#!/usr/bin/env python3
"""Loop introspection — the self-improvement engine.

Analyzes retro data across time windows to detect trends, extract patterns,
and produce structured recommendations. Output is consumed by deep_triage
and injected into the loop prompt context.

This is the piece that closes the feedback loop:
  cycle_retro → introspect → deep_triage → loop behavior changes

Run:  python3 scripts/loop_introspect.py
Output: .loop/retro/insights.json (structured insights + recommendations)
        Prints human-readable summary to stdout.

Called by: deep_triage.sh (before the LLM triage), timmy-loop.sh (every 50 cycles)
"""

from __future__ import annotations

import json
import sys
from collections import defaultdict
from datetime import datetime, timezone, timedelta
from pathlib import Path

REPO_ROOT = Path(__file__).resolve().parent.parent
CYCLES_FILE = REPO_ROOT / ".loop" / "retro" / "cycles.jsonl"
DEEP_TRIAGE_FILE = REPO_ROOT / ".loop" / "retro" / "deep-triage.jsonl"
TRIAGE_FILE = REPO_ROOT / ".loop" / "retro" / "triage.jsonl"
QUARANTINE_FILE = REPO_ROOT / ".loop" / "quarantine.json"
INSIGHTS_FILE = REPO_ROOT / ".loop" / "retro" / "insights.json"

# ── Helpers ──────────────────────────────────────────────────────────────

def load_jsonl(path: Path) -> list[dict]:
    """Load a JSONL file, skipping bad lines."""
    if not path.exists():
        return []
    entries = []
    for line in path.read_text().strip().splitlines():
        try:
            entries.append(json.loads(line))
        except (json.JSONDecodeError, ValueError):
            continue
    return entries


def parse_ts(ts_str: str) -> datetime | None:
    """Parse an ISO timestamp, tolerating missing tz."""
    if not ts_str:
        return None
    try:
        dt = datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
        if dt.tzinfo is None:
            dt = dt.replace(tzinfo=timezone.utc)
        return dt
    except (ValueError, TypeError):
        return None


def window(entries: list[dict], days: int) -> list[dict]:
    """Filter entries to the last N days."""
    cutoff = datetime.now(timezone.utc) - timedelta(days=days)
    result = []
    for e in entries:
        ts = parse_ts(e.get("timestamp", ""))
        if ts and ts >= cutoff:
            result.append(e)
    return result


# ── Analysis functions ───────────────────────────────────────────────────

def compute_trends(cycles: list[dict]) -> dict:
    """Compare recent window (last 7d) vs older window (7-14d ago)."""
    recent = window(cycles, 7)
    older = window(cycles, 14)
    # Remove recent from older to get the 7-14d window
    recent_set = {(e.get("cycle"), e.get("timestamp")) for e in recent}
    older = [e for e in older if (e.get("cycle"), e.get("timestamp")) not in recent_set]

    def stats(entries):
        if not entries:
            return {"count": 0, "success_rate": None, "avg_duration": None,
                    "lines_net": 0, "prs_merged": 0}
        successes = sum(1 for e in entries if e.get("success"))
        durations = [e["duration"] for e in entries if e.get("duration", 0) > 0]
        return {
            "count": len(entries),
            "success_rate": round(successes / len(entries), 3) if entries else None,
            "avg_duration": round(sum(durations) / len(durations)) if durations else None,
            "lines_net": sum(e.get("lines_added", 0) - e.get("lines_removed", 0) for e in entries),
            "prs_merged": sum(1 for e in entries if e.get("pr")),
        }

    recent_stats = stats(recent)
    older_stats = stats(older)

    trend = {
        "recent_7d": recent_stats,
        "previous_7d": older_stats,
        "velocity_change": None,
        "success_rate_change": None,
        "duration_change": None,
    }

    if recent_stats["count"] and older_stats["count"]:
        trend["velocity_change"] = recent_stats["count"] - older_stats["count"]
    if recent_stats["success_rate"] is not None and older_stats["success_rate"] is not None:
        trend["success_rate_change"] = round(
            recent_stats["success_rate"] - older_stats["success_rate"], 3
        )
    if recent_stats["avg_duration"] is not None and older_stats["avg_duration"] is not None:
        trend["duration_change"] = recent_stats["avg_duration"] - older_stats["avg_duration"]

    return trend


def type_analysis(cycles: list[dict]) -> dict:
    """Per-type success rates and durations."""
    by_type: dict[str, list[dict]] = defaultdict(list)
    for c in cycles:
        by_type[c.get("type", "unknown")].append(c)

    result = {}
    for t, entries in by_type.items():
        durations = [e["duration"] for e in entries if e.get("duration", 0) > 0]
        successes = sum(1 for e in entries if e.get("success"))
        result[t] = {
            "count": len(entries),
            "success_rate": round(successes / len(entries), 3) if entries else 0,
            "avg_duration": round(sum(durations) / len(durations)) if durations else 0,
            "max_duration": max(durations) if durations else 0,
        }
    return result


