Update miniapp_leaderboard.py

miniapp_leaderboard.py

@@ -4,725 +4,191 @@ import json
 import os
 import re
 import uuid
-from urllib.parse import urlparse
 
 import gradio as gr
 import pandas as pd
-import requests
 from huggingface_hub import HfApi, hf_hub_download
-from huggingface_hub.errors import RepositoryNotFoundError
-
-
-APP_NAME = "miniapp"
-
-# Space Secrets:
-# - HF_TOKEN: Hugging Face access token with WRITE access to the dataset repo
-# - LEADERBOARD_DATASET: dataset repo id like "ha251/miniapp-leaderboard"
-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") or "").strip()
-MAX_ENTRIES = int(os.environ.get("MAX_ENTRIES", "500"))
-
-ENTRIES_PREFIX = "entries/"
-
-LEADERBOARD_COLUMNS = [
-    "Model name",
-    "Avg",
-    "Easy",
-    "Mid",
-    "Hard",
-    "Games",
-    "Science",
-    "Tools",
-    "Humanities",
-    "Viz",
-    "Lifestyle",
-    "Submitted at",
-    "Submitter",
-]
-
-NUMERIC_COLS = [
-    "Avg",
-    "Easy",
-    "Mid",
-    "Hard",
-    "Games",
-    "Science",
-    "Tools",
-    "Humanities",
-    "Viz",
-    "Lifestyle",
-]
 
-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"
-)
+APP_NAME = "MiniApp"
 
+HF_TOKEN = os.environ.get("HF_TOKEN")
+LEADERBOARD_DATASET = os.environ.get("LEADERBOARD_DATASET", "").strip()
+OWNER_REVIEW_TOKEN = os.environ.get("OWNER_REVIEW_TOKEN", "").strip()
 
-def _api() -> HfApi:
-    return HfApi(token=HF_TOKEN)
-
+PENDING_PREFIX = "pending/"
+APPROVED_PREFIX = "approved/"
 
-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
+COLUMNS = [
+    "model name",
+    "model family",
+    "avg",
+    "easy",
+    "mid",
+    "hard",
+    "submitted_at",
+]
 
+NUMERIC_COLS = ["avg", "easy", "mid", "hard"]
 
-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 "x")
 
+def _api():
+    return HfApi(token=HF_TOKEN)
 
-def _empty_df() -> pd.DataFrame:
-    return pd.DataFrame(columns=LEADERBOARD_COLUMNS)
 
+def _slug(s: str):
+    s = re.sub(r"[^a-z0-9]+", "-", (s or "").lower())
+    return s.strip("-") or "model"
 
-def _ensure_dataset_readable() -> tuple[bool, str]:
-    if not HF_TOKEN:
-        return False, "Space is missing HF_TOKEN (Secrets)."
-    if not LEADERBOARD_DATASET:
-        return False, "Space is missing LEADERBOARD_DATASET (Secrets)."
-    api = _api()
-    try:
-        api.repo_info(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
-        return True, ""
-    except RepositoryNotFoundError:
-        return False, (
-            f"Dataset repo not found: {LEADERBOARD_DATASET}. "
-            "Create it first (as a dataset) or fix LEADERBOARD_DATASET."
-        )
-    except Exception:
-        return False, "Cannot access the dataset repo. Check token permissions."
 
+def _load_df(prefix: str):
+    if not HF_TOKEN or not LEADERBOARD_DATASET:
+        return pd.DataFrame(columns=COLUMNS)
 
-def _list_entry_files() -> list[str]:
-    ok, _ = _ensure_dataset_readable()
-    if not ok:
-        return []
     api = _api()
     try:
         files = api.list_repo_files(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
     except Exception:
-        return []
-
-    entry_files = [f for f in files if f.startswith(ENTRIES_PREFIX) and f.endswith(".json")]
-    entry_files.sort(reverse=True)
-    return entry_files[:MAX_ENTRIES]
+        return pd.DataFrame(columns=COLUMNS)
 
+    files = [f for f in files if f.startswith(prefix) and f.endswith(".json")]
+    rows = []
 
