app.py

# Code adapted from: https://huggingface.co/spaces/RaoFoundation/pretraining-leaderboard/blob/main/app.py

import datetime
import os

import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from dotenv import load_dotenv
from huggingface_hub import HfApi

import competitions
import utils

FONT = (
    """<link href="https://fonts.cdnfonts.com/css/jmh-typewriter" rel="stylesheet">"""
)
TITLE = """<h1 align="center" id="space-title" class="typewriter">Finetuning Subnet Leaderboard</h1>"""
HEADER = """<h2 align="center" class="typewriter"><a href="https://github.com/macrocosm-os/finetuning" target="_blank">Finetuning</a> is a <a href="https://bittensor.com/" target="_blank">Bittensor</a> subnet that rewards miners for producing finetuned models in defined competitions. The model with the best head-to-head score in each competition receive a steady emission of TAO.</h3>"""
EVALUATION_HEADER = """<h3 align="center">Shows the latest per-competition evaluation statistics as calculated by the Taoverse validator</h3>"""

HF_REPO_ID = "macrocosm-os/finetuning-leaderboard"
SECONDS_PER_BLOCK = 12

load_dotenv()

HF_TOKEN = os.environ.get("HF_TOKEN", None)
API = HfApi(token=HF_TOKEN)


def get_next_update_div(current_block: int, next_update_block: int) -> str:
    now = datetime.datetime.now()
    blocks_to_go = next_update_block - current_block
    next_update_time = now + datetime.timedelta(
        seconds=blocks_to_go * SECONDS_PER_BLOCK
    )
    delta = next_update_time - now
    return f"""<div align="center" style="font-size: larger;">Next reward update: <b>{blocks_to_go}</b> blocks (~{int(delta.total_seconds() // 60)} minutes)</div>"""


def get_last_updated_div() -> str:
    return f"""<div>Last Updated: {datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")} (UTC)</div>"""


def restart_space():
    API.restart_space(repo_id=HF_REPO_ID, token=HF_TOKEN)


def main():
    # To avoid leaderboard failures, infinitely try until we get all data
    # needed to populate the dashboard

    state_vars = utils.load_state_vars()
    model_data = state_vars["model_data"]
    vali_runs = state_vars["vali_runs"]
    scores = state_vars["scores"]
    validator_df = state_vars["validator_df"]
    benchmarks = state_vars.get("benchmarks", None)
    benchmark_timestamp = state_vars.get("benchmark_timestamp", None)

    demo = gr.Blocks(css=".typewriter {font-family: 'JMH Typewriter', sans-serif;}")
    with demo:
        gr.HTML(FONT)
        gr.HTML(TITLE)
        gr.HTML(HEADER)

        gr.Label(
            label="Emissions",
            value={
                f"{c.namespace}/{c.name} ({c.commit[0:8]}) · (τ{round(c.emission, 2):,})": c.incentive
                for c in model_data
                if c.incentive
            },
            num_top_classes=10,
        )
        if benchmarks is not None:
            with gr.Accordion("Top Model Benchmarks"):
                gr.components.Dataframe(benchmarks)
                gr.HTML("""<div>PPL computed using a stride of 512. See <a href='https://github.com/macrocosm-os/finetuning/blob/dev/scripts/run_benchmarks.py'>here</a> for the full code.</div>""")
                gr.HTML(f"""<div>Last Updated: {benchmark_timestamp.strftime("%Y-%m-%d %H:%M:%S")} (UTC)</div>""")

        with gr.Accordion("Competition Results"):
            gr.HTML(EVALUATION_HEADER)
            show_stale = gr.Checkbox(label="Show Stale", interactive=True)
            competition_leaderboards = []
            losses_1 = utils.get_losses_over_time(vali_runs, 1)
            comp_1 = competitions.COMPETITION_DETAILS[1]
            with gr.Accordion(f"{comp_1.name} Competition"):
                gr.HTML(comp_1.html_description)
                competition_leaderboards.append(gr.components.Dataframe(
                    value=utils.leaderboard_data(model_data, scores, 1, show_stale.value),
                    headers=["Name", "Win Rate", "Average Loss", "Weight", "UID", "Block"],
                    datatype=["markdown", "number", "number", "number", "number", "number"],
                    elem_id="comp1-table",
                    interactive=False,
                    visible=True,
                ))
                gr.LinePlot(
                    losses_1,
                    x="timestamp",
                    x_title="Date",
                    y="losses",
                    y_title="Average Loss",
                    interactive=True,
                    visible=True,
                    width=1024,
                    title="Best Average Loss Over Time",
                )
            comp_2 = competitions.COMPETITION_DETAILS[2]
            losses_2 = utils.get_losses_over_time(vali_runs, 2)
            # Covert the losses into % of correct answers.
            losses_2["losses"] = losses_2["losses"].apply(lambda x: 1 - x if x else None)
            with gr.Accordion(f"{comp_2.name} Competition"):
                gr.HTML(comp_2.html_description)
                competition_leaderboards.append(gr.components.Dataframe(
                    value=utils.leaderboard_data(model_data, scores, 2, show_stale.value),
                    headers=["Name", "Win Rate", "MC Score", "Weight", "UID", "Block"],
                    datatype=["markdown", "number", "number", "number", "number", "number"],
                    elem_id="comp2-table",
                    interactive=False,
                    visible=True,
                ))
                gr.LinePlot(
                    losses_2,
                    x="timestamp",
                    x_title="Date",
                    y="losses",
                    y_title="MC Score",
                    interactive=True,
                    visible=True,
                    width=1024,
                    title="Best MC Score Over Time",
                )
            gr.HTML("""
                    <ul><li><b>Name:</b> the 🤗 Hugging Face repo (click to go to the model card)</li>
                    <li><b>Win Rate:</b> % of head-to-head evals won vs. other eval'd models, given an epsilon advantage or disadvantage</li>
                    <li><b>Average Loss:</b> the last loss value on the evaluation data for the model as calculated by the OTF validator (lower is better)</li>
                    <li><b>MC Score:</b> the % of correct multiple choice answers given by the model as calculated by the OTF validator (higher is better)</li>
                    <li><b>UID:</b> the Bittensor UID of the miner</li>
                    <li><b>Weight:</b> the bittensor weight set for this model</li>
                    <li><b>Block:</b> the Bittensor block that the model was submitted in</li></ul><br/>More stats on <a href="https://taostats.io/subnets/netuid-37/" target="_blank">taostats</a>.""")
            show_stale.change(
                lambda stale: [utils.leaderboard_data(model_data, scores, 1, stale), utils.leaderboard_data(model_data, scores, 2, stale)],
                inputs=[show_stale],
                outputs=competition_leaderboards,
            )            

        with gr.Accordion("Validator Stats"):
            gr.components.Dataframe(
                utils.make_validator_dataframe(validator_df, model_data),
                interactive=False,
                visible=True,
            )
        gr.HTML(value=get_last_updated_div())

    scheduler = BackgroundScheduler()
    scheduler.add_job(
        restart_space, "interval", seconds=60 * 30
    )  # restart every 15 minutes
    scheduler.start()

    demo.launch()


main()