app.py

import datetime
import io
import json
import os
import re
from urllib.parse import urlparse

import gradio as gr
import pandas as pd
from huggingface_hub import HfApi, hf_hub_download


APP_NAME = "miniapp"
print("main mannnnn")

# 在 Space 里通过 Secrets 配置：
# - HF_TOKEN: 具有写 dataset 权限的 token（Settings -> Variables and secrets -> Secrets）
# - LEADERBOARD_DATASET: 形如 "your-username/miniapp-leaderboard"（repo_type=dataset）
HF_TOKEN = os.environ.get("HF_TOKEN") or os.environ.get("TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
LEADERBOARD_DATASET = os.environ.get("LEADERBOARD_DATASET", "").strip()

# 判断是否运行在 Hugging Face Spaces
IN_SPACES = bool(
    os.environ.get("SPACE_ID")
    or os.environ.get("SPACE_REPO_NAME")
    or os.environ.get("SPACE_AUTHOR_NAME")
    or os.environ.get("system", "") == "spaces"
)

MAX_ENTRIES = int(os.environ.get("MAX_ENTRIES", "200"))


def _is_valid_http_url(url: str) -> bool:
    try:
        parsed = urlparse(url)
        return parsed.scheme in ("http", "https") and bool(parsed.netloc)
    except Exception:
        return False


def _slug(s: str, max_len: int = 60) -> str:
    s = (s or "").strip().lower()
    s = re.sub(r"[^a-z0-9]+", "-", s)
    s = re.sub(r"-{2,}", "-", s).strip("-")
    return (s[:max_len] or "model")


def _api() -> HfApi:
    return HfApi(token=HF_TOKEN)


def _ensure_dataset_repo():
    if not HF_TOKEN:
        raise RuntimeError("未配置 HF_TOKEN（Space Secrets）。")
    if not LEADERBOARD_DATASET:
        raise RuntimeError("未配置 LEADERBOARD_DATASET（例如：your-username/miniapp-leaderboard）。")
    api = _api()
    try:
        api.repo_info(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
    except Exception:
        # 不存在则创建（public dataset；你也可以手动创建并设为 private）
        api.create_repo(repo_id=LEADERBOARD_DATASET, repo_type="dataset", private=False, exist_ok=True)


def _empty_df() -> pd.DataFrame:
    return pd.DataFrame(columns=["submitted_at", "username", "model_name", "model_api", "notes"])


def _load_submissions_df() -> pd.DataFrame:
    if not HF_TOKEN or not LEADERBOARD_DATASET:
        return _empty_df()

    api = _api()
    try:
        files = api.list_repo_files(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
    except Exception:
        return _empty_df()

    sub_files = sorted(
        [f for f in files if f.startswith("submissions/") and f.endswith(".json")],
        reverse=True,
    )[:MAX_ENTRIES]

    rows = []
    for filename in sub_files:
        try:
            path = hf_hub_download(
                repo_id=LEADERBOARD_DATASET,
                repo_type="dataset",
                filename=filename,
                token=HF_TOKEN,
            )
            with open(path, "r", encoding="utf-8") as fp:
                rows.append(json.load(fp))
        except Exception:
            continue

    if not rows:
        return _empty_df()

    df = pd.DataFrame(rows)
    for col in ["submitted_at", "username", "model_name", "model_api", "notes"]:
        if col not in df.columns:
            df[col] = ""
    df = df[["submitted_at", "username", "model_name", "model_api", "notes"]]
    df = df.sort_values(by=["submitted_at"], ascending=False, kind="stable")
    return df


def refresh():
    return _load_submissions_df()


def submit(model_name: str, model_api: str, notes: str, username: str | None):
    model_name = (model_name or "").strip()
    model_api = (model_api or "").strip()
    notes = (notes or "").strip()
    username = (username or "").strip() or "anonymous"

    if not model_name:
        return "请填写 **模型名称**。", _load_submissions_df()
    if not model_api:
        return "请填写 **模型 API**。", _load_submissions_df()
    if not _is_valid_http_url(model_api):
        return "**模型 API** 需要是合法的 `http(s)://...` URL。", _load_submissions_df()

    if not HF_TOKEN:
        return "Space 未配置 **HF_TOKEN**（Secrets），无法写入排行榜。", _load_submissions_df()
    if not LEADERBOARD_DATASET:
        return "Space 未配置 **LEADERBOARD_DATASET**（例如：`your-username/miniapp-leaderboard`）。", _load_submissions_df()

