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import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from gradio_space_ci import enable_space_ci
from src.display.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
FAQ_TEXT,
TITLE,
)
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, DYNAMIC_INFO_REPO, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, 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 src.scripts.update_all_request_files import update_dynamic_files
from src.tools.collections import update_collections
from src.tools.plots import (
create_metric_plot_obj,
create_plot_df,
create_scores_df,
)
# Start ephemeral Spaces on PRs (see config in README.md)
#enable_space_ci()
def restart_space():
"""
Restarts a Space instance specified by its repository ID.
This function is used to restart a Space instance within the Hugging Face platform.
It requires the repository ID and a valid API token for authentication.
Parameters as env variables
---------------------------
repo_id : str
The ID of the repository associated with the Space instance to be restarted.
token : str
A valid API token with the necessary permissions to restart the Space.
Returns
-------
None
This function does not return any value. It simply restarts the specified Space instance.
Example
-------
>>> restart_space(repo_id="example_repo_id", token="example_token")
"""
API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
def init_space():
"""
Initializes the Hugging Face Space environment.
This function initializes the Hugging Face Space environment by performing the following steps:
1. Downloads evaluation requests, dynamic information, and evaluation results.
2. Processes the raw data into a leaderboard DataFrame.
3. Updates collections with the original DataFrame.
4. Creates a plot DataFrame for visualization.
5. Retrieves evaluation queue DataFrames.
Returns
-------
tuple
A tuple containing the following elements:
- leaderboard_df : pandas.DataFrame
DataFrame containing the leaderboard data.
- original_df : pandas.DataFrame
Original DataFrame obtained from the evaluation results.
- plot_df : pandas.DataFrame
DataFrame suitable for creating plots.
- finished_eval_queue_df : pandas.DataFrame
DataFrame containing finished evaluation queue data.
- running_eval_queue_df : pandas.DataFrame
DataFrame containing running evaluation queue data.
- pending_eval_queue_df : pandas.DataFrame
DataFrame containing pending evaluation queue data.
Example
-------
>>> (
... leaderboard_df,
... original_df,
... plot_df,
... finished_eval_queue_df,
... running_eval_queue_df,
... pending_eval_queue_df,
... ) = init_space()
"""
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
)
except Exception:
restart_space()
try:
print(DYNAMIC_INFO_PATH)
snapshot_download(
repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
)
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
)
except Exception:
restart_space()
raw_data, original_df = get_leaderboard_df(
results_path=EVAL_RESULTS_PATH,
requests_path=EVAL_REQUESTS_PATH,
dynamic_path=DYNAMIC_INFO_FILE_PATH,
cols=COLS,
benchmark_cols=BENCHMARK_COLS
)
update_collections(original_df.copy())
leaderboard_df = original_df.copy()
plot_df = create_plot_df(create_scores_df(raw_data))
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
return leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
hide_models: list,
query: str,
):
"""
Updates a table DataFrame based on specified criteria.
This function filters the input DataFrame based on specified criteria and returns a new DataFrame with selected columns.
Parameters
----------
hidden_df : pandas.DataFrame
The DataFrame to be filtered and updated.
columns : list
List of column names to be included in the updated DataFrame.
type_query : list
List of types to filter models.
precision_query : str
Precision value to filter models.
size_query : list
List of sizes to filter models.
hide_models : list
List of models to be hidden.
query : str
Query string to filter rows in the DataFrame.
Returns
-------
updated_df : pandas.DataFrame
A DataFrame containing filtered and updated data based on the specified criteria.
Example
-------
>>> updated_df = update_table(
... hidden_df=original_df,
... columns=["Model", "Type", "Precision"],
... type_query=["type1", "type2"],
... precision_query="high",
... size_query=["large"],
... hide_models=["model1", "model2"],
... query="column1 > 0 and column2 == 'value'",
... )
"""
filtered_df = filter_models(df=hidden_df, type_query=type_query, size_query=size_query, precision_query=precision_query, hide_models=hide_models)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns)
return df
def load_query(request: gr.Request): # triggered only once at startup => read query parameter if it exists
"""
Loads a query parameter from a request object.
It returns the query parameter value for the "search_bar" component and for a hidden component that triggers a reload only if the value has changed.
Parameters
----------
request : gr.Request
The request object containing query parameters.
Returns
-------
tuple
A tuple containing two identical query parameter values:
- query_search_bar : str
The query parameter value for the "search_bar" component.
- query_hidden : str
The query parameter value for a hidden component that triggers a reload only if the value has changed.
Example
-------
>>> query_search_bar, query_hidden = load_query(request)
"""
query = request.query_params.get("query") or ""
return query, query # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
"""
Searches a DataFrame for rows containing a specified query.
This function filters the input DataFrame based on a specified query and returns a new DataFrame containing rows where the query matches any part of the specified column.
