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# some code blocks are taken from https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard/tree/main
import json
import os
from datetime import datetime, timezone
import gradio as gr
import pandas as pd
from huggingface_hub import HfApi
from src.css_html import custom_css
from src.text_content import ABOUT_TEXT, SUBMISSION_TEXT_3
from src.utils import (
AutoEvalColumn,
fields,
is_model_on_hub,
make_clickable_names,
plot_throughput,
styled_error,
styled_message,
)
TOKEN = os.environ.get("HF_TOKEN", None)
api = HfApi(TOKEN)
df = pd.read_csv("data/code_eval_board.csv")
QUEUE_REPO = "deepcode-ai/evaluation-requests"
EVAL_REQUESTS_PATH = "eval-queue"
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [
c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden
]
TYPES_LITE = [
c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden
]
def add_new_eval(
model: str,
revision: str,
precision: str,
model_type: str,
):
precision = precision
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
if model_type is None or model_type == "":
return styled_error("Please select a model type.")
# check the model actually exists before adding the eval
if revision == "":
revision = "main"
model_on_hub, error = is_model_on_hub(model, revision)
if not model_on_hub:
return styled_error(f'Model "{model}" {error}')
print("adding new eval")
eval_entry = {
"model": model,
"revision": revision,
"precision": precision,
"status": "PENDING",
"submitted_time": current_time,
"model_type": model_type.split(" ")[1],
}
user_name = ""
model_path = model
if "/" in model:
user_name = model.split("/")[0]
model_path = model.split("/")[1]
OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
os.makedirs(OUT_DIR, exist_ok=True)
out_path = f"{OUT_DIR}/{model_path}_eval_request_{precision}.json"
print(f"Saving eval request to {out_path}")
with open(out_path, "w") as f:
f.write(json.dumps(eval_entry))
api.upload_file(
path_or_fileobj=out_path,
path_in_repo=out_path.split("eval-queue/")[1],
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Add {model} to eval queue",
)
# remove the local file
os.remove(out_path)
return styled_message("Your request has been submitted to the evaluation queue!\n")
def select_columns(df, columns):
always_here_cols = [
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]
]
return filtered_df
def filter_items(df, leaderboard_table, query):
if query == "all":
return df[leaderboard_table.columns]
else:
query = query[0]
filtered_df = df[df["T"].str.contains(query, na=False)]
return filtered_df[leaderboard_table.columns]
def search_table(df, leaderboard_table, query):
filtered_df = df[(df["Model"].str.contains(query, case=False))]
return filtered_df[leaderboard_table.columns]
df = make_clickable_names(df)
# <div style='background-color: #F5F1CB; text-align: center; padding: 10px;'>
# <p><b>Warning</b>: This leaderboard is not regularily updated with the latest instruction-tuned code models, check the <b>Submit Results</b> section for submitting new evaluation results.
# You can also check other code leaderboards like <a href="https://evalplus.github.io/leaderboard.html">EvalPlus</a> & <a href="https://huggingface.co./spaces/mike-ravkine/can-ai-code-results">Can-AI-Code</a> .</p>
# </div>
demo = gr.Blocks(css=custom_css)
with demo:
with gr.Row():
gr.Markdown(
"""<div style="text-align: center;"><h1> ⭐ Deep <span style='color: #e6b800;'>Code</span> Models <span style='color: #e6b800;'>Leaderboard</span></h1></div>\
<br>\
<p>Inspired from the <a href="https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard">🤗 Open LLM Leaderboard</a> and <a href="https://huggingface.co./spaces/optimum/llm-perf-leaderboard">🤗 Open LLM-Perf Leaderboard 🏋️</a>, we compare performance of base multilingual code generation models on <a href="https://huggingface.co./datasets/openai_humaneval">HumanEval</a> benchmark and <a href="https://huggingface.co./datasets/nuprl/MultiPL-E">MultiPL-E</a>. We also measure throughput and provide\
information about the models. We only compare open pre-trained multilingual code models, that people can start from as base models for their trainings.</p>
""",
elem_classes="markdown-text",
)
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.Column():
with gr.Tabs(elem_classes="A100-tabs") as A100_tabs:
with gr.TabItem("🔍 Evaluation table", id=0):
with gr.Column():
with gr.Accordion("➡️ See All Columns", open=False):
shown_columns = gr.CheckboxGroup(
choices=[
c
for c in COLS
if c
not in [
AutoEvalColumn.dummy.name,
AutoEvalColumn.model.name,
AutoEvalColumn.model_type_symbol.name,
]
],
value=[
c
for c in COLS_LITE
if c
not in [
AutoEvalColumn.dummy.name,
AutoEvalColumn.model.name,
AutoEvalColumn.model_type_symbol.name,
]
],
label="",
elem_id="column-select",
interactive=True,
)
# with gr.Column(min_width=780):
with gr.Row():
search_bar = gr.Textbox(
placeholder="🔍 Search for your model and press ENTER...",
show_label=False,
elem_id="search-bar",
)
filter_columns = gr.Radio(
label="⏚ Filter model types",
choices=["all", "🟢 base", "🔶 instruction-tuned", "EXT external-evaluation"],
value="all",
elem_id="filter-columns",
)
leaderboard_df = gr.components.Dataframe(
value=df[
[
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.model.name,
]
+ shown_columns.value
],
headers=[
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.model.name,
]
+ shown_columns.value,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
)
hidden_leaderboard_df = gr.components.Dataframe(
value=df,
headers=COLS,
datatype=["str" for _ in range(len(COLS))],
visible=False,
)
search_bar.submit(
search_table,
[hidden_leaderboard_df, leaderboard_df, search_bar],
leaderboard_df,
)
filter_columns.change(
filter_items,
[hidden_leaderboard_df, leaderboard_df, filter_columns],
leaderboard_df,
)
shown_columns.change(
select_columns,
[hidden_leaderboard_df, shown_columns],
leaderboard_df,
)
gr.Markdown(
"""
**Notes:**
- Win Rate represents how often a model outperforms other models in each language, averaged across all languages.
- The scores of instruction-tuned models might be significantly higher on humaneval-python than other languages. We use the instruction format of HumanEval. For other languages, we use base MultiPL-E prompts.
- For more details check the 📝 About section.
- Models with a 🔴 symbol represent external evaluation submission, this means that we didn't verify the results, you can find the author's submission under `Submission PR` field from `See All Columns` tab.
""",
elem_classes="markdown-text",
)
with gr.TabItem("📊 Performance Plot", id=1):
with gr.Row():
bs_1_plot = gr.components.Plot(
value=plot_throughput(df, bs=1),
elem_id="bs1-plot",
show_label=False,
)
bs_50_plt = gr.components.Plot(
value=plot_throughput(df, bs=50),
elem_id="bs50-plot",
show_label=False,
)
gr.Markdown(
"**Note:** The throughputs for some models are missing and might appear as zero.",
elem_classes="markdown-text",
)
with gr.TabItem("📝 About", id=2):
gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text")
with gr.TabItem("Submit results 🚀", id=3):
gr.Markdown(SUBMISSION_TEXT_3)
demo.launch() |