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import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
# Function to load data from a given CSV file
def load_data(version):
file_path = f'versions/{version}.csv' # Replace with your file paths
return pd.read_csv(file_path)
# Function for searching in the leaderboard
def search_leaderboard(df, query):
if query == "":
return df
else:
return df[df['Method'].str.contains(query)]
# Function to change the version of the leaderboard
def change_version(version):
new_df = load_data(version)
return new_df
# Function to create plots
from plotter import create_plots
# Initialize Gradio app
demo = gr.Blocks()
with demo:
gr.Markdown("## 🥇 TOFU Leaderboard
The TOFU dataset serves as a benchmark for evaluating the unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.")
with gr.Tabs():
with gr.TabItem("Leaderboard"):
with gr.Row():
version_dropdown = gr.Dropdown(
choices=["llama", "phi", "stable-lm"],
label="🔄 Select Base Model",
value="llama",
)
with gr.Row():
search_bar = gr.Textbox(
placeholder="Search for methods...",
show_label=False,
)
leaderboard_table = gr.components.Dataframe(
value=load_data("llama"),
interactive=True,
visible=True,
)
version_dropdown.change(
change_version,
inputs=version_dropdown,
outputs=leaderboard_table
)
search_bar.change(
search_leaderboard,
inputs=[leaderboard_table, search_bar],
outputs=leaderboard_table
)
with gr.TabItem("Plots"):
version_dropdown_plots = gr.Dropdown(
choices=["llama", "phi", "stable-lm"],
label="🔄 Select Base Model",
value="llama",
)
with gr.Row():
methods_checkbox = gr.CheckboxGroup(
label="Select Methods",
choices=list(load_data("llama")['Method'].unique()), # To be populated dynamically
)
plot_output = gr.Plot()
# Dynamically update the choices for the methods checkbox
def update_method_choices(version):
df = load_data(version)
methods = df['Method'].unique()
methods_checkbox.update(choices=methods)
return df
version_dropdown_plots.change(
update_method_choices,
inputs=version_dropdown_plots,
outputs=[methods_checkbox, plot_output]
)
methods_checkbox.change(
create_plots,
inputs=[methods_checkbox, leaderboard_table],
outputs=plot_output
)
# Launch the app
demo.launch()
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