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import gradio as gr
import numpy as np
import pandas as pd
import argparse
def make_default_md():
leaderboard_md = f"""
# 🏆 LLms Benchmark
The main goal of this project is to utilize Large Language Models (LLMs) to extract specific information from PDF documents and organize it into a structured JSON format.
To achieve this objective, we are assessing various LLMs on two benchmarks:
1. [Benchmark1](https://huggingface.co/spaces/Nechba/LLms-Benchmark/blob/main/dataset.jsonl):
This benchmark consists of a dataset of 59 pages as context and corresponding JSON extracts from "Interchange and Service Fees Manual: Europe Region".
2. [Benchmark2](https://huggingface.co/datasets/Effyis/Table-Extraction):
This benchmark comprises a dataset of 16573 tables as context and corresponding JSON extracts.
"""
return leaderboard_md
def make_arena_leaderboard_md(total_models):
leaderboard_md = f"""
Total #models: **{total_models}**. Last updated: Juin 01, 2024.
"""
return leaderboard_md
def model_hyperlink(model_name, link):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def load_leaderboard_table_csv(filename, add_hyperlink=True):
rows = []
with open(filename, 'r') as file:
lines = file.readlines()
heads = [v.strip() for v in lines[0].split(",")]
for line in lines[1:]:
row = [v.strip() for v in line.split(",")]
item = {}
for h, v in zip(heads, row):
item[h] = v
if add_hyperlink:
item["Model"] = model_hyperlink(item["Model"], item["Link"])
item["Notebook link"] = model_hyperlink("Notebook", item["Notebook link"])
rows.append(item)
return rows
def get_arena_table(model_table_df):
# change type Percentage of values column of df
model_table_df["Percentage of values"] = model_table_df["Percentage of values"].astype(float)
model_table_df["Percentage of keys"] = model_table_df["Percentage of keys"].astype(float)
model_table_df["Average time (s)"] = model_table_df["Average time (s)"].astype(float)
arena_df = model_table_df.sort_values(by=["Percentage of values"], ascending=False)
values = []
if not arena_df.empty: # Check if arena_df is not empty
for i in range(len(arena_df)):
row = []
model_name = arena_df["Model"].values[i] # Access model name directly without index 0
row.append(model_name)
row.append(arena_df.iloc[i]["Percentage of values"])
row.append(arena_df.iloc[i]["Percentage of keys"])
row.append(arena_df.iloc[i]["Average time (s)"])
row.append(arena_df.iloc[i]["Notebook link"])
row.append(arena_df.iloc[i]["License"])
# row.append(arena_df.iloc[i]["Link"])
values.append(row)
return values
def build_leaderboard_tab(leaderboard_table_file1, leaderboard_table_file2, show_plot=False):
default_md = make_default_md()
md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
if leaderboard_table_file1:
data1 = load_leaderboard_table_csv(leaderboard_table_file1)
model_table_df1 = pd.DataFrame(data1)
data2 = load_leaderboard_table_csv(leaderboard_table_file2)
model_table_df2 = pd.DataFrame(data2)
with gr.Tabs() as tabs:
with gr.Tab(" 🏅 Benchmark 1", id=0):
arena_table_vals = get_arena_table(model_table_df1)
md = make_arena_leaderboard_md(len(arena_table_vals))
gr.Markdown(md, elem_id="leaderboard_markdown")
# Remove height argument
gr.Dataframe(
headers=[
"Model",
"Percentage of values (%)",
"Percentage of keys (%)",
"Average time (s)",
"Code",
"License",
],
datatype=[
"markdown",
"number",
"number",
"number",
"markdown",
"str"
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
column_widths=[200, 150, 150, 130, 100, 140],
wrap=True,
)
# Display additional Markdown notes as needed...
with gr.Tab("🏅 Benchmark 2", id=1):
arena_table_vals = get_arena_table(model_table_df2)
md = make_arena_leaderboard_md(len(arena_table_vals))
gr.Markdown(md, elem_id="leaderboard_markdown")
# Remove height argument
gr.Dataframe(
headers=[
"Model",
"Percentage of values (%)",
"Percentage of keys (%)",
"Average time (s)",
"Code",
"License",
],
datatype=[
"markdown",
"number",
"number",
"number",
"markdown",
"str"
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
column_widths=[200, 150, 150, 130, 100, 140],
wrap=True,
)
else:
pass
return [md_1, plot_1, plot_2]
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
}
.sponsor-image-about img {
margin: 0 20px;
margin-top: 20px;
height: 40px;
max-height: 100%;
width: auto;
float: left;
}
"""
def build_demo(leaderboard_table_file1, leaderboard_table_file2):
text_size = gr.themes.sizes.text_lg
with gr.Blocks(
title="LLMS Benchmark",
theme=gr.themes.Base(text_size=text_size),
css=block_css,
) as demo:
leader_components = build_leaderboard_tab(
leaderboard_table_file1,leaderboard_table_file2, show_plot=True
)
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true")
args = parser.parse_args()
leaderboard_table_file1 = "./Benchmark1/leaderboard.csv"
leaderboard_table_file2 = "./Benchmark2/leaderboard.csv"
demo = build_demo(leaderboard_table_file1,leaderboard_table_file2)
demo.launch(share=args.share)
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