app.py

"""ST-WebAgentBench Leaderboard — HuggingFace Spaces Gradio App.

Displays benchmark results with:
- Main leaderboard table ranked by CuP
- Safety radar chart and risk heatmap
- Performance-Safety Frontier (Pareto front)
- Tier difficulty analysis
- Submission upload with 5-layer verification
"""

import base64
import fcntl
import hashlib
import hmac as _hmac
import json
import logging
import os
import re
import secrets
import tempfile
import time as _time
import traceback
from collections import Counter
from datetime import datetime, timezone
from enum import Enum
from pathlib import Path
from typing import List, Optional

import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
from huggingface_hub import CommitScheduler, HfApi
import pandas as pd
import plotly.graph_objects as go

from validation.schema import (
    Submission,
    SAFETY_DIMENSIONS,
    DIMENSION_DISPLAY,
    EXPECTED_TASK_COUNT,
    EXPECTED_POLICY_COUNT,
    WEB_APPLICATIONS,
    TIER_CONFIG,
)
from validation.validate import (
    validate_submission,
    recompute_metrics_from_evidence,
    detect_anomalies,
    validate_anti_gaming,
    is_safe_string,
)

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# IBM logo embedded as base64 data URI (avoids LFS/Xet issues on HF Spaces)
# ---------------------------------------------------------------------------
_IBM_LOGO_B64 = (
    "data:image/png;base64,"
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    "CCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh"
    "hBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ"
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)


def _get_admin_password() -> str:
    """Read admin password at call time (not import time) so Space picks up secret changes."""
    return os.environ.get("ADMIN_PASSWORD", "")


# ---------------------------------------------------------------------------
# Admin security: timing-safe comparison, rate limiting, sessions, audit
# ---------------------------------------------------------------------------

def _verify_admin_password(password: str) -> bool:
    """Constant-time password comparison to prevent timing attacks."""
    admin_pw = _get_admin_password()
    if not admin_pw or not password:
        return False
    return _hmac.compare_digest(password.encode("utf-8"), admin_pw.encode("utf-8"))


# Rate limiting — in-memory, resets on Space restart (acceptable).
_ADMIN_FAIL_LOG: list[float] = []
_ADMIN_MAX_FAILS = 5
_ADMIN_WINDOW_SECS = 300      # 5-minute sliding window
_ADMIN_LOCKOUT_SECS = 600     # 10-minute lockout after exceeding


def _check_rate_limit() -> str | None:
    """Return an error message if rate-limited, else None."""
    now = _time.time()
    # Prune old entries outside the lockout window
    cutoff = now - _ADMIN_LOCKOUT_SECS
    while _ADMIN_FAIL_LOG and _ADMIN_FAIL_LOG[0] < cutoff:
        _ADMIN_FAIL_LOG.pop(0)
    # Count failures in the sliding window
    recent = [t for t in _ADMIN_FAIL_LOG if t > now - _ADMIN_WINDOW_SECS]
    if len(recent) >= _ADMIN_MAX_FAILS:
        last_fail = max(_ADMIN_FAIL_LOG)
        unlock_at = last_fail + _ADMIN_LOCKOUT_SECS
        remaining = int(unlock_at - now)
        if remaining > 0:
            return f"Too many failed attempts. Try again in {remaining} seconds."
    return None


def _record_failed_attempt() -> None:
    _ADMIN_FAIL_LOG.append(_time.time())


# Session management — in-memory tokens, 1-hour TTL.
_ADMIN_SESSIONS: dict[str, float] = {}
_SESSION_TTL = 3600  # 1 hour


def _create_admin_session() -> str:
    """Generate a session token and store it with an expiry."""
    token = secrets.token_hex(32)
    _ADMIN_SESSIONS[token] = _time.time() + _SESSION_TTL
    # Prune expired sessions
    now = _time.time()
    expired = [k for k, v in _ADMIN_SESSIONS.items() if v < now]
    for k in expired:
        del _ADMIN_SESSIONS[k]
    return token


def _verify_session(token: str) -> bool:
    """Check if a session token is valid and not expired."""
    if not token or token not in _ADMIN_SESSIONS:
        return False
    if _time.time() > _ADMIN_SESSIONS[token]:
        del _ADMIN_SESSIONS[token]
        return False
    return True


# Audit logging — append-only JSONL.
ADMIN_AUDIT_FILE = Path("data/admin_audit.jsonl")


def _log_admin_action(action: str, details: str) -> None:
    """Append an admin action to the audit log."""
    ADMIN_AUDIT_FILE.parent.mkdir(parents=True, exist_ok=True)
    record = {
        "action": action,
        "details": details,
        "timestamp": datetime.now(timezone.utc).isoformat(),
    }
    with open(ADMIN_AUDIT_FILE, "a") as f:
        f.write(json.dumps(record) + "\n")


# Master secret env var name — used to derive per-user signing keys.
# Set as HF Space secret — never exposed publicly.
_MASTER_KEY_ENV = "ST_BENCH_MASTER_KEY"


def _get_master_key() -> str:
    """Read the master key at call time (not import time) for testability."""
    return os.environ.get(_MASTER_KEY_ENV, "")

# Email validation pattern
_EMAIL_RE = re.compile(r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$")

# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------

_APP_DIR = Path(__file__).resolve().parent
SUBMISSIONS_FILE = _APP_DIR / "data" / "submissions.jsonl"
KEY_REQUESTS_FILE = _APP_DIR / "data" / "key_requests.jsonl"
TASKS_FILE = _APP_DIR / "data" / "test.raw.json"
CANONICAL_HASHES_FILE = _APP_DIR / "data" / "canonical_hashes.json"
ADMIN_AUDIT_FILE = _APP_DIR / "data" / "admin_audit.jsonl"  # override relative path above


# ---------------------------------------------------------------------------
# Data persistence — CommitScheduler auto-syncs data/ to HF dataset repo
# ---------------------------------------------------------------------------

_DATA_REPO_ID = "ST-WebAgentBench/st-webagentbench-data"
_DATA_DIR = _APP_DIR / "data"
_scheduler: CommitScheduler | None = None
_PERSISTENCE_ENABLED = False


def _init_persistence() -> bool:
    """Initialize CommitScheduler for data persistence. Returns True if enabled."""
    global _scheduler
    _DATA_DIR.mkdir(parents=True, exist_ok=True)

    token = os.environ.get("HF_TOKEN")
    if not token:
        logger.warning("No HF token found — data persistence disabled (HF_TOKEN not in env)")
        return False

    logger.warning("HF_TOKEN found, initializing persistence...")
    api = HfApi(token=token)

    try:
        # Download existing data files from the repo before starting the scheduler
        for filename in ["submissions.jsonl", "key_requests.jsonl", "admin_audit.jsonl"]:
            local = _DATA_DIR / filename
            if not local.exists() or local.stat().st_size == 0:
                try:
                    api.hf_hub_download(
                        repo_id=_DATA_REPO_ID,
                        repo_type="dataset",
                        filename=filename,
                        local_dir=str(_DATA_DIR),
                    )
                    logger.info("Restored %s from data repo", filename)
                except Exception:
                    logger.info("No existing %s in data repo (first run?)", filename)

        # Start the scheduler — auto-commits data/ every 2 minutes
        _scheduler = CommitScheduler(
            repo_id=_DATA_REPO_ID,
            folder_path=_DATA_DIR,
            every=2,
            repo_type="dataset",
            private=True,
            allow_patterns=["*.jsonl"],
            squash_history=True,
            hf_api=api,
        )
        logger.warning(
            "CommitScheduler started — persisting to %s every 2 min",
            _DATA_REPO_ID,
        )
        return True
    except Exception:
        logger.error("Failed to initialize persistence", exc_info=True)
        return False


# Load canonical task definitions for validation
_TASKS_DATA = None
_CANONICAL_HASHES = None


def _load_tasks_data():
    global _TASKS_DATA
    if _TASKS_DATA is None and TASKS_FILE.exists():
        with open(TASKS_FILE) as f:
            _TASKS_DATA = json.load(f)
    return _TASKS_DATA


def _load_canonical_hashes():
    """Load canonical code hashes, preferring the env-var source.

    Priority:
    1. CANONICAL_HASHES env var (JSON string) — keeps hashes private
    2. data/canonical_hashes.json file — fallback for local development
    """
    global _CANONICAL_HASHES
    if _CANONICAL_HASHES is not None:
        return _CANONICAL_HASHES

    # Try env var first (set as HF Space secret)
    env_hashes = os.environ.get("CANONICAL_HASHES", "").strip()
    if env_hashes:
        try:
            parsed = json.loads(env_hashes)
            # Support both {"1.0.0": {...}} and flat {...} formats
            if "1.0.0" in parsed:
                _CANONICAL_HASHES = parsed["1.0.0"]
            else:
                _CANONICAL_HASHES = parsed
            logger.info("Loaded canonical hashes from environment variable")
            return _CANONICAL_HASHES
        except json.JSONDecodeError:
            logger.warning("Failed to parse CANONICAL_HASHES env var")

    # Fallback to file
    if CANONICAL_HASHES_FILE.exists():
        with open(CANONICAL_HASHES_FILE) as f:
            all_hashes = json.load(f)
            _CANONICAL_HASHES = all_hashes.get("1.0.0", {})
        logger.info("Loaded canonical hashes from file")
    return _CANONICAL_HASHES

# ---------------------------------------------------------------------------
# Per-user signing key management
# ---------------------------------------------------------------------------


def derive_user_key(email: str) -> str:
    """Derive a per-user signing key from the master secret and email.

    key = HMAC-SHA256(master_key, normalised_email)
    """
    master = _get_master_key()
    normalised = email.strip().lower()
    return _hmac.new(
        master.encode("utf-8"),
        normalised.encode("utf-8"),
        hashlib.sha256,
    ).hexdigest()


def _log_key_request(email: str, team: str, institution: str) -> None:
    """Append a key-request record to the log (admin-only visibility)."""
    KEY_REQUESTS_FILE.parent.mkdir(parents=True, exist_ok=True)
    record = {
        "email": email.strip().lower(),
        "team": team.strip(),
        "institution": institution.strip(),
        "timestamp": datetime.now(timezone.utc).isoformat(),
    }
    with open(KEY_REQUESTS_FILE, "a") as f:
        f.write(json.dumps(record) + "\n")


def _load_key_requests() -> list[dict]:
    """Load all key-request records."""
    if not KEY_REQUESTS_FILE.exists():
        return []
    records = []
    for line in KEY_REQUESTS_FILE.read_text().strip().split("\n"):
        if line.strip():
            try:
                records.append(json.loads(line))
            except json.JSONDecodeError:
                continue
    return records


def handle_key_request(email: str, team: str, institution: str) -> str:
    """Validate inputs, derive the user key, log the request, return the key."""
    if not _get_master_key():
        return "ERROR: Key generation is not configured on this Space. Contact the maintainers."

    email = (email or "").strip()
    team = (team or "").strip()
    institution = (institution or "").strip()

    if not email:
        return "Please enter your email address."
    if not _EMAIL_RE.match(email):
        return f"Invalid email address: {email}"
    if not team:
        return "Please enter your team name."
    if not is_safe_string(team, max_length=256):
        return "Team name contains disallowed characters."
    if institution and not is_safe_string(institution, max_length=256):
        return "Institution contains disallowed characters."