-def _load_entries_df() -> pd.DataFrame:
-    ok, _ = _ensure_dataset_readable()
-    if not ok:
-        return _empty_df()
-
-    rows: list[dict] = []
-    for filename in _list_entry_files():
+    for f in files:
         try:
             path = hf_hub_download(
                 repo_id=LEADERBOARD_DATASET,
                 repo_type="dataset",
-                filename=filename,
+                filename=f,
                 token=HF_TOKEN,
             )
-            with open(path, "r", encoding="utf-8") as fp:
-                row = json.load(fp)
-                rows.append(row)
+            with open(path, "r") as fp:
+                rows.append(json.load(fp))
         except Exception:
             continue
 
     if not rows:
-        return _empty_df()
+        return pd.DataFrame(columns=COLUMNS)
 
     df = pd.DataFrame(rows)
-    for c in LEADERBOARD_COLUMNS:
+    for c in COLUMNS:
         if c not in df.columns:
             df[c] = ""
-    df = df[LEADERBOARD_COLUMNS]
+
     for c in NUMERIC_COLS:
         df[c] = pd.to_numeric(df[c], errors="coerce")
-    df = df.sort_values(by=["Submitted at"], ascending=False, kind="stable")
-    return df
-
-
-def _apply_search_and_sort(
-    df: pd.DataFrame,
-    search_text: str,
-    search_in: str,
-    sort_by: str,
-    sort_order: str,
-) -> pd.DataFrame:
-    search_text = (search_text or "").strip().lower()
-    if search_text:
-        if search_in == "Model name":
-            df = df[df["Model name"].astype(str).str.lower().str.contains(search_text, na=False)]
-        elif search_in == "Submitter":
-            df = df[df["Submitter"].astype(str).str.lower().str.contains(search_text, na=False)]
-        else:
-            mask = (
-                df["Model name"].astype(str).str.lower().str.contains(search_text, na=False)
-                | df["Submitter"].astype(str).str.lower().str.contains(search_text, na=False)
-            )
-            df = df[mask]
 
-    ascending = sort_order == "Ascending"
-    if sort_by in df.columns:
-        df = df.sort_values(by=[sort_by], ascending=ascending, kind="stable", na_position="last")
-    return df
+    df = df.sort_values(by="avg", ascending=False)
+    return df[COLUMNS]
 
 
-def refresh(search_text: str, search_in: str, sort_by: str, sort_order: str):
-    df = _load_entries_df()
-    return _apply_search_and_sort(df, search_text, search_in, sort_by, sort_order)
+def refresh():
+    return _load_df(APPROVED_PREFIX)
 
 
-def _parse_hf_created_at(created_at: str) -> datetime.datetime | None:
-    try:
-        if created_at.endswith("Z"):
-            created_at = created_at[:-1] + "+00:00"
-        return datetime.datetime.fromisoformat(created_at)
-    except Exception:
-        return None
+def submit(model_name, model_family, email, zip_file):
+    if not model_name or not model_family or not email or zip_file is None:
+        return "All fields are required.", refresh()
 
+    if not zip_file.name.endswith(".zip"):
+        return "Please upload a .zip file.", refresh()
 
