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import gradio as gr
import pandas as pd
import numpy as np
from collections import defaultdict
from gradio_leaderboard import Leaderboard, SelectColumns
# Load the DataFrame from the CSV file for detailed pass@k metrics
df = pd.read_csv('results.csv')
# Function to estimate pass@k
def estimate_pass_at_k(num_samples, num_correct, k):
def estimator(n, c, k):
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
return np.array([estimator(n, c, k) for n, c in zip(num_samples, num_correct)])
# Function to calculate pass@k
def calculate_pass_at_k(df, model, scenario, k_values=[1, 5, 10]):
filtered_df = df[(df['Model'] == model) & (df['Scenario'] == scenario)]
num_samples = filtered_df['Runs'].values
num_correct = filtered_df['Successes'].values
pass_at_k = {f"pass@{k}": estimate_pass_at_k(num_samples, num_correct, k).mean() for k in k_values}
return pass_at_k
# Function to filter data and calculate pass@k
def filter_data(model, scenario):
pass_at_k = calculate_pass_at_k(df, model, scenario)
return pd.DataFrame([pass_at_k])
# Initialize the leaderboard
def init_leaderboard(dataframe):
if dataframe is None or dataframe.empty:
raise ValueError("Leaderboard DataFrame is empty or None.")
return Leaderboard(
value=dataframe,
datatype=["markdown", "number", "number", "number"], # Specify the types of your columns
select_columns=SelectColumns(
default_selection=["Model", "pass@1", "pass@5", "pass@10"], # Columns to display by default
cant_deselect=[], # Columns that cannot be deselected
label="Select Columns to Display:",
),
search_columns=["Model"], # Columns that can be searched
hide_columns=[], # Columns to hide
filter_columns=[], # Filters for the columns
bool_checkboxgroup_label="Hide models",
interactive=False,
)
# Gradio interface
models = df['Model'].unique()
scenarios = df['Scenario'].unique()
demo = gr.Blocks()
with demo:
gr.Markdown("# 🏆 WebApp1K Detailed Leaderboard")
model_input = gr.Dropdown(choices=models, label="Select Model")
scenario_input = gr.Dropdown(choices=scenarios, label="Select Scenario")
output = gr.DataFrame(headers=["pass@1", "pass@5", "pass@10"])
filter_button = gr.Button("Filter")
filter_button.click(filter_data, inputs=[model_input, scenario_input], outputs=output)
output.render()
# Initialize leaderboard with the complete DataFrame
complete_pass_at_k = df.groupby('Model').apply(lambda x: pd.Series({
'pass@1': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 1).mean(),
'pass@5': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 5).mean(),
'pass@10': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 10).mean()
})).reset_index()
leaderboard = init_leaderboard(complete_pass_at_k)
leaderboard.render()
# Launch the Gradio interface
demo.launch()
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