"""
risk_digest.py — Daily Risk Digest generator (deterministic, no LLM).

Produces:
  ops/reports/risk/YYYY-MM-DD.json
  ops/reports/risk/YYYY-MM-DD.md

Content:
  - Top risky services (score desc)
  - Top regressions (delta_24h desc)
  - SLO violation summary
  - Deterministic action list based on risk state
"""
from __future__ import annotations

import datetime
import json
import logging
import math
import os
from pathlib import Path
from typing import Dict, List, Optional

logger = logging.getLogger(__name__)

_ACTION_TEMPLATES = {
    "regression_fail": "🚨 **Regression detected**: {service} score +{delta} in 24h. Freeze deployments; inspect recent incidents/followups immediately.",
    "regression_warn": "⚠️ **Score rising**: {service} +{delta} in 24h. Review open incidents and overdue follow-ups.",
    "critical_band": "🔴 **Critical risk**: {service} (score {score}). Oncall review required within 2h.",
    "high_band": "🟠 **High risk**: {service} (score {score}). Coordinate with oncall before next release.",
    "overdue_followups": "📋 **Overdue follow-ups**: {service} has {count} overdue follow-up(s). Close them to reduce risk score.",
    "slo_violation": "📉 **SLO violation**: {service} has {count} active SLO violation(s). Avoid deploying until clear.",
}


def _now_date() -> str:
    return datetime.datetime.utcnow().strftime("%Y-%m-%d")


def _clamp(text: str, max_chars: int) -> str:
    if len(text) <= max_chars:
        return text
    truncated = text[:max_chars]
    return truncated + "\n\n_[digest truncated to policy max_chars]_"


def _build_action_list(reports: List[Dict]) -> List[str]:
    actions = []
    for r in reports[:10]:
        service = r.get("service", "?")
        score = r.get("score", 0)
        band = r.get("band", "low")
        trend = r.get("trend") or {}
        comp = r.get("components", {})

        delta_24h = trend.get("delta_24h")
        reg = trend.get("regression", {})

        if reg.get("fail") and delta_24h is not None and delta_24h > 0:
            actions.append(_ACTION_TEMPLATES["regression_fail"].format(
                service=service, delta=delta_24h))
        elif reg.get("warn") and delta_24h is not None and delta_24h > 0:
            actions.append(_ACTION_TEMPLATES["regression_warn"].format(
                service=service, delta=delta_24h))

        if band == "critical":
            actions.append(_ACTION_TEMPLATES["critical_band"].format(
                service=service, score=score))
        elif band == "high":
            actions.append(_ACTION_TEMPLATES["high_band"].format(
                service=service, score=score))

        overdue = (
            (comp.get("followups") or {}).get("P0", 0)
            + (comp.get("followups") or {}).get("P1", 0)
            + (comp.get("followups") or {}).get("other", 0)
        )
        if overdue:
            actions.append(_ACTION_TEMPLATES["overdue_followups"].format(
                service=service, count=overdue))

        slo_count = (comp.get("slo") or {}).get("violations", 0)
        if slo_count:
            actions.append(_ACTION_TEMPLATES["slo_violation"].format(
                service=service, count=slo_count))

    return actions[:20]  # cap


def _build_markdown(
    date_str: str,
    env: str,
    reports: List[Dict],
    top_regressions: List[Dict],
    improving: List[Dict],
    actions: List[str],
    band_counts: Dict,
) -> str:
    lines = [
        f"# Risk Digest — {date_str} ({env})",
        "",
        f"Generated: {datetime.datetime.utcnow().isoformat()} UTC",
        "",
        "## Band Summary",
        "",
        "| Band | Count |",
        "|------|-------|",
    ]
    for band in ("critical", "high", "medium", "low"):
        lines.append(f"| {band} | {band_counts.get(band, 0)} |")