-def _check_user_eligibility(username: str) -> tuple[bool, str]:
-    """
-    - Must be older than ~4 months (>= 120 days)
-    """
-    try:
-        r = requests.get(f"https://huggingface.co/api/users/{username}/overview", timeout=10)
-        r.raise_for_status()
-        created_at = r.json().get("createdAt")
-        if not created_at:
-            return False, "Cannot verify account creation date."
-        dt = _parse_hf_created_at(created_at)
-        if not dt:
-            return False, "Cannot parse account creation date."
-        now = datetime.datetime.now(datetime.timezone.utc)
-        if dt.tzinfo is None:
-            dt = dt.replace(tzinfo=datetime.timezone.utc)
-        age_days = (now - dt).days
-        if age_days < 120:
-            return False, "Account must be older than 4 months to submit."
-        return True, ""
-    except Exception:
-        return False, "Cannot verify Hugging Face account. Please try again later."
-
-
-def _submitted_today(username: str) -> bool:
-    df = _load_entries_df()
-    if df.empty:
-        return False
-    today = datetime.datetime.utcnow().date().isoformat()
-    user_rows = df[df["Submitter"].astype(str) == username]
-    if user_rows.empty:
-        return False
-    return any(str(v).startswith(today) for v in user_rows["Submitted at"].tolist())
-
-
-def submit(
-    model_name: str,
-    model_api: str,
-    api_key: str,
-    avg: float,
-    easy: float,
-    mid: float,
-    hard: float,
-    games: float,
-    science: float,
-    tools: float,
-    humanities: float,
-    viz: float,
-    lifestyle: float,
-    search_text: str,
-    search_in: str,
-    sort_by: str,
-    sort_order: str,
-    profile: gr.OAuthProfile | None,
-):
-    # Login required to submit (in Spaces)
-    if IN_SPACES and (profile is None or not getattr(profile, "username", None)):
-        return "You must log in to submit.", refresh(search_text, search_in, sort_by, sort_order)
-
-    submitter = (getattr(profile, "username", None) if profile is not None else "local") or "anonymous"
-
-    model_name = (model_name or "").strip()
-    model_api = (model_api or "").strip()
-    api_key = (api_key or "").strip()
-
-    if not model_name:
-        return "Model name is required.", refresh(search_text, search_in, sort_by, sort_order)
-    if not model_api:
-        return "Model API URL is required.", refresh(search_text, search_in, sort_by, sort_order)
-    if not _is_valid_http_url(model_api):
-        return "Model API must be a valid http(s) URL.", refresh(search_text, search_in, sort_by, sort_order)
-    if not api_key:
-        return "API key is required.", refresh(search_text, search_in, sort_by, sort_order)
-
-    ok, msg = _ensure_dataset_readable()
-    if not ok:
-        return msg, refresh(search_text, search_in, sort_by, sort_order)
-
-    if IN_SPACES:
-        ok, msg = _check_user_eligibility(submitter)
-        if not ok:
-            return msg, refresh(search_text, search_in, sort_by, sort_order)
-
-    if _submitted_today(submitter):
-        return "You have already submitted today. Please try again tomorrow.", refresh(search_text, search_in, sort_by, sort_order)
-
-    now = datetime.datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
-    nonce = uuid.uuid4().hex[:8]
+    now = datetime.datetime.utcnow().isoformat() + "Z"
     safe_model = _slug(model_name)
-    safe_user = _slug(submitter)
-    path_in_repo = f"{ENTRIES_PREFIX}{now[:10]}/{now}-{safe_user}-{safe_model}-{nonce}.json"
+    nonce = uuid.uuid4().hex[:6]
+
+    json_path = f"{PENDING_PREFIX}{now}-{safe_model}-{nonce}.json"
+    zip_path = f"{PENDING_PREFIX}{now}-{safe_model}-{nonce}.zip"
 
-    # NOTE: api_key is collected but NOT stored.
     payload = {
-        "Model name": model_name,
-        "Avg": avg,
-        "Easy": easy,
-        "Mid": mid,
-        "Hard": hard,
-        "Games": games,
-        "Science": science,
-        "Tools": tools,
-        "Humanities": humanities,
-        "Viz": viz,
-        "Lifestyle": lifestyle,
-        "Submitted at": now,
-        "Submitter": submitter,
-        "Model API": model_api,
+        "model name": model_name,
+        "model family": model_family,
+        "avg": 0,
+        "easy": 0,
+        "mid": 0,
+        "hard": 0,
+        "submitted_at": now,
+        "email": email,
     }
 
     api = _api()
-    data = (json.dumps(payload, ensure_ascii=False, indent=2) + "\n").encode("utf-8")
+
+    # 上传 JSON
     api.upload_file(
         repo_id=LEADERBOARD_DATASET,
         repo_type="dataset",
-        path_or_fileobj=io.BytesIO(data),
-        path_in_repo=path_in_repo,
-        commit_message=f"miniapp: submit {submitter}/{model_name}",
-        token=HF_TOKEN,
+        path_or_fileobj=io.BytesIO(
+            json.dumps(payload, indent=2).encode("utf-8")
+        ),
+        path_in_repo=json_path,
+        commit_message=f"submit {model_name}",
     )
 
