app.py

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,
  LLM_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_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval

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,
  )
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,
  )
except Exception:
  restart_space()

LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
(
  finished_eval_queue_df,
  running_eval_queue_df,
  pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

def init_leaderboard(dataframe):
  if dataframe is None or dataframe.empty:
    raise ValueError("Leaderboard DataFrame is empty or None.")
  return Leaderboard(
    value=dataframe,
    datatype=[c.type for c in fields(AutoEvalColumn)],
    select_columns=SelectColumns(
      default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
      cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
      label="Select Columns to Display:",
    ),
    search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
    hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
    filter_columns=[            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
            ColumnFilter(
                AutoEvalColumn.params.name, type="slider", min=0.01, max=150, label="Select the number of parameters (B)"
            ),
            ColumnFilter(AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def display_user_data(user_id):
    user_data = data.load_data()
    if user_id in user_data:
        return f"Points: {user_data[user_id]['points']}\nReferrals: {len(user_data[user_id]['referrals'])}"
    else:
        return "User not found"

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("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            leaderboard = init_leaderboard(LEADERBOARD_DF)
        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():
  with gr.Column():
    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
    model_type = gr.Dropdown(
      choices=[t.to_str(" : ") for t in ModelType if t.value != ModelType.Unknown.value],
      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.value != Precision.Unknown.value],
      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)")
    model_name_textbox = gr.Textbox(label="Model name")

submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
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,
    )
submit_button.click(
  add_new_eval,
  [
    model_name_textbox,
    base_model_name_textbox,
    revision_name_textbox,
    precision,
    weight_type,
    model_type,
  ],
  submission_result,
)

# ... (Rest of your code)
            
    start_button = gr.Button("Start", elem_id="start_button") 
    scheduler = BackgroundScheduler()
    scheduler.add_job(restart_space, "interval", seconds=1800)
    scheduler.start()
demo.queue(default_concurrency_limit=40).launch()