Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+import json
+from huggingface_hub import HfApi
+import pandas as pd
+
+
+def compute_df():
+    api = HfApi()
+    # download all files in https://huggingface.co/illuin-cde/baselines
+    files = [f for f in api.list_repo_files("illuin-cde/baselines") if f.startswith("metrics")]
+    print(files)
+
+    metrics = []
+    for file in files:
+        result_path = api.hf_hub_download("illuin-cde/baselines", filename=file)
+        with open(result_path, "r") as f:
+            dic = json.load(f)
+            dic.update(dic["metrics"])
+            del dic["metrics"]
+            metrics.append(dic)
+
+    df = pd.DataFrame(metrics)
+    df = df[["model", "dataset", "split", "is_contextual", "ndcg_at_1", "ndcg_at_5", "ndcg_at_10", "ndcg_at_100"]]
+    df["model"] = df["model"].apply(lambda x: x.split("/")[-1])
+    df["dataset"] = df["dataset"].apply(lambda x: x.split("/")[-1])
+    # round all numeric columns
+    df = df.round(3)
+
+    # sort by ndcg_at_5
+    df = df.sort_values("ndcg_at_5", ascending=False)
+
+    # gradio display
+    gradio_df = gr.Dataframe(df)
+    return gradio_df
+
+# refresh button and precompute
+gr.Interface(fn=compute_df, title="Results Leaderboard", inputs=None, outputs="dataframe").launch()