app.py

import os
import warnings

warnings.filterwarnings('ignore')
os.environ["PYTHONWARNINGS"] = "ignore"

import gradio as gr
import pandas as pd

from virny.datasets import ACSIncomeDataset, ACSPublicCoverageDataset, LawSchoolDataset
from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer


if __name__ == '__main__':
    # Define configs for sample datasets
    demo_configs = {
        'ACS_Income': {
            'data_loader': ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False,
                                            subsample_size=15_000, subsample_seed=42),
            'sensitive_attributes_dct': {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None},
            'model_metrics': pd.read_csv(os.path.join('.', 'data', 'acs_income_metrics.csv'), header=0),
        },
        'ACS_Public_Coverage': {
            'data_loader': ACSPublicCoverageDataset(state=['CA'], year=2018, with_nulls=False,
                                                    subsample_size=15_000, subsample_seed=42),
            'sensitive_attributes_dct': {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None},
            'model_metrics': pd.read_csv(os.path.join('.', 'data', 'acs_pub_cov_metrics.csv'), header=0),
        },
        'Law_School': {
            'data_loader': LawSchoolDataset(),
            'sensitive_attributes_dct': {'male': '0.0', 'race': 'Non-White', 'male&race': None},
            'model_metrics': pd.read_csv(os.path.join('.', 'data', 'law_school_metrics.csv'), header=0),
        },
    }

    # Create gradio demo objects for each sample dataset
    dataset_names = list(demo_configs.keys())
    sample_demos = []
    for dataset_name in dataset_names:
        sample_demo = MetricsInteractiveVisualizer(
            X_data=demo_configs[dataset_name]['data_loader'].X_data,
            y_data=demo_configs[dataset_name]['data_loader'].y_data,
            model_metrics=demo_configs[dataset_name]['model_metrics'],
            sensitive_attributes_dct=demo_configs[dataset_name]['sensitive_attributes_dct']
        ).create_web_app(start_app=False)
        sample_demos.append(sample_demo)

    # Build a web application with tabs for each sample dataset
    demo = gr.TabbedInterface(sample_demos, [name.replace('_', ' ') for name in dataset_names], theme=gr.themes.Soft())
    demo.launch(inline=False, debug=True, show_error=True)