import json
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.display.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
TABLE_DESC,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
NUMERIC_INTERVALS,
TYPES,
AutoEvalColumn,
ModelType,
fields,
WeightType,
Precision,
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, TOKEN, QUEUE_REPO, REPO_ID, RESULTS_REPO
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from captcha.image import ImageCaptcha
from PIL import Image
original_df = None
leaderboard_df = None
def restart_space():
API.restart_space(repo_id=REPO_ID, token=TOKEN)
def download_data():
global original_df
global leaderboard_df
try:
print(EVAL_REQUESTS_PATH,QUEUE_REPO)
snapshot_download(
repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
)
except Exception:
restart_space()
try:
print(EVAL_RESULTS_PATH, RESULTS_REPO)
snapshot_download(
repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
)
except Exception:
restart_space()
_, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
leaderboard_df = original_df.copy()
download_data()
"""
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
"""
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
query: str,
):
#filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
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:
print(query)
return df[(df[AutoEvalColumn.eval_name.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [
#AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.eval_name.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 != "" and query is not None:
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=[AutoEvalColumn.eval_name.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[AutoEvalColumn.still_on_hub.name] == True]
filtered_df = df
#type_emoji = [t[0] for t in type_query]
#filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
#filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
#numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
#params_column = pd.to_numeric(df[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
def validate_upload(input):
try:
with open(input, mode="r") as f:
data = json.load(f)
#raise gr.Error("Cannot divide by zero!")
except:
raise gr.Error("Cannot parse file")
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("🏅 Leaderboard", elem_id="llm-benchmark-tab-table", id=0) as tb_board:
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 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,
)
"""
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 ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_precision = gr.CheckboxGroup(
label="Precision",
choices=[i.value.name for i in Precision],
value=[i.value.name for i in Precision],
interactive=True,
elem_id="filter-columns-precision",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
"""
gr.Markdown(TABLE_DESC, elem_classes="markdown-text")
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+ shown_columns.value
],
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
wrap=False,
#column_widths=["2%", "2%"],
)
# 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("📝 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.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")
#precision = gr.Radio(["bfloat16", "float16", "4bit"], label="Precision", info="What precision are you using for inference?")
precision = gr.Dropdown(
choices=[i.value.name for i in Precision if i != Precision.Unknown],
label="Precision",
multiselect=False,
value="other",
interactive=True,
info="What weight precision were you using during the evaluation?"
)
hf_model_id = gr.Textbox(label="Model link (Optional)", info="URL to the model's Hugging Face repository, or it's official website")
contact_email = gr.Textbox(label="Your E-Mail")
file_output = gr.File()
upload_button = gr.UploadButton("Upload json", file_types=['.json'])
upload_button.upload(validate_upload, upload_button, file_output)
"""
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)")
"""
captcha_obj = ImageCaptcha(200, 150)
captcha_data = captcha_obj.generate("1234")
image = Image.open(captcha_data)
captcha_img = gr.Image(image)
submit_button = gr.Button("Submit Eval", interactive=True)
submission_result = gr.Markdown()
submit_button.click(
fn = add_new_eval,
inputs = [
model_name_textbox,
upload_button,
precision,
hf_model_id,
contact_email
],
outputs = [submission_result, model_name_textbox, precision, hf_model_id, contact_email],
)
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=3600)
#scheduler.start()
demo.queue(default_concurrency_limit=40).launch(server_name="0.0.0.0")