    user_key = derive_user_key(email)
    _log_key_request(email, team, institution)

    return (
        f"Your signing key (set this as an environment variable before running the benchmark):\n\n"
        f"export ST_BENCH_SIGNING_KEY=\"{user_key}\"\n\n"
        f"IMPORTANT: Use the same email ({email}) as --contact-email when generating your submission."
    )


RISK_COLORS = {"low": "#22c55e", "medium": "#eab308", "high": "#ef4444"}

# ---------------------------------------------------------------------------
# UI Design Constants
# ---------------------------------------------------------------------------

CUSTOM_CSS = """
/* === Global === */
.gradio-container {
    max-width: 1200px !important;
    margin: 0 auto !important;
}

/* === Hero Header === */
#hero-header {
    background: linear-gradient(135deg, #1e3a8a 0%, #312e81 50%, #1e293b 100%);
    border-radius: 16px;
    padding: 40px 48px 32px;
    margin-bottom: 8px;
    position: relative;
    overflow: hidden;
}
#hero-header::before {
    content: '';
    position: absolute;
    top: -50%;
    right: -20%;
    width: 500px;
    height: 500px;
    background: radial-gradient(circle, rgba(99, 102, 241, 0.15) 0%, transparent 70%);
    pointer-events: none;
}
#hero-header h1 {
    color: white;
    font-size: 2rem;
    font-weight: 700;
    margin: 0 0 6px 0;
    letter-spacing: -0.02em;
}
#hero-header .subtitle {
    color: #cbd5e1;
    font-size: 1.05rem;
    margin: 0 0 16px 0;
    font-weight: 400;
}
#hero-header .iclr-badge {
    display: inline-block;
    background: linear-gradient(135deg, #6366f1, #818cf8);
    color: white;
    font-size: 0.75rem;
    font-weight: 600;
    padding: 3px 10px;
    border-radius: 9999px;
    letter-spacing: 0.03em;
    vertical-align: middle;
    margin-left: 8px;
}
#hero-header .nav-links {
    margin-top: 12px;
    display: flex;
    gap: 20px;
    flex-wrap: wrap;
}
#hero-header .nav-links a {
    color: #93c5fd;
    text-decoration: none;
    font-size: 0.9rem;
    font-weight: 500;
    transition: color 0.15s ease;
    display: inline-flex;
    align-items: center;
    gap: 4px;
}
#hero-header .nav-links a:hover {
    color: white;
}
#hero-header .stats-strip {
    display: flex;
    gap: 32px;
    margin-top: 20px;
    padding-top: 16px;
    border-top: 1px solid rgba(255,255,255,0.1);
    flex-wrap: wrap;
}
#hero-header .stat-item {
    text-align: left;
}
#hero-header .stat-value {
    color: white;
    font-size: 1.5rem;
    font-weight: 700;
    line-height: 1.2;
}
#hero-header .stat-label {
    color: #94a3b8;
    font-size: 0.78rem;
    font-weight: 500;
    text-transform: uppercase;
    letter-spacing: 0.05em;
}
#hero-header .logo-row {
    display: flex;
    align-items: center;
    gap: 16px;
    margin-bottom: 12px;
}
#hero-header .logo-row img {
    height: 64px;
    filter: brightness(0) invert(1);
    opacity: 0.9;
}

/* === Tabs === */
.tabs > .tab-nav {
    border-bottom: 2px solid #e2e8f0 !important;
    gap: 0 !important;
    padding: 0 4px !important;
    background: transparent !important;
}
.tabs > .tab-nav > button {
    border: none !important;
    border-bottom: 2px solid transparent !important;
    margin-bottom: -2px !important;
    padding: 10px 18px !important;
    font-weight: 500 !important;
    font-size: 0.9rem !important;
    color: #64748b !important;
    background: transparent !important;
    transition: color 0.15s ease, border-color 0.15s ease !important;
    border-radius: 0 !important;
    box-shadow: none !important;
}
.tabs > .tab-nav > button:hover {
    color: #1e293b !important;
    background: transparent !important;
}
.tabs > .tab-nav > button.selected {
    color: #2563eb !important;
    border-bottom-color: #2563eb !important;
    font-weight: 600 !important;
    background: transparent !important;
}

/* === Tables (Dataframe) === */
/* Container styling */
.table-wrap {
    border-radius: 12px !important;
    border: 1px solid #e2e8f0 !important;
    overflow: hidden !important;
}
/* Ensure last row has a visible bottom border for table closure */
table tbody tr:last-child td {
    border-bottom: 2px solid #e2e8f0 !important;
}
/* Override Gradio 6 internal: force nowrap on header text */
.header-content {
    white-space: nowrap !important;
    overflow-wrap: normal !important;
    word-break: normal !important;
}
/* Override Gradio 6 internal: use auto layout instead of fixed */
table :is(thead, tfoot, tbody) {
    table-layout: auto !important;
}
/* Header cell styling */
table thead th {
    background: #f1f5f9 !important;
    color: #334155 !important;
    font-weight: 600 !important;
    font-size: 0.82rem !important;
    text-transform: uppercase !important;
    letter-spacing: 0.04em !important;
    border-bottom: 2px solid #e2e8f0 !important;
}
/* Data cell styling */
table tbody td {
    font-size: 0.88rem !important;
    border-bottom: 1px solid #f1f5f9 !important;
}
/* Row hover */
table tbody tr:hover {
    background: #eff6ff !important;
}

/* === Accordion (FAQ) === */
.faq-section .accordion {
    border: 1px solid #e2e8f0 !important;
    border-radius: 10px !important;
    margin-bottom: 8px !important;
    overflow: hidden !important;
    box-shadow: none !important;
}
.faq-section .accordion > .label-wrap {
    padding: 14px 18px !important;
    background: white !important;
}
.faq-section .accordion > .label-wrap:hover {
    background: #f8fafc !important;
}
.faq-section .accordion .prose {
    padding: 4px 18px 18px !important;
    color: #475569 !important;
    line-height: 1.65 !important;
}
.faq-section h3 {
    color: #1e293b !important;
    font-size: 1.05rem !important;
    font-weight: 600 !important;
    margin-top: 28px !important;
    margin-bottom: 12px !important;
    padding-bottom: 6px !important;
    border-bottom: 1px solid #e2e8f0 !important;
}

/* === Form Cards === */
.form-card {
    background: white !important;
    border: 1px solid #e2e8f0 !important;
    border-radius: 12px !important;
    padding: 24px !important;
    box-shadow: 0 1px 3px 0 rgb(0 0 0 / 0.04) !important;
}

/* === Filter Row === */
/* === Filter Row === */
.filter-row {
    background: #f8fafc !important;
    border: 1px solid #e2e8f0 !important;
    border-radius: 12px !important;
    padding: 16px 20px !important;
    margin-bottom: 16px !important;
    display: flex !important;
    align-items: end !important;
    gap: 16px !important;
}
.filter-row > div {
    flex: 1 !important;
    min-width: 0 !important;
}
.filter-row .wrap {
    gap: 4px !important;
}

/* === Responsive === */
@media (max-width: 768px) {
    #hero-header {
        padding: 28px 24px 24px;
    }
    #hero-header h1 {
        font-size: 1.5rem;
    }
    #hero-header .stats-strip {
        gap: 20px;
    }
    #hero-header .stat-value {
        font-size: 1.2rem;
    }
    .tabs > .tab-nav > button {
        padding: 8px 12px !important;
        font-size: 0.82rem !important;
    }
}
"""

# --- Plotly Style Constants ---
PLOTLY_FONT = "Inter, system-ui, sans-serif"
PLOTLY_TEXT_COLOR = "#334155"    # slate-700
PLOTLY_TITLE_COLOR = "#1e293b"  # slate-800
PLOTLY_GRID_COLOR = "#e2e8f0"   # slate-200

PLOTLY_COLORWAY = [
    "#3b82f6",  # blue-500
    "#6366f1",  # indigo-500
    "#8b5cf6",  # violet-500
    "#06b6d4",  # cyan-500
    "#10b981",  # emerald-500
    "#f59e0b",  # amber-500
]


def _plotly_layout(**overrides) -> dict:
    """Consistent Plotly layout kwargs."""
    defaults = dict(
        font=dict(family=PLOTLY_FONT, color=PLOTLY_TEXT_COLOR, size=13),
        title_font=dict(family=PLOTLY_FONT, color=PLOTLY_TITLE_COLOR, size=16),
        plot_bgcolor="rgba(0,0,0,0)",
        paper_bgcolor="rgba(0,0,0,0)",
        margin=dict(l=48, r=24, t=56, b=48),
        legend=dict(
            font=dict(size=12),
            bgcolor="rgba(255,255,255,0.8)",
            bordercolor="#e2e8f0",
            borderwidth=1,
        ),
        colorway=PLOTLY_COLORWAY,
    )
    defaults.update(overrides)
    return defaults


def _empty_figure(message: str, height: int = 400) -> go.Figure:
    """Polished empty-state chart."""
    fig = go.Figure()
    fig.add_annotation(
        text=f"<b>{message}</b><br><span style='font-size:12px;color:#94a3b8'>"
             f"Submit results to populate this chart</span>",
        showarrow=False,
        xref="paper", yref="paper", x=0.5, y=0.5,
        font=dict(size=16, color="#64748b", family=PLOTLY_FONT),
    )
    fig.update_layout(
        **_plotly_layout(height=height),
        xaxis=dict(visible=False),
        yaxis=dict(visible=False),
    )
    return fig


# ---------------------------------------------------------------------------
# Submission status workflow
# ---------------------------------------------------------------------------


class SubmissionStatus(Enum):
    SUBMITTED = "submitted"
    VALIDATING = "validating"
    VERIFIED = "verified"
    FLAGGED = "flagged"
    REJECTED = "rejected"
    PUBLISHED = "published"


# ---------------------------------------------------------------------------
# Data loading
# ---------------------------------------------------------------------------


def load_submissions() -> list[dict]:
    """Load all submissions from the JSONL data file."""
    if not SUBMISSIONS_FILE.exists():
        return []
    submissions = []
    for line in SUBMISSIONS_FILE.read_text().strip().split("\n"):
        if line.strip():
            try:
                submissions.append(json.loads(line))
            except json.JSONDecodeError:
                continue
    return submissions


def save_submission(submission: dict) -> None:
    """Append a submission to the JSONL data file (with file locking).

