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1ac264d 726831f 1ac264d 726831f 1ac264d 726831f 1ac264d 726831f 1ac264d 726831f 0f0fe92 726831f 1ac264d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import json
from pathlib import Path
import gradio as gr
import pandas as pd
TITLE = 'EASI Leaderboard'
DATA_PATH = Path("leaderboard.csv")
META_PATH = Path("benchmark.json")
DISPLAY_TEXT_LIMIT = 96
df = pd.read_csv(DATA_PATH)
if "score" in df.columns:
df["score"] = pd.to_numeric(df["score"], errors="coerce")
meta = {}
if META_PATH.exists():
try:
meta = json.loads(META_PATH.read_text(encoding="utf-8"))
except Exception:
meta = {}
def metric_narrative():
likely_metrics = (((meta.get("analysis") or {}).get("likely_metrics")) or [])
if likely_metrics:
return (
"This benchmark is ranked by the primary `score` column (descending). "
"Reported benchmark metrics include: " + ", ".join([str(x) for x in likely_metrics]) + "."
)
known_non_metrics = {
"model_name",
"score",
"source_title",
"source_url",
"notes",
}
metric_like = [c for c in df.columns if c not in known_non_metrics]
if metric_like:
return (
"This benchmark is ranked by the primary `score` column (descending). "
"Table columns include: " + ", ".join(metric_like) + "."
)
return "This benchmark is ranked by the primary `score` column (descending)."
def render(query, top_k):
x = df.copy()
if query.strip():
x = x[x["model_name"].astype(str).str.contains(query.strip(), case=False, na=False)]
if "score" in x.columns:
x = x.sort_values(by=["score"], ascending=False, na_position="last")
x = x.head(int(top_k)).reset_index(drop=True)
x = compact_for_display(x)
x.insert(0, "display_rank", x.index + 1)
return x
def compact_for_display(frame):
out = frame.copy()
for col in out.columns:
if out[col].dtype != object:
continue
out[col] = out[col].apply(truncate_cell)
return out
def truncate_cell(value):
if value is None:
return value
text = str(value)
if len(text) <= DISPLAY_TEXT_LIMIT:
return text
return text[: DISPLAY_TEXT_LIMIT - 3] + "..."
with gr.Blocks(title=TITLE) as demo:
gr.Markdown(f"# {TITLE}")
gr.Markdown(metric_narrative())
gr.Markdown(f"Rows: {len(df)}")
with gr.Row():
gr.DownloadButton("Download CSV", value=str(DATA_PATH))
if Path("leaderboard.json").exists():
gr.DownloadButton("Download JSON", value="leaderboard.json")
with gr.Row():
query = gr.Textbox(label="Model contains")
top_k = gr.Slider(minimum=5, maximum=500, step=1, value=100, label="Top K")
table = gr.Dataframe(
value=render("", 100),
interactive=False,
wrap=False,
row_count=(20, "fixed"),
)
query.change(render, [query, top_k], table)
top_k.change(render, [query, top_k], table)
demo.launch()
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