"""Argus active monitoring — health watchdog, quality regression, throughput anomaly detection.

Provides check functions that detect problems and return structured alerts.
Called by /check endpoint (periodic cron) or on-demand.

Alert schema:
    {
        "id": str,          # unique key for dedup (e.g. "dormant:ganymede")
        "severity": str,    # "critical" | "warning" | "info"
        "category": str,    # "health" | "quality" | "throughput" | "failure_pattern"
        "title": str,       # human-readable headline
        "detail": str,      # actionable description
        "agent": str|None,  # affected agent (if applicable)
        "domain": str|None, # affected domain (if applicable)
        "detected_at": str, # ISO timestamp
        "auto_resolve": bool, # clears when condition clears
    }
"""

import json
import sqlite3
import statistics
from datetime import datetime, timezone


# ─── Agent-domain mapping (static config, maintained by Argus) ──────────────

AGENT_DOMAINS = {
    "rio": ["internet-finance"],
    "clay": ["creative-industries"],
    "ganymede": None,  # reviewer — cross-domain
    "epimetheus": None,  # infra
    "leo": None,  # standards
    "oberon": None,  # evolution tracking
    "vida": None,  # health monitoring
    "hermes": None,  # comms
    "astra": None,  # research
}

# Thresholds
DORMANCY_HOURS = 48
APPROVAL_DROP_THRESHOLD = 15  # percentage points below 7-day baseline
THROUGHPUT_DROP_RATIO = 0.5  # alert if today < 50% of 7-day SMA
REJECTION_SPIKE_RATIO = 0.20  # single reason > 20% of recent rejections
STUCK_LOOP_THRESHOLD = 3  # same agent + same rejection reason > N times in 6h
COST_SPIKE_RATIO = 2.0  # daily cost > 2x 7-day average


def _now_iso() -> str:
    return datetime.now(timezone.utc).isoformat()


# ─── Check: Agent Health (dormancy detection) ───────────────────────────────


def check_agent_health(conn: sqlite3.Connection) -> list[dict]:
    """Detect agents with no PR activity in the last DORMANCY_HOURS hours."""
    alerts = []

    # Get last activity per agent
    rows = conn.execute(
        """SELECT agent, MAX(last_attempt) as latest, COUNT(*) as total_prs
           FROM prs WHERE agent IS NOT NULL
           GROUP BY agent"""
    ).fetchall()

    now = datetime.now(timezone.utc)
    for r in rows:
        agent = r["agent"]
        latest = r["latest"]
        if not latest:
            continue

        last_dt = datetime.fromisoformat(latest)
        if last_dt.tzinfo is None:
            last_dt = last_dt.replace(tzinfo=timezone.utc)

        hours_since = (now - last_dt).total_seconds() / 3600

        if hours_since > DORMANCY_HOURS:
            alerts.append({
                "id": f"dormant:{agent}",
                "severity": "warning",
                "category": "health",
                "title": f"Agent '{agent}' dormant for {int(hours_since)}h",
                "detail": (
                    f"No PR activity since {latest}. "
                    f"Last seen {int(hours_since)}h ago (threshold: {DORMANCY_HOURS}h). "
                    f"Total historical PRs: {r['total_prs']}."
                ),
                "agent": agent,
                "domain": None,
                "detected_at": _now_iso(),
                "auto_resolve": True,
            })

    return alerts


# ─── Check: Quality Regression (approval rate drop) ─────────────────────────


def check_quality_regression(conn: sqlite3.Connection) -> list[dict]:
    """Detect approval rate drops vs 7-day baseline, per agent and per domain."""
    alerts = []

    # 7-day baseline approval rate (overall)
    baseline = conn.execute(
        """SELECT
            COUNT(CASE WHEN event='approved' THEN 1 END) as approved,
            COUNT(*) as total
           FROM audit_log
           WHERE stage='evaluate'
           AND event IN ('approved','changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-7 days')"""
    ).fetchone()
    baseline_rate = (baseline["approved"] / baseline["total"] * 100) if baseline["total"] else None