    _ensure_dataset_repo()
    api = _api()

    now = datetime.datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
    safe_model = _slug(model_name)
    safe_user = _slug(username)
    path_in_repo = f"submissions/{now[:10]}/{now}-{safe_user}-{safe_model}.json"

    payload = {
        "submitted_at": now,
        "username": username,
        "model_name": model_name,
        "model_api": model_api,
        "notes": notes,
    }
    data = (json.dumps(payload, ensure_ascii=False, indent=2) + "\n").encode("utf-8")
    bio = io.BytesIO(data)

    api.upload_file(
        repo_id=LEADERBOARD_DATASET,
        repo_type="dataset",
        path_or_fileobj=bio,
        path_in_repo=path_in_repo,
        commit_message=f"miniapp: submit {username}/{model_name}",
        token=HF_TOKEN,
    )

    return "已提交并写入 leaderboard。", _load_submissions_df()


def build_demo() -> gr.Blocks:
    with gr.Blocks(title=f"{APP_NAME} leaderboard") as demo:
        gr.Markdown(
            f"## {APP_NAME} leaderboard\n\n"
            "提交你的模型信息后，会写入一个 Hugging Face **Dataset**，并在下方表格展示。\n\n"
            f"- 当前 `LEADERBOARD_DATASET`: `{LEADERBOARD_DATASET or '(未配置)'}`\n"
        )

        with gr.Row():
            with gr.Column(scale=2):
                model_name = gr.Textbox(label="模型名称（必填）", placeholder="例如：my-agent-v1")
                model_api = gr.Textbox(
                    label="模型 API（必填）",
                    placeholder="例如：https://api.example.com/v1/chat/completions",
                )
                notes = gr.Textbox(label="备注（可选）", lines=4)

                # 纯前端版：不强制 OAuth；如果你想“只能登录用户提交”，后续再加 LoginButton
                if IN_SPACES:
                    username = gr.Textbox(
                        label="用户名（可选）",
                        placeholder="建议填你的 HF 用户名（也可留空）",
                    )
                else:
                    username = gr.Textbox(label="用户名（本地调试用）", value="local")

                submit_btn = gr.Button("提交", variant="primary")
                status = gr.Markdown()

            with gr.Column(scale=3):
                leaderboard = gr.Dataframe(
                    label="Leaderboard（按提交时间倒序）",
                    value=_load_submissions_df(),
                    interactive=False,
                    wrap=True,
                )
                refresh_btn = gr.Button("刷新")

        submit_btn.click(
            submit,
            inputs=[model_name, model_api, notes, username],
            outputs=[status, leaderboard],
        )
        refresh_btn.click(refresh, inputs=[], outputs=[leaderboard])

    return demo


demo = build_demo()


def main():
    demo.launch()


if __name__ == "__main__":
    main()

import datetime
import io
import json
import os
import re
from urllib.parse import urlparse

import gradio as gr
import pandas as pd
from huggingface_hub import HfApi, hf_hub_download


APP_NAME = "miniapp"

# 在 Space 里通过 Secrets 配置：
# - HF_TOKEN: 具有写 dataset 权限的 token（Settings -> Variables and secrets -> Secrets）
# - LEADERBOARD_DATASET: 形如 "your-username/miniapp-leaderboard"（repo_type=dataset）
HF_TOKEN = os.environ.get("HF_TOKEN") or os.environ.get("TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
LEADERBOARD_DATASET = os.environ.get("LEADERBOARD_DATASET", "").strip()

