"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space."""
import ast
import argparse
import glob
import pickle
import gradio as gr
import numpy as np
import pandas as pd
# notebook_url = "https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing"
notebook_url = "https://colab.research.google.com/drive/1KdwokPjirkTmpO_P1WByFNFiqxWQquwH#scrollTo=o_CpbkGEbhrK"
basic_component_values = [None] * 6
leader_component_values = [None] * 5
def make_default_md(arena_df, elo_results):
total_votes = sum(arena_df["num_battles"]) // 2
total_models = len(arena_df)
leaderboard_md = f"""
# 🏆 LMSYS Chatbot Arena Leaderboard
| [Vote](https://chat.lmsys.org) | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
LMSYS [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) is a crowdsourced open platform for LLM evals.
We've collected over **200,000** human preference votes to rank LLMs with the Elo ranking system.
"""
return leaderboard_md
def make_arena_leaderboard_md(arena_df):
total_votes = sum(arena_df["num_battles"]) // 2
total_models = len(arena_df)
leaderboard_md = f"""
Total #models: **{total_models}**. Total #votes: **{total_votes}**. Last updated: Jan 9, 2024.
Contribute your vote 🗳️ at [chat.lmsys.org](https://chat.lmsys.org)! Find more analysis in the [notebook]({notebook_url}).
"""
return leaderboard_md
def make_full_leaderboard_md(elo_results):
leaderboard_md = f"""
Two more benchmarks are displayed: **MT-Bench** and **MMLU**.
- [MT-Bench](https://arxiv.org/abs/2306.05685): a set of challenging multi-turn questions. We use GPT-4 to grade the model responses.
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot): a test to measure a model's multitask accuracy on 57 tasks.
💻 Code: The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge).
The MMLU scores are mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval).
Higher values are better for all benchmarks. Empty cells mean not available.
"""
return leaderboard_md
def make_leaderboard_md_live(elo_results):
leaderboard_md = f"""
# Leaderboard
Last updated: {elo_results["last_updated_datetime"]}
{elo_results["leaderboard_table"]}
"""
return leaderboard_md
def update_elo_components(max_num_files, elo_results_file):
log_files = get_log_files(max_num_files)
# Leaderboard
if elo_results_file is None: # Do live update
battles = clean_battle_data(log_files)
elo_results = report_elo_analysis_results(battles)
leader_component_values[0] = make_leaderboard_md_live(elo_results)
leader_component_values[1] = elo_results["win_fraction_heatmap"]
leader_component_values[2] = elo_results["battle_count_heatmap"]
leader_component_values[3] = elo_results["bootstrap_elo_rating"]
leader_component_values[4] = elo_results["average_win_rate_bar"]
# Basic stats
basic_stats = report_basic_stats(log_files)
md0 = f"Last updated: {basic_stats['last_updated_datetime']}"
md1 = "### Action Histogram\n"
md1 += basic_stats["action_hist_md"] + "\n"
md2 = "### Anony. Vote Histogram\n"
md2 += basic_stats["anony_vote_hist_md"] + "\n"
md3 = "### Model Call Histogram\n"
md3 += basic_stats["model_hist_md"] + "\n"
md4 = "### Model Call (Last 24 Hours)\n"
md4 += basic_stats["num_chats_last_24_hours"] + "\n"
basic_component_values[0] = md0
basic_component_values[1] = basic_stats["chat_dates_bar"]
basic_component_values[2] = md1
basic_component_values[3] = md2
basic_component_values[4] = md3
basic_component_values[5] = md4
def update_worker(max_num_files, interval, elo_results_file):
while True:
tic = time.time()
update_elo_components(max_num_files, elo_results_file)
durtaion = time.time() - tic
print(f"update duration: {durtaion:.2f} s")
time.sleep(max(interval - durtaion, 0))
def load_demo(url_params, request: gr.Request):
logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
return basic_component_values + leader_component_values
def model_hyperlink(model_name, link):
return f'{model_name}'
def load_leaderboard_table_csv(filename, add_hyperlink=True):
lines = open(filename).readlines()
heads = [v.strip() for v in lines[0].split(",")]
rows = []
for i in range(1, len(lines)):
row = [v.strip() for v in lines[i].split(",")]
for j in range(len(heads)):
item = {}
for h, v in zip(heads, row):
if h == "Arena Elo rating":
if v != "-":
v = int(ast.literal_eval(v))
else:
v = np.nan
elif h == "MMLU":
if v != "-":
v = round(ast.literal_eval(v) * 100, 1)
else:
v = np.nan
elif h == "MT-bench (win rate %)":
if v != "-":
v = round(ast.literal_eval(v[:-1]), 1)
else:
v = np.nan
elif h == "MT-bench (score)":
if v != "-":
v = round(ast.literal_eval(v), 2)
else:
v = np.nan
item[h] = v
if add_hyperlink:
item["Model"] = model_hyperlink(item["Model"], item["Link"])
rows.append(item)
return rows
def build_basic_stats_tab():
empty = "Loading ..."