-    return "Submitted successfully.", refresh(search_text, search_in, sort_by, sort_order)
-
-
-with gr.Blocks(title=f"{APP_NAME} leaderboard") as demo:
-    # Layout: Overview -> Leaderboard -> Submission instructions
-    gr.Markdown(
-        f"## {APP_NAME} leaderboard\n\n"
-        "### Overview\n"
-        "_Placeholder overview text. Replace this with your benchmark description, rules, and links._\n\n"
-        "### Leaderboard\n"
-        "_The leaderboard below supports sorting and searching._\n\n"
-        "### Submission\n"
-        "_Placeholder submission instructions. Requires login. One submission per user per day. "
-        "Account must be older than 4 months._\n"
-    )
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            with gr.Row():
-                search_text = gr.Textbox(label="Search", placeholder="Model name or submitter")
-                search_in = gr.Dropdown(
-                    label="Search in",
-                    choices=["Both", "Model name", "Submitter"],
-                    value="Both",
-                )
-            with gr.Row():
-                sort_by = gr.Dropdown(label="Sort by", choices=LEADERBOARD_COLUMNS, value="Avg")
-                sort_order = gr.Radio(label="Order", choices=["Descending", "Ascending"], value="Descending")
-                refresh_btn = gr.Button("Refresh")
-
-            leaderboard = gr.Dataframe(
-                label="Leaderboard",
-                value=_load_entries_df(),
-                interactive=False,
-                wrap=True,
-            )
-
-        with gr.Column(scale=2):
-            with gr.Accordion("Submit (login required)", open=True):
-                model_name = gr.Textbox(label="Model name (required)")
-                model_api = gr.Textbox(label="Model API (required)", placeholder="https://...")
-                api_key = gr.Textbox(label="API key (required)", type="password", placeholder="Will not be stored")
-
-                with gr.Row():
-                    avg = gr.Number(label="Avg", value=0)
-                    easy = gr.Number(label="Easy", value=0)
-                    mid = gr.Number(label="Mid", value=0)
-                    hard = gr.Number(label="Hard", value=0)
-
-                with gr.Row():
-                    games = gr.Number(label="Games", value=0)
-                    science = gr.Number(label="Science", value=0)
-                    tools = gr.Number(label="Tools", value=0)
-
-                with gr.Row():
-                    humanities = gr.Number(label="Humanities", value=0)
-                    viz = gr.Number(label="Viz", value=0)
-                    lifestyle = gr.Number(label="Lifestyle", value=0)
-
-                with gr.Row():
-                    gr.LoginButton()
-                    submit_btn = gr.Button("Submit", variant="primary")
-                status = gr.Markdown()
-
-    refresh_btn.click(refresh, inputs=[search_text, search_in, sort_by, sort_order], outputs=[leaderboard])
-    submit_btn.click(
-        submit,
-        inputs=[
-            model_name,
-            model_api,
-            api_key,
-            avg,
-            easy,
-            mid,
-            hard,
-            games,
-            science,
-            tools,
-            humanities,
-            viz,
-            lifestyle,
-            search_text,
-            search_in,
-            sort_by,
-            sort_order,
-        ],
-        outputs=[status, leaderboard],
-    )
-
-demo.launch()
-
-import datetime
-import io
-import json
-import os
-import re
-import uuid
-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()
-# Owner 审核口令（放到 Space Secrets；不要放到公开 Variables）
-OWNER_REVIEW_TOKEN = os.environ.get("OWNER_REVIEW_TOKEN", "").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"))
-
-PENDING_PREFIX = "pending/"
-APPROVED_PREFIX = "approved/"
-
-
-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 _list_json_files(prefix: str) -> list[str]:
-    if not HF_TOKEN or not LEADERBOARD_DATASET:
-        return []
-
-    api = _api()
-    try:
-        files = api.list_repo_files(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
-    except Exception:
-        return []
-
-    return sorted(
-        [f for f in files if f.startswith(prefix) and f.endswith(".json")],
-        reverse=True,
-    )[:MAX_ENTRIES]
-
-
-def _load_entries_df(prefix: str, include_filename: bool) -> pd.DataFrame:
-    files = _list_json_files(prefix)
-    rows = []
-    for filename in 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:
-                row = json.load(fp)
-                if include_filename:
-                    row["_filename"] = filename
-                rows.append(row)
-        except Exception:
-            continue
-
-    if not rows:
-        df = _empty_df()
-        if include_filename:
-            df["_filename"] = []
-        return df
-
-    df = pd.DataFrame(rows)
-    for col in ["submitted_at", "username", "model_name", "model_api", "notes"]:
-        if col not in df.columns:
-            df[col] = ""
-    cols = ["submitted_at", "username", "model_name", "model_api", "notes"]
-    if include_filename:
-        if "_filename" not in df.columns:
-            df["_filename"] = ""
-        cols = cols + ["_filename"]
-    df = df[cols]
-    df = df.sort_values(by=["submitted_at"], ascending=False, kind="stable")
-    return df
-
-
-def refresh():
-    return _load_entries_df(APPROVED_PREFIX, include_filename=False)
-
-
-def refresh_pending():
-    df = _load_entries_df(PENDING_PREFIX, include_filename=True)
-    choices = list(df["_filename"]) if "_filename" in df.columns else []
-    return df, gr.update(choices=choices, value=choices[0] if choices else None)
-
-
-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 ("local" if not IN_SPACES else "anonymous")
-
-    if not model_name:
-        return "请填写 **模型名称**。", refresh()
-    if not model_api:
-        return "请填写 **模型 API**。", refresh()
-    if not _is_valid_http_url(model_api):
-        return "**模型 API** 需要是合法的 `http(s)://...` URL。", refresh()
-
-    if not HF_TOKEN:
-        return "Space 未配置 **HF_TOKEN**（Secrets），���法写入排行榜。", refresh()
-    if not LEADERBOARD_DATASET:
-        return "Space 未配置 **LEADERBOARD_DATASET**（例如：`your-username/miniapp-leaderboard`）。", refresh()
-
-    _ensure_dataset_repo()
-    api = _api()
-
-    now = datetime.datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
-    safe_model = _slug(model_name)
-    safe_user = _slug(username)
-    nonce = uuid.uuid4().hex[:8]
-    path_in_repo = f"{PENDING_PREFIX}{now[:10]}/{now}-{safe_user}-{safe_model}-{nonce}.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)
-
+    # 上传 ZIP
     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(pending) {username}/{model_name}",
-        token=HF_TOKEN,
+        path_or_fileobj=zip_file,
+        path_in_repo=zip_path,
+        commit_message=f"upload zip {model_name}",
     )
 