# 判断是否运行在 Hugging Face Spaces
IN_SPACES = bool(
    os.environ.get("SPACE_ID")
    or os.environ.get("SPACE_REPO_NAME")
    or os.environ.get("SPACE_AUTHOR_NAME")
    or os.environ.get("system", "") == "spaces"
)

MAX_ENTRIES = int(os.environ.get("MAX_ENTRIES", "200"))


def _is_valid_http_url(url: str) -> bool:
    try:
        parsed = urlparse(url)
        return parsed.scheme in ("http", "https") and bool(parsed.netloc)
    except Exception:
        return False


def _slug(s: str, max_len: int = 60) -> str:
    s = (s or "").strip().lower()
    s = re.sub(r"[^a-z0-9]+", "-", s)
    s = re.sub(r"-{2,}", "-", s).strip("-")
    return (s[:max_len] or "model")


def _api() -> HfApi:
    return HfApi(token=HF_TOKEN)


def _ensure_dataset_repo():
    if not HF_TOKEN:
        raise RuntimeError("未配置 HF_TOKEN（Space Secrets）。")
    if not LEADERBOARD_DATASET:
        raise RuntimeError("未配置 LEADERBOARD_DATASET（例如：your-username/miniapp-leaderboard）。")
    api = _api()
    try:
        api.repo_info(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
    except Exception:
        # 不存在则创建（public dataset；你也可以手动创建并设为 private）
        api.create_repo(repo_id=LEADERBOARD_DATASET, repo_type="dataset", private=False, exist_ok=True)


def _load_submissions_df() -> pd.DataFrame:
    if not HF_TOKEN or not LEADERBOARD_DATASET:
        return pd.DataFrame(columns=["submitted_at", "username", "model_name", "model_api", "notes"])

    api = _api()
    try:
        files = api.list_repo_files(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
    except Exception:
        return pd.DataFrame(columns=["submitted_at", "username", "model_name", "model_api", "notes"])

    sub_files = sorted(
        [f for f in files if f.startswith("submissions/") and f.endswith(".json")],
        reverse=True,
    )[:MAX_ENTRIES]

    rows = []
    for filename in sub_files:
        try:
            path = hf_hub_download(
                repo_id=LEADERBOARD_DATASET,
                repo_type="dataset",
                filename=filename,
                token=HF_TOKEN,
            )
            with open(path, "r", encoding="utf-8") as fp:
                rows.append(json.load(fp))
        except Exception:
            continue

    if not rows:
        return pd.DataFrame(columns=["submitted_at", "username", "model_name", "model_api", "notes"])

    df = pd.DataFrame(rows)
    # 统一列顺序
    for col in ["submitted_at", "username", "model_name", "model_api", "notes"]:
        if col not in df.columns:
            df[col] = ""
    df = df[["submitted_at", "username", "model_name", "model_api", "notes"]]
    df = df.sort_values(by=["submitted_at"], ascending=False, kind="stable")
    return df


def refresh():
    return _load_submissions_df()


def submit(model_name: str, model_api: str, notes: str, username: str | None):
    model_name = (model_name or "").strip()
    model_api = (model_api or "").strip()
    notes = (notes or "").strip()
    username = (username or "").strip() or "anonymous"

    if not model_name:
        return "请填写 **模型名称**。", _load_submissions_df()
    if not model_api:
        return "请填写 **模型 API**。", _load_submissions_df()
    if not _is_valid_http_url(model_api):
        return "**模型 API** 需要是合法的 `http(s)://...` URL。", _load_submissions_df()

    if not HF_TOKEN:
        return "Space 未配置 **HF_TOKEN**（Secrets），无法写入排行榜。", _load_submissions_df()
    if not LEADERBOARD_DATASET:
        return "Space 未配置 **LEADERBOARD_DATASET**（例如：`your-username/miniapp-leaderboard`）。", _load_submissions_df()