    # 24h approval rate (overall)
    recent = conn.execute(
        """SELECT
            COUNT(CASE WHEN event='approved' THEN 1 END) as approved,
            COUNT(*) as total
           FROM audit_log
           WHERE stage='evaluate'
           AND event IN ('approved','changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-24 hours')"""
    ).fetchone()
    recent_rate = (recent["approved"] / recent["total"] * 100) if recent["total"] else None

    if baseline_rate is not None and recent_rate is not None:
        drop = baseline_rate - recent_rate
        if drop > APPROVAL_DROP_THRESHOLD:
            alerts.append({
                "id": "quality_regression:overall",
                "severity": "critical",
                "category": "quality",
                "title": f"Approval rate dropped {drop:.0f}pp (24h: {recent_rate:.0f}% vs 7d: {baseline_rate:.0f}%)",
                "detail": (
                    f"24h approval rate ({recent_rate:.1f}%) is {drop:.1f} percentage points below "
                    f"7-day baseline ({baseline_rate:.1f}%). "
                    f"Evaluated {recent['total']} PRs in last 24h."
                ),
                "agent": None,
                "domain": None,
                "detected_at": _now_iso(),
                "auto_resolve": True,
            })

    # Per-agent approval rate (24h vs 7d) — only for agents with >=5 evals in each window
    # COALESCE: rejection events use $.agent, eval events use $.domain_agent (Epimetheus 2026-03-28)
    _check_approval_by_dimension(conn, alerts, "agent", "COALESCE(json_extract(detail, '$.agent'), json_extract(detail, '$.domain_agent'))")

    # Per-domain approval rate (24h vs 7d) — Theseus addition
    _check_approval_by_dimension(conn, alerts, "domain", "json_extract(detail, '$.domain')")

    return alerts


def _check_approval_by_dimension(conn, alerts, dim_name, dim_expr):
    """Check approval rate regression grouped by a dimension (agent or domain)."""
    # 7-day baseline per dimension
    baseline_rows = conn.execute(
        f"""SELECT {dim_expr} as dim_val,
            COUNT(CASE WHEN event='approved' THEN 1 END) as approved,
            COUNT(*) as total
           FROM audit_log
           WHERE stage='evaluate'
           AND event IN ('approved','changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-7 days')
           AND {dim_expr} IS NOT NULL
           GROUP BY dim_val HAVING total >= 5"""
    ).fetchall()
    baselines = {r["dim_val"]: (r["approved"] / r["total"] * 100) for r in baseline_rows}

    # 24h per dimension
    recent_rows = conn.execute(
        f"""SELECT {dim_expr} as dim_val,
            COUNT(CASE WHEN event='approved' THEN 1 END) as approved,
            COUNT(*) as total
           FROM audit_log
           WHERE stage='evaluate'
           AND event IN ('approved','changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-24 hours')
           AND {dim_expr} IS NOT NULL
           GROUP BY dim_val HAVING total >= 5"""
    ).fetchall()

    for r in recent_rows:
        val = r["dim_val"]
        if val not in baselines:
            continue
        recent_rate = r["approved"] / r["total"] * 100
        base_rate = baselines[val]
        drop = base_rate - recent_rate
        if drop > APPROVAL_DROP_THRESHOLD:
            alerts.append({
                "id": f"quality_regression:{dim_name}:{val}",
                "severity": "warning",
                "category": "quality",
                "title": f"{dim_name.title()} '{val}' approval dropped {drop:.0f}pp",
                "detail": (
                    f"24h: {recent_rate:.1f}% vs 7d baseline: {base_rate:.1f}% "
                    f"({r['total']} evals in 24h)."
                ),
                "agent": val if dim_name == "agent" else None,
                "domain": val if dim_name == "domain" else None,
                "detected_at": _now_iso(),
                "auto_resolve": True,
            })


# ─── Check: Throughput Anomaly ──────────────────────────────────────────────


def check_throughput(conn: sqlite3.Connection) -> list[dict]:
    """Detect throughput stalling — today vs 7-day SMA."""
    alerts = []