def repeat_failures(cycles: list[dict]) -> list[dict]:
    """Issues that have failed multiple times — quarantine candidates."""
    failures: dict[int, list] = defaultdict(list)
    for c in cycles:
        if not c.get("success") and c.get("issue"):
            failures[c["issue"]].append({
                "cycle": c.get("cycle"),
                "reason": c.get("reason", ""),
                "duration": c.get("duration", 0),
            })
    # Only issues with 2+ failures
    return [
        {"issue": k, "failure_count": len(v), "attempts": v}
        for k, v in sorted(failures.items(), key=lambda x: -len(x[1]))
        if len(v) >= 2
    ]


def duration_outliers(cycles: list[dict], threshold_multiple: float = 3.0) -> list[dict]:
    """Cycles that took way longer than average — something went wrong."""
    durations = [c["duration"] for c in cycles if c.get("duration", 0) > 0]
    if len(durations) < 5:
        return []
    avg = sum(durations) / len(durations)
    threshold = avg * threshold_multiple

    outliers = []
    for c in cycles:
        dur = c.get("duration", 0)
        if dur > threshold:
            outliers.append({
                "cycle": c.get("cycle"),
                "issue": c.get("issue"),
                "type": c.get("type"),
                "duration": dur,
                "avg_duration": round(avg),
                "multiple": round(dur / avg, 1) if avg > 0 else 0,
                "reason": c.get("reason", ""),
            })
    return outliers


def triage_effectiveness(deep_triages: list[dict]) -> dict:
    """How well is the deep triage performing?"""
    if not deep_triages:
        return {"runs": 0, "note": "No deep triage data yet"}

    total_reviewed = sum(d.get("issues_reviewed", 0) for d in deep_triages)
    total_refined = sum(len(d.get("issues_refined", [])) for d in deep_triages)
    total_created = sum(len(d.get("issues_created", [])) for d in deep_triages)
    total_closed = sum(len(d.get("issues_closed", [])) for d in deep_triages)
    timmy_available = sum(1 for d in deep_triages if d.get("timmy_available"))

    # Extract Timmy's feedback themes
    timmy_themes = []
    for d in deep_triages:
        fb = d.get("timmy_feedback", "")
        if fb:
            timmy_themes.append(fb[:200])

    return {
        "runs": len(deep_triages),
        "total_reviewed": total_reviewed,
        "total_refined": total_refined,
        "total_created": total_created,
        "total_closed": total_closed,
        "timmy_consultation_rate": round(timmy_available / len(deep_triages), 2),
        "timmy_recent_feedback": timmy_themes[-1] if timmy_themes else "",
        "timmy_feedback_history": timmy_themes,
    }


def generate_recommendations(
    trends: dict,
    types: dict,
    repeats: list,
    outliers: list,
    triage_eff: dict,
) -> list[dict]:
    """Produce actionable recommendations from the analysis."""
    recs = []

    # 1. Success rate declining?
    src = trends.get("success_rate_change")
    if src is not None and src < -0.1:
        recs.append({
            "severity": "high",
            "category": "reliability",
            "finding": f"Success rate dropped {abs(src)*100:.0f}pp in the last 7 days",
            "recommendation": "Review recent failures. Are issues poorly scoped? "
                              "Is main unstable? Check if triage is producing bad work items.",
        })

    # 2. Velocity dropping?
    vc = trends.get("velocity_change")
    if vc is not None and vc < -5:
        recs.append({
            "severity": "medium",
            "category": "throughput",
            "finding": f"Velocity dropped by {abs(vc)} cycles vs previous week",
            "recommendation": "Check for loop stalls, long-running cycles, or queue starvation.",
        })

    # 3. Duration creep?
    dc = trends.get("duration_change")
    if dc is not None and dc > 120:  # 2+ minutes longer
        recs.append({
            "severity": "medium",
            "category": "efficiency",
            "finding": f"Average cycle duration increased by {dc}s vs previous week",
            "recommendation": "Issues may be growing in scope. Enforce tighter decomposition "
                              "in deep triage. Check if tests are getting slower.",
        })

    # 4. Type-specific problems
    for t, info in types.items():
        if info["count"] >= 3 and info["success_rate"] < 0.5:
            recs.append({
                "severity": "high",
                "category": "type_reliability",
                "finding": f"'{t}' issues fail {(1-info['success_rate'])*100:.0f}% of the time "
                           f"({info['count']} attempts)",
                "recommendation": f"'{t}' issues need better scoping or different approach. "
                                  f"Consider: tighter acceptance criteria, smaller scope, "
                                  f"or delegating to Kimi with more context.",
            })
        if info["avg_duration"] > 600 and info["count"] >= 3:  # >10 min avg
            recs.append({
                "severity": "medium",
                "category": "type_efficiency",
                "finding": f"'{t}' issues average {info['avg_duration']//60}m{info['avg_duration']%60}s "
                           f"(max {info['max_duration']//60}m)",
                "recommendation": f"Break '{t}' issues into smaller pieces. Target <5 min per cycle.",
            })

