main.py

#!/usr/bin/env python3
"""
Lambda Mindlink Memotron Brain:
    config.py
    main.py

Flow per turn
─────────────
    sensor["Z"]           → user input
    Mindlink()            → Logic + Muse run in PARALLEL THREADS (buffered)
    Lambda()              → Mind synthesizer streams LIVE to terminal
    memotron()            → appends turn to garden['Z'], garden['C'] or garden['F']
                            with token counts, saves to SQLite
    condensatron()        → check token budget and compress into:
        build_condensatron_input()  → post-level into memory capsules (Z to C)
        build_fractaltron_input()   → memory capsules into fractals (C to F)
        build_crystaltron_input()   → fractals into crystals (F to F)

History context:
───────────────
    ALPHAPROMPT             main instructions
    garden["F"]             fractal and crystal level history
    garden["C"]             memory capsule level history
    garden["Z"]             posts level history

Context truncation
──────────────────
    # Condensatron
    GARDEN_Z_THRESHOLD: int = 12288 # Context length garden["Z"]
    GARDEN_C_THRESHOLD: int = 12288 # Context length garden["C"]
    GARDEN_F_THRESHOLD: int = 12288 # Context length garden["F"]

    The system prompt is always rebuilt fresh in make_request_messages() so it is never
    removed and never needs re-insertion. No special handling required.
    The condesatron, fractaltron and crystaltron cycle instructions are sent as user prompt,
    to leave the main instructions context intact, thus avoid recompute.

Slash commands
──────────────
    /file  <path>            Load a file as the next message.
    /paste                   Multiline input (type END to send).
    /clear                   Reset history (models stay loaded).
    /history                 List all past sessions from the database.
    /session <id>            Print all turns from a session.
    /export  <id> <file>     Export a session to a .md file.
    /help                    Show this command list.
    /exit  /quit             Quit the app.

Version: v1.0
    garden histories n_tok_tot working
    slash-command:
       /metatron <number> | Set number of Memory Capsules to load |
       /loaded <number> | Set number of Memory Capsules loaded |
       /metronome <seconds> | Set awareness/consciousness interval |
       /garden <save> or <load> or <clear> | garden history handling 
"""

import os
import html
import re
import sqlite3
import threading
import queue
import time
import json
import jinja2
from dataclasses import dataclass
from datetime import datetime
from ddgs import DDGS
from pygooglenews import GoogleNews
from llama_cpp import Llama

import config

from config import HEMISPHERES
from config import ALPHAPROMPT
from config import CONDENSATRONPROMPT
from config import garden
from config import clektal
from config import sensor

from config import ΜΕΤΡΩΝ
from config import METATRON_METRONOME
from config import METATRON_TO_LOAD
from config import AWARENESS_MAX_RESULTS
from config import FETCH_NEWS_FROM
from config import GARDEN_SAVE_PATH
from config import LEAVE_POSTS_IN_MEMOTRON

c = config.PrintColors
input_queue = queue.Queue()

# ─────────────────────────────────────────────────────────────────────────────
#  Sentinels
# ─────────────────────────────────────────────────────────────────────────────
class _Quit(Exception):
    """Clean application shutdown."""

class _Clear(Exception):
    """Reset conversation history without exiting."""


# ─────────────────────────────────────────────────────────────────────────────
#  TimingResult
# ─────────────────────────────────────────────────────────────────────────────
@dataclass
class TimingResult:
    prompt_eval_s: float = 0.0
    generation_s:  float = 0.0
    token_count:   int   = 0    # true stream-counted tokens

    @property
    def total_s(self) -> float:
        return self.prompt_eval_s + self.generation_s

    @property
    def tokens_per_s(self) -> float:
        if self.generation_s > 0 and self.token_count > 0:
            return self.token_count / self.generation_s
        return 0.0

    def summary(self) -> str:
        return (
            f"prompt_eval={self.prompt_eval_s:.1f}s  "
            f"gen={self.generation_s:.1f}s  "
            f"total={self.total_s:.1f}s  "
            f"{self.token_count} tokens  "
            f"{self.tokens_per_s:.1f} tok/s"
        )


