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import os
import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
CLS_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_leaderboard_df
from src.submission.submit import process_submission
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,
ignore_patterns=["*.csv"]
)
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,
ignore_patterns=["*.csv"]
)
except Exception:
restart_space()
os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True)
os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)
LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
def init_leaderboard(dataframe):
if dataframe is None or dataframe.empty:
print("Initializing empty leaderboard")
return Leaderboard(
value=pd.DataFrame(columns=[c.name for c in fields(AutoEvalColumn)]),
search_columns=['Model Name'],
interactive=True
)
else:
print("Initializing leaderboard with data")
return Leaderboard(
value=dataframe,
datatype=[c.type for c in fields(AutoEvalColumn)],
search_columns=['Model Name'],
hide_columns=['Student ID', 'eval_name'],
interactive=False
)
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("🏅 Performance Benchmark", elem_id="benchmark-tab-table", id=0):
leaderboard = init_leaderboard(LEADERBOARD_DF)
with gr.TabItem("📝 About", elem_id="benchmark-tab-table", id=2):
gr.Markdown(CLS_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="benchmark-tab-table", id=3):
with gr.Column():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
gr.Markdown("## Submit Your Results")
with gr.Row():
student_id = gr.Textbox(label="Student ID", value='455')
model_name = gr.Textbox(label="Model Name", value='pixelCNN++')
csv_upload = gr.UploadButton(
label="Upload Predictions CSV",
file_types=[".csv"],
file_count="single"
)
submit_button = gr.Button("Submit Results")
submission_result = gr.Markdown()
submit_button.click(
process_submission,
inputs=[student_id, model_name, csv_upload],
outputs=submission_result
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()
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