Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr, pandas as pd, datetime as dt
+from huggingface_hub import hf_hub_download
+
+DATASET = "Mdrnfox/peft-bench-metrics"
+PQ_PATH = "data/peft_bench.parquet"
+
+def load_table():
+    local = hf_hub_download(DATASET, PQ_PATH, repo_type="dataset")
+    df_long = pd.read_parquet(local)
+
+    wide = (
+        df_long
+        .query("metric != 'alias'")            # drop alias
+        .pivot_table(index=["model_id", "task"],
+                     columns="metric",
+                     values="value")
+        .reset_index()
+        .sort_values(["task", "model_id"])
+    )
+    return wide
+
+def refresh():
+    return gr.DataFrame.update(value=load_table(),
+                               headers=None), f"Last updated {dt.datetime.utcnow():%Y-%m-%d %H:%M UTC}"
+
+with gr.Blocks(title="PEFT-Bench") as demo:
+    gr.Markdown("# PEFT-Bench Leaderboard")
+    df = gr.DataFrame(value=load_table(), interactive=False, wrap=True)
+    t  = gr.Markdown(f"Last updated {dt.datetime.utcnow():%Y-%m-%d %H:%M UTC}")
+    gr.Button("↻ Refresh").click(refresh, outputs=[df, t])
+
+demo.launch()