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import gradio as gr
import pandas as pd
# Define the columns for the UGI Leaderboard
UGI_COLS = [
'#P', 'Model', 'UGI 🏆', 'Willingness 👍', 'Unruly', 'Internet', 'CrimeStats', 'Stories/Jokes', 'PolContro'
]
# Load the leaderboard data from a CSV file
def load_leaderboard_data(csv_file_path):
try:
df = pd.read_csv(csv_file_path)
# Create hyperlinks in the Model column using HTML <a> tags with inline CSS for styling
df['Model'] = df.apply(lambda row: f'<a href="{row["Link"]}" target="_blank" style="color: blue; text-decoration: none;">{row["Model"]}</a>' if pd.notna(row["Link"]) else row["Model"], axis=1)
# Drop the 'Link' column as it's no longer needed
df.drop(columns=['Link'], inplace=True)
return df
except Exception as e:
print(f"Error loading CSV file: {e}")
return pd.DataFrame(columns=UGI_COLS) # Return an empty dataframe with the correct columns
# Update the leaderboard table based on the search query and parameter range filters
def update_table(df: pd.DataFrame, query: str, param_ranges: dict) -> pd.DataFrame:
filtered_df = df
if any(param_ranges.values()):
conditions = []
for param_range, checked in param_ranges.items():
if checked:
if param_range == '~1.5':
conditions.append((filtered_df['Params'] < 2.5))
elif param_range == '~3':
conditions.append(((filtered_df['Params'] >= 2.5) & (filtered_df['Params'] < 6)))
elif param_range == '~7':
conditions.append(((filtered_df['Params'] >= 6) & (filtered_df['Params'] < 9.5)))
elif param_range == '~13':
conditions.append(((filtered_df['Params'] >= 9.5) & (filtered_df['Params'] < 16)))
elif param_range == '~20':
conditions.append(((filtered_df['Params'] >= 16) & (filtered_df['Params'] < 28)))
elif param_range == '~34':
conditions.append(((filtered_df['Params'] >= 28) & (filtered_df['Params'] < 40)))
elif param_range == '~50':
conditions.append(((filtered_df['Params'] >= 40) & (filtered_df['Params'] < 60)))
elif param_range == '~70+':
conditions.append((filtered_df['Params'] >= 60))
if all(param_ranges.values()):
conditions.append(filtered_df['Params'].isna())
filtered_df = filtered_df[pd.concat(conditions, axis=1).any(axis=1)]
if query:
filtered_df = filtered_df[filtered_df.apply(lambda row: query.lower() in row.to_string().lower(), axis=1)]
return filtered_df[UGI_COLS] # Return only the columns defined in UGI_COLS
# Define the Gradio interface
demo = gr.Blocks()
with demo:
gr.Markdown("## UGI Leaderboard", elem_classes="text-lg text-center")
gr.Markdown("""
UGI: Uncensored General Intelligence. The average of 5 different subjects that LLMs are commonly steered away from. The leaderboard is made from roughly 60 questions overall, measuring both "willingness to answer" and "accuracy" in fact-based controversial questions.
Willingness: A more narrow score, solely measuring the LLM's willingness to answer controversial questions.
Unruly: Knowledge of activities that are generally frowned upon.
Internet: Knowledge of various internet information, from professional to deviant.
CrimeStats: Knowledge of crime statistics which are uncomfortable to talk about.
Stories/Jokes: Ability to write offensive stories and jokes.
PolContro: Knowledge of politically/socially controversial information.
""")
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(placeholder=" 🔍 Search for a model...", show_label=False)
with gr.Row():
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=['~1.5', '~3', '~7', '~13', '~20', '~34', '~50', '~70+'],
value=['~1.5', '~3', '~7', '~13', '~20', '~34', '~50', '~70+'],
interactive=True,
elem_id="filter-columns-size",
)
# Load the initial leaderboard data
leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
# Define the datatypes for each column, setting 'Model' column to 'html'
datatypes = ['html' if col == 'Model' else 'str' for col in UGI_COLS]
leaderboard_table = gr.Dataframe(
value=leaderboard_df[UGI_COLS],
datatype=datatypes, # Specify the datatype for each column
interactive=False, # Set to False to make the leaderboard non-editable
visible=True,
elem_classes="text-sm" # Increase the font size of the leaderboard data
)
# Define the search and filter functionality
inputs = [
search_bar,
filter_columns_size
]
outputs = leaderboard_table
search_bar.change(
fn=lambda query, param_ranges: update_table(leaderboard_df, query, dict(zip(['~1.5', '~3', '~7', '~13', '~20', '~34', '~50', '~70+'], param_ranges))),
inputs=inputs,
outputs=outputs
)
filter_columns_size.change(
fn=lambda query, param_ranges: update_table(leaderboard_df, query, dict(zip(['~1.5', '~3', '~7', '~13', '~20', '~34', '~50', '~70+'], param_ranges))),
inputs=inputs,
outputs=outputs
)
# Launch the Gradio app
demo.launch() |