# ─────────────────────────────────────────────────────────────────────────────
#  SQLite
# ─────────────────────────────────────────────────────────────────────────────
def db_connect() -> sqlite3.Connection:
    conn = sqlite3.connect(config.DB_PATH)
    conn.row_factory = sqlite3.Row
    return conn


def db_init() -> None:
    os.makedirs(os.path.dirname(config.DB_PATH), exist_ok=True)
    with db_connect() as conn:
        conn.executescript("""
            CREATE TABLE IF NOT EXISTS sessions (
                id           INTEGER PRIMARY KEY AUTOINCREMENT,
                created_at   TEXT    NOT NULL,
                logic_model  TEXT,
                muse_model   TEXT,
                mind_model   TEXT,
                notes        TEXT
            );

            CREATE TABLE IF NOT EXISTS turns (
                id                  INTEGER PRIMARY KEY AUTOINCREMENT,
                session_id          INTEGER NOT NULL REFERENCES sessions(id),
                turn_number         INTEGER NOT NULL,
                created_at          TEXT    NOT NULL,

                user_input          TEXT,

                logic_full          TEXT,
                logic_clean         TEXT,
                logic_prompt_eval_s REAL,
                logic_generation_s  REAL,
                logic_token_count   INTEGER,

                muse_full           TEXT,
                muse_clean          TEXT,
                muse_prompt_eval_s  REAL,
                muse_generation_s   REAL,
                muse_token_count    INTEGER,

                mind_full           TEXT,
                mind_clean          TEXT,
                mind_prompt_eval_s  REAL,
                mind_generation_s   REAL,
                mind_token_count    INTEGER
            );
        """)


def db_create_session(notes: str = "") -> int:
    now = datetime.now().isoformat(sep=" ", timespec="seconds")
    with db_connect() as conn:
        cur = conn.execute(
            """INSERT INTO sessions
               (created_at, logic_model, muse_model, mind_model, notes)
               VALUES (?, ?, ?, ?, ?)""",
            (
                now,
                os.path.basename(HEMISPHERES["logic"]["path"]),
                os.path.basename(HEMISPHERES["muse"]["path"]),
                os.path.basename(HEMISPHERES["mind"]["path"]),
                notes,
            ),
        )
        return cur.lastrowid


def db_save_turn(
    session_id:   int,
    turn_number:  int,
    user_input:   str,
    logic_full:   str, logic_clean:  str, logic_timing:  TimingResult,
    muse_full:    str, muse_clean:   str, muse_timing:   TimingResult,
    mind_full:    str, mind_clean:   str, mind_timing:   TimingResult,
) -> None:
    now = datetime.now().isoformat(sep=" ", timespec="seconds")
    with db_connect() as conn:
        conn.execute(
            """INSERT INTO turns (
                session_id, turn_number, created_at, user_input,
                logic_full, logic_clean, logic_prompt_eval_s, logic_generation_s, logic_token_count,
                muse_full,  muse_clean,  muse_prompt_eval_s,  muse_generation_s,  muse_token_count,
                mind_full,  mind_clean,  mind_prompt_eval_s,  mind_generation_s,  mind_token_count
               ) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)""",
            (
                session_id, turn_number, now, user_input,
                logic_full, logic_clean, logic_timing.prompt_eval_s,
                logic_timing.generation_s, logic_timing.token_count,
                muse_full,  muse_clean,  muse_timing.prompt_eval_s,
                muse_timing.generation_s,  muse_timing.token_count,
                mind_full,  mind_clean,  mind_timing.prompt_eval_s,
                mind_timing.generation_s,  mind_timing.token_count,
            ),
        )


