compression_app.py

"""
TODO:
- 统计 tokenizer_impl
- 统计 OOV
- 统计 reversal
- 增加 math，code
"""

import gradio as gr
from compression_util import get_compression_leaderboard, common_corpuses


with gr.Blocks() as demo:
    # gr.Markdown("## Convertor")
    # with gr.Accordion("Convertor", open=False):
    #     gr.Markdown("Tokenize {} corpus")
    #     with gr.Row(elem_classes="no-border"):
    #         gr.Button("File Size", min_width=50)
    #         file_size = gr.Textbox(
    #             show_label=False,
    #             min_width=50,
    #             # elem_classes="textbox-as-text"
    #         )
    #         gr.Dropdown(
    #             choices=['MB', 'GB', 'TB'],
    #             show_label=False,
    #             min_width=15,
    #             # elem_classes="textbox-as-text"
    #         )
    #         # gr.Markdown('<h2 align="center">≈</h2>')
    #         # gr.HTML('<h2 style="margin: auto;">≈</h2>')
    #         gr.Button(
    #             "≈",
    #             min_width=10,
    #             elem_classes="button-white h2-font"
    #
    #         )
    #
    #         gr.Button(
    #             "Tokens",
    #             min_width=50
    #         )
    #         gr.Textbox(
    #             show_label=False,
    #             min_width=50
    #         )
    #         gr.Dropdown(
    #             ['million', 'billion', 'trillion'],
    #             show_label=False,
    #             min_width=15,
    #             elem_classes="button-white"
    #         )

    gr.Markdown("## 🛠️ Setting")  # ⚙
    with gr.Accordion("Please select the corpus and measure of compression rate.", open=True):
        # file size 💽 🖴, tokens 🧮
        # Total amount of disk used
        with gr.Row():
            with gr.Column():
                compress_rate_corpus = gr.Dropdown(
                    common_corpuses,  # , "code"
                    value=["cc100/en", "cc100/zh-Hans", "cc100/fr", "cc100/es"],
                    label="corpus",
                    multiselect=True
                    # info=""
                )

                # unit of file_size: gigabyte terabyte
                # unit of token_num: million billion trillion
                # The most common units of measurement include length (meter, inch, foot), weight (gram, kilogram, pound), volume (liter, gallon, milliliter), time (second, minute, hour)
                compress_rate_unit = gr.Radio(
                    ["b_tokens/g_bytes", "t_tokens/t_bytes"],
                    value="b_tokens/g_bytes",
                    label="measure",  # evaluation metric
                )

            gr.Markdown(
                # "Note:\n\n"
                "- `corpus`: tokenization is performed on the selected subsets of [cc100](https://huggingface.co/datasets/cc100) corpus.\n"
                "- `b_tokens/g_bytes` measures how many billion tokens per gigabytes corpus.\n"
                "- `t_tokens/t_bytes` measures how many trillion tokens per terabytes corpus.\n"
                # "- `g_bytes/b_tokens` measures how many gigabytes corpus per billion tokens.\n"
                # "- `t_bytes/t_tokens` measures how many terabytes corpus per trillion tokens.\n"
                "- `char/token` measures how many chars per token on the tokenized corpus.\n"
                "- `oov_ratio`: out-of-vocabulary ratio on the selected corpus, 👉 get [oov charset](https://huggingface.co/spaces/eson/tokenizer-arena/raw/main/stats/compression_rate.json)\n\n"
                "You can reproduce this procedure with [compression_util.py](https://huggingface.co/spaces/eson/tokenizer-arena/blob/main/compression_util.py)."
            )

    gr.Markdown("## 🏆 Compression Rate Leaderboard")
    search_bar = gr.Textbox(
        placeholder="🔍 Search by tokenizer or organization (e.g., 'llama', 'openai') and press ENTER...",
        show_label=False,
        elem_id="search-bar",
    )
    compress_rate_table = gr.Dataframe(datatype="html")

    # func call
    compress_rate_corpus.change(
        get_compression_leaderboard,
        inputs=[compress_rate_corpus, compress_rate_unit, search_bar],
        outputs=compress_rate_table
    )
    compress_rate_unit.change(
        get_compression_leaderboard,
        inputs=[compress_rate_corpus, compress_rate_unit, search_bar],
        outputs=compress_rate_table
    )
    # file_size.change(
    #     get_all_compress_rate,
    #     outputs=compress_rate_table
    # )

    search_bar.submit(
        get_compression_leaderboard,
        inputs=[
            compress_rate_corpus,
            compress_rate_unit,
            search_bar,
        ],
        outputs=compress_rate_table
    )

    demo.load(
        get_compression_leaderboard,
        inputs=[compress_rate_corpus, compress_rate_unit],
        outputs=compress_rate_table
    )

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