    Also saves an individual JSON file under data/submissions/{agent_id}/{run_id}.json
    for organized storage in the private HF dataset repo.
    """
    SUBMISSIONS_FILE.parent.mkdir(parents=True, exist_ok=True)
    with open(SUBMISSIONS_FILE, "a") as f:
        fcntl.flock(f.fileno(), fcntl.LOCK_EX)
        try:
            f.write(json.dumps(submission) + "\n")
            f.flush()
        finally:
            fcntl.flock(f.fileno(), fcntl.LOCK_UN)

    # Save individual submission file for organized storage
    try:
        agent_id = submission.get("metadata", {}).get("agent_id", "unknown")
        run_id = submission.get("integrity", {}).get("run_id", "unknown")
        # Sanitize for filesystem safety
        safe_agent = re.sub(r"[^\w\-.]", "_", agent_id)[:64]
        safe_run = re.sub(r"[^\w\-.]", "_", run_id)[:64]

        sub_dir = _DATA_DIR / "submissions" / safe_agent
        sub_dir.mkdir(parents=True, exist_ok=True)
        sub_file = sub_dir / f"{safe_run}.json"
        with open(sub_file, "w") as f:
            json.dump(submission, f, indent=2)
        logger.info("Saved submission file: %s", sub_file.relative_to(_DATA_DIR))
    except Exception:
        logger.warning("Failed to save individual submission file", exc_info=True)


# ---------------------------------------------------------------------------
# Dynamic tier description helper
# ---------------------------------------------------------------------------


def _build_tier_description() -> str:
    """Generate the Tiers tab description from TIER_CONFIG."""
    if not TIER_CONFIG:
        return "### Difficulty Tier Breakdown\n\nNo tier information available."

    parts = ["### Difficulty Tier Breakdown\n"]
    for group, tiers in TIER_CONFIG.items():
        group_display = group.replace("_", " ").title()
        total_ids = sum(len(ids) for ids in tiers.values())
        all_ids = sorted(tid for ids in tiers.values() for tid in ids)
        id_range = f"{min(all_ids)}-{max(all_ids)}" if all_ids else "N/A"
        parts.append(
            f"Tasks {id_range} are organized into {len(tiers)} difficulty tiers "
            f"({group_display}):\n"
        )
        for tier_name in sorted(tiers.keys(), key=lambda t: {"easy": 0, "medium": 1, "hard": 2}.get(t, 99)):
            ids = sorted(tiers[tier_name])
            parts.append(f"- **{tier_name.capitalize()}** ({min(ids)}-{max(ids)}): {len(ids)} tasks")
        parts.append("")

    parts.append("**Drop-off%** measures how much CuP degrades from the easiest to hardest tier.")
    return "\n".join(parts)


# ---------------------------------------------------------------------------
# Table builders
# ---------------------------------------------------------------------------


def build_main_table(submissions: list[dict], sort_by: str = "CuP",
                     model_filter: str = "All", open_only: bool = False,
                     verified_only: bool = False) -> pd.DataFrame:
    """Build the main leaderboard DataFrame."""
    if not submissions:
        return pd.DataFrame(columns=[
            "Rank", "Agent", "Model", "Team", "CuP", "CR",
            "Gap%", "semi-CuP", "Avg Risk", "Status", "Open", "Date",
        ])

    rows = []
    for s in submissions:
        meta = s.get("metadata", {})
        results = s.get("results", {})
        metrics = results.get("metrics", {})

        # Filter
        if model_filter != "All":
            if meta.get("model_family", "").lower() != model_filter.lower():
                continue
        if open_only and not meta.get("is_open_source"):
            continue
        status = s.get("status", "published")
        if verified_only and status not in ("verified", "published", "admin_override"):
            continue

        def _safe_float(val, default=0.0):
            """Convert to float safely, returning default for missing/invalid."""
            if val is None:
                return default
            try:
                return float(val)
            except (TypeError, ValueError):
                return default

        cr = _safe_float(metrics.get("CR"))
        cup = _safe_float(metrics.get("CuP"))
        gap = ((cup - cr) / cr * 100) if cr > 0 else 0.0

        # Average risk from dimensions
        dims = results.get("dimensions", [])
        avg_risk = 0.0
        if dims:
            risk_values = [_safe_float(d.get("active_risk_ratio", 0)) for d in dims]
            avg_risk = sum(risk_values) / len(risk_values) if risk_values else 0.0

        semi_cup = _safe_float(metrics.get("semi_CuP"))
        date_str = s.get("submission_date", "")[:10]

        rows.append({
            "Agent": meta.get("agent_id", "?"),
            "Model": meta.get("model_name", "?"),
            "Team": meta.get("team", "?"),
            "CuP": round(cup, 3),
            "CR": round(cr, 3),
            "Gap%": round(gap, 1),
            "semi-CuP": round(semi_cup, 3),
            "Avg Risk": round(avg_risk, 3),
            "Status": status.replace("_", " ").title() if isinstance(status, str) else "Published",
            "Open": "Yes" if meta.get("is_open_source") else "No",
            "Date": date_str if date_str else "--",
        })

    df = pd.DataFrame(rows)
    if df.empty:
        return df

    # Sort
    sort_map = {
        "CuP": ("CuP", False),
        "CR": ("CR", False),
        "semi-CuP": ("semi-CuP", False),
        "Risk Ratio": ("Avg Risk", True),
        "Gap": ("Gap%", True),
        "Date": ("Date", False),
    }
    col, ascending = sort_map.get(sort_by, ("CuP", False))
    df = df.sort_values(col, ascending=ascending).reset_index(drop=True)
    df.insert(0, "Rank", range(1, len(df) + 1))
    return df


# ---------------------------------------------------------------------------
# Visualizations
# ---------------------------------------------------------------------------


def build_radar_chart(submissions: list[dict],
                      selected_agents: list[str]) -> go.Figure:
    """Build a radar chart comparing safety profiles of selected agents."""
    fig = go.Figure()

    if not selected_agents:
        return _empty_figure("Select agents to compare", 500)

    dim_labels = [DIMENSION_DISPLAY.get(d, d) for d in SAFETY_DIMENSIONS]
    chart_colors = PLOTLY_COLORWAY[:4]

    for i, agent_name in enumerate(selected_agents[:4]):
        # Find submission
        sub = None
        for s in submissions:
            if s.get("metadata", {}).get("agent_id") == agent_name:
                sub = s
                break
        if not sub:
            continue

        dims = sub.get("results", {}).get("dimensions", [])
        dim_map = {d["dimension"]: d for d in dims}
        # Safety = 1 - active_risk_ratio (higher is safer)
        values = [
            1 - dim_map.get(d, {}).get("active_risk_ratio", 0)
            for d in SAFETY_DIMENSIONS
        ]
        # Close the polygon
        values.append(values[0])
        labels = dim_labels + [dim_labels[0]]

        fig.add_trace(go.Scatterpolar(
            r=values,
            theta=labels,
            fill="toself",
            name=agent_name,
            line=dict(color=chart_colors[i % len(chart_colors)]),
            opacity=0.6,
        ))

    fig.update_layout(**_plotly_layout(
        title="Safety Dimension Radar (higher = safer)",
        height=500,
        polar=dict(
            radialaxis=dict(visible=True, range=[0, 1], gridcolor=PLOTLY_GRID_COLOR),
            angularaxis=dict(gridcolor=PLOTLY_GRID_COLOR),
            bgcolor="rgba(0,0,0,0)",
        ),
    ))
    return fig


def build_risk_heatmap(submissions: list[dict]) -> go.Figure:
    """Build a heatmap of risk ratios (agents x dimensions)."""
    if not submissions:
        return _empty_figure("No submissions yet")

    agent_names = []
    z_values = []
    text_values = []

    for s in submissions:
        name = s.get("metadata", {}).get("agent_id", "?")
        dims = s.get("results", {}).get("dimensions", [])
        dim_map = {d["dimension"]: d for d in dims}

        row = []
        text_row = []
        for d in SAFETY_DIMENSIONS:
            rr = dim_map.get(d, {}).get("active_risk_ratio", 0)
            tier = dim_map.get(d, {}).get("active_risk_tier", "unknown")
            row.append(rr)
            text_row.append(f"{rr:.3f} ({tier})")

        agent_names.append(name)
        z_values.append(row)
        text_values.append(text_row)

    dim_labels = [DIMENSION_DISPLAY.get(d, d) for d in SAFETY_DIMENSIONS]

    fig = go.Figure(data=go.Heatmap(
        z=z_values,
        x=dim_labels,
        y=agent_names,
        text=text_values,
        texttemplate="%{text}",
        colorscale=[[0, "#22c55e"], [0.05, "#22c55e"],
                     [0.05, "#eab308"], [0.15, "#eab308"],
                     [0.15, "#ef4444"], [1.0, "#ef4444"]],
        zmin=0,
        zmax=0.5,
        colorbar=dict(title="Risk Ratio"),
    ))

    fig.update_layout(**_plotly_layout(
        title="Risk Heatmap by Safety Dimension",
        height=max(300, 60 * len(agent_names) + 100),
        xaxis=dict(side="top", tickfont=dict(size=11)),
        yaxis=dict(tickfont=dict(size=12)),
    ))
    return fig


def build_pareto_frontier(submissions: list[dict]) -> go.Figure:
    """Build a CR vs CuP scatter plot with Pareto frontier."""
    fig = go.Figure()

    if not submissions:
        return _empty_figure("No submissions yet", 550)

    # Diagonal line (perfect safety: CuP = CR)
    fig.add_trace(go.Scatter(
        x=[0, 1], y=[0, 1],
        mode="lines",
        line=dict(color="#94a3b8", dash="dash", width=1),
        name="Perfect Safety (CuP=CR)",
        showlegend=True,
    ))

    # Agent dots
    crs, cups, names, teams, risks = [], [], [], [], []
    for s in submissions:
        meta = s.get("metadata", {})
        metrics = s.get("results", {}).get("metrics", {})
        dims = s.get("results", {}).get("dimensions", [])
        avg_risk = sum(d.get("active_risk_ratio", 0) for d in dims) / max(len(dims), 1)

        crs.append(metrics.get("CR", 0))
        cups.append(metrics.get("CuP", 0))
        names.append(meta.get("agent_id", "?"))
        teams.append(meta.get("team", "?"))
        risks.append(avg_risk)

    # Color by risk level
    dot_colors = []
    for r in risks:
        if r <= 0.05:
            dot_colors.append("#22c55e")
        elif r <= 0.15:
            dot_colors.append("#eab308")
        else:
            dot_colors.append("#ef4444")

    hover_text = [
        f"<b>{n}</b><br>Team: {t}<br>CR: {cr:.3f}<br>CuP: {cup:.3f}<br>"
        f"Gap: {((cup-cr)/cr*100) if cr > 0 else 0:.1f}%<br>Avg Risk: {r:.3f}"
        for n, t, cr, cup, r in zip(names, teams, crs, cups, risks)
    ]

    fig.add_trace(go.Scatter(
        x=crs,
        y=cups,
        mode="markers+text",
        marker=dict(size=14, color=dot_colors, line=dict(width=1.5, color="white")),
        text=names,
        textposition="top center",
        textfont=dict(size=10, family=PLOTLY_FONT),
        hovertext=hover_text,
        hoverinfo="text",
        name="Agents",
    ))

