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import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
AutoEvalColumn,
ModelType,
fields,
WeightType,
Precision
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
def restart_space():
API.restart_space(repo_id=REPO_ID)
### Space initialisation
# try:
# print(EVAL_REQUESTS_PATH)
# snapshot_download(
# repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
# )
# except Exception:
# restart_space()
# try:
# print(EVAL_RESULTS_PATH)
# snapshot_download(
# repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
# )
# except Exception:
# restart_space()
LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
def init_leaderboard(dataframe):
if dataframe.empty:
dataframe = pd.DataFrame(columns=[c.name for c in fields(AutoEvalColumn)])
return Leaderboard(
value=dataframe,
datatype=[c.type for c in fields(AutoEvalColumn)],
select_columns=SelectColumns(
default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
label="Select Columns to Display:",
),
search_columns=[AutoEvalColumn.model.name],
hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
filter_columns=[],
bool_checkboxgroup_label="Hide models",
interactive=False,
)
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
leaderboard = init_leaderboard(LEADERBOARD_DF)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown("""
We welcome community submissions of new model evaluation results. Those submissions will be listed as 'External', and authors must upload their generated outputs for peer review.
## Evaluation
Evaluation [Setup](https://huggingface.co/docs/hub/spaces-overview) and [Usage](https://huggingface.co/docs/hub/spaces-overview). This will generate a markdown report summarizing the results.
## Submission
To submit your results, create a Pull Request in the [Community Tab](https://huggingface.co/spaces/doubao-bench/web-bench-leaderboard/discussions) to add them to the `src/custom-eval-results` folder in this repository:
* Create a new folder named with your provider and model names (e.g., `ollama_mistral-small`, using underscores to separate parts).
* Each folder stores the evaluation results of only one model.
* Add a `base_meta.json` file with the following fields:
* **Model**: the name of your model
* **Model Link**: the link to the model page
* **Provider**: the name of the provider
* **Openness**: the openness of the model
* **Agent**: the agent used for evaluation, `Web-Agent` or your custom agent name
* Put your generated reports (e.g. `eval-20258513-102235.zip`) in your folder.
* The title of the PR should be: `[Community Submission] Model: org/model, Username: your_username`.
* **Tips**: `gen_meta.json` will be created after our review.
We will promptly merge and review your submission. Once the review is complete, we will publish the results on the leaderboard.
""")
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(share=True) |