # Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", True)])
-auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", True)])
-auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", True, False)])
+auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False, True)])
+auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False, True)])
+auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
 auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", True)])
-auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", True)])
+auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False, True)])
 auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", True)])
-auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", True)])
-auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", True)])
+auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False, True)])
+auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, True)])
 auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, True)])
 
 # We use make dataclass to dynamically fill the scores from Tasks