app.py

import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
import json
import os
import datetime
import urllib.parse

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


def restart_space():
    API.restart_space(repo_id=REPO_ID)


def save_submission_and_notify(model_name, contact_email, weight_link, json_results, paper_link, description):
    """Save submission to file and provide instructions for email"""
    try:
        # Validate JSON format if provided
        if json_results.strip():
            try:
                json.loads(json_results)
            except json.JSONDecodeError:
                return "❌ Invalid JSON format in results field"

        # Create submission data
        submission_data = {
            "timestamp": datetime.datetime.now().isoformat(),
            "model_name": model_name,
            "contact_email": contact_email,
            "weight_link": weight_link,
            "paper_link": paper_link,
            "description": description,
            "json_results": json_results,
        }

        # Save to submissions directory
        os.makedirs("submissions", exist_ok=True)
        filename = (
            f"submissions/{model_name.replace('/', '_')}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
        )

        with open(filename, "w") as f:
            json.dump(submission_data, f, indent=2)

        # Create mailto link for user
        subject = f"SearchAgent Leaderboard Submission: {model_name}"
        body = f"""New model submission for SearchAgent Leaderboard:

Model Name: {model_name}
Contact Email: {contact_email}
Weight Link: {weight_link}
Paper Link: {paper_link}
Description: {description}

JSON Results:
{json_results}"""

        # URL encode the email content
        mailto_link = (
            f"mailto:shyuli@tencent.com?subject={urllib.parse.quote(subject)}&body={urllib.parse.quote(body[:500])}"
        )

        return f"""✅ Submission saved successfully!
        
📧 **Please send your submission to: shyuli@tencent.com**

You can either:
1. Click here to open your email client: [Send Email](mailto:shyuli@tencent.com)
2. Or copy the submission details above and send manually

Your submission has been saved to: {filename}

We'll review your model and get back to you at {contact_email}."""

    except Exception as e:
        return f"❌ Failed to save submission: {str(e)}"


### Space initialisation
# Use local data for demo purposes
try:
    print(EVAL_REQUESTS_PATH)
    # For demo, use local eval-queue directory if it exists
    import os

    if not os.path.exists(EVAL_REQUESTS_PATH):
        os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True)
    # snapshot_download(
    #     repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    # )
except Exception as e:
    print(f"Could not setup eval requests path: {e}")
try:
    print(EVAL_RESULTS_PATH)
    # For demo, use local eval-results directory if it exists
    if not os.path.exists(EVAL_RESULTS_PATH):
        os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)
    # snapshot_download(
    #     repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    # )
except Exception as e:
    print(f"Could not setup eval results path: {e}")


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],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.model_size.name, type="checkboxgroup", label="Model Size"),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )


demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 SearchAgent Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            leaderboard = init_leaderboard(LEADERBOARD_DF)
            gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

        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 Model", elem_id="llm-benchmark-tab-table", id=3):
            with gr.Column():
                gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

    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=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(share=True)