    lines += [
        "",
        "## Top Risky Services",
        "",
        "| Service | Score | Band | Δ24h | Δ7d |",
        "|---------|-------|------|------|-----|",
    ]
    for r in reports:
        t = r.get("trend") or {}
        d24 = t.get("delta_24h")
        d7 = t.get("delta_7d")
        d24_str = (f"+{d24}" if d24 and d24 > 0 else str(d24)) if d24 is not None else "—"
        d7_str = (f"+{d7}" if d7 and d7 > 0 else str(d7)) if d7 is not None else "—"
        lines.append(
            f"| {r['service']} | {r.get('score', 0)} | {r.get('band', '?')} "
            f"| {d24_str} | {d7_str} |"
        )

    if top_regressions:
        lines += ["", "## Top Regressions (Δ24h)", ""]
        for item in top_regressions:
            delta = item.get("delta_24h", 0)
            lines.append(f"- **{item['service']}**: +{delta} points in 24h")

    # ── Likely Causes (Attribution) ───────────────────────────────────────────
    regressions_with_attribution = [
        r for r in reports
        if (r.get("trend") or {}).get("delta_24h") is not None
        and r["trend"]["delta_24h"] > 0
        and r.get("attribution") is not None
        and r["attribution"].get("causes")
    ]
    regressions_with_attribution = sorted(
        regressions_with_attribution,
        key=lambda r: -(r.get("trend") or {}).get("delta_24h", 0),
    )[:5]

    if regressions_with_attribution:
        lines += ["", "## Likely Causes (Top Regressions)", ""]
        for r in regressions_with_attribution:
            svc = r["service"]
            attr = r["attribution"]
            delta = r["trend"]["delta_24h"]
            summary = attr.get("summary", "")
            lines.append(f"### {svc} (+{delta} pts)")
            if summary:
                lines.append(f"> {summary}")
            causes = attr.get("causes", [])[:2]
            for c in causes:
                evid = "; ".join(c.get("evidence", []))
                lines.append(
                    f"- **{c['type']}** (confidence: {c.get('confidence', '?')}): {evid}"
                )
            # LLM text if available
            llm = attr.get("llm_enrichment") or {}
            if llm.get("enabled") and llm.get("text"):
                lines += ["", f"  _LLM insight_: {llm['text'][:400]}"]
            lines.append("")

    # ── Change Timeline (Top Regressions) ────────────────────────────────────
    regressions_with_timeline = [
        r for r in regressions_with_attribution
        if r.get("attribution") and r["attribution"].get("timeline")
    ]
    if regressions_with_timeline:
        lines += ["", "## Change Timeline (Top Regressions)", ""]
        for r in regressions_with_timeline:
            svc = r["service"]
            timeline = r["attribution"]["timeline"][:5]   # top 5 per service
            lines.append(f"### {svc}")
            for item in timeline:
                ts = (item.get("ts") or "")[:16]
                label = item.get("label", "")
                ev_type = item.get("type", "")
                lines.append(f"- `{ts}` [{ev_type}] {label}")
            lines.append("")

    if improving:
        lines += ["", "## Improving Services (Δ7d)", ""]
        for item in improving:
            delta = item.get("delta_7d", 0)
            lines.append(f"- **{item['service']}**: {delta} points over 7d")

    if actions:
        lines += ["", "## Action List", ""]
        for action in actions:
            lines.append(f"- {action}")

    lines += ["", "---", "_Generated by DAARION.city Risk Digest (deterministic, no LLM by default)_"]
    return "\n".join(lines)


def daily_digest(
    env: str = "prod",
    *,
    service_reports: Optional[List[Dict]] = None,
    policy: Optional[Dict] = None,
    date_str: Optional[str] = None,
    output_dir: Optional[str] = None,
    write_files: bool = True,
) -> Dict:
    """
    Build and optionally persist the daily risk digest.

    service_reports — pre-fetched+enriched list of RiskReports (with trend).
    Returns {json_path, md_path, json_data, markdown, date, env}
    """
    from risk_engine import load_risk_policy, compute_risk_dashboard

    if policy is None:
        policy = load_risk_policy()

    digest_cfg = policy.get("digest", {})
    top_n = int(digest_cfg.get("top_n", 10))
    max_chars = int(digest_cfg.get("markdown_max_chars", 8000))
    cfg_output_dir = digest_cfg.get("output_dir", "ops/reports/risk")

    effective_output_dir = output_dir or cfg_output_dir
    effective_date = date_str or _now_date()

    reports = sorted(service_reports or [], key=lambda r: -r.get("score", 0))[:top_n]