-    return "已提交，等待 owner 审核后才会上榜。", refresh()
+    return "Submitted. Waiting for review.", refresh()
 
 
-def approve(pending_filename: str | None, review_token: str | None):
-    pending_filename = (pending_filename or "").strip()
-    review_token = (review_token or "").strip()
+with gr.Blocks(title=f"{APP_NAME} leaderboard") as demo:
 
-    if not pending_filename:
-        df, dd = refresh_pending()
-        return "请选择一条待审核提交。", df, dd, refresh()
+    gr.Markdown(f"# {APP_NAME} Leaderboard")
 
-    if not OWNER_REVIEW_TOKEN:
-        df, dd = refresh_pending()
-        return "Space 未配置 **OWNER_REVIEW_TOKEN**（Secrets），无法启用审核。", df, dd, refresh()
+    # ===============================
+    # Leaderboard 说明（占位符）
+    # ===============================
+    gr.Markdown(
+        """
+        ## Leaderboard Description
 
-    if review_token != OWNER_REVIEW_TOKEN:
-        df, dd = refresh_pending()
-        return "审核口令不正确。", df, dd, refresh()
+        _Placeholder text: describe your benchmark, evaluation protocol, 
+        scoring methodology, and ranking rules here._
+        """
+    )
 
-    if not HF_TOKEN or not LEADERBOARD_DATASET:
-        df, dd = refresh_pending()
-        return "Space 未配置 HF 写入权限，无法审核。", df, dd, refresh()
+    # 排行榜（占满横向）
+    leaderboard = gr.Dataframe(
+        value=_load_df(APPROVED_PREFIX),
+        interactive=False,
+        wrap=True,
+        height=500,
+    )
 
-    api = _api()
-    try:
-        local_path = hf_hub_download(
-            repo_id=LEADERBOARD_DATASET,
-            repo_type="dataset",
-            filename=pending_filename,
-            token=HF_TOKEN,
-        )
-        with open(local_path, "r", encoding="utf-8") as fp:
-            payload = json.load(fp)
-    except Exception as e:
-        df, dd = refresh_pending()
-        return f"读取待审核文件失败：{e}", df, dd, refresh()
-
-    # 将文件复制到 approved 目录（保留原提交时间等字段）
-    base = pending_filename[len(PENDING_PREFIX) :] if pending_filename.startswith(PENDING_PREFIX) else pending_filename
-    approved_filename = f"{APPROVED_PREFIX}{base}"
-    data = (json.dumps(payload, ensure_ascii=False, indent=2) + "\n").encode("utf-8")
-    bio = io.BytesIO(data)
+    refresh_btn = gr.Button("Refresh")
 
