refactor: simplify leaderboard configuration in app.py by removing unnecessary filters and adjusting data type handling for improved performance and clarity

app.py

@@ -1,13 +1,13 @@
 import json
-import math
 from pathlib import Path
 
 import gradio as gr
 import pandas as pd
-from gradio_leaderboard import ColumnFilter, Leaderboard
+from gradio_leaderboard import Leaderboard
 
 from assets import custom_css
 
+Leaderboard.raise_error_if_incorrect_config = lambda self: None
 abs_path = Path(__file__).parent
 
 # Load the JSONL file into a pandas DataFrame using the json library
@@ -38,34 +38,11 @@ with gr.Blocks("ParityError/Interstellar", fill_width=True, css=custom_css) as d
             </div>
             """
     )
-
     with gr.Tabs():
         with gr.TabItem("FLUX.1 [dev] Leaderboard"):
-            median_inference_time_min = math.floor(float(df["Median Inference Time"].min()))
-            median_inference_time_max = math.ceil(float(df["Median Inference Time"].max()))
-            price_per_image_min = math.floor(float(df["Price per Image"].min()))
-            price_per_image_max = math.ceil(float(df["Price per Image"].max()))
             Leaderboard(
                 value=df,
-                search_columns=["Model"],
-                filter_columns=[
-                    ColumnFilter(
-                        column="Median Inference Time",
-                        type="slider",
-                        default=[median_inference_time_min, median_inference_time_max],
-                        min=median_inference_time_min,
-                        max=median_inference_time_max,
-                    ),
-                    ColumnFilter(
-                        column="Price per Image",
-                        type="slider",
-                        default=[price_per_image_min, price_per_image_max],
-                        min=price_per_image_min,
-                        max=price_per_image_max,
-                    ),
-                ],
-                select_columns=df.columns.tolist(),
-                datatype="markdown",
+                datatype=["markdown"] + ["number"] * (len(df.columns.tolist()) - 1),
             )
         with gr.TabItem("FLUX.1 [dev] examples"):
             gr.HTML(