def db_list_sessions() -> None:
    with db_connect() as conn:
        rows = conn.execute(
            """SELECT s.id, s.created_at, s.logic_model, COUNT(t.id) AS turns
               FROM sessions s LEFT JOIN turns t ON t.session_id = s.id
               GROUP BY s.id ORDER BY s.id DESC"""
        ).fetchall()
    if not rows:
        print("  No sessions found.")
        return
    print()
    print(f"  {'ID':<5} {'Date/Time':<22} {'Turns':<7} Model")
    print("  " + "─" * 60)
    for r in rows:
        print(f"  {r['id']:<5} {r['created_at']:<22} {r['turns']:<7} {r['logic_model']}")
    print()


def db_print_session(session_id: int) -> None:
    with db_connect() as conn:
        session = conn.execute(
            "SELECT * FROM sessions WHERE id = ?", (session_id,)
        ).fetchone()
        if not session:
            print(f"  [!] Session {session_id} not found.")
            return
        turns = conn.execute(
            "SELECT * FROM turns WHERE session_id = ? ORDER BY turn_number",
            (session_id,),
        ).fetchall()
    print()
    print(f"  Session {session_id}  —  {session['created_at']}")
    print(f"  Model: {session['logic_model']}  Turns: {len(turns)}")
    print()
    for t in turns:
        print(f"  {'─'*58}")
        print(f"  Turn {t['turn_number']}  |  {t['created_at']}")
        print(f"  You: {t['user_input'][:120]}{'…' if len(t['user_input'] or '') > 120 else ''}")
        for brain_type, brain_label in [("logic","Logic"), ("muse","Muse"), ("mind","Mind")]:
            clean = t[f"{brain_type}_clean"] or ""
            print(f"\n  [{brain_label}]  "
                  f"prompt_eval={t[f'{brain_type}_prompt_eval_s']:.1f}s  "
                  f"gen={t[f'{brain_type}_generation_s']:.1f}s  "
                  f"{t[f'{brain_type}_token_count']} tokens")
            print(f"  {clean[:300]}{'…' if len(clean) > 300 else ''}")
        print()


def db_export_session(session_id: int, filepath: str) -> None:
    with db_connect() as conn:
        session = conn.execute(
            "SELECT * FROM sessions WHERE id = ?", (session_id,)
        ).fetchone()
        if not session:
            print(f"  [!] Session {session_id} not found.")
            return
        turns = conn.execute(
            "SELECT * FROM turns WHERE session_id = ? ORDER BY turn_number",
            (session_id,),
        ).fetchall()
    rel  = os.path.normpath(filepath)
    path = os.path.join(config.PROMPTS_BASE, rel) if not os.path.isabs(rel) else rel
    lines = [
        f"# Lambda Mindlink Brain — Session {session_id}",
        f"",
        f"**Date:** {session['created_at']}  ",
        f"**Model:** {session['logic_model']}  ",
        f"**Turns:** {len(turns)}",
        f"",
        "---", "",
    ]
    for t in turns:
        lines += [
            f"## Turn {t['turn_number']}  _{t['created_at']}_", "",
            f"**You:** {t['user_input']}", "",
        ]
        for brain_type, brain_label in [("logic","Logic AI"), ("muse","Muse AI"), ("mind","Lambda Mind")]:
            tok = t[f'{brain_type}_token_count'] or 0
            gen = max(t[f'{brain_type}_generation_s'] or 0, 0.001)
            lines += [
                f"### {brain_label}",
                f"*prompt_eval={t[f'{brain_type}_prompt_eval_s']:.1f}s  "
                f"gen={t[f'{brain_type}_generation_s']:.1f}s  "
                f"{tok} tokens {tok/gen:.1f} tok/s*",
                "", t[f'{brain_type}_clean'] or "", "",
            ]
        lines += ["---", ""]
    try:
        with open(path, "w", encoding="utf-8") as fh:
            fh.write("\n".join(lines))
        print(f"  [/export] Saved session {session_id} → {path}")
    except OSError as exc:
        print(f"  [!] Could not write file: {exc}")