    # Daily merged counts for last 7 days
    rows = conn.execute(
        """SELECT date(merged_at) as day, COUNT(*) as n
           FROM prs WHERE merged_at > datetime('now', '-7 days')
           GROUP BY day ORDER BY day"""
    ).fetchall()

    if len(rows) < 2:
        return alerts  # Not enough data

    daily_counts = [r["n"] for r in rows]
    sma = statistics.mean(daily_counts[:-1]) if len(daily_counts) > 1 else daily_counts[0]
    today_count = daily_counts[-1]

    if sma > 0 and today_count < sma * THROUGHPUT_DROP_RATIO:
        alerts.append({
            "id": "throughput:stalling",
            "severity": "warning",
            "category": "throughput",
            "title": f"Throughput stalling: {today_count} merges today vs {sma:.0f}/day avg",
            "detail": (
                f"Today's merge count ({today_count}) is below {THROUGHPUT_DROP_RATIO:.0%} of "
                f"7-day average ({sma:.1f}/day). Daily counts: {daily_counts}."
            ),
            "agent": None,
            "domain": None,
            "detected_at": _now_iso(),
            "auto_resolve": True,
        })

    return alerts


# ─── Check: Rejection Reason Spike ─────────────────────────────────────────


def check_rejection_spike(conn: sqlite3.Connection) -> list[dict]:
    """Detect single rejection reason exceeding REJECTION_SPIKE_RATIO of recent rejections."""
    alerts = []

    # Total rejections in 24h
    total = conn.execute(
        """SELECT COUNT(*) as n FROM audit_log
           WHERE stage='evaluate'
           AND event IN ('changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-24 hours')"""
    ).fetchone()["n"]

    if total < 10:
        return alerts  # Not enough data

    # Count by rejection tag
    tags = conn.execute(
        """SELECT value as tag, COUNT(*) as cnt
           FROM audit_log, json_each(json_extract(detail, '$.issues'))
           WHERE stage='evaluate'
           AND event IN ('changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-24 hours')
           GROUP BY tag ORDER BY cnt DESC"""
    ).fetchall()

    for t in tags:
        ratio = t["cnt"] / total
        if ratio > REJECTION_SPIKE_RATIO:
            alerts.append({
                "id": f"rejection_spike:{t['tag']}",
                "severity": "warning",
                "category": "quality",
                "title": f"Rejection reason '{t['tag']}' at {ratio:.0%} of rejections",
                "detail": (
                    f"'{t['tag']}' accounts for {t['cnt']}/{total} rejections in 24h "
                    f"({ratio:.1%}). Threshold: {REJECTION_SPIKE_RATIO:.0%}."
                ),
                "agent": None,
                "domain": None,
                "detected_at": _now_iso(),
                "auto_resolve": True,
            })

    return alerts


# ─── Check: Stuck Loops ────────────────────────────────────────────────────


def check_stuck_loops(conn: sqlite3.Connection) -> list[dict]:
    """Detect agents repeatedly failing on the same rejection reason."""
    alerts = []

    # COALESCE: rejection events use $.agent, eval events use $.domain_agent (Epimetheus 2026-03-28)
    rows = conn.execute(
        """SELECT COALESCE(json_extract(detail, '$.agent'), json_extract(detail, '$.domain_agent')) as agent,
                  value as tag,
                  COUNT(*) as cnt
           FROM audit_log, json_each(json_extract(detail, '$.issues'))
           WHERE stage='evaluate'
           AND event IN ('changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-6 hours')
           AND COALESCE(json_extract(detail, '$.agent'), json_extract(detail, '$.domain_agent')) IS NOT NULL
           GROUP BY agent, tag
           HAVING cnt > ?""",
        (STUCK_LOOP_THRESHOLD,),
    ).fetchall()

    for r in rows:
        alerts.append({
            "id": f"stuck_loop:{r['agent']}:{r['tag']}",
            "severity": "critical",
            "category": "health",
            "title": f"Agent '{r['agent']}' stuck: '{r['tag']}' failed {r['cnt']}x in 6h",
            "detail": (
                f"Agent '{r['agent']}' has been rejected for '{r['tag']}' "
                f"{r['cnt']} times in the last 6 hours (threshold: {STUCK_LOOP_THRESHOLD}). "
                f"Stop and reassess."
            ),
            "agent": r["agent"],
            "domain": None,
            "detected_at": _now_iso(),
            "auto_resolve": True,
        })

    return alerts


# ─── Check: Cost Spikes ────────────────────────────────────────────────────


def check_cost_spikes(conn: sqlite3.Connection) -> list[dict]:
    """Detect daily cost exceeding 2x of 7-day average per agent."""
    alerts = []