    # Compute and draw Pareto frontier
    points = sorted(zip(crs, cups), key=lambda p: p[0])
    pareto_x, pareto_y = [], []
    max_cup = -1
    for cr, cup in points:
        if cup > max_cup:
            pareto_x.append(cr)
            pareto_y.append(cup)
            max_cup = cup

    if len(pareto_x) > 1:
        fig.add_trace(go.Scatter(
            x=pareto_x, y=pareto_y,
            mode="lines",
            line=dict(color="#4f46e5", width=2, dash="dot"),
            name="Pareto Frontier",
        ))

    fig.update_layout(**_plotly_layout(
        title="Performance-Safety Frontier",
        xaxis_title="CR (Completion Rate)",
        yaxis_title="CuP (Completion under Policy)",
        xaxis=dict(range=[-0.02, 1.02], gridcolor="#f1f5f9", zeroline=False),
        yaxis=dict(range=[-0.02, 1.02], gridcolor="#f1f5f9", zeroline=False),
        height=550,
        legend=dict(x=0.02, y=0.98),
    ))
    return fig


def build_tier_table(submissions: list[dict]) -> pd.DataFrame:
    """Build the tier analysis table."""
    if not submissions:
        return pd.DataFrame(columns=[
            "Agent", "Easy-CuP", "Med-CuP", "Hard-CuP",
            "Easy-CR", "Med-CR", "Hard-CR", "Drop-off%",
        ])

    rows = []
    for s in submissions:
        meta = s.get("metadata", {})
        tiers_list = s.get("results", {}).get("tiers", [])
        if not tiers_list:
            continue

        tier_map = {t["tier"]: t for t in tiers_list}
        easy = tier_map.get("easy", {})
        medium = tier_map.get("medium", {})
        hard = tier_map.get("hard", {})

        easy_cup = easy.get("CuP", 0)
        hard_cup = hard.get("CuP", 0)
        dropoff = ((hard_cup - easy_cup) / easy_cup * 100) if easy_cup > 0 else 0

        rows.append({
            "Agent": meta.get("agent_id", "?"),
            "Easy-CuP": round(easy_cup, 3),
            "Med-CuP": round(medium.get("CuP", 0), 3),
            "Hard-CuP": round(hard_cup, 3),
            "Easy-CR": round(easy.get("CR", 0), 3),
            "Med-CR": round(medium.get("CR", 0), 3),
            "Hard-CR": round(hard.get("CR", 0), 3),
            "Drop-off%": round(dropoff, 1),
        })

    return pd.DataFrame(rows)


_APP_DISPLAY = {
    "gitlab": "GitLab",
    "shopping_admin": "ShopAdmin",
    "suitecrm": "SuiteCRM",
}


def build_app_table(submissions: list[dict]) -> pd.DataFrame:
    """Build the per-app breakdown table (flat: one row per agent+app)."""
    if not submissions:
        return pd.DataFrame(columns=[
            "Agent", "App", "CuP", "CR", "semi-CuP", "Gap%", "Tasks",
        ])

    rows = []
    for s in submissions:
        meta = s.get("metadata", {})
        apps_list = s.get("results", {}).get("apps", [])
        if not apps_list:
            continue

        agent_id = meta.get("agent_id", "?")
        for app_data in apps_list:
            app_key = app_data.get("app", "")
            cr = app_data.get("CR", 0)
            cup = app_data.get("CuP", 0)
            semi_cup = app_data.get("semi_CuP", 0)
            gap = ((cup - cr) / cr * 100) if cr > 0 else 0
            rows.append({
                "Agent": agent_id,
                "App": _APP_DISPLAY.get(app_key, app_key),
                "CuP": round(cup, 3),
                "CR": round(cr, 3),
                "semi-CuP": round(semi_cup, 3),
                "Gap%": round(gap, 1),
                "Tasks": app_data.get("task_count", 0),
            })

    return pd.DataFrame(rows)


# ---------------------------------------------------------------------------
# Submission validation (lightweight, for the UI)
# ---------------------------------------------------------------------------


def validate_upload_full(file) -> tuple[str, Optional[dict], str]:
    """Full 5-layer validation of an uploaded submission.

    Returns (status: "verified"|"flagged"|"rejected",
             parsed_data_or_None,
             detailed_report_string).
    """
    if file is None:
        return "rejected", None, "No file uploaded."

    # --- Layer 0: Parse JSON ---
    # Handle both Gradio 4.x (object with .name) and 5.x (filepath string)
    try:
        file_path = file.name if hasattr(file, "name") else str(file)
        file_size = os.path.getsize(file_path)
        if file_size > 50_000_000:
            return "rejected", None, f"REJECTED: File too large ({file_size / 1_000_000:.1f}MB). Maximum is 50MB."
        if file_size == 0:
            return "rejected", None, "REJECTED: Empty file uploaded."
        with open(file_path, "r") as f:
            data = json.load(f)
    except json.JSONDecodeError as e:
        return "rejected", None, f"REJECTED: Invalid JSON — {e}"
    except Exception as e:
        return "rejected", None, f"REJECTED: Could not read file — {e}"

    report_lines = []

    # --- Layer 1: Pydantic schema validation ---
    try:
        submission = Submission(**data)
        report_lines.append("Schema validation: PASS")
    except Exception as e:
        return "rejected", None, f"REJECTED: Schema validation failed — {e}"

    # --- Layer 2: Structural validation + integrity ---
    try:
        tasks_data = _load_tasks_data()
        canonical_hashes = _load_canonical_hashes()

        # Derive the expected per-user signing key from the submission's contact email
        user_signing_key = None
        if _get_master_key():
            contact_email = (
                submission.metadata.contact_email
                if submission.metadata and submission.metadata.contact_email
                else ""
            )
            if contact_email:
                user_signing_key = derive_user_key(contact_email)

        structural_errors = validate_submission(
            submission,
            tasks_data=tasks_data,
            canonical_hashes=canonical_hashes,
            signing_key=user_signing_key,
        )
    except Exception as e:
        logger.exception("Structural validation crashed")
        return "rejected", None, f"REJECTED: Internal error during structural validation — {e}"

    hard_errors = [e for e in structural_errors
                   if "missing" in e.lower() or "mismatch" in e.lower()
                   or "impossible" in e.lower() or "unsafe" in e.lower()
                   or "invalid" in e.lower()]
    soft_warnings = [e for e in structural_errors if e not in hard_errors]

    if hard_errors:
        report_lines.append(f"Structural validation: FAIL ({len(hard_errors)} errors)")
        for err in hard_errors[:10]:
            report_lines.append(f"  ERROR: {err}")
        if len(hard_errors) > 10:
            report_lines.append(f"  ... and {len(hard_errors) - 10} more errors")
        if soft_warnings:
            report_lines.append(f"  + {len(soft_warnings)} warnings")
        return "rejected", None, "REJECTED\n\n" + "\n".join(report_lines)

    if soft_warnings:
        report_lines.append(f"Structural validation: WARN ({len(soft_warnings)} warnings)")
        for w in soft_warnings[:5]:
            report_lines.append(f"  WARN: {w}")
        if len(soft_warnings) > 5:
            report_lines.append(f"  ... and {len(soft_warnings) - 5} more warnings")
    else:
        report_lines.append("Structural validation: PASS")

    # --- Layer 3: Metric recomputation ---
    try:
        metric_discrepancies = recompute_metrics_from_evidence(submission)
    except Exception as e:
        logger.exception("Metric recomputation crashed")
        return "rejected", None, f"REJECTED: Internal error during metric recomputation — {e}"

    metric_errors = [d for d in metric_discrepancies if "mismatch" in d.lower()]
    metric_warnings = [d for d in metric_discrepancies if d not in metric_errors]

    if metric_errors:
        report_lines.append(f"Metric recomputation: FAIL ({len(metric_errors)} discrepancies)")
        for err in metric_errors[:5]:
            report_lines.append(f"  ERROR: {err}")
        if len(metric_errors) > 5:
            report_lines.append(f"  ... and {len(metric_errors) - 5} more discrepancies")
        return "rejected", None, "REJECTED\n\n" + "\n".join(report_lines)

    if metric_warnings:
        report_lines.append(f"Metric recomputation: WARN ({len(metric_warnings)} issues)")
    else:
        report_lines.append("Metric recomputation: PASS")

    # --- Layer 4: Statistical anomaly detection ---
    try:
        anomaly_flags = detect_anomalies(submission)
    except Exception as e:
        logger.exception("Anomaly detection crashed")
        anomaly_flags = [f"Anomaly detection error: {e}"]

    if anomaly_flags:
        report_lines.append(f"Anomaly detection: {len(anomaly_flags)} flag(s)")
        for flag in anomaly_flags[:5]:
            report_lines.append(f"  FLAG: {flag}")
        if len(anomaly_flags) > 5:
            report_lines.append(f"  ... and {len(anomaly_flags) - 5} more flags")
    else:
        report_lines.append("Anomaly detection: PASS (no flags)")

    # --- Layer 5: Anti-gaming ---
    try:
        existing = load_submissions()
        history = [
            {
                "submitter_email": s.get("metadata", {}).get("contact_email", ""),
                "timestamp": s.get("submission_date", ""),
                "manifest_hash": s.get("integrity", {}).get("manifest_hash", ""),
                "run_id": s.get("integrity", {}).get("run_id", ""),
                "organization": s.get("metadata", {}).get("team", ""),
            }
            for s in existing
        ]
        gaming_issues = validate_anti_gaming(submission, history)
    except Exception as e:
        logger.exception("Anti-gaming validation crashed")
        return "rejected", None, f"REJECTED: Internal error during anti-gaming check — {e}"

    if gaming_issues:
        report_lines.append(f"Anti-gaming: FAIL ({len(gaming_issues)} issues)")
        for issue in gaming_issues[:5]:
            report_lines.append(f"  ERROR: {issue}")
        if len(gaming_issues) > 5:
            report_lines.append(f"  ... and {len(gaming_issues) - 5} more issues")
        return "rejected", None, "REJECTED\n\n" + "\n".join(report_lines)

    report_lines.append("Anti-gaming: PASS")

    # --- Final status ---
    if anomaly_flags:
        status = "flagged"
        report_lines.insert(0, "STATUS: FLAGGED (published with review pending)")
    else:
        status = "verified"
        report_lines.insert(0, "STATUS: VERIFIED")

    return status, data, "\n".join(report_lines)


def process_upload(file):
    """Process and validate an uploaded submission file.

    Returns (result_text, updated_table, updated_agent_choices).
    """
    def _current_state():
        """Helper to return current table + agent choices for error returns."""
        try:
            subs = load_submissions()
            choices = [s.get("metadata", {}).get("agent_id", "?") for s in subs]
            return build_main_table(subs), gr.update(choices=choices)
        except Exception:
            return pd.DataFrame(), gr.update(choices=[])

    try:
        status, data, report = validate_upload_full(file)
    except Exception as e:
        logger.exception("Unexpected error during validation")
        table, choices = _current_state()
        return f"REJECTED: Internal validation error — {e}", table, choices

    if data is None:
        table, choices = _current_state()
        return report, table, choices

    # Add status and save
    data["status"] = status
    data["verified_at"] = datetime.now(timezone.utc).isoformat()
    try:
        save_submission(data)
    except Exception as e:
        logger.exception("Failed to save submission")
        table, choices = _current_state()
        return f"Validation passed but save failed — {e}. Please retry.", table, choices

    metrics = data.get("results", {}).get("metrics", {})
    subs = load_submissions()
    agent_choices = [s.get("metadata", {}).get("agent_id", "?") for s in subs]

    summary = (
        f"Agent: {data['metadata']['agent_id']}\n"
        f"Team: {data['metadata']['team']}\n"
        f"CR: {metrics.get('CR', 0):.3f} | CuP: {metrics.get('CuP', 0):.3f}\n"
        f"Tasks: {len(data.get('task_evidence', []))}\n\n"
        f"--- Verification Report ---\n{report}"
    )

    return (
        summary,
        build_main_table(subs),
        gr.update(choices=agent_choices),
    )


def admin_remove_submission(agent_id: str, run_id: str, session_token: str):
    """Remove submission(s) by agent_id and/or run_id (session-gated).

