app.py

import gradio as gr
import pandas as pd
import random
import plotly.express as px
from huggingface_hub import snapshot_download
import os
import logging

from config import (
    SETUPS,
    LOCAL_RESULTS_DIR,
    CITATION_BUTTON_TEXT,
    CITATION_BUTTON_LABEL,
)
from parsing import read_all_configs

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    handlers=[
        # logging.FileHandler("app.log"),
        logging.StreamHandler()
    ],
)

logger = logging.getLogger(__name__)


try:
    print("Saving results locally at:", LOCAL_RESULTS_DIR)
    snapshot_download(
        repo_id="g8a9/fair-asr-results",
        local_dir=LOCAL_RESULTS_DIR,
        repo_type="dataset",
        tqdm_class=None,
        etag_timeout=30,
        ignore_patterns=["*samples*", "*transcripts*"],
        token=os.environ.get("TOKEN"),
    )
except Exception as e:
    raise e


def format_dataframe(df, times_100=False):
    if times_100:
        df = df.map(lambda x: (f"{x * 100:.3f}%" if isinstance(x, (int, float)) else x))
    else:
        df = df.map(lambda x: (f"{x:.4f}" if isinstance(x, (int, float)) else x))
    return df


with gr.Blocks() as fm_interface:
    fm = SETUPS[0]
    setup = fm["majority_group"] + "_" + fm["minority_group"]
    results = read_all_configs(setup)

    model_results = (
        results.pivot_table(
            index="Model", values="Gap", aggfunc=lambda x: 100 * x.abs().sum()
        )
        .reset_index()
        .sort_values("Gap")
    )
    best_model = model_results.iloc[0]["Model"]
    print("Best model:", best_model)
    # model_results = format_dataframe(model_results)
    # print(results.head())

    gr.Markdown("### Sum of Absolute Gaps ⬇️")
    gr.DataFrame(format_dataframe(model_results))

    gr.Markdown("#### F-M gaps by language")

    lang_results = results.pivot_table(
        index="Model",
        values="Gap",
        columns="Language",
    ).reset_index()
    gr.DataFrame(format_dataframe(lang_results, times_100=True))

    # gr.Plot(fig1)
    results["Gap"] = results["Gap"] * 100
    fig = px.bar(
        results,
        x="Language",
        y="Gap",
        color="Model",
        title="Gaps by Language and Model",
        labels={
            "Gap": "Sum of Absolute Gaps (%)",
            "Language": "Language",
            "Model": "Model",
        },
        barmode="group",
    )
    lang_order = (
        lang_results.set_index("Model")
        .loc[best_model]
        .sort_values(ascending=False)
        .index
    )
    print(lang_order)

    # [best_model].sort_values().index
    fig.update_layout(xaxis={"categoryorder": "array", "categoryarray": lang_order})
    gr.Plot(fig)
    # gr.Plot(fig2)

tabs = [fm_interface]
titles = ["F-M Setup"]

with gr.Blocks() as demo:
    gr.Markdown("# Twists, Humps, and Pebbles: ASR Leadeboard")
    gr.Markdown(
        """
Datasets currently included: 
- **Mozilla Common Voice v17**
"""
    )
    gr.TabbedInterface(tabs, titles)

    gr.Textbox(
        value=CITATION_BUTTON_TEXT,
        label=CITATION_BUTTON_LABEL,
        max_lines=6,
        show_copy_button=True,
    )

if __name__ == "__main__":
    demo.launch()