Delete app.py

app.py

@@ -1,775 +0,0 @@
-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)