    _ensure_dataset_repo()
    api = _api()

    now = datetime.datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
    safe_model = _slug(model_name)
    safe_user = _slug(username)
    path_in_repo = f"submissions/{now[:10]}/{now}-{safe_user}-{safe_model}.json"

    payload = {
        "submitted_at": now,
        "username": username,
        "model_name": model_name,
        "model_api": model_api,
        "notes": notes,
    }
    data = (json.dumps(payload, ensure_ascii=False, indent=2) + "\n").encode("utf-8")
    bio = io.BytesIO(data)

    api.upload_file(
        repo_id=LEADERBOARD_DATASET,
        repo_type="dataset",
        path_or_fileobj=bio,
        path_in_repo=path_in_repo,
        commit_message=f"miniapp: submit {username}/{model_name}",
        token=HF_TOKEN,
    )

    return "已提交并写入 leaderboard。", _load_submissions_df()


with gr.Blocks(title=f"{APP_NAME} leaderboard") as demo:
    gr.Markdown(
        f"## {APP_NAME} leaderboard\n\n"
        "提交你的模型信息后，会写入一个 Hugging Face **Dataset**，并在下方表格展示。\n\n"
        f"- 当前 `LEADERBOARD_DATASET`: `{LEADERBOARD_DATASET or '(未配置)'}`\n"
    )

    with gr.Row():
        with gr.Column(scale=2):
            model_name = gr.Textbox(label="模型名称（必填）", placeholder="例如：my-agent-v1")
            model_api = gr.Textbox(
                label="模型 API（必填）",
                placeholder="例如：https://api.example.com/v1/chat/completions",
            )
            notes = gr.Textbox(label="备注（可选）", lines=4)

            # 纯前端版：不强制 OAuth；在 Space 里建议你自己加 LoginButton 做鉴权
            if IN_SPACES:
                username = gr.Textbox(
                    label="用户名（可选）",
                    placeholder="建议填你的 HF 用户名（也可留空）",
                )
            else:
                username = gr.Textbox(label="用户名（本地调试用）", value="local")

            submit_btn = gr.Button("提交", variant="primary")
            status = gr.Markdown()
        with gr.Column(scale=3):
            leaderboard = gr.Dataframe(
                label="Leaderboard（按提交时间倒序）",
                value=_load_submissions_df(),
                interactive=False,
                wrap=True,
            )
            refresh_btn = gr.Button("刷新")

    submit_btn.click(
        submit,
        inputs=[model_name, model_api, notes, username],
        outputs=[status, leaderboard],
    )
    refresh_btn.click(refresh, inputs=[], outputs=[leaderboard])

def main():
    demo.launch()


if __name__ == "__main__":
    main()

# Display the results
if HAS_TOKEN and not LOCAL_DEBUG:
    try:
        eval_results = load_dataset(
            RESULTS_DATASET,
            YEAR_VERSION,
            token=TOKEN,
            download_mode="force_redownload",
            verification_mode=VerificationMode.NO_CHECKS,
        )
    except Exception as e:
        print(e)
        eval_results = None

    try:
        contact_infos = load_dataset(
            CONTACT_DATASET,
            YEAR_VERSION,
            token=TOKEN,
            download_mode="force_redownload",
            verification_mode=VerificationMode.NO_CHECKS,
        )
    except Exception as e:
        print(e)
        contact_infos = None
else:
    eval_results = None
    contact_infos = None

def get_dataframe_from_results(eval_results, split):
    if eval_results is None:
        return pd.DataFrame(columns=EMPTY_LEADERBOARD_COLUMNS)
    local_df = eval_results[split]
    local_df = local_df.map(lambda row: {"model": model_hyperlink(row["url"], row["model"])})
    local_df = local_df.remove_columns(["system_prompt", "url"])
    local_df = local_df.rename_column("model", "Agent name")
    local_df = local_df.rename_column("model_family", "Model family")
    local_df = local_df.rename_column("score", "Average score (%)")
    for i in [1, 2, 3]:
        local_df = local_df.rename_column(f"score_level{i}", f"Level {i} score (%)")
    local_df = local_df.rename_column("date", "Submission date")
    df = pd.DataFrame(local_df)
    df = df.sort_values(by=["Average score (%)"], ascending=False)