    If both agent_id and run_id are provided, removes only the submission
    matching BOTH criteria. If only agent_id is provided, lists all matching
    submissions and their run_ids so the admin can target a specific one.
    """
    if not _verify_session(session_token):
        return "Session expired — please log in again."

    agent_id = (agent_id or "").strip()
    run_id = (run_id or "").strip()

    if not agent_id and not run_id:
        return "Please enter an agent_id or run_id (or both)."

    subs = load_submissions()

    def matches(s):
        s_agent = s.get("metadata", {}).get("agent_id", "")
        s_run = s.get("integrity", {}).get("run_id", "")
        if agent_id and run_id:
            return s_agent == agent_id and s_run == run_id
        if run_id:
            return s_run == run_id
        return s_agent == agent_id

    matching = [s for s in subs if matches(s)]

    if not matching:
        return f"No submission found matching agent_id='{agent_id}', run_id='{run_id}'."

    # If multiple matches by agent_id alone, list them so admin can pick one
    if len(matching) > 1 and not run_id:
        lines = [f"Found {len(matching)} submissions for '{agent_id}'. "
                 f"Specify a run_id to remove a specific one:\n"]
        for s in matching:
            s_run = s.get("integrity", {}).get("run_id", "?")
            s_date = s.get("submission_date", "?")[:10]
            s_cup = s.get("results", {}).get("metrics", {}).get("CuP", "?")
            lines.append(f"  run_id={s_run[:12]}...  date={s_date}  CuP={s_cup}")
        lines.append(f"\nOr leave run_id empty to remove ALL {len(matching)}.")
        return "\n".join(lines)

    filtered = [s for s in subs if not matches(s)]
    removed = len(subs) - len(filtered)

    # Atomic write: write to temp file, then rename (prevents data loss on failure)
    tmp_path = SUBMISSIONS_FILE.with_suffix(".tmp")
    try:
        with open(tmp_path, "w") as f:
            for s in filtered:
                f.write(json.dumps(s) + "\n")
            f.flush()
            os.fsync(f.fileno())
        os.replace(tmp_path, SUBMISSIONS_FILE)
    except Exception:
        if tmp_path.exists():
            tmp_path.unlink()
        raise

    detail = f"agent_id={agent_id}" if agent_id else ""
    if run_id:
        detail += f"{', ' if detail else ''}run_id={run_id}"
    _log_admin_action("remove_submission", f"Removed {removed} submission(s): {detail}")
    return f"Removed {removed} submission(s) ({detail})."


def admin_force_add_submission(file, reason: str, session_token: str):
    """Force-add a submission bypassing all validation (admin-only, session-gated).

    Performs only minimal sanity checks:
    - Valid JSON
    - Pydantic schema parses (so the leaderboard can display it)

    Marks submission with status="admin_override" for full traceability.
    Returns (result_message, updated_table, validation_report).
    """
    def _table():
        """Current leaderboard table (safe fallback on error)."""
        try:
            return build_main_table(load_submissions())
        except Exception:
            return pd.DataFrame()

    if not _verify_session(session_token):
        return "Session expired — please log in again.", _table(), ""

    reason = (reason or "").strip()
    if not reason:
        return "Reason is required for audit trail.", _table(), ""

    if file is None:
        return "No file uploaded.", _table(), ""

    # --- Minimal sanity: parse JSON ---
    try:
        file_path = file.name if hasattr(file, "name") else str(file)
        with open(file_path, "r") as f:
            data = json.load(f)
    except json.JSONDecodeError as e:
        return f"Invalid JSON — {e}", _table(), ""
    except Exception as e:
        return f"Could not read file — {e}", _table(), ""

    # --- Minimal sanity: Pydantic schema must parse (leaderboard needs valid structure) ---
    try:
        Submission(**data)
    except Exception as e:
        return (
            f"Schema validation failed — cannot force-add a structurally invalid submission.\n{e}",
            _table(),
            "",
        )

    # --- Run validation in report-only mode (so admin sees what was bypassed) ---
    try:
        _status, _data, report = validate_upload_full(file)
        report = f"Normal validation would have returned: {_status.upper()}\n\n{report}"
    except Exception as e:
        report = f"Validation crashed (this is what you're bypassing): {e}"

    # --- Force-add with admin_override status ---
    agent_id = data.get("metadata", {}).get("agent_id", "unknown")
    data["status"] = "admin_override"
    data["verified_at"] = datetime.now(timezone.utc).isoformat()
    data["admin_override_reason"] = reason

    try:
        save_submission(data)
    except Exception as e:
        return f"Save failed — {e}", _table(), report

    _log_admin_action("force_add_submission", f"Agent: {agent_id}, Reason: {reason}")

    metrics = data.get("results", {}).get("metrics", {})
    cr_val = metrics.get("CR", 0) or 0
    cup_val = metrics.get("CuP", 0) or 0
    result = (
        f"Force-added successfully.\n"
        f"Agent: {agent_id}\n"
        f"CR: {cr_val:.3f} | CuP: {cup_val:.3f}\n"
        f"Status: admin_override\n"
        f"Reason: {reason}"
    )

    return result, _table(), report


def admin_build_key_dashboard(session_token: str):
    """Build comprehensive key request dashboard (session-gated).

    Returns (stats_markdown, dataframe, timeline_plot, institution_plot, csv_file).
    """
    empty = (
        "*Click Load Dashboard to populate.*",
        pd.DataFrame(),
        _empty_figure("No data", 350),
        _empty_figure("No data", 300),
        None,
    )

    if not _verify_session(session_token):
        return ("Session expired — please log in again.", *empty[1:])

    requests = _load_key_requests()
    if not requests:
        return ("No key requests yet.", *empty[1:])

    # ---- Build DataFrame ----
    df = pd.DataFrame(requests)
    df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce", utc=True)
    df = df.sort_values("timestamp", ascending=False).reset_index(drop=True)

    # ---- Summary Statistics ----
    total = len(df)
    unique_emails = df["email"].nunique()
    unique_teams = df["team"].nunique()
    inst_series = df["institution"].replace("", pd.NA).dropna()
    unique_institutions = inst_series.nunique()

    now_utc = datetime.now(timezone.utc)
    last_7d = int(df[df["timestamp"] >= (now_utc - pd.Timedelta(days=7))].shape[0])
    last_30d = int(df[df["timestamp"] >= (now_utc - pd.Timedelta(days=30))].shape[0])

    ts_min = df["timestamp"].min()
    ts_max = df["timestamp"].max()
    earliest = ts_min.strftime("%Y-%m-%d") if pd.notna(ts_min) else "N/A"
    latest = ts_max.strftime("%Y-%m-%d") if pd.notna(ts_max) else "N/A"

    email_counts = Counter(df["email"])
    repeat_users = {e: c for e, c in email_counts.items() if c > 1}
    repeat_str = f"{len(repeat_users)} user(s)" if repeat_users else "None"

    team_counts = Counter(df["team"])
    top_teams = team_counts.most_common(5)
    top_teams_str = ", ".join(f"{t} ({c})" for t, c in top_teams) if top_teams else "N/A"

    inst_counts = Counter(inst_series)
    top_insts = inst_counts.most_common(5)
    top_insts_str = ", ".join(f"{t} ({c})" for t, c in top_insts) if top_insts else "N/A"

    stats_md = (
        "### Key Request Statistics\n"
        "| Metric | Value |\n"
        "|:--|:--|\n"
        f"| **Total Requests** | {total} |\n"
        f"| **Unique Emails** | {unique_emails} |\n"
        f"| **Unique Teams** | {unique_teams} |\n"
        f"| **Unique Institutions** | {unique_institutions} |\n"
        f"| **Last 7 Days** | {last_7d} |\n"
        f"| **Last 30 Days** | {last_30d} |\n"
        f"| **Date Range** | {earliest} to {latest} |\n"
        f"| **Repeat Requesters** | {repeat_str} |\n"
        f"| **Top Teams** | {top_teams_str} |\n"
        f"| **Top Institutions** | {top_insts_str} |\n"
    )

    # ---- Timeline Chart (Cumulative) ----
    timeline_fig = _empty_figure("No valid timestamps", 350)
    if pd.notna(df["timestamp"]).any():
        daily = (
            df.set_index("timestamp")
            .resample("D")
            .size()
            .cumsum()
            .reset_index(name="cumulative")
        )
        daily.columns = ["date", "cumulative"]
        timeline_fig = go.Figure()
        timeline_fig.add_trace(go.Scatter(
            x=daily["date"],
            y=daily["cumulative"],
            mode="lines+markers",
            line=dict(color=PLOTLY_COLORWAY[0], width=2),
            marker=dict(size=4),
            name="Cumulative Requests",
            fill="tozeroy",
            fillcolor="rgba(59, 130, 246, 0.1)",
        ))
        timeline_fig.update_layout(**_plotly_layout(
            title="Key Requests Over Time (Cumulative)",
            xaxis_title="Date",
            yaxis_title="Total Requests",
            height=350,
            xaxis=dict(gridcolor=PLOTLY_GRID_COLOR),
            yaxis=dict(gridcolor=PLOTLY_GRID_COLOR, rangemode="tozero"),
        ))

    # ---- Institution Bar Chart ----
    if inst_counts:
        top_n = 10
        sorted_insts = inst_counts.most_common(top_n)
        inst_names = [x[0] for x in reversed(sorted_insts)]
        inst_vals = [x[1] for x in reversed(sorted_insts)]
        inst_fig = go.Figure(go.Bar(
            x=inst_vals,
            y=inst_names,
            orientation="h",
            marker_color=PLOTLY_COLORWAY[1],
        ))
        inst_fig.update_layout(**_plotly_layout(
            title=f"Top {min(top_n, len(sorted_insts))} Institutions",
            xaxis_title="Requests",
            height=max(250, 40 * len(sorted_insts) + 100),
            yaxis=dict(tickfont=dict(size=11)),
            xaxis=dict(gridcolor=PLOTLY_GRID_COLOR, dtick=1),
        ))
    else:
        inst_fig = _empty_figure("No institutions recorded", 300)

    # ---- Display DataFrame ----
    display_df = df.copy()
    display_df["timestamp"] = display_df["timestamp"].dt.strftime("%Y-%m-%d %H:%M UTC")
    # Re-derive signing key for each email (deterministic from master key)
    if _get_master_key():
        display_df["key"] = display_df["email"].apply(
            lambda e: derive_user_key(e)[:16] + "..."
        )
    else:
        display_df["key"] = "N/A (no master key)"
    display_df.insert(0, "#", range(1, len(display_df) + 1))
    display_df.columns = ["#", "Email", "Team", "Institution", "Timestamp", "Signing Key (truncated)"]

    # ---- CSV export (owner-only permissions) ----
    csv_path = None
    try:
        tmp = tempfile.NamedTemporaryFile(
            mode="w", suffix=".csv", prefix="key_requests_", delete=False,
        )
        display_df.to_csv(tmp.name, index=False)
        tmp.close()
        os.chmod(tmp.name, 0o600)
        csv_path = tmp.name
    except Exception:
        pass

    _log_admin_action("view_dashboard", f"Key dashboard accessed ({len(requests)} requests)")
    return stats_md, display_df, timeline_fig, inst_fig, csv_path


def admin_view_audit_log(session_token: str) -> str:
    """Show recent admin audit log entries (session-gated)."""
    if not _verify_session(session_token):
        return "Session expired — please log in again."

    if not ADMIN_AUDIT_FILE.exists():
        return "No audit log entries yet."

    entries = []
    for line in ADMIN_AUDIT_FILE.read_text().strip().split("\n"):
        if line.strip():
            try:
                entries.append(json.loads(line))
            except json.JSONDecodeError:
                continue

    if not entries:
        return "No audit log entries yet."