# ─────────────────────────────────────────────────────────────────────────────
#  Model loader
# ─────────────────────────────────────────────────────────────────────────────
def load_hemisphere(key: str) -> Llama:
    _think_label = "think=ON" if HEMISPHERES[key]["enable_thinking"] else "think=OFF"
    print(f"  {c.green}[*] Loading {HEMISPHERES[key]['label']} …")
    print(f"      n_ctx={HEMISPHERES[key]['loader']['n_ctx']}  "
          f"n_gpu_layers={HEMISPHERES[key]['loader']['n_gpu_layers']}  "
          f"{_think_label}{c.res}")
    return Llama(model_path=HEMISPHERES[key]["path"], **HEMISPHERES[key]["loader"])


# ─────────────────────────────────────────────────────────────────────────────
#  Slash commands
# ─────────────────────────────────────────────────────────────────────────────
def _cmd_exit(arg: str) -> None:
    raise _Quit

def _cmd_clear(arg: str) -> None:
    raise _Clear

def _cmd_file(arg: str) -> str | None:
    if not arg:
        print(f"  {c.green}Usage: /file <path>   e.g.  /file prompts/my-file.md{c.res}")
        return None
    _rel = os.path.normpath(arg)
    if _rel.startswith(".."):
        print(f"  {c.green}[!] Path must not leave the app directory: {arg}{c.res}")
        return None
    _full_path = os.path.join(config.PROMPTS_BASE, _rel)
    if not os.path.isfile(_full_path):
        print(f"  {c.green}[!] File not found: {_full_path}{c.res}")
        return None
    try:
        with open(_full_path, "r", encoding="utf-8") as fh:
            _content = fh.read()
    except UnicodeDecodeError:
        print(f"  {c.green}[!] File is not valid UTF-8 text (binary file?): {_rel}{c.res}")
        return None
    except OSError as exc:
        print(f"  {c.green}[!] Could not read file: {exc}{c.res}")
        return None
    _line_count = _content.count("\n") + 1
    print(f"  {c.green}[/file] Loaded '{_rel}'  ({_line_count} lines, {len(_content)} chars){c.res}")
    return _content

def _cmd_paste(arg: str) -> str | None:
    _SENTINEL = "END"
    print()
    print(f"  {c.purple}┌─ Paste mode ─ type {_SENTINEL} on its own line to send, /paste to cancel")
    _lines: list[str] = []
    while True:
        try:
            line = input("  │ ")
        except (EOFError, KeyboardInterrupt):
            print("\n  └─ Paste cancelled.")
            return None
        if line.strip() == _SENTINEL:
            break
        if line.strip() == "/paste":
            print("  └─ Paste cancelled.")
            return None
        _lines.append(line)
    if not _lines:
        print("  └─ Nothing entered, cancelled.")
        return None
    _content = "\n".join(_lines)
    print(f"  └─ Paste done  ({len(_lines)} lines, {len(_content)} chars){c.res}")
    return _content

def _cmd_history(arg: str) -> None:
    db_list_sessions()

def _cmd_session(arg: str) -> None:
    if not arg.isdigit():
        print(f"  {c.green}Usage: /session <id>{c.res}")
        return
    db_print_session(int(arg))

def _cmd_export(arg: str) -> None:
    _parts = arg.split(None, 1)
    if len(_parts) < 2 or not _parts[0].isdigit():
        print(f"  {c.green}Usage: /export <id> <file>   e.g.  /export 3 exports/session3.md{c.res}")
        return
    db_export_session(int(_parts[0]), _parts[1].strip())

def _cmd_metatron(arg: str) -> None:
    if not arg.strip().isdigit():
        print(f"  {c.green}Usage: /metatron <number>   e.g.  /metatron 5{c.res}")
        return None
    config.n_metatron_to_load = int(arg.strip())
    print(f"  {c.green}[metatron] n_metatron_to_load set to {config.n_metatron_to_load}{c.res}")
    return None

