import sys
from datetime import datetime

import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from gradio_leaderboard import Leaderboard
from huggingface_hub import snapshot_download
from loguru import logger

from src.about import (
    INTRODUCTION_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.envs import (
    API,
    COMPETITION_URL,
    CUTOFF_DATES,
    EVAL_RESULTS_PATH,
    EVAL_SPLITS,
    LEADERBOARD_REFRESH_INTERVAL,
    REGISTRATION_URL,
    REPO_ID,
    RESULTS_REPO,
    SUBMISSION_URL,
    TOKEN,
)
from src.hf_dataset_utils import download_dataset_snapshot
from src.populate import (
    fetch_bonus_leaderboard,
    fetch_overall_leaderboard,
    fetch_tossup_leaderboard,
)

logger.remove()
logger.add(sys.stderr, level="INFO", backtrace=True, diagnose=False)


# Load metrics manual content
def load_metrics_manual():
    try:
        with open("metrics_manual.md", "r") as f:
            return f.read()
    except Exception as e:
        logger.error(f"Error loading metrics manual: {e}")
        return "# Metrics Manual\n\nCould not load metrics manual content."


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


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()


def refresh_leaderboard(
    split: str = "tiny_eval",
    style: bool = True,
    date: datetime.date = None,
    profile: gr.OAuthProfile = None,
):
    download_dataset_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
    try:
        username = profile and profile.username
    except Exception:
        # If the user is not logged in, profile will be None
        username = None
    tossup_df = fetch_tossup_leaderboard(split, style, date, username)
    bonus_df = fetch_bonus_leaderboard(split, style, date, username)
    overall_df = fetch_overall_leaderboard(split, style, date, username)

    return tossup_df, bonus_df, overall_df


def create_leaderboard_interface(app, refresh_btn, split: str = "tiny_eval", date: datetime.date = None):
    leaderboard_timer = gr.Timer(LEADERBOARD_REFRESH_INTERVAL)

    tossup_df, bonus_df, overall_df = refresh_leaderboard(split, style=True, date=date)

    gr.HTML(
        "<div style='font-size: 18px;'>"
        "ℹ️ <b>E [Score]</b> is the <b>Expected Score</b> for a question. 🙋🏻 and 🤖 indicate the scores against just the Human and the AI players respectively.<br>"
        "ℹ️ <b>Cost</b> is the cost in USD of executing the pipeline <b>per question prefix</b>. (Typically we have upto ~20 prefixes per tossup question)"
        "ℹ️ <b>When does the cost matter?</b> When two models buzz at the same token, which they often do, a lighter (cost-effective) model takes precedence.<br>"
        "</div>"
    )
    tossup_leaderboard = gr.Dataframe(
        value=tossup_df,
        show_search=True,
        label=" 🛎️ Tossup Round Leaderboard",
        show_label=True,
        datatype=["str", "number", "number", "number", "number", "number", "number"],
        elem_id="tossup-table",
        interactive=False,  # Ensure it's not interactive
    )

    gr.HTML(
        "<div style='font-size: 18px;'>"
        "ℹ️ <b>Cost for Bonus pipeline</b> is the cost in USD of executing the pipeline <b>per bonus part</b>. (We have exactly 3 parts per bonus question)"
        "</div>"
    )
    bonus_leaderboard = gr.Dataframe(
        value=bonus_df,
        show_search=True,
        label=" 🧐 Bonus Round Leaderboard",
        show_label=True,
        datatype=["str", "number", "number", "number", "number", "number", "number", "number", "number"],
        elem_id="bonus-table",
        interactive=False,  # Ensure it's not interactive
    )

    overall_leaderboard = gr.Dataframe(
        value=overall_df,
        show_search=True,
        label=" 🥇 Overall Leaderboard",
        show_label=True,
        datatype=["str", "str", "str", "number", "number", "number", "number", "number", "number"],
    )

    gr.on(
        triggers=[leaderboard_timer.tick, refresh_btn.click, app.load],
        fn=refresh_leaderboard,
        inputs=[gr.State(split), gr.State(True), gr.State(date)],
        outputs=[tossup_leaderboard, bonus_leaderboard, overall_leaderboard],
    )


with gr.Blocks(css=custom_css) as demo:
    gr.HTML(TITLE)
    with gr.Row():
        with gr.Column(scale=5):
            gr.Markdown(
                f"## 📋 Register [here]({REGISTRATION_URL}) to participate in our [Human-AI Cooperative Trivia Competition]({COMPETITION_URL}).\n"
                f"## 🎲 Create and submit your quizbowl AI agents at our [submission site]({SUBMISSION_URL}).",
                elem_classes="welcome-text",
            )
            logged_note = gr.Markdown(
                "## 👉 **Note:** <span style='background-color: lightblue; padding: 10px; margin:4px'>Rows in blue with **(*)**</span> are your submissions past the cutoff date and are only visible to you.",
                visible=False,
            )

        with gr.Column(scale=2):
            beautify_date = datetime.strptime(CUTOFF_DATES["Week 2"], "%Y-%m-%d").strftime("%B %d, %Y")
            gr.Markdown(f"## 📅 Next Cutoff Date: &nbsp;&nbsp; <span style='color:crimson'>{beautify_date}</span>")
            gr.LoginButton("Login to privately view your scores on past weeks.")
            refresh_btn = gr.Button("🔄 Refresh")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        for i, (name, split) in enumerate(EVAL_SPLITS.items()):
            with gr.TabItem(f"🏅 {name}", elem_id="llm-benchmark-tab-table", id=i):
                leaderboard_timer = gr.Timer(LEADERBOARD_REFRESH_INTERVAL)
                cutoff_date = CUTOFF_DATES[name]
                date = datetime.strptime(cutoff_date, "%Y-%m-%d").date()
                create_leaderboard_interface(demo, refresh_btn, split, date)

        # Add the Metrics Guide tab
        with gr.TabItem("📊 Metrics Guide", elem_id="metrics-guide-tab"):
            gr.Markdown(load_metrics_manual())

    def check_user_logged_in(x: gr.OAuthProfile | None = None):
        return gr.update(visible=x is not None)

    demo.load(check_user_logged_in, outputs=[logged_note])


# scheduler = BackgroundScheduler()
# scheduler.add_job(restart_space, "interval", seconds=1800)
# scheduler.start()
demo.queue(default_concurrency_limit=40).launch()