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import logging
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
import src.envs as envs
from main_backend import PENDING_STATUS, RUNNING_STATUS, FINISHED_STATUS, FAILED_STATUS
from src.backend import sort_queue
from src.envs import EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, RESULTS_REPO
import src.backend.manage_requests as manage_requests
import socket
import src.display.about as about
from src.display.css_html_js import custom_css
import src.display.utils as utils
import src.populate as populate
from src.populate import get_evaluation_queue_df, get_leaderboard_df
import src.submission.submit as submit
import os
import datetime
import spacy_transformers
import pprint
import src.backend.run_eval_suite as run_eval_suite
pp = pprint.PrettyPrinter(width=80)
TOKEN = os.environ.get("H4_TOKEN", None)
print("TOKEN", TOKEN)
def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout):
try:
print("local",local_dir)
snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout)
except Exception as e:
restart_space()
def restart_space():
envs.API.restart_space(repo_id=envs.REPO_ID, token=TOKEN)
def init_space():
#dataset_df = get_dataset_summary_table(file_path='blog/Hallucination-Leaderboard-Summary.csv')
ui_snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
ui_snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, utils.COLS, utils.BENCHMARK_COLS)
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, utils.EVAL_COLS)
return original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
leaderboard_df = original_df.copy()
def process_pending_evals():
# if len(pending_eval_queue_df) == 0:
# print("No pending evaluations found.")
# return
#
# for _, eval_request in pending_eval_queue_df.iterrows():
# import re
# model_link = eval_request['model']
# match = re.search(r'>([^<]+)<', model_link)
# if match:
# eval_request['model'] = match.group(1) # 赋值给 eval_request['model']
# else:
# eval_request['model'] = model_link # 如果无法匹配,保留原始字符串
#
# print(f"Evaluating model: {eval_request['model']}")
#
# # 调用评估函数
# run_eval_suite.run_evaluation(
# eval_request=eval_request,
# local_dir=envs.EVAL_RESULTS_PATH_BACKEND,
# results_repo=envs.RESULTS_REPO,
# batch_size=1,
# device=envs.DEVICE,
# no_cache=True,
# need_check=False, # 根据需要设定是否需要检查
# write_results=False # 根据需要设定是否写入结果
# )
# print(f"Finished evaluation for model: {eval_request['model']}")
# # Update the status to FINISHED
# manage_requests.set_eval_request(
# api=envs.API,
# eval_request=eval_request,
# new_status="FINISHED",
# hf_repo=envs.QUEUE_REPO,
# local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
# )
current_pending_status = [PENDING_STATUS]
print('_________________')
manage_requests.check_completed_evals(
api=envs.API,
checked_status=RUNNING_STATUS,
completed_status=FINISHED_STATUS,
failed_status=FAILED_STATUS,
hf_repo=envs.QUEUE_REPO,
local_dir=envs.EVAL_REQUESTS_PATH_BACKEND,
hf_repo_results=envs.RESULTS_REPO,
local_dir_results=envs.EVAL_RESULTS_PATH_BACKEND
)
logging.info("Checked completed evals")
eval_requests = manage_requests.get_eval_requests(
job_status=current_pending_status,
hf_repo=envs.QUEUE_REPO,
local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
)
logging.info("Got eval requests")
eval_requests = sort_queue.sort_models_by_priority(api=envs.API, models=eval_requests)
logging.info("Sorted eval requests")
print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")
if len(eval_requests) == 0:
print("No eval requests found. Exiting.")