Parameters
----------
df : pandas.DataFrame
The DataFrame to be searched.
query : str
The query string to search for within the DataFrame.
Returns
-------
filtered_df : pandas.DataFrame
A DataFrame containing rows where the query matches any part of the specified column.
Example
-------
>>> filtered_df = search_table(df=original_df, query="example_query")
"""
return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
"""
Selects specified columns from a DataFrame.
This function selects specified columns from the input DataFrame and returns a new DataFrame containing only those columns.
Parameters
----------
df : pandas.DataFrame
The DataFrame from which columns are to be selected.
columns : list
List of column names to be selected from the DataFrame.
Returns
-------
filtered_df : pandas.DataFrame
A DataFrame containing only the specified columns.
Example
-------
>>> filtered_df = select_columns(df=original_df, columns=["column1", "column2", "column3"])
"""
always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
dummy_col = [AutoEvalColumn.dummy.name]
#AutoEvalColumn.model_type_symbol.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] + dummy_col
]
return filtered_df
def filter_queries(query: str, filtered_df: pd.DataFrame):
"""Added by Abishek"""
"""
Filters DataFrame rows based on specified query strings.
This function filters the input DataFrame based on specified query strings and returns a new DataFrame containing rows that match any of the queries.
Parameters
----------
query : str
The query string containing one or more search queries separated by semicolons (;).
filtered_df : pandas.DataFrame
The DataFrame to be filtered based on the queries.
Returns
-------
filtered_df : pandas.DataFrame
A DataFrame containing rows that match any of the specified queries.
Example
-------
>>> filtered_df = filter_queries(
... query="query1; query2; query3",
... filtered_df=original_df,
... )
"""
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=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
)
return filtered_df
def filter_models(
df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, hide_models: list
) -> pd.DataFrame:
"""
Filters DataFrame rows based on specified criteria.
This function filters the input DataFrame based on specified criteria such as model type, size, precision, and models to hide.
Parameters
----------
df : pandas.DataFrame
The DataFrame to be filtered.
type_query : list
List of tuples containing model types to include in the filtering. Each tuple consists of a model type abbreviation and its corresponding emoji.
size_query : list
List of size categories to include in the filtering.
precision_query : list
List of precision values to include in the filtering.
hide_models : list
List of model categories to hide from the DataFrame.
Returns
-------
filtered_df : pandas.DataFrame
A DataFrame containing rows that meet the specified filtering criteria.
Example
-------
>>> filtered_df = filter_models(
... df=original_df,
... type_query=[("Type1", "πŸ”₯"), ("Type2", "⭐")],
... size_query=["Large", "Medium"],
... precision_query=["High", "Medium"],
... hide_models=["Private or deleted", "Contains a merge/moerge", "MoE", "Flagged"],
... )
"""
# Show all models
if "Private or deleted" in hide_models:
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
else:
filtered_df = df
if "Contains a merge/moerge" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False]
if "MoE" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False]
if "Flagged" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
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
leaderboard_df = filter_models(
df=leaderboard_df,
type_query=[t.to_str(" : ") for t in ModelType],
size_query=list(NUMERIC_INTERVALS.keys()),
precision_query=[i.value.name for i in Precision],
hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], # Deleted, merges, flagged, MoEs
)
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("πŸ… LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" πŸ” 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.Row():
hide_models = gr.CheckboxGroup(
label="Hide models",
choices = ["Private or deleted", "Contains a merge/moerge", "Flagged", "MoE"],
value=["Private or deleted", "Contains a merge/moerge", "Flagged"],
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",
)
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,
datatype=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[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
search_bar,
],
leaderboard_table,
)
# Define a hidden component that will trigger a reload only if a query parameter has been set
hidden_search_bar = gr.Textbox(value="", visible=False)
hidden_search_bar.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
search_bar,
],
leaderboard_table,
)
# Check query parameter once at startup and update search bar + hidden component
demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, hide_models]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
search_bar,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("πŸ“ˆ Metrics through time", elem_id="llm-benchmark-tab-table", id=4):
with gr.Row():
with gr.Column():
chart = create_metric_plot_obj(
plot_df,
[AutoEvalColumn.average.name],
title="Average of Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.Column():
chart = create_metric_plot_obj(
plot_df,
BENCHMARK_COLS,
title="Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.TabItem("πŸ“ About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
with gr.TabItem("πŸš€ 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"βœ… 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"πŸ”„ 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("# βœ‰οΈβœ¨ 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")
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
model_type = gr.Dropdown(
choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
label="Model type",
multiselect=False,
value=ModelType.FT.to_str(" : "),
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,
private,
weight_type,
model_type,
],
submission_result,
)
with gr.Row():
with gr.Accordion("πŸ“™ 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=10800) # restarted every 3h
scheduler.add_job(update_dynamic_files, "cron", minute=30) # launched every hour on the hour
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()