app.py

import gradio as gr
import pandas as pd
from utils_display import make_clickable_model

banner_url = "https://huggingface.co/spaces/deepdml/open_universal_arabic_quranic_asr_leaderboard/main/banner.png"
BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 10vw; max-width: 600px;"> </div>'

INTRODUCTION_TEXT = "📖**Open Universal Arabic Quranic ASR Leaderboard**📖 benchmarks multi-dialect Arabic Quranic ASR models on various multi-dialect datasets.<br>Apart from the WER%/CER% for each test set, we also report the Average WER%/CER% and rank the models based on the Average WER, from lowest to highest.<br>To reproduce the benchmark numbers and request a model that is not listed, you can launch an issue/PR in our [GitHub repo](https://github.com/Natural-Language-Processing-Elm/open_universal_arabic_asr_leaderboard)😊.<br>For more detailed analysis such as models' robustness, speaker adaption, model efficiency and memory usage, please check our [paper](https://arxiv.org/pdf/2412.13788)."

CITATION_BUTTON_TEXT = """
@article{wang2024open,
  title={Open Universal Arabic Quranic ASR Leaderboard},
  author={Jimenez, David},
  year={2025}
}
"""

METRICS_TAB_TEXT = """
## Metrics
We report both the Word Error Rate (WER) and Character Error Rate (CER).
## Reproduction
The Open Universal Arabic Quranic ASR Leaderboard will be a continuous benchmark project. 
\nWe open-source the evaluation scripts at our [GitHub repo](https://github.com/Natural-Language-Processing-Elm/open_universal_arabic_asr_leaderboard).
\nPlease launch a discussion in our GitHub repo to let us know if you want to learn about the performance of a new model.

## Benchmark datasets
| Test Set                                                                                        | Num Samples |
|-------------------------------------------------------------------------------------------------|-------------|
| [Tarteel AI's EveryAyah](https://huggingface.co/datasets/tarteel-ai/everyayah)                  | 23.473      |

## In-depth Analysis
We also provide a comprehensive analysis of the models' robustness, speaker adaptation, inference efficiency and memory consumption.
\nPlease check our [paper](https://arxiv.org/pdf/2412.13788) to learn more.
"""


def styled_message(message):
    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"

UPDATES = "Oct 20th 2025:[Created repo]<br>"

results = {
    "Model": ["tarteel-ai/whisper-tiny-ar-quran", "Habib-HF/tarbiyah-ai-whisper-medium-merged", "nvidia/stt_ar_fastconformer_hybrid_large_pcd_v1.0", 
              "IbrahimSalah/Wav2vecLarge_quran_syllables_recognition", "facebook/mms-1b-all", "facebook/hf-seamless-m4t-medium", "facebook/seamless-m4t-v2-large",
              "IJyad/whisper-large-v3-Tarteel"],
    "Average WER⬇️": [4.99, 81.59, 1.33, 281.27, 48.13, 25.77,  26.2, 25.39],
    "Average CER":   [1.82, 76.65, 0.49,  73.18, 16.58, 15.38, 18.08, 17.75],
    "Tarteel WER":   [4.99, 81.59, 1.33, 281.27, 48.13, 25.77,  26.2, 25.39],
    "Tarteel CER":   [1.82, 76.65, 0.49,  73.18, 16.58, 15.38, 18.08, 17.75],
}

original_df = pd.DataFrame(results)
# original_df["Model"] = original_df["Model"].apply(lambda x: x.replace(x, make_clickable_model(x)))
original_df.sort_values(by="Average WER⬇️", inplace=True)

TYPES = ['str', 'number', 'number', 'number', 'number']

LEADERBOARD_CSS = """
html, body {
  overflow-y: auto !important;
}

#leaderboard-table th .header-content {
    min-width: 150px;
    white-space: nowrap;
}
"""

def request_model(model_text):
    return styled_message("🤗 Please launch a discussion in our GitHub repo, thank you. 🤗")

with gr.Blocks(fill_width=False, fill_height=False, css=LEADERBOARD_CSS) as demo:
    gr.HTML(BANNER, elem_id="banner")
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0):
            leaderboard_table = gr.Dataframe(
                value=original_df,
                datatype=TYPES,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
            )

        with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
            gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")

        with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=2):
            with gr.Column():
                gr.Markdown("# ✉️✨ Request results for a new model here!", elem_classes="markdown-text")
                model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)")
                mdw_submission_result = gr.Markdown()
                btn_submit = gr.Button(value="🚀 Request")
                btn_submit.click(request_model, [model_name_textbox], mdw_submission_result)

    gr.Markdown(UPDATES, elem_classes="markdown-text")

    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            gr.Textbox(
                value=CITATION_BUTTON_TEXT, lines=7,
                label="Copy the BibTeX snippet to cite this source",
                elem_id="citation-button",
                show_copy_button=True,
            )

demo.launch(allowed_paths=["banner.png"], ssr_mode=False)