    numeric_cols = [c for c in local_df.column_names if "score" in c]
    df[numeric_cols] = df[numeric_cols].multiply(100).round(decimals=2)
    #df = df.style.format("{:.2%}", subset=numeric_cols)

    return df

#eval_dataframe_val = get_dataframe_from_results(eval_results=eval_results, split="validation")
eval_dataframe_test = get_dataframe_from_results(eval_results=eval_results, split="test")

# Gold answers
if HAS_TOKEN and not LOCAL_DEBUG:
    gold_dataset = load_dataset(
        INTERNAL_DATA_DATASET,
        f"{YEAR_VERSION}_all",
        token=TOKEN,
    )
    gold_results = {
        split: {row["task_id"]: row for row in gold_dataset[split]}
        for split in ["test", "validation"]
    }
else:
    gold_results = {"test": {}, "validation": {}}


def restart_space():
    if IN_SPACES and HAS_TOKEN:
        api.restart_space(repo_id=LEADERBOARD_PATH, token=TOKEN)

TYPES = ["markdown", "number", "number", "number", "number", "str", "str", "str"]

def add_new_eval(
    #val_or_test: str,
    model: str,
    model_family: str,
    system_prompt: str,
    url: str,
    path_to_file: str,
    organisation: str,
    mail: str,
    profile: gr.OAuthProfile, 
):
    val_or_test = "test"
    try:
        if not HAS_TOKEN or LOCAL_DEBUG:
            return format_error(
                "Submissions are disabled in local mode. Set env TOKEN (Hugging Face token) and rerun to enable submissions."
            )
        # Was the profile created less than 2 month ago?
        user_data = requests.get(f"https://huggingface.co/api/users/{profile.username}/overview")
        creation_date = json.loads(user_data.content)["createdAt"]
        if datetime.datetime.now() - datetime.datetime.strptime(creation_date, '%Y-%m-%dT%H:%M:%S.%fZ') < datetime.timedelta(days=60):
            return format_error("This account is not authorized to submit on GAIA.")
            

        contact_infos = load_dataset(CONTACT_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS, trust_remote_code=True)
        user_submission_dates = sorted(row["date"] for row in contact_infos[val_or_test] if row["username"] == profile.username)
        # if len(user_submission_dates) > 0 and user_submission_dates[-1] == datetime.datetime.today().strftime('%Y-%m-%d'):
        #     return format_error("You already submitted once today, please try again tomorrow.")


        is_validation = val_or_test == "validation"
        # Very basic email parsing
        _, parsed_mail = parseaddr(mail)
        if not "@" in parsed_mail:
            return format_warning("Please provide a valid email adress.")

        print("Adding new eval")

        # Check if the combination model/org already exists and prints a warning message if yes
        if model.lower() in set([m.lower() for m in eval_results[val_or_test]["model"]]) and organisation.lower() in set([o.lower() for o in eval_results[val_or_test]["organisation"]]):
            return format_warning("This model has been already submitted.")
        
        if path_to_file is None:
            return format_warning("Please attach a file.")