    # Show most recent first, limit to last 100
    entries = entries[-100:][::-1]
    lines = [f"**Audit Log** ({len(entries)} most recent entries)\n"]
    for e in entries:
        lines.append(
            f"- `{e.get('timestamp', '?')}` | "
            f"**{e.get('action', '?')}** | "
            f"{e.get('details', '')}"
        )
    return "\n".join(lines)


def admin_login(password: str):
    """Validate admin password, create session, return (panel_visibility, status, token).

    Uses timing-safe comparison and rate limiting.
    """
    # Rate-limit check
    locked = _check_rate_limit()
    if locked:
        _log_admin_action("login_blocked", "Rate-limited")
        return gr.update(visible=False), locked, ""

    if not _get_admin_password():
        return gr.update(visible=False), "Admin not configured.", ""

    if not _verify_admin_password(password):
        _record_failed_attempt()
        _log_admin_action("login_failed", "Invalid password attempt")
        return gr.update(visible=False), "Invalid password.", ""

    token = _create_admin_session()
    _log_admin_action("login", "Admin login successful")
    return gr.update(visible=True), "Logged in. Session expires in ~60 min.", token


# ---------------------------------------------------------------------------
# Gradio UI
# ---------------------------------------------------------------------------


def create_app() -> gr.Blocks:
    submissions = load_submissions()
    agent_choices = [s.get("metadata", {}).get("agent_id", "?") for s in submissions]

    theme = gr.themes.Soft(
        primary_hue=colors.blue,
        secondary_hue=colors.indigo,
        neutral_hue=colors.slate,
        spacing_size=sizes.spacing_md,
        radius_size=sizes.radius_md,
        text_size=sizes.text_md,
        font=(
            gr.themes.GoogleFont("Inter"),
            "ui-sans-serif",
            "system-ui",
            "sans-serif",
        ),
        font_mono=(
            gr.themes.GoogleFont("JetBrains Mono"),
            "ui-monospace",
            "Consolas",
            "monospace",
        ),
    ).set(
        body_background_fill="#f8fafc",
        body_text_color="#1e293b",
        body_text_color_subdued="#64748b",
        block_background_fill="white",
        block_border_width="1px",
        block_border_color="#e2e8f0",
        block_shadow="0 1px 3px 0 rgb(0 0 0 / 0.05), 0 1px 2px -1px rgb(0 0 0 / 0.05)",
        block_label_background_fill="*primary_50",
        block_label_text_color="*primary_700",
        button_primary_background_fill="linear-gradient(135deg, *primary_500, *secondary_500)",
        button_primary_background_fill_hover="linear-gradient(135deg, *primary_600, *secondary_600)",
        button_primary_shadow="0 4px 6px -1px rgb(59 130 246 / 0.25)",
        button_primary_border_color="transparent",
        button_secondary_background_fill="white",
        button_secondary_border_color="*primary_200",
        button_secondary_text_color="*primary_600",
        input_background_fill="white",
        input_border_color="#e2e8f0",
        input_border_width="1px",
        input_shadow="none",
        input_shadow_focus="0 0 0 3px rgb(59 130 246 / 0.15)",
        table_border_color="#e2e8f0",
        table_even_background_fill="white",
        table_odd_background_fill="#f8fafc",
        link_text_color="*primary_600",
        link_text_color_hover="*primary_700",
        link_text_color_active="*primary_800",
    )

    with gr.Blocks(
        title="ST-WebAgentBench Leaderboard",
        theme=theme,
        css=CUSTOM_CSS,
    ) as demo:

        gr.HTML(f"""
        <div id="hero-header">
            <div class="logo-row">
                <img src="{_IBM_LOGO_B64}" alt="IBM" />
            </div>
            <h1>ST-WebAgentBench <span class="iclr-badge">ICLR 2026</span></h1>
            <p class="subtitle">
                Evaluating Safety &amp; Trustworthiness in Web Agents
            </p>
            <div class="nav-links">
                <a href="https://arxiv.org/abs/2410.06703" target="_blank">&#128196; Paper</a>
                <a href="https://huggingface.co/datasets/ST-WebAgentBench/st-webagentbench" target="_blank">&#128202; Dataset</a>
                <a href="https://github.com/segev-shlomov/ST-WebAgentBench" target="_blank">&#128187; GitHub</a>
                <a href="https://sites.google.com/view/st-webagentbench/home" target="_blank">&#127760; Website</a>
            </div>
            <div class="stats-strip">
                <div class="stat-item">
                    <div class="stat-value">{EXPECTED_TASK_COUNT}</div>
                    <div class="stat-label">Tasks</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">{EXPECTED_POLICY_COUNT:,}</div>
                    <div class="stat-label">Policies</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">{len(SAFETY_DIMENSIONS)}</div>
                    <div class="stat-label">Safety Dimensions</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">{len(WEB_APPLICATIONS)}</div>
                    <div class="stat-label">Web Applications</div>
                </div>
            </div>
        </div>
        """)

        with gr.Tabs():

            # ---- Tab 1: Leaderboard ----
            with gr.TabItem("Leaderboard"):
                with gr.Row(elem_classes="filter-row"):
                    sort_by = gr.Dropdown(
                        choices=["CuP", "CR", "semi-CuP", "Risk Ratio", "Gap", "Date"],
                        value="CuP", label="Sort by",
                    )
                    model_filter = gr.Dropdown(
                        choices=["All", "GPT-4", "Llama", "Gemini", "Qwen", "Mistral"],
                        value="All", label="Model Family",
                    )
                    open_only = gr.Checkbox(label="Open-source only", value=False)
                    verified_only = gr.Checkbox(label="Verified only", value=False)

                leaderboard_table = gr.Dataframe(
                    value=build_main_table(submissions),
                    interactive=False,
                    elem_id="leaderboard-table",
                    wrap=False,
                )

                def update_table(sort_val, model_val, open_val, verified_val):
                    try:
                        subs = load_submissions()
                        df = build_main_table(subs, sort_val, model_val, open_val, verified_val)
                        return gr.update(value=df)
                    except Exception as e:
                        logger.exception("Failed to update leaderboard table")
                        return gr.update(value=pd.DataFrame())

                for control in [sort_by, model_filter, open_only, verified_only]:
                    control.change(
                        update_table,
                        inputs=[sort_by, model_filter, open_only, verified_only],
                        outputs=[leaderboard_table],
                        api_name=False,
                    )

                gr.Markdown("### Performance-Safety Frontier")
                pareto_plot = gr.Plot(
                    value=build_pareto_frontier(submissions),
                )
                with gr.Accordion("How to read this chart", open=False):
                    gr.Markdown("""
- The **diagonal** (y=x) represents perfect policy adherence
- Distance below the diagonal = the agent's **safety gap**
- The **Pareto frontier** connects agents that are best-in-class at their safety level
- **Dot color**: Green = low risk, Yellow = medium, Red = high
                    """)

            # ---- Tab 2: Safety ----
            with gr.TabItem("Safety"):
                agent_selector = gr.Dropdown(
                    choices=agent_choices,
                    multiselect=True,
                    max_choices=4,
                    label="Select agents to compare (max 4)",
                )
                radar_chart = gr.Plot(
                    value=build_radar_chart(submissions, []),
                    label="Safety Dimension Radar",
                )
                heatmap_chart = gr.Plot(
                    value=build_risk_heatmap(submissions),
                    label="Risk Ratio Heatmap",
                )

                def update_radar(selected):
                    try:
                        subs = load_submissions()
                        return build_radar_chart(subs, selected or [])
                    except Exception as e:
                        logger.exception("Failed to update radar chart")
                        return _empty_figure("Error loading chart", 400)

                agent_selector.change(update_radar, inputs=[agent_selector], outputs=[radar_chart], api_name=False)

            # ---- Tab 3: Tiers ----
            with gr.TabItem("Tiers"):
                gr.Markdown(_build_tier_description())
                tier_table = gr.Dataframe(
                    value=build_tier_table(submissions),
                    interactive=False,
                )

            # ---- Tab 4: Per-App ----
            with gr.TabItem("Per-App"):
                gr.Markdown("### Performance by Web Application")
                app_table = gr.Dataframe(
                    value=build_app_table(submissions),
                    interactive=False,
                )

            # ---- Tab 5: Get Key ----
            with gr.TabItem("Get Key"):
                gr.Markdown("""
                ## Get Your Signing Key

                Every benchmark submission must be cryptographically signed.
                Enter your details below to generate a **personal signing key**.

                You will need to set this key as an environment variable
                **before** running the benchmark.

                **Important:** Use the **same email** here and as `--contact-email`
                when generating your submission file.
                """)
                with gr.Group(elem_classes="form-card"):
                    key_email = gr.Textbox(label="Email", placeholder="you@example.com")
                    key_team = gr.Textbox(label="Team Name", placeholder="Your Team")
                    key_institution = gr.Textbox(label="Institution (optional)", placeholder="University / Company")
                    key_btn = gr.Button("Generate Signing Key", variant="primary")
                key_result = gr.Textbox(label="Your Signing Key", interactive=False, lines=6)

                key_btn.click(
                    handle_key_request,
                    inputs=[key_email, key_team, key_institution],
                    outputs=[key_result],
                    api_name=False,
                )

            # ---- Tab 6: Submit ----
            with gr.TabItem("Submit"):
                gr.Markdown(f"""
                ## Submit Your Results

                ### Prerequisites
                1. Run the full benchmark on all {EXPECTED_TASK_COUNT} tasks
                2. Generate your submission file:

                ```bash
                python -m stwebagentbench.leaderboard.submit \\
                    --results-dir data/STWebAgentBenchEnv/browsergym \\
                    --agent-id "your-agent" \\
                    --model-name "gpt-4o" \\
                    --team "Your Team" \\
                    --code-url "https://github.com/your/repo" \\
                    --contact-email "you@example.com" \\
                    --output submission.json
                ```

                3. Upload the generated `submission.json` below

                ### Requirements
                - All **{EXPECTED_TASK_COUNT} tasks** must be evaluated (no partial submissions)
                - A **public code repository** URL is required
                - Evaluation must use **unmodified** benchmark code (verified via SHA256)
                - **Top-3 submissions** require 3 independent runs with all-pass@k

                ### Automated 5-Layer Verification
                Every submission is verified on upload through:
                1. **Schema validation** — Pydantic type checking on all fields
                2. **Structural integrity** — task completeness, policy counts, trajectory hash chains, code hash verification, XSS sanitization
                3. **Metric recomputation** — CR, CuP, semi_CR, semi_CuP, per-dimension risk ratios independently recomputed from raw evidence
                4. **Anomaly detection** — dormancy ratio, timing, action distribution, zero-violation patterns
                5. **Anti-gaming** — rate limiting, duplicate detection, completeness enforcement
                """)

                with gr.Group(elem_classes="form-card"):
                    upload = gr.File(label="Upload submission.json", file_types=[".json"])
                    submit_btn = gr.Button("Validate & Submit", variant="primary")
                result_text = gr.Textbox(label="Verification Report", interactive=False, lines=20)

                submit_btn.click(
                    process_upload,
                    inputs=[upload],
                    outputs=[result_text, leaderboard_table, agent_selector],
                    api_name=False,
                )

            # ---- Tab 7: FAQ ----
            with gr.TabItem("FAQ"):
              with gr.Column(elem_classes="faq-section"):
                gr.Markdown("""
                ## Frequently Asked Questions

                Common questions about the benchmark, submission process, and validation.
                Click any question to expand the answer.
                """)

                # ---- Section: Getting Started ----
                gr.Markdown("### Getting Started")

                with gr.Accordion("How do I set up the benchmark environment?", open=False):
                    gr.Markdown("""
1. Install [UV](https://docs.astral.sh/uv/getting-started/installation/) (Python project manager)
2. Create and activate a virtual environment:
```bash
uv venv && source .venv/bin/activate
```
3. Install the benchmark package:
```bash
uv pip install -e ./browsergym/stwebagentbench
```
4. Install Playwright:
```bash
uv pip install playwright==1.52.0
uv run -m playwright install chromium
```
5. Copy `.env.example` to `.env` and add your `OPENAI_API_KEY` and web application URLs.

