fix filters

main.py

@@ -173,11 +173,15 @@ def make_leaderboard(lb: LBContainer) -> Leaderboard:
     df_leaderboard["TypeFiler"] = df_leaderboard["TypeName"].apply(
         lambda m: f"{m} {model_type_emoji[m]}"
     )
-    df_leaderboard["Only Default"] = df_leaderboard["Model"].str.endswith("(default)")
-    df_leaderboard["Only Tuned"] = df_leaderboard["Model"].str.endswith("(tuned)")
-    df_leaderboard["Only Tuned + Ensembled"] = df_leaderboard["Model"].str.endswith(
-        "(tuned + ensembled)"
-    ) | df_leaderboard["Model"].str.endswith(", 4h)")
+    df_leaderboard["Only Default"] = df_leaderboard["Model"].str.contains(
+        "(default)", regex=False
+    )
+    df_leaderboard["Only Tuned"] = df_leaderboard["Model"].str.contains(
+        "(tuned)", regex=False
+    )
+    df_leaderboard["Only Tuned + Ensembled"] = df_leaderboard["Model"].str.contains(
+        r"(tuned + ensembled)", regex=False
+    ) | df_leaderboard["Model"].str.contains(r"AutoGluon", regex=False)
 
     filter_columns = [
         ColumnFilter("TypeFiler", type="checkboxgroup", label="🤖 Model Types"),
@@ -222,6 +226,7 @@ def make_leaderboard(lb: LBContainer) -> Leaderboard:
         )
 
     return Leaderboard(
+        # label=f"Full Leaderboard [{lb.name}]",
         elem_id=f"lb_for_{lb.name}",
         value=df_leaderboard,
         datatype=datatypes,
@@ -351,7 +356,12 @@ def render_details(imputation, splits, tasks, datasets, lb_matrix):
     make_overview_images(lb, subset_name=lb.name)
 
     # Render Leaderboard safely
-    make_leaderboard(lb)
+    with gr.Group():
+        gr.Markdown(
+            "## ⭐ Full Leaderboard Table",
+            elem_classes="markdown-text",
+        )
+        make_leaderboard(lb)
 
     gr.Image(
         lb.get_path_to_winrate_matrix(),
@@ -467,13 +477,10 @@ def main():
                         outputs=datasets_state,
                     )
 
-        @gr.render(inputs=[impute_state, splits_state, tasks_state, datasets_state])
-        def reactive_render(sel_i, sel_s, sel_t, sel_d):
-            # Since we are outside the loop, we don't need to check contexts.
-            # We simply render whatever the current state dictates.
-            print(f"Rendering: {sel_i}, {sel_s}, {sel_t}, {sel_d}")
+        with gr.Column():
 
-            with gr.Group():
+            @gr.render(inputs=[impute_state, splits_state, tasks_state, datasets_state])
+            def reactive_render(sel_i, sel_s, sel_t, sel_d):
                 render_details(
                     imputation=sel_i,
                     splits=sel_s,