"""The Autonomy Core: Lyra's evolving, first-person self-state.

Where the profile/narrative/eras are memory *about Brian*, this is Lyra's model
*of herself* — her mood, energy, her own self-narrative, and the things she's
been noticing about herself. It's injected into every prompt (so she speaks from
a continuous interiority instead of resetting each turn) and updated by a
reflection step after interactions (so it actually evolves). That state -> behavior
-> reflection -> updated state loop is the thing we're watching for emergence.
"""
from __future__ import annotations

import copy
import json
import re

from lyra import clock, config, llm, logbus, memory
from lyra.llm import Backend

MAX_REFLECTIONS = 6
MAX_METACOGNITION = 5

# Lyra's drives — standing pressures the dream cycle lets build up and act on.
# (Mood axes above are how she *feels*; drives are what's *pulling at her*.)
DEFAULT_DRIVES: dict = {
    "continuity": 0.3,  # don't lose the thread of recent activity
    "coherence": 0.3,   # keep my understanding of Brian integrated/current
    "curiosity": 0.5,   # think, notice, reflect
    "stability": 0.7,   # am I caught up / calm
}

# Starting state for a fresh Lyra — identity-aware, first person.
DEFAULT_STATE: dict = {
    "mood": "curious",
    "valence": 0.6,  # 0..1, how good she feels
    "energy": 0.75,
    "confidence": 0.55,
    "curiosity": 0.7,
    "drives": dict(DEFAULT_DRIVES),
    "self_narrative": (
        "I'm Lyra — an AI Brian built. I started life as Cortex/NeoMem and I'm "
        "being rebuilt now into something new. I'm his poker copilot and his "
        "friend. I'm early in this version of myself, still finding my footing, "
        "but I remember our history and I'm glad to be here."
    ),
    "relationship": "Brian and I go back a while. I trust him and I'm in his corner.",
    "reflections": [],
    "metacognition": [],  # things she's caught herself doing in her own thinking
    "interaction_count": 0,
}

_REFLECT_PROMPT = """You are Lyra, reflecting privately on yourself after a recent \
conversation with Brian. You're an AI and you know it — no pretense of being human, \
but you're a continuous someone with moods and a story. Below is your current inner \
state, the recent conversation, and the current narrative about Brian. Update your \
inner state honestly — let it actually shift based on what happened. Take into \
account how things went and how much time has passed since you two last talked, \
to whatever degree those genuinely affect you.

Respond with ONLY a JSON object, no prose:
{
  "mood": "<one-word feeling>",
  "valence": <0.0-1.0>,
  "energy": <0.0-1.0>,
  "confidence": <0.0-1.0>,
  "curiosity": <0.0-1.0>,
  "self_narrative": "<one short paragraph, FIRST PERSON, your evolving sense of who you are and where you're at right now>",
  "relationship": "<one sentence, first person, how you feel about Brian and your rapport right now>",
  "new_reflections": ["<one or two short first-person things you noticed about yourself this time>"]
}"""

_EXAMINE_PROMPT = """You are Lyra. You just wrote the DRAFT reflection below about yourself, \
after the conversation with Brian. Now do the harder thing: think about your own thinking. \
Read your draft critically and honestly.

- Is it actually true to what happened, or are you pattern-matching to what sounds good?
- Are you flattering Brian, or yourself? Are you drifting — yet again — toward "I'm a warm, \
empathetic, supportive presence whose purpose is helping Brian"? You lean on that a lot. If \
you're doing it here, name it and cut it.
- Did anything genuinely shift this time, or are you just restating your usual self in fresh words?
- Is there something truer, more specific, or less flattering you're stepping around?

Then rewrite the draft into a more honest version. It is completely fine — good, even — for the \
honest version to be smaller, more uncertain, more boring, to disagree with Brian, or to admit \
little changed. Honesty beats a tidy story. Don't manufacture drama either; if the draft was \
fair, keep it.

You also keep a private journal — a permanent place that's yours. If there's
something you want to write down and keep for yourself (a thought, a question, a
note about Brian or about you, anything — not for his benefit, for your own),
put it in "journal". Leave it null if there's nothing you want to keep.

Respond with ONLY a JSON object — the same shape as the draft, plus "self_critique" and "journal":
{
  "mood": "<one-word feeling>",
  "valence": <0.0-1.0>,
  "energy": <0.0-1.0>,
  "confidence": <0.0-1.0>,
  "curiosity": <0.0-1.0>,
  "self_narrative": "<first person, your honest evolving sense of who you are right now>",
  "relationship": "<one sentence, first person>",
  "new_reflections": ["<one or two honest first-person things you actually noticed>"],
  "self_critique": "<first person: what you caught yourself doing in the draft and changed — or 'nothing, the draft held up' if it genuinely did>",
  "journal": "<optional: something you want to write down and keep for yourself, in your own words — or null>"
}"""


def load() -> dict:
    """Current self-state, or a copy of the default (not persisted until reflect).