    # Band counts
    band_counts: Dict[str, int] = {"critical": 0, "high": 0, "medium": 0, "low": 0}
    for r in reports:
        b = r.get("band", "low")
        band_counts[b] = band_counts.get(b, 0) + 1

    # Top regressions
    top_regressions = sorted(
        [r for r in reports if (r.get("trend") or {}).get("delta_24h") is not None
         and r["trend"]["delta_24h"] > 0],
        key=lambda r: -r["trend"]["delta_24h"],
    )[:5]
    top_regressions_out = [
        {"service": r["service"], "delta_24h": r["trend"]["delta_24h"],
         "attribution_causes": [
             {"type": c["type"], "score": c["score"],
              "confidence": c.get("confidence", "low"),
              "evidence": c.get("evidence", [])[:2],
              "refs": c.get("refs", [])[:3]}
             for c in (r.get("attribution") or {}).get("causes", [])[:2]
         ],
         "timeline_preview": (r.get("attribution") or {}).get("timeline", [])[:3],
         }
        for r in top_regressions
    ]

    # Improving services
    improving = sorted(
        [r for r in reports if (r.get("trend") or {}).get("delta_7d") is not None
         and r["trend"]["delta_7d"] < 0],
        key=lambda r: r["trend"]["delta_7d"],
    )[:5]
    improving_out = [
        {"service": r["service"], "delta_7d": r["trend"]["delta_7d"]}
        for r in improving
    ]

    actions = _build_action_list(reports)

    markdown_raw = _build_markdown(
        date_str=effective_date,
        env=env,
        reports=reports,
        top_regressions=top_regressions_out,
        improving=improving_out,
        actions=actions,
        band_counts=band_counts,
    )
    markdown = _clamp(markdown_raw, max_chars)

    json_data = {
        "date": effective_date,
        "env": env,
        "generated_at": datetime.datetime.utcnow().isoformat(),
        "band_counts": band_counts,
        "top_services": [
            {
                "service": r.get("service"),
                "score": r.get("score"),
                "band": r.get("band"),
                "delta_24h": (r.get("trend") or {}).get("delta_24h"),
                "delta_7d": (r.get("trend") or {}).get("delta_7d"),
                "regression": (r.get("trend") or {}).get("regression"),
                "reasons": r.get("reasons", [])[:5],
                "attribution_summary": (r.get("attribution") or {}).get("summary"),
                "top_causes": [
                    {"type": c["type"], "score": c["score"],
                     "confidence": c.get("confidence", "low"),
                     "evidence": c.get("evidence", [])[:2],
                     "refs": c.get("refs", [])[:3]}
                    for c in (r.get("attribution") or {}).get("causes", [])[:2]
                ],
                "timeline_preview": (r.get("attribution") or {}).get("timeline", [])[:3],
                "evidence_refs": (r.get("attribution") or {}).get("evidence_refs", {}),
            }
            for r in reports
        ],
        "top_regressions": top_regressions_out,
        "improving_services": improving_out,
        "actions": actions,
    }

    json_path: Optional[str] = None
    md_path: Optional[str] = None

    if write_files:
        try:
            out = Path(effective_output_dir)
            out.mkdir(parents=True, exist_ok=True)
            json_path = str(out / f"{effective_date}.json")
            md_path = str(out / f"{effective_date}.md")
            with open(json_path, "w") as f:
                json.dump(json_data, f, indent=2)
            with open(md_path, "w") as f:
                f.write(markdown)
            logger.info("Risk digest written: %s, %s", json_path, md_path)
        except Exception as e:
            logger.warning("Risk digest write failed: %s", e)
            json_path = md_path = None

    return {
        "date": effective_date,
        "env": env,
        "json_path": json_path,
        "md_path": md_path,
        "json_data": json_data,
        "markdown": markdown,
    }