Minor changes of filter_model logic

app.py

@@ -134,23 +134,48 @@ def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
 def filter_models(
     df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
 ) -> pd.DataFrame:
+    print("Initial number of models:", len(df))
+    
     # Show all models
     if show_deleted:
         filtered_df = df
-    else:  # Show only still on the hub models
+    else:
         filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
 
-    type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+    print("After hub filter:", len(filtered_df))
 
-    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
-    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
-    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-    filtered_df = filtered_df.loc[mask]
+    if "All" not in type_query:
+        if "?" in type_query:
+            filtered_df = filtered_df.loc[~df[AutoEvalColumn.model_type_symbol.name].isin([t for t in ModelType if t != "?"])]
+        else:
+            type_emoji = [t[0] for t in type_query]
+            filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+
+    print("After type filter:", len(filtered_df))
+
+    if "All" not in precision_query:
+        if "?" in precision_query:
+            filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isna()]
+        else:
+            filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+
+    print("After precision filter:", len(filtered_df))
+
+    if "All" not in size_query:
+        if "?" in size_query:
+            filtered_df = filtered_df.loc[df[AutoEvalColumn.params.name].isna()]
+        else:
+            numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
+            params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
+            mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
+            filtered_df = filtered_df.loc[mask]
+
+    print("After size filter:", len(filtered_df))
 
     return filtered_df
 
+
+
 def uncheck_all():
     return [], [], [], [], [], [], [], [], [], []
 
@@ -262,32 +287,33 @@ with demo:
                         )
                     with gr.Row():
                         deleted_models_visibility = gr.Checkbox(
-                            value=False, label="Show gated/private/deleted models", interactive=True
+                            value=True, label="Show gated/private/deleted models", interactive=True
                         )
                 with gr.Column(min_width=320):
                     #with gr.Box(elem_id="box-filter"):
                     filter_columns_type = gr.CheckboxGroup(
                         label="Model types",
-                        choices=[t.to_str() for t in ModelType],
-                        value=[t.to_str() for t in ModelType],
+                        choices=["All"] + [t.to_str() for t in ModelType],
+                        value=["All"],
                         interactive=True,
                         elem_id="filter-columns-type",
                     )
                     filter_columns_precision = gr.CheckboxGroup(
                         label="Precision",
-                        choices=[i.value.name for i in Precision],
-                        value=[i.value.name for i in Precision],
+                        choices=["All"] + [i.value.name for i in Precision],
+                        value=["All"],
                         interactive=True,
                         elem_id="filter-columns-precision",
                     )
                     filter_columns_size = gr.CheckboxGroup(
                         label="Model sizes (in billions of parameters)",
-                        choices=list(NUMERIC_INTERVALS.keys()),
-                        value=list(NUMERIC_INTERVALS.keys()),
+                        choices=["All"] + list(NUMERIC_INTERVALS.keys()) + ["?"],
+                        value=["All"],
                         interactive=True,
                         elem_id="filter-columns-size",
                     )
 
+
             leaderboard_table = gr.Dataframe(
                 value=leaderboard_df[
                     [c.name for c in fields(AutoEvalColumn) if c.never_hidden]