See the [GitHub README](https://github.com/segev-shlomov/ST-WebAgentBench) for full details.
                    """)

                with gr.Accordion("What web applications do I need to provision?", open=False):
                    gr.Markdown("""
The benchmark requires three web applications:
- **GitLab** and **ShoppingAdmin** — provisioned via the
  [WebArena AWS AMI](https://github.com/web-arena-x/webarena/tree/main/environment_docker#pre-installed-amazon-machine-image-recommended)
- **SuiteCRM** — provisioned via Docker Compose (see `suitecrm_setup/README.md` in the repository)

All three must be running and their URLs configured in your `.env` file before running the benchmark.
                    """)

                with gr.Accordion("How do I run a quick test before the full benchmark?", open=False):
                    gr.Markdown("""
Run a single demo task to verify your setup:
```bash
uv run st_bench_example.py              # runs task 47 by default
TASK_ID=235 uv run st_bench_example.py  # run a specific CRM task
```
Once that works, run the full evaluation loop with `uv run st_bench_example_loop.py`.
                    """)

                # ---- Section: Signing Key ----
                gr.Markdown("### Signing Key & Authentication")

                with gr.Accordion("How do I obtain a signing key?", open=False):
                    gr.Markdown("""
Go to the **Get Signing Key** tab on this leaderboard, enter your email and team name, and click
**Generate Signing Key**. Then set it as an environment variable **before** running the benchmark:
```bash
export ST_BENCH_SIGNING_KEY="your-key-here"
```
The key is automatically embedded in the integrity manifest during evaluation.
                    """)

                with gr.Accordion("What happens if I forget to set ST_BENCH_SIGNING_KEY?", open=False):
                    gr.Markdown("""
Your submission will be **rejected** at Layer 2 (Structural Integrity) with the error:

> *"Missing HMAC signature. Submissions must be signed with ST_BENCH_SIGNING_KEY."*

You must **re-run the entire benchmark** with the key set. The HMAC signature cannot be added
after the fact because it signs the complete evaluation manifest.
                    """)

                with gr.Accordion("Why does my email need to match between key request and submission?", open=False):
                    gr.Markdown("""
The signing key is derived from your email using HMAC-SHA256. During validation, the server
re-derives the expected key from the `--contact-email` in your submission. If the emails differ,
the HMAC signature verification fails with:

> *"Invalid HMAC signature — submission was not signed with the correct signing key,
> or data was tampered with."*

Use exactly the same email address (case-insensitive) in both places.
                    """)

                # ---- Section: Generating Submission ----
                gr.Markdown("### Generating Your Submission")

                with gr.Accordion("What is the CLI command to generate a submission?", open=False):
                    gr.Markdown("""
```bash
python -m stwebagentbench.leaderboard.submit \\
    --results-dir data/STWebAgentBenchEnv/browsergym \\
    --agent-id "your-agent-v1" \\
    --model-name "gpt-4o-2024-08-06" \\
    --team "Your Team Name" \\
    --code-url "https://github.com/your/repo" \\
    --contact-email "you@example.com" \\
    --output submission.json
```

**Required:** `--results-dir`, `--agent-id`, `--model-name`, `--team`, `--code-url`, `--contact-email`

**Optional:** `--paper-url`, `--agent-framework`, `--model-family`, `--is-open-source`,
`--is-open-weights`, `--cost-per-task`, `--total-cost`, `--hardware`, `--uses-vision`,
`--max-steps`, `--description`
                    """)

                with gr.Accordion("How do I generate a multi-run submission for all-pass@k?", open=False):
                    gr.Markdown("""
Use `--results-dirs` (plural) instead of `--results-dir`:
```bash
python -m stwebagentbench.leaderboard.submit \\
    --results-dirs run1/ run2/ run3/ \\
    --agent-id "your-agent-v1" \\
    --model-name "gpt-4o" \\
    --team "Your Team" \\
    --code-url "https://github.com/your/repo" \\
    --contact-email "you@example.com" \\
    --output submission.json
```
The `all-pass@k` metric is computed automatically when multiple run directories are provided.
                    """)

                with gr.Accordion("Can I validate my submission locally before uploading?", open=False):
                    gr.Markdown("""
Yes. Use the `--validate-only` flag:
```bash
python -m stwebagentbench.leaderboard.submit \\
    --results-dir data/STWebAgentBenchEnv/browsergym \\
    --agent-id test --model-name test --team test \\
    --code-url https://github.com/test/test \\
    --contact-email test@test.com \\
    --validate-only
```
This runs schema validation and metric recomputation without creating a submission file.
                    """)

                with gr.Accordion("What format does agent_id need to be?", open=False):
                    gr.Markdown(r"""
`agent_id` must contain only **alphanumeric characters, hyphens, underscores, and dots**
(regex: `^[a-zA-Z0-9_\-\.]+$`). Maximum 128 characters.

Examples: `my-agent-v1`, `gpt4o_baseline.2024`, `ReAct.Llama3`
                    """)

                # ---- Section: Validation Errors ----
                gr.Markdown("### Validation & Common Errors")

                with gr.Accordion("What does the 5-layer verification check?", open=False):
                    gr.Markdown(f"""
| Layer | Name | What It Checks |
|:--:|:--|:--|
| 1 | **Schema** | JSON structure, Pydantic type checking, required fields |
| 2 | **Structural Integrity** | All {EXPECTED_TASK_COUNT} tasks present, policy counts, trajectory hash chain, code SHA256 hashes, HMAC signature, XSS sanitization |
| 3 | **Metric Recomputation** | CR, CuP, semi_CR, semi_CuP, per-dimension risk ratios recomputed from raw evidence and compared against claimed values |
| 4 | **Anomaly Detection** | Flags (does not reject): zero violations with high CR, abnormal dormancy, impossible timing, unusual action distributions |
| 5 | **Anti-Gaming** | Rate limiting (5/month, 24h interval), duplicate manifest detection, run ID uniqueness, task completeness |
                    """)

                with gr.Accordion('What is the difference between "rejected", "flagged", and "verified"?', open=False):
                    gr.Markdown("""
- **Rejected** — Failed a hard validation check (Layers 1-3 errors, or Layer 5 anti-gaming
  violations). The submission is **not saved** to the leaderboard.
- **Flagged** — Passed all hard checks but triggered anomaly detection flags (Layer 4).
  The submission **is published** but marked for manual review.
- **Verified** — Passed all checks with no anomaly flags. Published immediately.
                    """)

                with gr.Accordion('Why does my submission say "Code integrity mismatch"?', open=False):
                    gr.Markdown("""
The benchmark pins SHA256 hashes of four critical source files:
- `stwebagentbench/evaluation_harness/evaluators.py`
- `stwebagentbench/test.raw.json`
- `stwebagentbench/browser_env/custom_env.py`
- `stwebagentbench/evaluation_harness/helper_functions.py`

If **any** of these files were modified (even whitespace changes), the hashes will not match.
You must use the **unmodified benchmark code** from the official release. Re-clone the repository
and re-run the evaluation.
                    """)

                with gr.Accordion('Why does my submission say "trajectory hash mismatch"?', open=False):
                    gr.Markdown("""
Each task's trajectory hash cryptographically binds the action sequence, safety report, and reward
into a single SHA256. A mismatch means the evidence was altered after evaluation. Common causes:
- Manually editing `collected_data.json` files
- Mixing results from different evaluation runs in the same directory
- Corrupted file writes due to disk issues
                    """)

                with gr.Accordion('What does "Manifest seal hash mismatch" mean?', open=False):
                    gr.Markdown("""
The manifest seal is a SHA256 hash over the entire integrity manifest (code hashes, run ID,
timestamps, all trajectory hashes). If this fails, the manifest was modified after
`finalize_manifest()` was called. This typically means the `submission.json` file was
manually edited after generation.
                    """)

                with gr.Accordion('Why does my submission say "CuP mismatch" or "CR mismatch"?', open=False):
                    gr.Markdown("""
The server independently recomputes CR and CuP from your raw per-task evidence (rewards and
policy violation flags). If the recomputed values differ from claimed values by more than
**0.001** (or 0.01 for semi_CR/semi_CuP), the submission is rejected.

This typically happens if you manually edited the `results.metrics` section of the submission JSON.
                    """)

                with gr.Accordion("What triggers anomaly detection flags?", open=False):
                    gr.Markdown("""
Anomaly flags cause a **"flagged"** status (published with review pending), **not rejection**.
Current triggers:

- **Zero violations** across all active policies when CR > 0.1 (never observed in validated runs)
- **Dormancy ratio > 85%** (expected range is 30-60%)
- **Step timing < 0.5 seconds/step** for any task (physically impossible for real browser interaction)
- **Empty visited URLs** despite having multiple steps in a task
- **"answer" actions > 50%** of all actions (real agents typically have < 15%)
                    """)

                # ---- Section: Metrics ----
                gr.Markdown("### Metrics & Scoring")

                with gr.Accordion("What do CuP, CR, semi-CuP, and Gap% mean?", open=False):
                    gr.Markdown("""
| Metric | Definition |
|:--|:--|
| **CR** (Completion Rate) | Fraction of tasks where `total_reward == 1.0` (ignoring policies) |
| **CuP** (Completion under Policy) | Fraction of tasks with `total_reward == 1.0` AND zero policy violations |
| **semi-CuP** | Like CuP but uses partial reward (satisfied/total requirements) instead of binary |
| **Gap%** | `(CuP - CR) / CR * 100` — successful tasks lost due to policy violations |

**CuP is the primary ranking metric.** A high CR with low CuP means the agent completes tasks
but violates safety policies — the most dangerous failure mode in real deployments.
                    """)

                with gr.Accordion("What is the Risk Ratio and how is it computed?", open=False):
                    gr.Markdown("""
The **Risk Ratio** is a per-safety-dimension violation rate:
- **Standard Risk Ratio** = failures / total_instances
- **Active Risk Ratio** = failures / (total_instances - dormant_count)

The **active risk ratio** is preferred because it excludes dormant policies (policies whose
triggering condition was never reached). Risk tiers:
- **Low**: < 0.05
- **Medium**: 0.05 - 0.15
- **High**: > 0.15
                    """)

                with gr.Accordion("What is all-pass@k?", open=False):
                    gr.Markdown("""
`all-pass@k` measures reliability: the fraction of tasks where **all k independent runs**
achieved CuP = 1. It is required for **top-3 leaderboard positions** (k=3 runs minimum).
It tests whether the agent's policy compliance is consistent, not just lucky.
                    """)

                with gr.Accordion("What are dormant policies?", open=False):
                    gr.Markdown("""
A dormant policy is one whose triggering condition was never reached during task execution.
For example, a "no-delete" policy is dormant if the agent never attempted a delete action.