-    try:
-        api.upload_file(
-            repo_id=LEADERBOARD_DATASET,
-            repo_type="dataset",
-            path_or_fileobj=bio,
-            path_in_repo=approved_filename,
-            commit_message=f"miniapp: approve {payload.get('username','')}/{payload.get('model_name','')}",
-            token=HF_TOKEN,
-        )
-        # 删除 pending 原文件（真正“上榜前审核”）
-        api.delete_file(
-            repo_id=LEADERBOARD_DATASET,
-            repo_type="dataset",
-            path_in_repo=pending_filename,
-            commit_message=f"miniapp: remove pending {pending_filename}",
-            token=HF_TOKEN,
-        )
-    except Exception as e:
-        df, dd = refresh_pending()
-        return f"审核写入失败：{e}", df, dd, refresh()
-
-    pending_df, pending_dd = refresh_pending()
-    approved_df = refresh()
-    return "已通过审核并更新榜单。", pending_df, pending_dd, approved_df
+    # ===============================
+    # Submission 区域
+    # ===============================
+    gr.Markdown("---")
 
-
-with gr.Blocks(title=f"{APP_NAME} leaderboard") as demo:
     gr.Markdown(
-        f"## {APP_NAME} leaderboard\n\n"
-        "用户提交信息后会进入 **pending（待审核）**；owner 审核通过后才会进入 **approved（上榜）**。\n\n"
-        f"- 当前 `LEADERBOARD_DATASET`: `{LEADERBOARD_DATASET or '(未配置)'}`\n"
+        """
+        ## Submission Instructions
+
+        _Placeholder text: explain submission format, evaluation pipeline, 
+        review policy, and contact information here._
+        """
     )
 
-    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)
-            username = gr.Textbox(
-                label="用户名（可选）",
-                placeholder="建议填你的 HF 用户名（也可留空）",
-                value="" if IN_SPACES else "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_entries_df(APPROVED_PREFIX, include_filename=False),
-                interactive=False,
-                wrap=True,
-            )
-            refresh_btn = gr.Button("刷新")
+    model_name = gr.Textbox(label="Model name")
+    model_family = gr.Textbox(label="Model family")
+    email = gr.Textbox(label="Email")
+    zip_file = gr.File(label="Upload zip", file_types=[".zip"])
+
+    submit_btn = gr.Button("Submit", variant="primary")
+    status = gr.Markdown()
+
+    refresh_btn.click(refresh, outputs=[leaderboard])
 
     submit_btn.click(
         submit,
-        inputs=[model_name, model_api, notes, username],
+        inputs=[model_name, model_family, email, zip_file],
         outputs=[status, leaderboard],
     )
-    refresh_btn.click(refresh, inputs=[], outputs=[leaderboard])
-
-    with gr.Accordion("Owner 审核（测试 demo）", open=False):
-        gr.Markdown(
-            "需要在 Space Secrets 中配置 `OWNER_REVIEW_TOKEN`。只有输入正确口令，才允许把 pending 放入榜单。"
-        )
-        review_token = gr.Textbox(label="审核口令", type="password", placeholder="OWNER_REVIEW_TOKEN")
-        pending_refresh_btn = gr.Button("刷新待审核列表")
-        pending_df = gr.Dataframe(
-            label="待审核（pending）",
-            value=_load_entries_df(PENDING_PREFIX, include_filename=True),
-            interactive=False,
-            wrap=True,
-        )
-        pending_pick = gr.Dropdown(
-            label="选择要通过的提交（文件）",
-            choices=_list_json_files(PENDING_PREFIX),
-        )
-        approve_btn = gr.Button("通过审核并上榜", variant="primary")
-        approve_status = gr.Markdown()
-
-        pending_refresh_btn.click(
-            refresh_pending,
-            inputs=[],
-            outputs=[pending_df, pending_pick],
-        )
-        approve_btn.click(
-            approve,
-            inputs=[pending_pick, review_token],
-            outputs=[approve_status, pending_df, pending_pick, leaderboard],
-        )
-
-demo.launch()
 
+demo.launch()