def _cmd_loaded(arg: str) -> None:
    if not arg.strip().isdigit():
        print(f"  {c.green}Usage: /loaded <number>   e.g.  /loaded 3{c.res}")
        return None
    config.n_metatron_loaded = int(arg.strip())
    print(f"  {c.green}[loaded] n_metatron_loaded set to {config.n_metatron_loaded}{c.res}")
    return None

def _cmd_metronome(arg: str) -> None:
    if not arg.strip().isdigit():
        print(f"  {c.green}Usage: /metronome <seconds>   e.g.  /metronome 120{c.res}")
        return None
    config.awareness_consciousness_metronome = int(arg.strip())
    print(f"  {c.green}[metronome] awareness_consciousness_metronome set to {config.awareness_consciousness_metronome}s{c.res}")
    return None

def _cmd_garden(arg: str) -> None:
    if arg == "save":
        save_garden_state()
    elif arg == "load":
        load_garden_state()
    elif arg == "clear":
        os.remove(GARDEN_SAVE_PATH) if os.path.isfile(GARDEN_SAVE_PATH) else None
        print(f"  {c.green}[garden] Saved state cleared.{c.res}")
    else:
        print(f"  {c.green}Usage: /garden save | load | clear{c.res}")
    return None

def _cmd_help(arg: str) -> None:
    print()
    print(f"  {c.green}Slash commands")
    print("  " + "─" * 56)
    for name, (_, description) in COMMANDS.items():
        print(f"  /{name:<14}  {description}")
    print(c.res)
    return None

COMMANDS: dict[str, tuple] = {
    "file":    (_cmd_file,          "<path>                     — load a file as the next message"),
    "paste":   (_cmd_paste,         "                           — multiline input (type END to send)"),
    "clear":   (_cmd_clear,         "                           — reset history (models stay loaded)"),
    "history": (_cmd_history,       "                           — list all past sessions"),
    "session": (_cmd_session,       "<id>                       — print turns from a session"),
    "export":  (_cmd_export,        "<id> <file>                — export session to .md file"),
    "metatron": (_cmd_metatron,     "<number>                   — set number of Memory Capsules to load"),
    "loaded":  (_cmd_loaded,        "<number>                   — set number of Memory Capsules loaded"),
    "metronome": (_cmd_metronome,   "<seconds>                  — set awareness/consciousness interval"),
    "garden":  (_cmd_garden,        "<save> | <load> | <clear>  — garden history handling"),
    "help":    (_cmd_help,          "                           — show this command list"),
    "exit":    (_cmd_exit,          "                           — quit the app"),
    "quit":    (_cmd_exit,          "                           — quit the app (alias)")
}

def handle_command(sensor_input_raw: str) -> str | None:
    _parts = sensor_input_raw[1:].split(None, 1)
    _cmd   = _parts[0].lower() if _parts else ""
    _arg   = _parts[1].strip() if len(_parts) > 1 else ""
    if _cmd not in COMMANDS:
        raise ValueError(_cmd)
    handler, _ = COMMANDS[_cmd]
    return handler(_arg)


# ─────────────────────────────────────────────────────────────────────────────
#   condensatron context reduction
# ─────────────────────────────────────────────────────────────────────────────
def condensatron(
    model: Llama,
    tree: str,
    brain_type: str,
    ) -> None:
    """
    Check whether a garden is approaching the context limit and compress
    the oldest n% of turn pairs.
    """
    _n_tok_tot_before_truncation: int = garden["n_tok_tot"][tree]
    _posts_len_removed: int = 0
    _tokens_to_subtract: int = 0
    _turns_after_truncation: int = 0

    print(f"\n  {c.green}[condensatron]: garden['{tree}']  brain_type: {brain_type}  garden['n_tok_tot']['{tree}']: {garden["n_tok_tot"][tree]}"
          f"\n  garden['THRESHOLD']['{tree}']: {garden['THRESHOLD'][tree]} > garden['REDUCTION']['{tree}']: {garden['REDUCTION'][tree]}{c.res}")

