| 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() | |