Update files

app.py

@@ -146,13 +146,15 @@ def plot_acc_rate(rate_compare_results_df: pl.DataFrame, width: int = 1000, heig
 
 
 def update_data(
-    dataset: str, benchmark: str, intermediate: bool, mim: bool, log_x: bool, search_bar: str
+    dataset: str, benchmark: str, intermediate: bool, mim: bool, dist: bool, log_x: bool, search_bar: str
 ) -> tuple[alt.LayerChart, pl.DataFrame]:
     compare_results_df = pl.read_csv(f"results_{dataset}.csv")
     if intermediate is False:
         compare_results_df = compare_results_df.filter(pl.col("Intermediate") == intermediate)
     if mim is False:
         compare_results_df = compare_results_df.filter(pl.col("MIM") == mim)
+    if dist is False:
+        compare_results_df = compare_results_df.filter(pl.col("Distilled") == dist)
 
     x_scale_type = "log" if log_x is True else "linear"
 
@@ -269,6 +271,7 @@ def app() -> None:
                     info="Show models that underwent intermediate training (extra data)",
                 )
                 mim = gr.Checkbox(label="MIM", value=True, info="Show models with Masked Image Modeling pre-training")
+                dist = gr.Checkbox(label="Distilled", value=True, info="Show distilled models")
                 log_x = gr.Checkbox(label="Log scale X-axis", value=False)
 
             with gr.Column():
@@ -287,7 +290,7 @@ def app() -> None:
         plot = gr.Plot(container=False)
         table = gr.Dataframe(show_search="search")
 
-        inputs = [dataset_dropdown, benchmark_dropdown, intermediate, mim, log_x, search_bar]
+        inputs = [dataset_dropdown, benchmark_dropdown, intermediate, mim, dist, log_x, search_bar]
         outputs = [plot, table]
         leaderboard.load(update_data, inputs=inputs, outputs=outputs)
 
@@ -295,6 +298,7 @@ def app() -> None:
         benchmark_dropdown.change(update_data, inputs=inputs, outputs=outputs)
         intermediate.change(update_data, inputs=inputs, outputs=outputs)
         mim.change(update_data, inputs=inputs, outputs=outputs)
+        dist.change(update_data, inputs=inputs, outputs=outputs)
         log_x.change(update_data, inputs=inputs, outputs=outputs)
         search_bar.change(update_data, inputs=inputs, outputs=outputs)