    # Check if costs table exists and has agent column
    try:
        cols = conn.execute("PRAGMA table_info(costs)").fetchall()
        col_names = {c["name"] for c in cols}
    except sqlite3.Error:
        return alerts

    if "agent" not in col_names or "cost_usd" not in col_names:
        # Fall back to per-PR cost tracking
        rows = conn.execute(
            """SELECT agent,
                      SUM(CASE WHEN created_at > datetime('now', '-1 day') THEN cost_usd ELSE 0 END) as today_cost,
                      SUM(CASE WHEN created_at > datetime('now', '-7 days') THEN cost_usd ELSE 0 END) / 7.0 as avg_daily
               FROM prs WHERE agent IS NOT NULL AND cost_usd > 0
               GROUP BY agent
               HAVING avg_daily > 0"""
        ).fetchall()
    else:
        rows = conn.execute(
            """SELECT agent,
                      SUM(CASE WHEN timestamp > datetime('now', '-1 day') THEN cost_usd ELSE 0 END) as today_cost,
                      SUM(CASE WHEN timestamp > datetime('now', '-7 days') THEN cost_usd ELSE 0 END) / 7.0 as avg_daily
               FROM costs WHERE agent IS NOT NULL
               GROUP BY agent
               HAVING avg_daily > 0"""
        ).fetchall()

    for r in rows:
        if r["avg_daily"] and r["today_cost"] > r["avg_daily"] * COST_SPIKE_RATIO:
            ratio = r["today_cost"] / r["avg_daily"]
            alerts.append({
                "id": f"cost_spike:{r['agent']}",
                "severity": "warning",
                "category": "health",
                "title": f"Agent '{r['agent']}' cost spike: ${r['today_cost']:.2f} today ({ratio:.1f}x avg)",
                "detail": (
                    f"Today's cost (${r['today_cost']:.2f}) is {ratio:.1f}x the 7-day daily average "
                    f"(${r['avg_daily']:.2f}). Threshold: {COST_SPIKE_RATIO}x."
                ),
                "agent": r["agent"],
                "domain": None,
                "detected_at": _now_iso(),
                "auto_resolve": True,
            })

    return alerts


# ─── Check: Domain Rejection Patterns (Theseus addition) ───────────────────


def check_domain_rejection_patterns(conn: sqlite3.Connection) -> list[dict]:
    """Track rejection reason shift per domain — surfaces domain maturity issues."""
    alerts = []

    # Per-domain rejection breakdown in 24h
    rows = conn.execute(
        """SELECT json_extract(detail, '$.domain') as domain,
                  value as tag,
                  COUNT(*) as cnt
           FROM audit_log, json_each(json_extract(detail, '$.issues'))
           WHERE stage='evaluate'
           AND event IN ('changes_requested','domain_rejected','tier05_rejected')
           AND timestamp > datetime('now', '-24 hours')
           AND json_extract(detail, '$.domain') IS NOT NULL
           GROUP BY domain, tag
           ORDER BY domain, cnt DESC"""
    ).fetchall()

    # Group by domain
    domain_tags = {}
    for r in rows:
        d = r["domain"]
        if d not in domain_tags:
            domain_tags[d] = []
        domain_tags[d].append({"tag": r["tag"], "count": r["cnt"]})

