Spaces:
Running
Running
File size: 7,059 Bytes
8e67ebe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
import json
import logging
import os
import subprocess
import time
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from gradio_leaderboard import Leaderboard, SelectColumns
from gradio_space_ci import enable_space_ci
from huggingface_hub import snapshot_download
from src.display.about import (
FAQ_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
# BENCHMARK_COLS,
AutoEvalColumn,
fields,
)
from src.envs import (
API,
EVAL_RESULTS_PATH,
H4_TOKEN,
REPO_ID,
RESET_JUDGEMENT_ENV,
)
os.environ['GRADIO_ANALYTICS_ENABLED']='false'
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Start ephemeral Spaces on PRs (see config in README.md)
enable_space_ci()
def restart_space():
API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
def time_diff_wrapper(func):
def wrapper(*args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
end_time = time.time()
diff = end_time - start_time
logging.info(f"Time taken for {func.__name__}: {diff} seconds")
return result
return wrapper
@time_diff_wrapper
def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, backoff_factor=1.5):
"""Download dataset with exponential backoff retries."""
attempt = 0
while attempt < max_attempts:
try:
logging.info(f"Downloading {repo_id} to {local_dir}")
snapshot_download(
repo_id=repo_id,
local_dir=local_dir,
repo_type=repo_type,
tqdm_class=None,
etag_timeout=30,
max_workers=8,
)
logging.info("Download successful")
return
except Exception as e:
wait_time = backoff_factor ** attempt
logging.error(f"Error downloading {repo_id}: {e}, retrying in {wait_time}s")
time.sleep(wait_time)
attempt += 1
raise Exception(f"Failed to download {repo_id} after {max_attempts} attempts")
def init_space(full_init: bool = True):
"""Initializes the application space, loading only necessary data."""
if full_init:
# These downloads only occur on full initialization
# try:
# download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
# download_dataset(DYNAMIC_INFO_REPO, DYNAMIC_INFO_PATH)
download_dataset("Vikhrmodels/openbench-eval", EVAL_RESULTS_PATH)
# print(subprocess.Popen('ls src'))
subprocess.run(['rsync', '-avzP', '--ignore-existing', f'{EVAL_RESULTS_PATH[2:]}/external/*', 'src/gen/data/arena-hard-v0.1/model_answer/'])
subprocess.run(['rsync', '-avzP', '--ignore-existing', f'{EVAL_RESULTS_PATH[2:]}/model_judgment/*', 'src/gen/data/arena-hard-v0.1/model_judgement/'])
# except Exception:
# restart_space()
# Always retrieve the leaderboard DataFrame
original_df = pd.DataFrame.from_records(json.load(open('eval-results/evals/upd.json','r')))
leaderboard_df = original_df.copy()
return leaderboard_df
# Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set.
# This controls whether a full initialization should be performed.
do_full_init = os.getenv("LEADERBOARD_FULL_INIT", "True") == "True"
# Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable.
# This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag.
leaderboard_df = init_space(full_init=do_full_init)
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):
pass
leaderboard = Leaderboard(
value=leaderboard_df,
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 or c.dummy],
label="Select Columns to Display:",
),
search_columns=[
AutoEvalColumn.model.name,
# AutoEvalColumn.fullname.name,
# AutoEvalColumn.license.name
],
)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=4):
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit ", elem_id="llm-benchmark-tab-table", id=5):
with gr.Row():
gr.Markdown("# ✨ Submit your model here!", elem_classes="markdown-text")
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
def upload_file(file):
print(file.name)
file_path = file.name.split('/')[-1] if '/' in file.name else file.name
print(file_path)
API.upload_file(path_or_fileobj=file.name,path_in_repo='./external/'+file_path,repo_id='Vikhrmodels/openbench-eval',repo_type='dataset')
os.environ[RESET_JUDGEMENT_ENV] = '1'
return file.name
if model_name_textbox:
file_output = gr.File()
upload_button = gr.UploadButton("Click to Upload & Submit Answers", file_types=['*'], file_count="single")
upload_button.upload(upload_file, upload_button, file_output)
# print(os.system('cd src/gen && ../../.venv/bin/python gen_judgment.py'))
# print(os.system('cd src/gen/ && python show_result.py --output'))
def update_board():
need_reset = os.environ.get(RESET_JUDGEMENT_ENV)
if need_reset != '1':
return
os.environ[RESET_JUDGEMENT_ENV] = '0'
subprocess.run(['python','../gen/gen_judgement.py'])
subprocess.Popen('python3 ../gen/show_result.py --output')
if __name__ == "__main__":
os.environ[RESET_JUDGEMENT_ENV] = '1'
scheduler = BackgroundScheduler()
scheduler.add_job(update_board, "interval", minutes=10)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(debug=True)
|