        # SAVE UNSCORED SUBMISSION
        if LOCAL_DEBUG:
            print("mock uploaded submission")
        else:
            api.upload_file(
                repo_id=SUBMISSION_DATASET, 
                path_or_fileobj=path_to_file.name, 
                path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_raw_{datetime.datetime.today()}.jsonl",
                repo_type="dataset", 
                token=TOKEN
            )

        # SAVE CONTACT
        contact_info = {
            "model": model,
            "model_family": model_family,
            "url": url,
            "organisation": organisation,
            "username": profile.username,
            "mail": mail,
            "date": datetime.datetime.today().strftime('%Y-%m-%d')
        }
        contact_infos[val_or_test]= contact_infos[val_or_test].add_item(contact_info)
        if LOCAL_DEBUG:
            print("mock uploaded contact info")
        else:
            contact_infos.push_to_hub(CONTACT_DATASET, config_name = YEAR_VERSION, token=TOKEN)

        # SCORE SUBMISSION
        file_path = path_to_file.name        
        scores = {"all": 0, 1: 0, 2: 0, 3: 0}
        num_questions = {"all": 0, 1: 0, 2: 0, 3: 0}
        task_ids = []
        with open(f"scored/{organisation}_{model}.jsonl", "w") as scored_file:
            with open(file_path, 'r') as f:
                for ix, line in enumerate(f):
                    try:
                        task = json.loads(line)
                    except Exception:
                        return format_error(f"Line {ix} is incorrectly formatted. Please fix it and resubmit your file.")

                    if "model_answer" not in task:
                        return format_error(f"Line {ix} contains no model_answer key. Please fix it and resubmit your file.")
                    answer = task["model_answer"]
                    task_id = task["task_id"]
                    try:
                        level = int(gold_results[val_or_test][task_id]["Level"])
                    except KeyError:
                        return format_error(f"{task_id} not found in split {val_or_test}. Are you sure you submitted the correct file?")

                    score = question_scorer(task['model_answer'], gold_results[val_or_test][task_id]["Final answer"])
                    
                    scored_file.write(
                        json.dumps({
                            "id": task_id,
                            "model_answer": answer,
                            "score": score,
                            "level": level
                        }) + "\n"
                    )
                    task_ids.append(task_id)

                    scores["all"] += score
                    scores[level] += score
                    num_questions["all"] += 1
                    num_questions[level] += 1

        # Check if there's any duplicate in the submission
        if len(task_ids) != len(set(task_ids)):
            return format_error("There are duplicates in your submission. Please check your file and resubmit it.")

        if any([num_questions[level] != ref_level_len[val_or_test][level] for level in [1, 2, 3]]):
            return format_error(f"Your submission has {num_questions[1]} questions for level 1, {num_questions[2]} for level 2, and {num_questions[3]} for level 3, but it should have {ref_level_len[val_or_test][1]}, {ref_level_len[val_or_test][2]}, and {ref_level_len[val_or_test][3]} respectively. Please check your submission.")

        # SAVE SCORED SUBMISSION
        if LOCAL_DEBUG:
            print("mock uploaded scored submission")
        else:
            api.upload_file(
                repo_id=SUBMISSION_DATASET, 
                path_or_fileobj=f"scored/{organisation}_{model}.jsonl",
                path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_scored_{datetime.datetime.today()}.jsonl", 
                repo_type="dataset", 
                token=TOKEN
            )

            # Save scored file
            if is_validation:
                api.upload_file(
                    repo_id=SUBMISSION_DATASET_PUBLIC, 
                    path_or_fileobj=f"scored/{organisation}_{model}.jsonl",
                    path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_scored_{datetime.datetime.today()}.jsonl", 
                    repo_type="dataset", 
                    token=TOKEN
                )

        # SAVE TO LEADERBOARD DATA
        eval_entry = {
            "model": model,
            "model_family": model_family,
            "system_prompt": system_prompt,
            "url": url,
            "organisation": organisation,
            "score": scores["all"]/ref_scores_len[val_or_test],
            "score_level1": scores[1]/num_questions[1],
            "score_level2": scores[2]/num_questions[2],
            "score_level3": scores[3]/num_questions[3],
            "date": datetime.datetime.today().strftime('%Y-%m-%d')
        }
        if num_questions[1] + num_questions[2] + num_questions[3] != ref_scores_len[val_or_test]:
            return format_error(f"Your submission has {len(scores['all'])} questions for the {val_or_test} set, but it should have {ref_scores_len[val_or_test]}. Please check your submission.")
        # Catching spam submissions of 100%
        if all((eval_entry[k] == 1 for k in ["score_level1", "score_level2", "score_level3"])):
            return format_error(f"There was a problem with your submission. Please open a discussion.")