basic_component_values[:] = [empty, None, empty, empty, empty, empty]
md0 = gr.Markdown(empty)
gr.Markdown("#### Figure 1: Number of model calls and votes")
plot_1 = gr.Plot(show_label=False)
with gr.Row():
with gr.Column():
md1 = gr.Markdown(empty)
with gr.Column():
md2 = gr.Markdown(empty)
with gr.Row():
with gr.Column():
md3 = gr.Markdown(empty)
with gr.Column():
md4 = gr.Markdown(empty)
return [md0, plot_1, md1, md2, md3, md4]
def get_full_table(arena_df, model_table_df):
values = []
for i in range(len(model_table_df)):
row = []
model_key = model_table_df.iloc[i]["key"]
model_name = model_table_df.iloc[i]["Model"]
# model display name
row.append(model_name)
if model_key in arena_df.index:
idx = arena_df.index.get_loc(model_key)
row.append(round(arena_df.iloc[idx]["rating"]))
else:
row.append(np.nan)
row.append(model_table_df.iloc[i]["MT-bench (score)"])
row.append(model_table_df.iloc[i]["MMLU"])
# Organization
row.append(model_table_df.iloc[i]["Organization"])
# license
row.append(model_table_df.iloc[i]["License"])
values.append(row)
values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)
return values
def get_arena_table(arena_df, model_table_df):
# sort by rating
arena_df = arena_df.sort_values(by=["rating"], ascending=False)
values = []
for i in range(len(arena_df)):
row = []
model_key = arena_df.index[i]
model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
0
]
# rank
row.append(i + 1)
# model display name
row.append(model_name)
# elo rating
row.append(round(arena_df.iloc[i]["rating"]))
upper_diff = round(
arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"]
)
lower_diff = round(
arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"]
)
row.append(f"+{upper_diff}/-{lower_diff}")
# num battles
row.append(round(arena_df.iloc[i]["num_battles"]))
# Organization
row.append(
model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
)
# license
row.append(
model_table_df[model_table_df["key"] == model_key]["License"].values[0]
)
values.append(row)
return values
def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False):
if elo_results_file is None: # Do live update
default_md = "Loading ..."