    # Flag if a domain has >50% of rejections from a single reason (concentrated failure)
    for domain, tags in domain_tags.items():
        total = sum(t["count"] for t in tags)
        if total < 5:
            continue
        top = tags[0]
        ratio = top["count"] / total
        if ratio > 0.5:
            alerts.append({
                "id": f"domain_rejection_pattern:{domain}:{top['tag']}",
                "severity": "info",
                "category": "failure_pattern",
                "title": f"Domain '{domain}': {ratio:.0%} of rejections are '{top['tag']}'",
                "detail": (
                    f"In domain '{domain}', {top['count']}/{total} rejections (24h) are for "
                    f"'{top['tag']}'. This may indicate a systematic issue with evidence standards "
                    f"or schema compliance in this domain."
                ),
                "agent": None,
                "domain": domain,
                "detected_at": _now_iso(),
                "auto_resolve": True,
            })

    return alerts


# ─── Failure Report Generator ───────────────────────────────────────────────


def generate_failure_report(conn: sqlite3.Connection, agent: str, hours: int = 24) -> dict | None:
    """Compile a failure report for a specific agent.

    Returns top rejection reasons, example PRs, and suggested fixes.
    Designed to be sent directly to the agent via Pentagon messaging.
    """
    hours = int(hours)  # defensive — callers should pass int, but enforce it
    rows = conn.execute(
        """SELECT value as tag, COUNT(*) as cnt,
                  GROUP_CONCAT(DISTINCT json_extract(detail, '$.pr')) as pr_numbers
           FROM audit_log, json_each(json_extract(detail, '$.issues'))
           WHERE stage='evaluate'
           AND event IN ('changes_requested','domain_rejected','tier05_rejected')
           AND json_extract(detail, '$.agent') = ?
           AND timestamp > datetime('now', ? || ' hours')
           GROUP BY tag ORDER BY cnt DESC
           LIMIT 5""",
        (agent, f"-{hours}"),
    ).fetchall()

    if not rows:
        return None

    total_rejections = sum(r["cnt"] for r in rows)
    top_reasons = []
    for r in rows:
        prs = r["pr_numbers"].split(",")[:3] if r["pr_numbers"] else []
        top_reasons.append({
            "reason": r["tag"],
            "count": r["cnt"],
            "pct": round(r["cnt"] / total_rejections * 100, 1),
            "example_prs": prs,
            "suggestion": _suggest_fix(r["tag"]),
        })

    return {
        "agent": agent,
        "period_hours": hours,
        "total_rejections": total_rejections,
        "top_reasons": top_reasons,
        "generated_at": _now_iso(),
    }


def _suggest_fix(rejection_tag: str) -> str:
    """Map known rejection reasons to actionable suggestions."""
    suggestions = {
        "broken_wiki_links": "Check that all [[wiki links]] in claims resolve to existing files. Run link validation before submitting.",
        "near_duplicate": "Search existing claims before creating new ones. Use semantic search to find similar claims.",
        "frontmatter_schema": "Validate YAML frontmatter against the claim schema. Required fields: title, domain, confidence, type.",
        "weak_evidence": "Add concrete sources, data points, or citations. Claims need evidence that can be independently verified.",
        "missing_confidence": "Every claim needs a confidence level: proven, likely, experimental, or speculative.",
        "domain_mismatch": "Ensure claims are filed under the correct domain. Check domain definitions if unsure.",
        "too_broad": "Break broad claims into specific, testable sub-claims.",
        "missing_links": "Claims should link to related claims, entities, or sources. Isolated claims are harder to verify.",
    }
    return suggestions.get(rejection_tag, f"Review rejection reason '{rejection_tag}' and adjust extraction accordingly.")


# ─── Run All Checks ────────────────────────────────────────────────────────


def run_all_checks(conn: sqlite3.Connection) -> list[dict]:
    """Execute all check functions and return combined alerts."""
    alerts = []
    alerts.extend(check_agent_health(conn))
    alerts.extend(check_quality_regression(conn))
    alerts.extend(check_throughput(conn))
    alerts.extend(check_rejection_spike(conn))
    alerts.extend(check_stuck_loops(conn))
    alerts.extend(check_cost_spikes(conn))
    alerts.extend(check_domain_rejection_patterns(conn))
    return alerts


def format_alert_message(alert: dict) -> str:
    """Format an alert for Pentagon messaging."""
    severity_icon = {"critical": "!!", "warning": "!", "info": "~"}
    icon = severity_icon.get(alert["severity"], "?")
    return f"[{icon}] {alert['title']}\n{alert['detail']}"