Spaces:
Running
Running
File size: 11,830 Bytes
71cd8ad 90def7d 71cd8ad f2ef0e9 90def7d 1f40cb7 71cd8ad 9afe185 4855a8c 9afe185 71cd8ad 5b9a852 cf16aef 970e6bf 5b9a852 baf0072 4855a8c 970e6bf fe96d5f cf16aef 1d59da3 5b9a852 aa53fd4 71cd8ad f8f2c33 71cd8ad a648d00 71cd8ad 9afe185 71cd8ad f8f2c33 71cd8ad f8f2c33 71cd8ad |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 |
import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns, SearchColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
import os
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)
import jsonlines
# Initialize an empty list to store the JSON objects
json_list = []
# Open the JSONL file
with jsonlines.open('commit_results.jsonl') as reader:
for obj in reader:
# Append each JSON object to the list
json_list.append(obj)
# _test_data = pd.DataFrame({"Score": [54,46,53], "Name": ["MageBench", "MageBench", "MageBench"], "BaseModel": ["GPT-4o", "GPT-4o", "LLaMA"], "Env.": ["Sokoban", "Sokoban", "Football"],
# "Target-research": ["Model-Eval-Global", "Model-Eval-Online", "Agent-Eval-Prompt"], "Subset": ["mini", "all", "mini"], "Link": ["xxx", "xxx", "xxx"]})
json_list = sorted(json_list, key=lambda x: x['Score'], reverse=True)
committed = pd.DataFrame(json_list)
(
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.")
return Leaderboard(
value=dataframe, #dataframe,
select_columns=SelectColumns(
default_selection=["Score", "Name", "BaseModel", "Env.", "Target-research", "Subset", "Link"],
cant_deselect=["Score", "Name",],
label="Select Columns to Display:",
),
search_columns=SearchColumns(primary_column="Name", secondary_columns=["BaseModel", "Target-research"],
placeholder="Search by work name or basemodel. To search by country, type 'basemodel:<query>'",
label="Search"),
filter_columns=[
ColumnFilter("Target-research", type="checkboxgroup", label="Comparison settings for target researches (Single Selection)"),
# ColumnFilter("BaseModel", type="dropdown", label="Select The base lmm model that fultill the task."),
ColumnFilter("Env.", type="checkboxgroup", label="Environment (Single Selection)"),
ColumnFilter("Subset", type="checkboxgroup", label="Subset (Single Selection)"),
ColumnFilter("State", type="checkboxgroup", label="Result state (checked or under-review)"),
# ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
# ColumnFilter(
# AutoEvalColumn.params.name,
# type="slider",
# min=0.01,
# max=150,
# label="Select the number of parameters (B)",
# ),
# ColumnFilter(
# AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
# ),
],
interactive=False,
)
# =================test
if os.path.exists("./text.txt"):
print(open("./text.txt").read())
else:
print("not exists")
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Video('demo.mp4', elem_id="video-player", label="Introduction Video")
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(committed) # 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 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.Row():
score_input = gr.Textbox(label="Score (float)", placeholder="请输入分数")
name_input = gr.Textbox(label="Name (str)", placeholder="请输入名称")
base_model_input = gr.Textbox(label="BaseModel (str)", placeholder="请输入基模型名称")
with gr.Row():
env_dropdown = gr.Dropdown(
choices=["Sokoban", "Football", "WebUI"],
label="Env.",
value="Sokoban"
)
target_research_dropdown = gr.Dropdown(
choices=["Model-Eval-Online", "Model-Eval-Global"],
label="Target-research",
value="Model-Eval-Online"
)
subset_dropdown = gr.Dropdown(
choices=["mini", "all"],
label="Subset",
value="mini"
)
link_input = gr.Textbox(label="Link (str)", placeholder="请输入链接")
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
def submit_eval(score, name, base_model, env, target_research, subset, link):
# 在这里处理提交逻辑，可以将信息保存到数据库或进行其他处理
result = (
f"Score: {score}\n"
f"Name: {name}\n"
f"BaseModel: {base_model}\n"
f"Env: {env}\n"
f"Target-research: {target_research}\n"
f"Subset: {subset}\n"
f"Link: {link}"
)
open("./text.txt", "w").write(result)
return result
submit_button.click(
submit_eval,
[score_input, name_input, base_model_input, env_dropdown, target_research_dropdown, subset_dropdown, link_input],
submission_result
)
# 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=EVAL_COLS,
# datatype=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=EVAL_COLS,
# datatype=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=EVAL_COLS,
# datatype=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 ModelType if t != ModelType.Unknown],
# label="Model type",
# multiselect=False,
# value=None,
# interactive=True,
# )
# with gr.Column():
# precision = gr.Dropdown(
# choices=[i.value.name for i in Precision if i != Precision.Unknown],
# label="Precision",
# multiselect=False,
# value="float16",
# interactive=True,
# )
# weight_type = gr.Dropdown(
# choices=[i.value.name for i in 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(
# 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=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() |