    # 5. Repeat failures
    for rf in repeats[:3]:
        recs.append({
            "severity": "high",
            "category": "repeat_failure",
            "finding": f"Issue #{rf['issue']} has failed {rf['failure_count']} times",
            "recommendation": "Quarantine or rewrite this issue. Repeated failure = "
                              "bad scope or missing prerequisite.",
        })

    # 6. Outliers
    if len(outliers) > 2:
        recs.append({
            "severity": "medium",
            "category": "outliers",
            "finding": f"{len(outliers)} cycles took {outliers[0].get('multiple', '?')}x+ "
                       f"longer than average",
            "recommendation": "Long cycles waste resources. Add timeout enforcement or "
                              "break complex issues earlier.",
        })

    # 7. Code growth
    recent = trends.get("recent_7d", {})
    net = recent.get("lines_net", 0)
    if net > 500:
        recs.append({
            "severity": "low",
            "category": "code_health",
            "finding": f"Net +{net} lines added in the last 7 days",
            "recommendation": "Lines of code is a liability. Balance feature work with "
                              "refactoring. Target net-zero or negative line growth.",
        })

    # 8. Triage health
    if triage_eff.get("runs", 0) == 0:
        recs.append({
            "severity": "high",
            "category": "triage",
            "finding": "Deep triage has never run",
            "recommendation": "Enable deep triage (every 20 cycles). The loop needs "
                              "LLM-driven issue refinement to stay effective.",
        })

    # No recommendations = things are healthy
    if not recs:
        recs.append({
            "severity": "info",
            "category": "health",
            "finding": "No significant issues detected",
            "recommendation": "System is healthy. Continue current patterns.",
        })

    return recs


# ── Main ─────────────────────────────────────────────────────────────────

def main() -> None:
    cycles = load_jsonl(CYCLES_FILE)
    deep_triages = load_jsonl(DEEP_TRIAGE_FILE)

    if not cycles:
        print("[introspect] No cycle data found. Nothing to analyze.")
        return

    # Run all analyses
    trends = compute_trends(cycles)
    types = type_analysis(cycles)
    repeats = repeat_failures(cycles)
    outliers = duration_outliers(cycles)
    triage_eff = triage_effectiveness(deep_triages)
    recommendations = generate_recommendations(trends, types, repeats, outliers, triage_eff)

    insights = {
        "generated_at": datetime.now(timezone.utc).isoformat(),
        "total_cycles_analyzed": len(cycles),
        "trends": trends,
        "by_type": types,
        "repeat_failures": repeats[:5],
        "duration_outliers": outliers[:5],
        "triage_effectiveness": triage_eff,
        "recommendations": recommendations,
    }

    # Write insights
    INSIGHTS_FILE.parent.mkdir(parents=True, exist_ok=True)
    INSIGHTS_FILE.write_text(json.dumps(insights, indent=2) + "\n")

    # Current epoch from latest entry
    latest_epoch = ""
    for c in reversed(cycles):
        if c.get("epoch"):
            latest_epoch = c["epoch"]
            break

    # Human-readable output
    header = f"[introspect] Analyzed {len(cycles)} cycles"
    if latest_epoch:
        header += f"  ·  current epoch: {latest_epoch}"
    print(header)

    print(f"\n  TRENDS (7d vs previous 7d):")
    r7 = trends["recent_7d"]
    p7 = trends["previous_7d"]
    print(f"    Cycles:       {r7['count']:>3d}  (was {p7['count']})")
    if r7["success_rate"] is not None:
        arrow = "↑" if (trends["success_rate_change"] or 0) > 0 else "↓" if (trends["success_rate_change"] or 0) < 0 else "→"
        print(f"    Success rate: {r7['success_rate']*100:>4.0f}%  {arrow}")
    if r7["avg_duration"] is not None:
        print(f"    Avg duration: {r7['avg_duration']//60}m{r7['avg_duration']%60:02d}s")
    print(f"    PRs merged:   {r7['prs_merged']:>3d}  (was {p7['prs_merged']})")
    print(f"    Lines net:    {r7['lines_net']:>+5d}")

    print(f"\n  BY TYPE:")
    for t, info in sorted(types.items(), key=lambda x: -x[1]["count"]):
        print(f"    {t:12s}  n={info['count']:>2d}  "
              f"ok={info['success_rate']*100:>3.0f}%  "
              f"avg={info['avg_duration']//60}m{info['avg_duration']%60:02d}s")

    if repeats:
        print(f"\n  REPEAT FAILURES:")
        for rf in repeats[:3]:
            print(f"    #{rf['issue']}  failed {rf['failure_count']}x")

    print(f"\n  RECOMMENDATIONS ({len(recommendations)}):")
    for i, rec in enumerate(recommendations, 1):
        sev = {"high": "🔴", "medium": "🟡", "low": "🟢", "info": "ℹ️ "}.get(rec["severity"], "?")
        print(f"    {sev} {rec['finding']}")
        print(f"       → {rec['recommendation']}")

    print(f"\n  Written to: {INSIGHTS_FILE}")


if __name__ == "__main__":
    main()