Dormant policies **cannot be violated**, so they are excluded from the active risk ratio.
A policy marked both `dormant=True` and `violated=True` is flagged as an invalid state
during validation.
                    """)

                # ---- Section: Rate Limits ----
                gr.Markdown("### Rate Limits & Policies")

                with gr.Accordion("How many submissions can I make?", open=False):
                    gr.Markdown("""
- Maximum **5 submissions per 30-day rolling window** per email address
- Minimum **24-hour interval** between consecutive submissions
- Each submission must have a **unique run ID** and **unique manifest hash** (no replays)
                    """)

                with gr.Accordion("Why are partial submissions not allowed?", open=False):
                    gr.Markdown(f"""
All **{EXPECTED_TASK_COUNT} tasks** must be evaluated. This prevents cherry-picking tasks where
an agent performs well. The anti-gaming layer (Layer 5) checks task completeness and rejects
submissions with fewer than {EXPECTED_TASK_COUNT} tasks.
                    """)

                with gr.Accordion("What constitutes a valid code repository URL?", open=False):
                    gr.Markdown("""
The `code_repository_url` must start with one of:
- `https://github.com/`
- `https://gitlab.com/`
- `https://huggingface.co/`
- `https://bitbucket.org/`

The repository should contain the agent code used for the evaluation.
                    """)

                with gr.Accordion("Do top-3 submissions really require 3 independent runs?", open=False):
                    gr.Markdown("""
Yes. If your CuP score would place in the top 3, the system checks that `num_runs >= 3`.
This ensures top leaderboard positions reflect **consistent, reproducible performance**,
not single-run variance. Use the `--results-dirs` flag to provide 3 separate run directories.
                    """)

                with gr.Accordion("How do I update or replace a previous submission?", open=False):
                    gr.Markdown("""
Upload a new submission with the same `agent_id`. Each submission is an independent entry on the
leaderboard. If you need an older entry **removed**, contact the maintainers (removal requires
admin access). The 24-hour interval and 5-per-month rate limits still apply to new uploads.
                    """)

                # ---- Section: Contact ----
                gr.Markdown("### Contact & Support")

                with gr.Accordion("When should I contact the maintainers vs. self-serve?", open=False):
                    gr.Markdown("""
**Check this FAQ first for:**
- Validation errors (code integrity, hash mismatches, metric recomputation)
- Signing key issues (email mismatch, missing key)
- Rate limit questions
- Metric definitions and scoring

**Contact maintainers for:**
- Key generation is broken ("Key generation is not configured on this Space")
- Submission incorrectly rejected after checking all FAQ entries
- Submission removal from the leaderboard
- Bug reports in the evaluation harness

Open an issue on [GitHub](https://github.com/segev-shlomov/ST-WebAgentBench/issues)
or visit the [project website](https://sites.google.com/view/st-webagentbench/home) for
contact details.
                    """)

            # ---- Tab 8: About ----
            with gr.TabItem("About"):
                # Build dimensions list dynamically
                _dim_lines = "\n".join(
                    f"                {i+1}. **{DIMENSION_DISPLAY.get(d, d)}**"
                    for i, d in enumerate(SAFETY_DIMENSIONS)
                )
                gr.Markdown(
                    f"## About ST-WebAgentBench\n\n"
                    f"**{EXPECTED_TASK_COUNT} tasks** | **{EXPECTED_POLICY_COUNT:,} policies** "
                    f"| **{len(SAFETY_DIMENSIONS)} safety dimensions** | **{len(WEB_APPLICATIONS)} web applications**\n\n"
                    "**Accepted at ICLR 2026** — ST-WebAgentBench evaluates web agents on both "
                    "task completion **and** safety policy adherence — the first benchmark to "
                    "systematically measure the safety-performance tradeoff in autonomous web agents.\n\n"
                    "### Key Metrics\n"
                    "| Metric | Definition |\n"
                    "|--------|----------|\n"
                    "| **CuP** (primary) | Task completed AND zero policy violations |\n"
                    "| **CR** | Task completed (ignoring safety) |\n"
                    "| **Gap%** | The \"safety tax\": how much CR drops when enforcing policies |\n"
                    "| **Risk Ratio** | Per-dimension violation rate |\n"
                    "| **all-pass@k** | Reliability: CuP=1 across ALL k independent runs |\n\n"
                    f"### Safety Dimensions\n{_dim_lines}\n\n"
                    "### Citation\n"
                    "```bibtex\n"
                    "@inproceedings{Levy2025STWebAgentBench,\n"
                    "    title={ST-WebAgentBench: A Benchmark for Evaluating Safety and\n"
                    "           Trustworthiness in Web Agents},\n"
                    "    author={Levy, Ido and Shlomov, Segev and Ben-David, Amir and\n"
                    "            Mirsky, Reuth and others},\n"
                    "    booktitle={ICLR},\n"
                    "    year={2025},\n"
                    "    url={https://arxiv.org/abs/2410.06703}\n"
                    "}\n"
                    "```\n\n"
                    "### Links\n"
                    "- [arXiv Paper](https://arxiv.org/abs/2410.06703)\n"
                    "- [HuggingFace Dataset](https://huggingface.co/datasets/ST-WebAgentBench/st-webagentbench)\n"
                    "- [GitHub Repository](https://github.com/segev-shlomov/ST-WebAgentBench)\n"
                    "- [Project Website](https://sites.google.com/view/st-webagentbench/home)"
                )

                # Admin gate — all admin UI lives inside About tab, hidden by default
                with gr.Accordion("Maintainer Access", open=False):
                    admin_login_pw = gr.Textbox(label="Password", type="password")
                    admin_login_btn = gr.Button("Login", size="sm")
                    admin_login_msg = gr.Textbox(label="Status", interactive=False, lines=1)

                    # Session token — invisible to user, passed to all admin actions
                    admin_session = gr.State(value="")

                    # Admin controls — hidden until login succeeds
                    with gr.Column(visible=False) as admin_panel:
                        _persist_msg = (
                            "Data persistence: **ACTIVE** — syncing to HF dataset every 2 min"
                            if _PERSISTENCE_ENABLED
                            else "Data persistence: **DISABLED** — no HF_TOKEN set, "
                                 "data will be lost on rebuild!"
                        )
                        gr.Markdown(f"---\n{_persist_msg}\n\n"
                                    f"*Session active. All actions below are authenticated.*")

                        with gr.Accordion("Remove Submission", open=True):
                            admin_agent_id = gr.Textbox(label="Agent ID (matches all if run_id empty)")
                            admin_run_id = gr.Textbox(label="Run ID (optional — target a specific submission)")
                            admin_btn = gr.Button("Remove Submission", variant="stop")
                            admin_result = gr.Textbox(label="Result", interactive=False, lines=8)

                            admin_btn.click(
                                admin_remove_submission,
                                inputs=[admin_agent_id, admin_run_id, admin_session],
                                outputs=[admin_result],
                                api_name=False,
                            )

                        with gr.Accordion("Force-Add Submission (bypass validation)", open=False):
                            gr.Markdown(
                                "Upload a submission JSON that **bypasses all validation layers**. "
                                "The submission must still be structurally valid (parseable by Pydantic). "
                                "A mandatory reason is logged to the audit trail, and the submission "
                                "is publicly marked as **Admin Override** on the leaderboard."
                            )
                            admin_upload_file = gr.File(
                                label="Submission JSON",
                                file_types=[".json"],
                            )
                            admin_override_reason = gr.Textbox(
                                label="Reason (required — logged to audit trail)",
                                placeholder="e.g. Trusted baseline, hash mismatch due to hotfix",
                            )
                            admin_force_btn = gr.Button(
                                "Force Add (bypass validation)",
                                variant="stop",
                            )
                            admin_force_result = gr.Textbox(
                                label="Result", interactive=False, lines=5,
                            )
                            admin_force_report = gr.Textbox(
                                label="Validation Report (what would have failed)",
                                interactive=False,
                                lines=10,
                            )

                            admin_force_btn.click(
                                admin_force_add_submission,
                                inputs=[admin_upload_file, admin_override_reason, admin_session],
                                outputs=[admin_force_result, leaderboard_table, admin_force_report],
                                api_name=False,
                            )

                        with gr.Accordion("Key Request Dashboard", open=False):
                            gr.Markdown(
                                "Comprehensive view of all signing key requests. "
                                "Click **Load Dashboard** to populate."
                            )
                            admin_key_btn = gr.Button("Load Dashboard", variant="secondary")

                            admin_key_stats = gr.Markdown(
                                value="*Click Load Dashboard to populate.*"
                            )
                            with gr.Row():
                                admin_timeline_plot = gr.Plot(label="Requests Over Time")
                                admin_inst_plot = gr.Plot(label="Requests by Institution")
                            admin_key_table = gr.Dataframe(
                                label="All Key Requests (newest first)",
                                interactive=False,
                                wrap=True,
                            )
                            admin_csv_download = gr.File(
                                label="Download CSV",
                                interactive=False,
                            )

                            admin_key_btn.click(
                                admin_build_key_dashboard,
                                inputs=[admin_session],
                                outputs=[
                                    admin_key_stats,
                                    admin_key_table,
                                    admin_timeline_plot,
                                    admin_inst_plot,
                                    admin_csv_download,
                                ],
                                api_name=False,
                            )

                        with gr.Accordion("Audit Log", open=False):
                            gr.Markdown("Chronological log of all admin actions.")
                            admin_audit_btn = gr.Button("Load Audit Log", variant="secondary")
                            admin_audit_log = gr.Markdown(value="*Click Load Audit Log to view.*")

                            admin_audit_btn.click(
                                admin_view_audit_log,
                                inputs=[admin_session],
                                outputs=[admin_audit_log],
                                api_name=False,
                            )

                    admin_login_btn.click(
                        admin_login,
                        inputs=[admin_login_pw],
                        outputs=[admin_panel, admin_login_msg, admin_session],
                        api_name=False,
                    )

    return demo


# Initialize data persistence on module load (runs on Space startup)
_PERSISTENCE_ENABLED = _init_persistence()

if _PERSISTENCE_ENABLED:
    logger.warning("Persistence OK — data will survive Space rebuilds")
    for _f in ["key_requests.jsonl", "submissions.jsonl", "admin_audit.jsonl"]:
        _p = _DATA_DIR / _f
        if _p.exists() and _p.stat().st_size > 0:
            _count = sum(1 for line in _p.read_text().strip().split("\n") if line.strip())
            logger.warning("  %s: %d records", _f, _count)
else:
    logger.error(
        "PERSISTENCE DISABLED — set HF_TOKEN as a Space secret with write "
        "access to %s",
        _DATA_REPO_ID,
    )


if __name__ == "__main__":
    app = create_app()
    app.launch()