🐛 Add patch to gradio_leaderboard before import

app.py

@@ -1,5 +1,49 @@
 import os
 import re
+from pathlib import Path
+
+
+def patch_gradio_leaderboard():
+    """Patch gradio_leaderboard JS to fix crash on tab switch with Gradio 5.x.
+
+    See PR.md for details.
+    """
+    import gradio_leaderboard
+    pkg_dir = Path(gradio_leaderboard.__file__).parent
+    js_file = pkg_dir / "templates" / "component" / "Index-CzS_eGV6.js"
+    if not js_file.exists():
+        return
+
+    src = js_file.read_text()
+
+    patches = [
+        # Fix 1 & 2: Guard r[39]/a[39] filter callback (undefined during Svelte outro)
+        (
+            'r[0].filter(\n        /*func*/\n        r[39]\n      ).map(qd)',
+            '(r[39] ? r[0].filter(r[39]) : r[0]).map(qd)',
+        ),
+        (
+            'a[0].filter(\n          /*func*/\n          a[39]\n        ).map(qd))',
+            '(a[39] ? a[0].filter(a[39]) : a[0]).map(qd))',
+        ),
+        # Fix 3: Lx (Boolean) extracted from Rx (globals) which is undefined in Gradio 5
+        (
+            '{ Boolean: Lx } = Rx,',
+            'Lx = (Rx && Rx.Boolean) || Boolean,',
+        ),
+    ]
+
+    patched = False
+    for old, new in patches:
+        if old in src:
+            src = src.replace(old, new)
+            patched = True
+
+    if patched:
+        js_file.write_text(src)
+
+
+patch_gradio_leaderboard()
 
 import gradio as gr
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
@@ -131,12 +175,10 @@ with demo:
 
     with gr.Tabs():
         with gr.Tab("🏆 Leaderboard"):
-            # leaderboard = init_leaderboard(LEADERBOARD_DF)
-            leaderboard = gr.DataFrame(LEADERBOARD_DF)
+            leaderboard = init_leaderboard(LEADERBOARD_DF)
 
         with gr.Tab("🏃 Benchmark Runs"):
-            # benchmark_runs = init_benchmark_runs(BENCHMARK_RUN_DF)
-            benchmark_runs = gr.DataFrame(BENCHMARK_RUN_DF)
+            benchmark_runs = init_benchmark_runs(BENCHMARK_RUN_DF)
             gr.Markdown("\* `internal` refers to internal benchmarks performed by the model provider where the harness/environment were not made public")
 
         with gr.Tab("📝 About"):