app.py

import gradio as gr
import pandas as pd
import plotly.express as px
from model_handler import ModelHandler
from data_handler import unified_exam_result_table, mmlu_result_table, unified_exam_chart, mmlu_chart

global_unified_exam_df = None
global_mmlu_df = None
global_output_armenian = None
global_output_mmlu = None

def refresh_data():
    global global_mmlu_df, global_unified_exam_df, global_output_armenian, global_output_mmlu

    model_handler = ModelHandler()
    global_mmlu_df, global_unified_exam_df = model_handler.get_arm_bench_data()

    global_output_armenian = unified_exam_result_table(global_unified_exam_df)
    global_output_mmlu = mmlu_result_table(global_mmlu_df)

    unified_chart = unified_exam_chart(global_output_armenian, 'Average')
    mmlu_chart_output = mmlu_chart(global_output_mmlu, 'Average')

    return global_output_armenian, global_output_mmlu, unified_chart, mmlu_chart_output, 'Average', 'Average'

def main():
    global global_mmlu_df, global_unified_exam_df, global_output_armenian, global_output_mmlu
    model_handler = ModelHandler()
    global_mmlu_df, global_unified_exam_df = model_handler.get_arm_bench_data()

    global_output_armenian = unified_exam_result_table(global_unified_exam_df)
    global_output_mmlu = mmlu_result_table(global_mmlu_df)

    with gr.Blocks() as app:
        with gr.Tabs():
            with gr.TabItem("Armenian Unified Exams"):
                gr.Markdown("# Armenian Unified Test Exams")
                gr.Markdown(
                    """
                    This benchmark contains results of various Language Models on Armenian Unified Test Exams for Armenian language and literature, Armenian history and mathematics. The scoring system is a 20-point scale, where 0-8 is a Fail, 8-18 is a Pass, and 18-20 is a Distinction.
                    """
                )
                table_output_armenian = gr.DataFrame(value=global_output_armenian)
                plot_column_dropdown_unified_exam = gr.Dropdown(choices=['Average', 'Armenian language and literature', 'Armenian history', 'Mathematics'], value='Average', label='Select Column to Plot')
                plot_output_armenian = gr.Plot(lambda column: unified_exam_chart(global_output_armenian, column), inputs=plot_column_dropdown_unified_exam)
            with gr.TabItem("MMLU-Pro-Hy"):
                gr.Markdown("# MMLU-Pro Translated to Armenian (MMLU-Pro-Hy)")
                gr.Markdown(
                    """
                    This benchmark contains results of various Language Models on the MMLU-Pro benchmark, translated into Armenian. MMLU-Pro is a massive multi-task test in MCQA format. The scores represent accuracy.
                    """
                )
                table_output_mmlu = gr.DataFrame(value=global_output_mmlu)
                subject_cols = ['Average','Biology', 'Business', 'Chemistry', 'Computer Science', 'Economics', 'Engineering', 'Health', 'History', 'Law', 'Math', 'Philosophy', 'Physics', 'Psychology','Other']
                plot_column_dropdown_mmlu = gr.Dropdown(choices=subject_cols, value='Average', label='Select Column to Plot')
                plot_output_mmlu = gr.Plot(lambda column: mmlu_chart(global_output_mmlu, column), inputs=plot_column_dropdown_mmlu)
            with gr.TabItem("About"):
                gr.Markdown("# Overview")
                gr.Markdown(
                    """
                    This benchmark is developed and maintained by [Metric](https://metric.am/). It evaluates the capabilities of Large Language Models on Armenian-specific tasks, including Armenian Unified Test Exams and a translated version of the MMLU-Pro benchmark (MMLU-Pro-Hy). It is designed to measure the models' understanding and generation capabilities in the Armenian language.

                    """
                )

                gr.Markdown("# Dataset")
                gr.Markdown("""  
                    - [Armenian Unified Exams](https://dimord.am/public/tests): collection of High School graduation test exams used in 2025 in Armenia. The highest achievable score per test is 20. The data is extracted from PDFs and manually prepared for LLM evaluation.
                    - MMLU-Pro-Hy: a massive multi-task test in MCQA format, inspired by the original [MMLU benchmark](https://arxiv.org/abs/2406.01574), adapted for the Armenian language. Currently, a stratified sample is sued for evaluation summing up to 1000 questions in total. The Armenian version is generated through machine-translation. Resulting dataset went extensive post-processing to ensure high quality subsample is selected for evaluation..
                    """
                )
                gr.Markdown("## Submission Guide")
                gr.Markdown(
                    """
                    To submit a model for evaluation, please follow these steps:
                    1. **Evaluate your model**:
                        - Follow the evaluation script provided here: [https://github.com/Metricam/ArmBench-LLM](https://github.com/Metricam/ArmBench-LLM)
                        - For more details about the evaluation and submission process, read the README in the ArmBench-LLM GitHub repository.

                    2. **Format your submission file**:
                        - After evaluation, you will get a `results.json` file. Ensure the file follows this format:
                        
                        ```json
                            {
                                "mmlu_results": [
                                    {
                                        "category": "category_name",
                                        "score": score_value
                                    },
                                    ...
                                ],
                                "unified_exam_results": [
                                    {
                                        "category": "category_name",
                                        "score": score_value
                                    },
                                    ...
                                ]
                            }
                        ```
                    3. **Submit your model**:
                        - Add the `ArmBench-LLM` tag and the `results.json` file to your model card.
                        - Click on the "Refresh Data" button in this app, and you will see your model's results.
                    """
                )
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("## Contributing")
                        gr.Markdown(
                            """
                            You can contribute to this benchmark in several ways:
                            - Provide API credits for evaluating additional API-based models.
                            - Citing our work in your research and publications.
                            - Contributing to the development of the benchmark itself with data or with evaluation results.
                            """
                        )
                    with gr.Column():
                        gr.Markdown("")
                        gr.Markdown("")
                        gr.Markdown("")
                        gr.Markdown("")
                        gr.Image("logo.png", width=200, show_label=False, show_download_button=False, show_fullscreen_button=False, show_share_button=False)
                
                gr.Markdown("# About Metric")
                gr.Markdown(
                    """
                    [Metric](https://metric.am/) is an AI Research Lab based in Yerevan, Armenia. It is specialized in training custom embedding and generation models for use cases such as Document AI or low-represented languages. If you are interested in our research or advisory services, drop an email to [email protected].

                    """
                )

        refresh_button = gr.Button("Refresh Data")
        refresh_button.click(
            fn=refresh_data,
            outputs=[table_output_armenian, table_output_mmlu, plot_output_armenian, plot_output_mmlu, plot_column_dropdown_unified_exam, plot_column_dropdown_mmlu],
        )

    app.launch(share=True, debug=True)

if __name__ == "__main__":
    main()