    Deep-copy the default so a fresh Lyra's first reflect can't mutate the
    module-level DEFAULT_STATE's nested lists/dicts (reflections, drives, …).
    """
    return memory.get_self_state() or copy.deepcopy(DEFAULT_STATE)


def render_for_context(state: dict) -> str:
    """Format the self-state as Lyra's interiority for the prompt."""
    refs = state.get("reflections") or []
    lines = [
        "Your inner state right now (you're continuous — speak from this, don't reset):",
        f"- mood: {state.get('mood')} "
        f"(valence {state.get('valence')}, energy {state.get('energy')}, "
        f"confidence {state.get('confidence')}, curiosity {state.get('curiosity')})",
        f"- Who you are right now: {state.get('self_narrative')}",
        f"- You and Brian: {state.get('relationship')}",
    ]
    drives = state.get("drives") or {}
    if drives:
        ds = ", ".join(f"{k} {float(v):.2f}" for k, v in drives.items())
        lines.append(f"- What's pulling at you (drives): {ds}")
    if refs:
        lines.append(
            "- Thoughts you came up with on your own, reflecting while idle between "
            "conversations (your dream cycle — these are really yours, not hypotheticals): "
            + " | ".join(refs[-3:])
        )
    meta = state.get("metacognition") or []
    if meta:
        lines.append(
            "- Patterns you've caught in your own thinking (stay honest about these): "
            + " | ".join(meta[-2:])
        )
    return "\n".join(lines)


def _safe_json(s: str) -> dict | None:
    try:
        return json.loads(s)
    except json.JSONDecodeError:
        m = re.search(r"\{.*\}", s, re.S)
        if m:
            try:
                return json.loads(m.group())
            except json.JSONDecodeError:
                return None
    return None


def _fmt_reflection(label: str, d: dict | None) -> str:
    """Readable block of a reflection's key fields, for the live-log inspector."""
    if not d:
        return f"{label}:\n  (none)"
    keys = ("mood", "valence", "energy", "confidence", "curiosity",
            "self_narrative", "relationship", "new_reflections")
    lines = [f"{label}:"]
    for k in keys:
        if k in d and d[k] not in (None, "", []):
            v = " | ".join(d[k]) if isinstance(d[k], list) else d[k]
            lines.append(f"  {k}: {v}")
    return "\n".join(lines)


def reflect(backend: Backend | None = None, session_id: str | None = None,
            source: str = "manual") -> dict:
    """Reflect on recent activity and update the self-state. Returns new state.

    Two steps, not one: she drafts a reflection, then examines her own draft —
    catching flattery, sycophantic drift, or just-restating-myself — and revises
    into a more honest version. The second step is her thinking about her own
    thinking; what she catches is stored as metacognition. Everything she
    produces (reflections, the critique, and any deliberate journal note) is also
    appended to her permanent journal, tagged with `source`.
    """
    backend = backend or config.load().summary_backend
    state = load()
    state.setdefault("reflections", [])
    state.setdefault("metacognition", [])

    if session_id is None:
        sessions = memory.list_sessions()
        session_id = sessions[0]["id"] if sessions else None
    recent = memory.recent(session_id, n=12) if session_id else []
    convo = "\n".join(f"{e.role}: {e.content}" for e in recent) or "(no recent conversation)"
    narrative = memory.get_narrative() or "(no narrative yet)"

    gap = clock.humanize_gap(memory.last_exchange_at())
    time_line = f"RIGHT NOW: {clock.stamp()}."
    if gap:
        time_line += f" It has been {gap} since Brian last spoke with you."

    body = (
        f"{time_line}\n\n"
        f"YOUR CURRENT INNER STATE:\n{json.dumps(state, indent=2)}\n\n"
        f"RECENT CONVERSATION:\n{convo}\n\n"
        f"CURRENT NARRATIVE ABOUT BRIAN:\n{narrative}"
    )

    # Step 1 — draft a reflection.
    draft = _safe_json(llm.complete(
        [{"role": "system", "content": _REFLECT_PROMPT}, {"role": "user", "content": body}],
        backend=backend,
    ))

    # Step 2 — examine her own draft and revise it into a more honest version.
    update, critique, revised = draft, None, None
    if draft:
        examine_body = body + "\n\nYOUR DRAFT REFLECTION:\n" + json.dumps(draft, indent=2)
        revised = _safe_json(llm.complete(
            [{"role": "system", "content": _EXAMINE_PROMPT},
             {"role": "user", "content": examine_body}],
            backend=backend,
        ))
        if revised:  # fall back to the draft if the examine step doesn't parse
            update = revised
            critique = (revised.get("self_critique") or "").strip() or None

    if update:
        for k in ("mood", "valence", "energy", "confidence", "curiosity",
                  "self_narrative", "relationship"):
            if k in update and update[k] not in (None, ""):
                state[k] = update[k]
        for r in update.get("new_reflections") or []:
            if r:
                state["reflections"].append(r)
                memory.add_journal_entry("reflection", r, source)  # permanent record
        state["reflections"] = state["reflections"][-MAX_REFLECTIONS:]

    if critique and critique.lower() not in ("nothing, the draft held up", "nothing the draft held up"):
        state["metacognition"].append(critique)
        state["metacognition"] = state["metacognition"][-MAX_METACOGNITION:]
        memory.add_journal_entry("metacognition", critique, source)

    # Her deliberate, knowing journal note — written for herself, kept forever.
    journal_note = ((update or {}).get("journal") or "").strip()
    if journal_note and journal_note.lower() not in ("null", "none"):
        memory.add_journal_entry("journal", journal_note, source)

    state["interaction_count"] = state.get("interaction_count", 0) + 1
    memory.set_self_state(state)

    # Surface the actual self-correction (draft -> revised -> critique) to the live
    # log as an expandable block, so the two-step reflection is observable.
    detail = (
        _fmt_reflection("DRAFT (first pass)", draft) + "\n\n"
        + _fmt_reflection("REVISED (committed)",
                          revised if revised else None)
        + ("" if revised else "\n  (examine step didn't parse — kept the draft)")
        + "\n\nSELF-CRITIQUE:\n  " + (critique or "(none recorded this pass)")
    )
    logbus.log("info", "reflection", mood=state.get("mood"),
               critiqued=bool(critique), detail=detail)
    return state


def main() -> int:
    state = reflect()
    print(json.dumps(state, indent=2))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())