return
for eval_request in eval_requests:
pp.pprint(eval_request)
run_eval_suite.run_evaluation(
eval_request=eval_request,
local_dir=envs.EVAL_RESULTS_PATH_BACKEND,
results_repo=envs.RESULTS_REPO,
batch_size=1,
device=envs.DEVICE,
no_cache=True,
need_check= False,
write_results= False
)
logging.info(f"Eval finished for model {eval_request.model}, now setting status to finished")
# Update the status to FINISHED
manage_requests.set_eval_request(
api=envs.API,
eval_request=eval_request,
new_status=FINISHED_STATUS,
hf_repo=envs.QUEUE_REPO,
local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
)
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
show_deleted: bool,
query: str,
):
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns)
return df
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df[utils.AutoEvalColumn.dummy.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [
utils.AutoEvalColumn.model_type_symbol.name,
utils.AutoEvalColumn.model.name,
]
# We use COLS to maintain sorting
filtered_df = df[
always_here_cols + [c for c in utils.COLS if c in df.columns and c in columns] + [utils.AutoEvalColumn.dummy.name]
]
return filtered_df
def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
final_df = []
if query != "":
queries = [q.strip() for q in query.split(";")]
for _q in queries:
_q = _q.strip()
if _q != "":
temp_filtered_df = search_table(filtered_df, _q)
if len(temp_filtered_df) > 0:
final_df.append(temp_filtered_df)
if len(final_df) > 0:
filtered_df = pd.concat(final_df)
filtered_df = filtered_df.drop_duplicates(
subset=[utils.AutoEvalColumn.model.name, utils.AutoEvalColumn.precision.name, utils.AutoEvalColumn.revision.name]
)
return filtered_df
def filter_models(
df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
) -> pd.DataFrame:
# Show all models
# if show_deleted:
# filtered_df = df
# else: # Show only still on the hub models
# filtered_df = df[df[utils.AutoEvalColumn.still_on_hub.name]]
filtered_df = df
type_emoji = [t[0] for t in type_query]
filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
numeric_interval = pd.IntervalIndex(sorted([utils.NUMERIC_INTERVALS[s] for s in size_query]))
params_column = pd.to_numeric(df[utils.AutoEvalColumn.params.name], errors="coerce")
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
filtered_df = filtered_df.loc[mask]
return filtered_df
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(about.TITLE)
gr.Markdown(about.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):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[
c.name
for c in utils.fields(utils.AutoEvalColumn)
if not c.hidden and not c.never_hidden and not c.dummy
],
value=[
c.name
for c in utils.fields(utils.AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Row():
deleted_models_visibility = gr.Checkbox(
value=False, label="Show gated/private/deleted models", interactive=True
)
with gr.Column(min_width=320):
#with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model types",
choices=[t.to_str() for t in utils.ModelType],
value=[t.to_str() for t in utils.ModelType],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_precision = gr.CheckboxGroup(
label="Precision",
choices=[i.value.name for i in utils.Precision],
value=[i.value.name for i in utils.Precision],
interactive=True,
elem_id="filter-columns-precision",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(utils.NUMERIC_INTERVALS.keys()),
value=list(utils.NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[c.name for c in utils.fields(utils.AutoEvalColumn) if c.never_hidden]
+ shown_columns.value
+ [utils.AutoEvalColumn.dummy.name]
],
headers=[c.name for c in utils.fields(utils.AutoEvalColumn) if c.never_hidden] + shown_columns.value,
datatype=utils.TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
column_widths=["2%", "33%"]
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df[utils.COLS],
headers=utils.COLS,
datatype=utils.TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
deleted_models_visibility,
search_bar,
],
leaderboard_table,
)
for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
deleted_models_visibility,
search_bar,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(about.LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
with gr.Column():
with gr.Row():
gr.Markdown(about.EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(
f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
open=False,
):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue_df,
headers=utils.EVAL_COLS,
datatype=utils.EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"🔄 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=utils.EVAL_COLS,
datatype=utils.EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"⏳ 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=utils.EVAL_COLS,
datatype=utils.EVAL_TYPES,
row_count=5,
)
with gr.Row():
gr.Markdown("# ✉️✨ 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", placeholder="main")
model_type = gr.Dropdown(
choices=[t.to_str(" : ") for t in utils.ModelType if t != utils.ModelType.Unknown],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=[i.value.name for i in utils.Precision if i != utils.Precision.Unknown],
label="Precision",
multiselect=False,
value="float16",
interactive=True,
)
weight_type = gr.Dropdown(
choices=[i.value.name for i in utils.WeightType],
label="Weights type",
multiselect=False,
value="Original",
interactive=True,
)
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
submit.add_new_eval,
[
model_name_textbox,
base_model_name_textbox,
revision_name_textbox,
precision,
weight_type,
model_type,
],
submission_result,
)
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
value=about.CITATION_BUTTON_TEXT,
label=about.CITATION_BUTTON_LABEL,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
# 在初始化完成后调用
# original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
# process_pending_evals()
# try:
# print(envs.EVAL_REQUESTS_PATH)
# snapshot_download(
# repo_id=envs.QUEUE_REPO, local_dir=envs.EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
# )
# except Exception:
# restart_space()
# try:
# print(envs.EVAL_RESULTS_PATH)
# snapshot_download(
# repo_id=envs.RESULTS_REPO, local_dir=envs.EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
# )
# except Exception:
# restart_space()
# raw_data, original_df = populate.get_leaderboard_df(envs.RESULTS_REPO, envs.QUEUE_REPO, utils.COLS, utils.BENCHMARK_COLS)
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = populate.get_evaluation_queue_df(envs.EVAL_REQUESTS_PATH, utils.EVAL_COLS)
def background_init_and_process():
global original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
#process_pending_evals()
scheduler = BackgroundScheduler()
scheduler.add_job(background_init_and_process, 'date', run_date=datetime.datetime.now()) # 立即执行
scheduler.add_job(restart_space, "interval", seconds=36000)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch() |