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"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space."""
import argparse
import gradio as gr
import numpy as np
import pandas as pd
import gradio as gr
import pandas as pd
from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS
LAST_UPDATED = "Feb 28th 2024"
css = """
.markdown-text{font-size: 15pt}
.markdown-text-small{font-size: 13pt}
th {
text-align: center;
}
td {
font-size: 15px; /* Adjust the font size as needed */
text-align: center;
}
"""
column_names = {
"model": "Model",
"Overall": "All 🎯",
"Turn 1": "Turn 1️⃣",
"Turn 2": "Turn 2️⃣",
}
model_info = {
"gpt-4": {"hf_name": "https://platform.openai.com/", "pretty_name": "gpt-4"},
"gpt-3.5-turbo": {"hf_name": "https://platform.openai.com/", "pretty_name": "gpt-3.5-turbo"},
"Llama-2-70b-hf": {"hf_name": "meta-llama/Llama-2-70b-hf", "pretty_name": "Llama-2-70B"},
"Llama-2-13b-hf": {"hf_name": "meta-llama/Llama-2-13b-hf", "pretty_name": "Llama-2-13B"},
"Llama-2-7b-hf": {"hf_name": "meta-llama/Llama-2-7b-hf", "pretty_name": "Llama-2-7B"},
"Mixtral-8x7B-v0.1": {"hf_name": "mistralai/Mixtral-8x7B-v0.1", "pretty_name": "Mixtral-8x7B"},
"Mistral-7b-v0.1": {"hf_name": "mistralai/Mistral-7B-v0.1", "pretty_name": "Mistral-7B"},
"Yi-34B": {"hf_name": "01-ai/Yi-34B", "pretty_name": "Yi-34B"},
"Yi-6B": {"hf_name": "01-ai/Yi-6B", "pretty_name": "Yi-6B"},
"gemma-7b": {"hf_name": "google/gemma-7b", "pretty_name": "Gemma-7B"},
"gemma-2b": {"hf_name": "google/gemma-2b", "pretty_name": "Gemma-2B"},
"phi-2": {"hf_name": "microsoft/phi-2", "pretty_name": "Phi-2 @hf"},
"olmo": {"hf_name": "allenai/OLMo-7B", "pretty_name": "OLMo-7B @hf"},
"phi-2-vllm": {"hf_name": "microsoft/phi-2", "pretty_name": "Phi-2 (2.7B)"},
"olmo-7b-vllm": {"hf_name": "allenai/OLMo-7B", "pretty_name": "OLMo-7B"},
"falcon-7b": {"hf_name": "microsoft/falcon-7b", "pretty_name": "Falcon-7B"},
"mpt-7b": {"hf_name": "mosaicml/mpt-7b", "pretty_name": "MPT-7B"},
"amber": {"hf_name": "LLM360/Amber", "pretty_name": "Amber (7B)"},
}
def formatter(x):
x = round(x, 2)
return x
def make_clickable_model(model_name, model_info):
if model_info[model_name]['hf_name'].startswith("http"):
link = model_info[model_name]['hf_name']
else:
link = f"https://huggingface.co/{model_info[model_name]['hf_name']}"
if model_name.startswith("gpt"):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted; background-color: lightgray;">{model_info[model_name]["pretty_name"]}</a>'
else:
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_info[model_name]["pretty_name"]}</a>'
def build_demo(original_df, TYPES):
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
# gr.HTML(BANNER, elem_id="banner")
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
# with gr.Tabs(elem_classes="tab-buttons") as tabs:
# with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0):
leaderboard_table = gr.components.Dataframe(
value=original_df,
datatype=TYPES,
height=1000,
wrap=False,
elem_id="leaderboard-table",
interactive=False,
visible=True,
min_width=60,
)
# with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
# gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")
gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text-small")
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
gr.Textbox(
value=CITATION_TEXT, lines=18,
label="Copy the BibTeX to cite URIAL and MT-Bench",
elem_id="citation-button",
show_copy_button=True)
# ).style(show_copy_button=True)
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true")
parser.add_argument("--result_file", help="Path to results table", default="leaderboard_data.jsonl")
args = parser.parse_args()
bench_results = args.result_file
original_df = pd.read_json(bench_results, lines=True)
print(original_df.columns)
for col in original_df.columns:
if col == "model":
original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x, model_info)))
else:
original_df[col] = original_df[col].apply(formatter) # For numerical values
# Define the first column explicitly, add 'Overall' as the second column, and then append the rest excluding 'Overall'
new_order = [original_df.columns[0], 'Overall'] + [col for col in original_df.columns if col not in [original_df.columns[0], 'Overall']]
# Reorder the DataFrame columns using the new order
reordered_df = original_df[new_order]
reordered_df.sort_values(by='Overall', inplace=True, ascending=False)
reordered_df.rename(columns=column_names, inplace=True)
# COLS = [c.name for c in fields(AutoEvalColumn)]
# TYPES = [c.type for c in fields(AutoEvalColumn)]
TYPES = ["markdown", "number"]
demo = build_demo(reordered_df, TYPES)
demo.launch(share=args.share)
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