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import os | |
import shutil | |
import gradio as gr | |
from pathlib import Path | |
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, | |
Detail_Tasks, | |
) | |
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) | |
LEADERBOARD_DF_N_CORRECT = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS, version="n_correct") | |
LEADERBOARD_DF_1_CORRECT_VAR = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS, version="1_correct_var") | |
( | |
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 is None or dataframe.empty: | |
raise ValueError("Leaderboard DataFrame is empty or None.") | |
print(dataframe.columns) | |
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 and c.name in dataframe.columns], | |
cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden and c.name in dataframe.columns], | |
label="Select Columns to Display:", | |
), | |
search_columns=[AutoEvalColumn.model.name], | |
hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden and c.name in dataframe.columns], | |
filter_columns=[ | |
ColumnFilter(AutoEvalColumn.output_format.name, type="checkboxgroup", label="Output Format"), | |
], | |
interactive=False, | |
) | |
# def upload_file(file): | |
# UPLOAD_FOLDER = "./data" | |
# if not os.path.exists(UPLOAD_FOLDER): | |
# os.mkdir(UPLOAD_FOLDER) | |
# shutil.copy(file, UPLOAD_FOLDER) | |
# gr.Info("File Uploaded!!!") | |
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("π 1 Correct", elem_id="llm-benchmark-tab-table", id=0): | |
leaderboard = init_leaderboard(LEADERBOARD_DF) | |
with gr.TabItem("π 1 Correct + Variations", elem_id="llm-benchmark-tab-table", id=4): | |
leaderboard = init_leaderboard(LEADERBOARD_DF_1_CORRECT_VAR) | |
with gr.TabItem("π N Correct", elem_id="llm-benchmark-tab-table", id=1): | |
leaderboard = init_leaderboard(LEADERBOARD_DF_N_CORRECT) | |
# with gr.TabItem("π About", elem_id="llm-benchmark-tab-table-n-correct", id=2): | |
# gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
with gr.TabItem("π Submit here! ", elem_id="llm-benchmark-tab-table", id=3): | |
with gr.Column(): | |
with gr.Row(): | |
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") | |
with gr.Column(): | |
with gr.Accordion( | |
f"β Finished Evaluations ({len(finished_eval_queue_df)})", | |
open=False, | |
): | |
with gr.Row(): | |
finished_eval_table = gr.components.Dataframe( | |
value=finished_eval_queue_df, | |
headers=EVAL_COLS, | |
datatype=EVAL_TYPES, | |
row_count=5, | |
) | |
with gr.Accordion( | |
f"π Running Evaluation Queue ({len(running_eval_queue_df)})", | |
open=False, | |
): | |
with gr.Row(): | |
running_eval_table = gr.components.Dataframe( | |
value=running_eval_queue_df, | |
headers=EVAL_COLS, | |
datatype=EVAL_TYPES, | |
row_count=5, | |
) | |
with gr.Accordion( | |
f"β³ Pending Evaluation Queue ({len(pending_eval_queue_df)})", | |
open=False, | |
): | |
with gr.Row(): | |
pending_eval_table = gr.components.Dataframe( | |
value=pending_eval_queue_df, | |
headers=EVAL_COLS, | |
datatype=EVAL_TYPES, | |
row_count=5, | |
) | |
with gr.Row(): | |
gr.Markdown("# βοΈβ¨ Submit your model here!", elem_classes="markdown-text") | |
with gr.Row(): | |
with gr.Column(): | |
model_name_textbox = gr.Textbox(label="Model name") | |
revision_name_textbox = gr.Textbox(label="Revision commit", value="main") | |
output_format = gr.Textbox(label="Output format", value="Out-GEN") | |
version = gr.Dropdown( | |
["1_correct", "1_correct_var", "n_correct",], value="1_correct", multiselect=False, label="Task version", | |
) | |
with gr.Row(): | |
u = gr.UploadButton("Upload a file", file_count="single") | |
submit_button = gr.Button("Submit Eval") | |
submission_result = gr.Markdown() | |
submit_button.click( | |
add_new_eval, | |
[ | |
model_name_textbox, | |
output_format, | |
revision_name_textbox, | |
u, | |
version, | |
], | |
submission_result, | |
) | |
with gr.Row(): | |
with gr.Accordion("π Citation", open=False): | |
citation_button = gr.Textbox( | |
value=CITATION_BUTTON_TEXT, | |
label=CITATION_BUTTON_LABEL, | |
lines=20, | |
elem_id="citation-button", | |
show_copy_button=True, | |
) | |
scheduler = BackgroundScheduler() | |
scheduler.add_job(restart_space, "interval", seconds=1800) | |
scheduler.start() | |
demo.queue(default_concurrency_limit=40).launch() |