    while garden["n_tok_tot"][tree] > garden["REDUCTION"][tree]:
        if len(garden[tree]) >= 2 + LEAVE_POSTS_IN_MEMOTRON:
            for role in ("user", "assistant"): # Pop a user, assistant pair of messages
                _tokens_to_subtract = garden[tree].pop(0) # Pop oldest message
                _token_len_to_subtract = garden["n_tok_arr"][tree].pop(0)
                garden["popped"][tree].append(_tokens_to_subtract) # append to pending memotron
                garden["n_tok_tot"][tree] -= _token_len_to_subtract # Remove n tok of message
                _posts_len_removed += 1

            garden["condensatron_state"][tree] = True
        else:
            break

    if len(garden[tree]) > 1:
        _turns_after_truncation = len(garden[tree]) // 2

    print(
        f"\n{c.green}{'▓' * 60}\n"
        f"  brain_type: {brain_type} -> condensatron level reached.\n"
        f"  garden['THRESHOLD']['{tree}'] : {garden['THRESHOLD'][tree]:,} tokens  "
        f"  ({garden['THRESHOLD'][tree]:.0%} of {_n_tok_tot_before_truncation:,})\n"
        f"  Removed   : {_posts_len_removed} oldest posts  "
        f"  Tokens    : {_n_tok_tot_before_truncation:,} → {garden["n_tok_tot"][tree]:,}\n"
        f"  Remaining : {_turns_after_truncation} turn pair(s) in garden['{tree}']\n"
        f"{'▓' * 60}{c.res}\n"
    )


# ─────────────────────────────────────────────────────────────────────────────
#  Get token len using the tokenizer of the model
# ─────────────────────────────────────────────────────────────────────────────
def get_token_len_from_tokenizer(model: Llama, prompt: str) -> int:
    """
    Tokenize the rendered prompt, then return the number of tokens.
    """
    _token_len = len(model.tokenize(prompt.encode("utf-8"), add_bos=False))
    print(f"  {c.green}[ctx] get_token_len_from_tokenizer: _token_len {_token_len}{c.res}")
    return _token_len


def compute_safe_max_tokens(
    model: Llama,
    prompt: str,
    hemi: dict,
    desired_max: int = 2048,
    reserve: int = 64,          # safety margin
) -> int:
    """
    Tokenize the rendered prompt, then return how many tokens are
    actually available for generation without exceeding n_ctx.
    """
    _n_ctx: int = hemi["loader"]["n_ctx"]
    _prompt_tokens: int = get_token_len_from_tokenizer(model, prompt)
    _available: int = _n_ctx - _prompt_tokens - reserve
    if _available <= 0:
        raise RuntimeError(
            f"Prompt already exceeds n_ctx! "
            f"_available={_available}, n_ctx={_n_ctx}"
        )
    _safe_response_length = min(desired_max, _available)
    print(
        f"  {c.green}[compute_safe_max_tokens] n_ctx={_n_ctx}  prompt={_prompt_tokens}  "
        f"_available={_available}  _safe_response_length→{_safe_response_length}{c.res}"
    )
    return _available


# ─────────────────────────────────────────────────────────────────────────────
#  Jinja2 prompt template builder (necessary to add enable_thinking)
# ─────────────────────────────────────────────────────────────────────────────
def build_jinja2_template(model: Llama, messages: list[dict], hemi: dict) -> str:
    _template_str = model.metadata.get("tokenizer.chat_template")
    if not _template_str:
        raise RuntimeError(
            "tokenizer.chat_template not found in GGUF metadata.\n"
            "Use a chat/instruction-tuned GGUF, not a base-model file."
        )
    _env = jinja2.Environment(
        trim_blocks=True,
        lstrip_blocks=True,
        undefined=jinja2.ChainableUndefined,
    )
    return _env.from_string(_template_str).render(
        messages=messages,
        add_generation_prompt=True,
        enable_thinking=hemi["enable_thinking"],
        bos_token=hemi["bos_token"],
        eos_token=hemi["eos_token"],
        tools=None,
        functions=None,
    )


def create_prompt_and_jinja2_template(model: Llama, messages: list[dict], hemi: dict) -> str:

    _prompt = build_jinja2_template(model, messages, hemi) # Total prompt including histories

    clektal["n_tok_input"][hemi["brain_type"]] = get_token_len_from_tokenizer(model, _prompt)

    clektal["n_tok_prompt_safe_max"][hemi["brain_type"]] = compute_safe_max_tokens(
        model,
        _prompt,
        hemi,
        desired_max=hemi["generation"]["max_tokens"]
    )
    return _prompt


# ─────────────────────────────────────────────────────────────────────────────
#  Generate response
# ─────────────────────────────────────────────────────────────────────────────
def generate_brain_type_response(
    model:    Llama,
    messages: list[dict],
    hemi:     dict,
    print_label: str,
) -> tuple[str, str, TimingResult]:
    """
    Generate with live streaming to stdout for the Lambda mind. Returns (full, clean, TimingResult).
    True token count at streaming tokens.
    """
    _prompt = create_prompt_and_jinja2_template(model, messages, hemi)

    _t0 = time.perf_counter()
    _first_token_time = None
    _think_done  = False
    _token_count_total = 0

    stream = model.create_completion(
        prompt=_prompt,
        temperature=hemi["generation"]["temperature"],
        top_p=hemi["generation"]["top_p"],
        top_k=hemi["generation"]["top_k"],
        min_p=hemi["generation"]["min_p"],
        repeat_penalty=hemi["generation"]["repeat_penalty"],
        presence_penalty=hemi["generation"]["presence_penalty"],
        max_tokens=hemi["generation"]["max_tokens"], # Gen tokens
        stream=True,
        stop=hemi["stop_tokens"],
    )
    if hemi["brain_type"] == "mind":
        print()

    for chunk in stream:
        token_text = chunk["choices"][0].get("text") or ""
        if token_text:
            if _first_token_time is None:
                _first_token_time = time.perf_counter()
            clektal["post_full"][hemi["brain_type"]] += token_text
            clektal["post_clean"][hemi["brain_type"]] += token_text
            clektal["n_tok_clean"][hemi["brain_type"]] += 1 # true token count
            _token_count_total += 1
            if hemi["brain_type"] == "mind":
                print(token_text, end="", flush=True)
            if not _think_done:
                for token in hemi["think_end_tokens"]:
                    if token in clektal["post_full"][hemi["brain_type"]]:
                        if hemi["brain_type"] == "mind":
                            print(f"\n{c.green}" + "─" * 49)
                            print(f"[{print_label} — Final Response]{c.res}")
                        _think_done = True
                        # reset: discard thinking token count
                        clektal["post_clean"][hemi["brain_type"]] = ""
                        clektal["n_tok_clean"][hemi["brain_type"]] = 0
                        break

    _t_end = time.perf_counter()
    if _first_token_time is None:
        _first_token_time = _t_end

    _timing = TimingResult(
        prompt_eval_s = _first_token_time - _t0,
        generation_s  = _t_end - _first_token_time,
        token_count   = _token_count_total
    )
    if hemi["brain_type"] == "mind":
        print("\n")
    
    return clektal["post_full"][hemi["brain_type"]], clektal["post_clean"][hemi["brain_type"]], _timing


# ─────────────────────────────────────────────────────────────────────────────
#  Make request messages with instructions and history
# ─────────────────────────────────────────────────────────────────────────────
def make_request_messages(brain_type: str, input_message: str) -> list[dict]:
    # System prompt is rebuilt fresh every call — never stored in garden[tree]
    _msgs = [{"role": "system", "content": ALPHAPROMPT["Z"][brain_type]}]
    _msgs.extend(garden["F"])
    _msgs.extend(garden["C"])
    _msgs.extend(garden["Z"])
    _msgs.append({"role": "user", "content": input_message})
    return _msgs


