update

README.md

@@ -8,7 +8,7 @@ app_file: app.py
 pinned: true
 license: apache-2.0
 short_description: 'BOOM: Benchmark Of Observability Metrics'
-sdk_version: 5.19.0
+sdk_version: 6.11.0
 tags:
 - leaderboard
 ---

app.py

@@ -1,7 +1,5 @@
 import gradio as gr
-from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
 from src.populate import get_model_info_df, get_merged_df
 
 from src.about import (
@@ -107,31 +105,28 @@ def init_leaderboard(dataframe, model_info_df):
         ]
     )
     merged_df = merged_df[cols]
+    
+    # Remove hidden columns
+    hidden_cols = [c.name for c in fields(AutoEvalColumn) if c.hidden]
+    merged_df = merged_df.drop(columns=[col for col in hidden_cols if col in merged_df.columns], errors='ignore')
+    
+    # Build datatype list
     col2type_dict = {c.name: c.type for c in fields(AutoEvalColumn)}
     datatype_list = [col2type_dict[col] if col in col2type_dict else "number" for col in merged_df.columns]
-    model_info_col_list = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default]
-    default_selection_list = list(dataframe.columns) + model_info_col_list
-    return Leaderboard(
+    
+    # Use native Gradio 6 Dataframe with search and filter
+    return gr.Dataframe(
         value=merged_df,
         datatype=datatype_list,
-        select_columns=SelectColumns(
-            default_selection=default_selection_list,
-            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
-            label="Select Columns to Display:",
-        ),
-        search_columns=[AutoEvalColumn.model.name],
-        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
-        filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-        ],
-        bool_checkboxgroup_label="Hide models",
+        show_search="filter",  # Enable search + per-column filtering
         column_widths=[40, 180] + [160 for _ in range(len(merged_df.columns) - 2)],
         wrap=True,
         interactive=False,
+        max_height=600,
     )
 
 
-demo = gr.Blocks(css=custom_css)
+demo = gr.Blocks()
 with demo:
     gr.HTML(TITLE)
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
@@ -162,10 +157,7 @@ with demo:
                 label=CITATION_BUTTON_LABEL,
                 lines=20,
                 elem_id="citation-button",
-                show_copy_button=True,
             )
 
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
-scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+# Queue memory leak fixed in Gradio 6.11+, no need for scheduled restarts
+demo.queue(default_concurrency_limit=40).launch(css=custom_css)

requirements.txt

@@ -1,9 +1,7 @@
-APScheduler
 black
 datasets
-gradio
+gradio>=6.11.0
 gradio[oauth]
-gradio_leaderboard==0.0.13
 gradio_client
 huggingface-hub>=0.18.0
 matplotlib

src/display/utils.py

@@ -1,4 +1,4 @@
-from dataclasses import dataclass, make_dataclass
+from dataclasses import dataclass, make_dataclass, field
 from enum import Enum
 
 import pandas as pd
@@ -13,7 +13,7 @@ def fields(raw_class):
 # These classes are for user facing column names,
 # to avoid having to change them all around the code
 # when a modif is needed
-@dataclass
+@dataclass(frozen=True)
 class ColumnContent:
     name: str
     type: str
@@ -47,12 +47,12 @@ AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=
 ## For the queue columns in the submission tab
 @dataclass(frozen=True)
 class EvalQueueColumn:  # Queue column
-    model = ColumnContent("model", "markdown", True)
-    revision = ColumnContent("revision", "str", True)
-    private = ColumnContent("private", "bool", True)
-    precision = ColumnContent("precision", "str", True)
-    weight_type = ColumnContent("weight_type", "str", "Original")
-    status = ColumnContent("status", "str", True)
+    model: ColumnContent = ColumnContent("model", "markdown", True)
+    revision: ColumnContent = ColumnContent("revision", "str", True)
+    private: ColumnContent = ColumnContent("private", "bool", True)
+    precision: ColumnContent = ColumnContent("precision", "str", True)
+    weight_type: ColumnContent = ColumnContent("weight_type", "str", "Original")
+    status: ColumnContent = ColumnContent("status", "str", True)
 
 
 ## All the model information that we might need