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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-v2")
if f.startswith("metrics")
]
print(files)
metrics = []
cols = ["model", "is_contextual"]
for file in files:
result_path = api.hf_hub_download("illuin-cde/baselines-v2", filename=file)
with open(result_path, "r") as f:
dic = json.load(f)
metrics_cur = dic["metrics"]
for k, v in metrics_cur.items():
dic.update({k: v["ndcg_at_5"]})
cols.append(k)
del dic["metrics"]
metrics.append(dic)
df = pd.DataFrame(metrics)
df = df[cols]
df["model"] = df["model"].apply(lambda x: x.split("/")[-1])
# round all numeric columns
# avg all numeric columns
df["avg"] = df.iloc[:, 2:].mean(axis=1)
df = df.round(3)
# sort by ndcg_at_5
df = df.sort_values(by="avg", 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()
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