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import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
import os
os.environ['CURL_CA_BUNDLE'] = ''
from src.display.about import (
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
LLM_DATASET_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
TYPES,
AutoEvalColumn,
fields,
BENCHMARK_COLS_GROUP,
COLS_GROUP,
EVAL_COLS_GROUP,
EVAL_TYPES_GROUP,
TYPES_GROUP,
AutoEvalColumnGroup,
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, TOKEN, QUEUE_REPO, REPO_ID, RESULTS_REPO, EVAL_RESULTS_GROUP_PATH, RESULTS_GROUP_REPO
from src.populate import get_evaluation_queue_df, get_leaderboard_df, get_evaluation_queue_df_group, get_leaderboard_group_df
from src.submission.submit import add_new_eval
def restart_space():
API.restart_space(repo_id=REPO_ID, token=TOKEN)
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,
force_download=True,
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,
force_download=True,
token=TOKEN
)
snapshot_download(
repo_id=RESULTS_GROUP_REPO,
local_dir=EVAL_RESULTS_GROUP_PATH,
repo_type="dataset",
tqdm_class=None,
etag_timeout=30,
force_download=True,
token=TOKEN)
except Exception:
restart_space()
raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS)
raw_data_grouped, original_df_grouped = get_leaderboard_group_df(EVAL_RESULTS_GROUP_PATH, COLS_GROUP, BENCHMARK_COLS_GROUP)
leaderboard_grouped_df = original_df_grouped.copy()
leaderboard_df = original_df.copy()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
(
finished_eval_queue_g_df,
running_eval_queue_g_df,
pending_eval_queue_g_df,
) = get_evaluation_queue_df_group(EVAL_REQUESTS_PATH, EVAL_COLS_GROUP)
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
query: str,
):
filtered_df = filter_queries(query, hidden_df)
df = select_columns(filtered_df, columns)
return df
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [
AutoEvalColumn.model_submission_date.name,
AutoEvalColumn.model.name,
]
# We use COLS to maintain sorting
filtered_df = df[
always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [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:
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=[AutoEvalColumn.model.name, AutoEvalColumn.model_submission_date.name]
)
return filtered_df
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
with gr.Row():
with gr.Column(scale=9):
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Column(scale=2, min_width=1):
gr.Image('src/display/BirLLama.jpeg', scale=2,
show_label=False,
interactive=False,
show_share_button=False,
show_download_button=False)
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.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 fields(AutoEvalColumnGroup)
if not c.hidden and not c.never_hidden and not c.dummy
],
value=[
c.name
for c in fields(AutoEvalColumnGroup)
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,
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_grouped_df[
[c.name for c in fields(AutoEvalColumnGroup) if c.never_hidden]
+ shown_columns.value
+ [AutoEvalColumnGroup.dummy.name]
],
headers=[c.name for c in fields(AutoEvalColumnGroup) if c.never_hidden] + shown_columns.value + [AutoEvalColumnGroup.dummy.name],
datatype=TYPES_GROUP,
elem_id="leaderboard-table",
interactive=False,
visible=True,
column_widths=["15%", "30%"]
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df_grouped[COLS_GROUP],
headers=COLS_GROUP,
datatype=TYPES_GROUP,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
],
leaderboard_table,
)
for selector in [shown_columns]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("🏅 LLM Benchmark FineGrained", elem_id="llm-benchmark-tab-table-1", id=1):
with gr.Row():
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 fields(AutoEvalColumn)
if not c.hidden and not c.never_hidden and not c.dummy
],
value=[
c.name
for c in fields(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,
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+ shown_columns.value
+ [AutoEvalColumn.dummy.name]
],
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + [AutoEvalColumn.dummy.name],
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
column_widths=["15%", "30%"]
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
],
leaderboard_table,
)
for selector in [shown_columns]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=2):
with gr.Column():
with gr.Row():
gr.Markdown(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=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():
with gr.Row():
model_name_textbox = gr.Textbox(label="Model name")
with gr.Column():
with gr.Row():
weight_type = gr.Dropdown(
choices=['safetensors', 'gguf'],
label="Weights type",
multiselect=False,
value='safgit petensors',
interactive=True,
)
with gr.Column():
with gr.Row():
gguf_filename_textbox = gr.Textbox(label="GGUF filename")
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
weight_type,
gguf_filename_textbox
],
submission_result,
)
with gr.TabItem("📝 Evaluation Datasets", elem_id="llm-benchmark-tab-table", id=4):
gr.Markdown(LLM_DATASET_TEXT, elem_classes="markdown-text")
gr.HTML("""<h1 align="center" id="space-title"> Contributor Companies and Teams </h1>""")
with gr.Row():
with gr.Column(scale=35):
pass
with gr.Column(scale=10, min_width=1, elem_classes='center-column'):
gr.Image('src/display/localdocs.jpeg',
scale = 1,
height=160,
show_label=False,
interactive=False,
show_share_button=False,
show_download_button=False)
gr.HTML("""<h1 align="center" id="company tile"> LocalDocs </h1>""")
with gr.Column(scale=10, min_width=1, elem_classes='center-column'):
gr.Image('src/display/prodata.png',
scale = 1,
height=160,
show_label=False,
interactive=False,
show_share_button=False,
show_download_button=False)
gr.HTML("""<h1 align="center" id="company tile"> PRODATA </h1>""")
with gr.Column(scale=10, min_width=1, elem_classes='center-column'):
gr.Image('src/display/bhosai.jpeg',
scale = 1,
height=160,
show_label=False,
interactive=False,
show_share_button=False,
show_download_button=False)
gr.HTML("""<h1 align="center" id="company tile"> BHOSAI </h1>""")
with gr.Column(scale=35):
pass
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1000)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()
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