Update app.py

app.py

@@ -77,7 +77,7 @@ 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
+    dataset: str, benchmark: str, intermediate: bool, mim: 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:
@@ -87,6 +87,9 @@ def update_data(
 
     x_scale_type = "log" if log_x is True else "linear"
 
+    # Filter models
+    compare_results_df = compare_results_df.filter(pl.col("Model name").str.contains(search_bar))
+
     # Parameter count
     if benchmark == "Parameters":
         param_compare_results_df = compare_results_df.unique(subset=["Model name"]).sort(
@@ -122,6 +125,7 @@ def update_data(
             for col in output_df.columns
         ]
     )
+
     return (chart, output_df.drop("Mistakes", "Samples"))
 
 
@@ -174,10 +178,20 @@ def app() -> None:
             with gr.Column():
                 pass
 
+        with gr.Row():
+            with gr.Column():
+                pass
+
+            with gr.Column(scale=2):
+                search_bar = gr.Textbox(label="Model Filter", placeholder="e.g. convnext, efficient|mobile")
+
+            with gr.Column():
+                pass
+
         plot = gr.Plot(container=False)
-        table = gr.Dataframe(show_search=True)
+        table = gr.Dataframe(show_search="search")
 
-        inputs = [dataset_dropdown, benchmark_dropdown, intermediate, mim, log_x]
+        inputs = [dataset_dropdown, benchmark_dropdown, intermediate, mim, log_x, search_bar]
         outputs = [plot, table]
         leaderboard.load(update_data, inputs=inputs, outputs=outputs)
 
@@ -186,6 +200,7 @@ def app() -> None:
         intermediate.change(update_data, inputs=inputs, outputs=outputs)
         mim.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)
 
     leaderboard.launch()