        # Testing for duplicates - to see if we want to add something like it as it would allow people to try to see the content of other submissions
        #eval_entry_no_date = {k: v for k, v in eval_entry if k != "date"}
        #columns_no_date = [c for c in eval_results[val_or_test].column_names if c != "date"]
        #if eval_entry_no_date in eval_results[val_or_test].select_columns(columns_no_date):
        #    return format_error(f"Your submission is an exact duplicate from an existing submission.")

        eval_results[val_or_test] = eval_results[val_or_test].add_item(eval_entry)
        print(eval_results)
        if LOCAL_DEBUG:
            print("mock uploaded results to lb")
        else:
            eval_results.push_to_hub(RESULTS_DATASET, config_name = YEAR_VERSION, token=TOKEN)


        return format_log(f"Model {model} submitted by {organisation} successfully.\nPlease wait a few hours and refresh the leaderboard to see your score displayed.")
    except Exception as e:
        print(e)
        return format_error(f"An error occurred, please open a discussion and indicate at what time you encountered the error.\n")


def refresh():
    if HAS_TOKEN and not LOCAL_DEBUG:
        try:
            eval_results = load_dataset(
                RESULTS_DATASET,
                YEAR_VERSION,
                token=TOKEN,
                download_mode="force_redownload",
                verification_mode=VerificationMode.NO_CHECKS,
            )
        except Exception as e:
            print(e)
            eval_results = None
    else:
        eval_results = None
    return get_dataframe_from_results(eval_results=eval_results, split="test")

def upload_file(files):
    file_paths = [file.name for file in files]
    return file_paths


demo = gr.Blocks()
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_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,
                elem_id="citation-button",
            ) #.style(show_copy_button=True)

    gr.Markdown("Results: Test")
    leaderboard_table_test = gr.components.Dataframe(
        value=eval_dataframe_test, datatype=TYPES, interactive=False,
        column_widths=["20%"] 
    )
    #with gr.Tab("Results: Validation"):
    #    leaderboard_table_val = gr.components.Dataframe(
    #        value=eval_dataframe_val, datatype=TYPES, interactive=False,
    #        column_widths=["20%"] 
    #    )

    refresh_button = gr.Button("Refresh")
    refresh_button.click(
        refresh,
        inputs=[],
        outputs=[
            #leaderboard_table_val,
            leaderboard_table_test,
        ],
    )
    with gr.Accordion("Submit a new model for evaluation"):
        with gr.Row():
            gr.Markdown(SUBMISSION_TEXT, elem_classes="markdown-text")
        with gr.Row():
            with gr.Column():
                #level_of_test = gr.Radio(["test"], value="test", label="Split")
                model_name_textbox = gr.Textbox(label="Agent name")
                model_family_textbox = gr.Textbox(label="Model family")
                system_prompt_textbox = gr.Textbox(label="System prompt example")
                url_textbox = gr.Textbox(label="Url to model information")
            with gr.Column():
                organisation = gr.Textbox(label="Organisation")
                mail = gr.Textbox(label="Contact email (will be stored privately, & used if there is an issue with your submission)")
                file_output = gr.File()


        with gr.Row():
            gr.LoginButton()
            submit_button = gr.Button("Submit Eval On Test")
        submission_result = gr.Markdown()
        submit_button.click(
            add_new_eval,
            [
                #level_of_test,
                model_name_textbox,
                model_family_textbox,
                system_prompt_textbox,
                url_textbox,
                file_output,
                organisation,
                mail
            ],
            submission_result,
        )

if IN_SPACES and HAS_TOKEN:
    scheduler = BackgroundScheduler()
    scheduler.add_job(restart_space, "interval", seconds=3600)
    scheduler.start()
demo.launch(debug=True)