p1 = p2 = p3 = p4 = None
else:
with open(elo_results_file, "rb") as fin:
elo_results = pickle.load(fin)
p1 = elo_results["win_fraction_heatmap"]
p2 = elo_results["battle_count_heatmap"]
p3 = elo_results["bootstrap_elo_rating"]
p4 = elo_results["average_win_rate_bar"]
arena_df = elo_results["leaderboard_table_df"]
default_md = make_default_md(arena_df, elo_results)
md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
if leaderboard_table_file:
data = load_leaderboard_table_csv(leaderboard_table_file)
model_table_df = pd.DataFrame(data)
with gr.Tabs() as tabs:
# arena table
arena_table_vals = get_arena_table(arena_df, model_table_df)
with gr.Tab("Arena Elo", id=0):
md = make_arena_leaderboard_md(arena_df)
gr.Markdown(md, elem_id="leaderboard_markdown")
gr.Dataframe(
headers=[
"Rank",
"🤖 Model",
"⭐ Arena Elo",
"📊 95% CI",
"🗳️ Votes",
"Organization",
"License",
],
datatype=[
"str",
"markdown",
"number",
"str",
"number",
"str",
"str",
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
height=700,
column_widths=[50, 200, 100, 100, 100, 150, 150],
wrap=True,
)
with gr.Tab("Full Leaderboard", id=1):
md = make_full_leaderboard_md(elo_results)
gr.Markdown(md, elem_id="leaderboard_markdown")
full_table_vals = get_full_table(arena_df, model_table_df)
gr.Dataframe(
headers=[
"🤖 Model",
"⭐ Arena Elo",
"📈 MT-bench",
"📚 MMLU",
"Organization",
"License",
],
datatype=["markdown", "number", "number", "number", "str", "str"],
value=full_table_vals,
elem_id="full_leaderboard_dataframe",
column_widths=[200, 100, 100, 100, 150, 150],
height=700,
wrap=True,
)
if not show_plot:
gr.Markdown(
""" ## Visit our [HF space](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) for more analysis!
If you want to see more models, please help us [add them](https://github.com/lm-sys/FastChat/blob/main/docs/arena.md#how-to-add-a-new-model).
""",
elem_id="leaderboard_markdown",
)
else:
pass
leader_component_values[:] = [default_md, p1, p2, p3, p4]
if show_plot:
gr.Markdown(
f"""## More Statistics for Chatbot Arena\n
Below are figures for more statistics. The code for generating them is also included in this [notebook]({notebook_url}).
You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/).
""",
elem_id="leaderboard_markdown"
)
with gr.Row():
with gr.Column():
gr.Markdown(
"#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles"
)
plot_1 = gr.Plot(p1, show_label=False)
with gr.Column():
gr.Markdown(
"#### Figure 2: Battle Count for Each Combination of Models (without Ties)"
)
plot_2 = gr.Plot(p2, show_label=False)
with gr.Row():
with gr.Column():
gr.Markdown(
"#### Figure 3: Bootstrap of Elo Estimates (1000 Rounds of Random Sampling)"
)
plot_3 = gr.Plot(p3, show_label=False)
with gr.Column():
gr.Markdown(
"#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
)
plot_4 = gr.Plot(p4, show_label=False)
gr.Markdown(acknowledgment_md)
if show_plot:
return [md_1, plot_1, plot_2, plot_3, plot_4]
return [md_1]
block_css = """
#notice_markdown {
font-size: 104%
}
#notice_markdown th {
display: none;
}
#notice_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_markdown {
font-size: 104%
}
#leaderboard_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_dataframe td {
line-height: 0.1em;
}
footer {
display:none !important
}
.image-container {
display: flex;
align-items: center;
padding: 1px;
}
.image-container img {
margin: 0 30px;
height: 20px;
max-height: 100%;
width: auto;
max-width: 20%;
}
"""
acknowledgment_md = """
### Acknowledgment
"""
def build_demo(elo_results_file, leaderboard_table_file):
text_size = gr.themes.sizes.text_lg
with gr.Blocks(
title="Chatbot Arena Leaderboard",
theme=gr.themes.Base(text_size=text_size),
css=block_css,
) as demo:
leader_components = build_leaderboard_tab(
elo_results_file, leaderboard_table_file, show_plot=True
)
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true")
args = parser.parse_args()
elo_result_files = glob.glob("elo_results_*.pkl")
elo_result_files.sort(key=lambda x: int(x[12:-4]))
elo_result_file = elo_result_files[-1]
leaderboard_table_files = glob.glob("leaderboard_table_*.csv")
leaderboard_table_files.sort(key=lambda x: int(x[18:-4]))
leaderboard_table_file = leaderboard_table_files[-1]
demo = build_demo(elo_result_file, leaderboard_table_file)
demo.launch(share=args.share)