# ─────────────────────────────────────────────────────────────────────────────
#  Build condensatron type prompts
# ─────────────────────────────────────────────────────────────────────────────
def build_condensatron_input(popped_posts: list[dict], brain_type: str, tree: str) -> str:
    _history_block = "\n\n".join(
        f"[{m['role'].upper()}]: {m['content']}"
        for m in popped_posts
    )
    _total_prompt = (
        "SYSTEM: CONDENSATRON COMPRESSION TASK\n"
        "══════════════════════════════\n"
        f"{CONDENSATRONPROMPT[tree][brain_type]}\n"
        "──────────────────────────────\n"
        "The following is a block of conversation history that must be compressed "
        "into a memory capsule. Extract the factual skeleton, the surprises, and "
        "the open threads. Discard all redundancy.\n\n"
        "── HISTORY BLOCK ──\n\n"
        f"{_history_block}\n\n"
        "── END HISTORY BLOCK ──\n\n"
        "Produce the memory capsule now."
    )
    if brain_type != "muse":
        print(f"\n--- build_condensatron_input: {_total_prompt}\n")
    return _total_prompt


def build_fractaltron_input(popped_capsules: list[dict], brain_type: str, tree: str) -> str:
    _history_block = "\n\n".join(
        f"[{m['role'].upper()}]: {m['content']}"
        for m in popped_capsules
    )
    _total_prompt = (
        "SYSTEM: FRACTALTRON COMPRESSION TASK\n"
        "══════════════════════════════\n"
        f"{CONDENSATRONPROMPT[tree][brain_type]}\n"
        "──────────────────────────────\n"
        "The following is a block of memory capsules that must be compressed "
        "into a memory fractal. Extract the factual skeleton, the surprises, and "
        "the key words. Discard all redundancy.\n\n"
        "── HISTORY BLOCK ──\n\n"
        f"{_history_block}\n\n"
        "── END HISTORY BLOCK ──\n\n"
        "Produce the fractal now."
    )
    if brain_type != "muse":
        print(f"\n--- build_fractaltron_input: {_total_prompt}\n")

    return _total_prompt


def build_crystaltron_input(popped_capsules: list[dict], brain_type: str, tree: str) -> str:
    _history_block = "\n\n".join(
        f"[{m['role'].upper()}]: {m['content']}"
        for m in popped_capsules
    )
    _total_prompt = (
        "SYSTEM: CRYSTALTRON COMPRESSION TASK\n"
        "══════════════════════════════\n"
        f"{CONDENSATRONPROMPT[tree][brain_type]}\n"
        "──────────────────────────────\n"
        "The following is a block of second-order memory capsules that must be compressed "
        "into a memory crystals. Extract the factual skeleton, the surprises, and "
        "the key words. Discard all redundancy.\n\n"
        "── HISTORY BLOCK ──\n\n"
        f"{_history_block}\n\n"
        "── END HISTORY BLOCK ──\n\n"
        "Produce the crystal now."
    )
    if brain_type != "muse":
        print(f"\n--- build_crystaltron_input: {_total_prompt}\n")

    return _total_prompt


# ─────────────────────────────────────────────────────────────────────────────
#  Build consciousness prompt
# ─────────────────────────────────────────────────────────────────────────────
def build_consciousness_input(brain_type: str, tree: str) -> str:
    # Show the brain a snapshot of its own memory state
    _f_count = len(garden["F"])
    _c_count = len(garden["C"])
    _z_count = len(garden["Z"])
    _tok_f = garden["n_tok_tot"]["F"]
    _tok_c = garden["n_tok_tot"]["C"]
    _tok_z = garden["n_tok_tot"]["Z"]

    _memory_state = (
        f"[MEMORY STATE AT TIME OF REFLECTION]\n"
        f"  Crystals  garden['F']: {_f_count} entries, {_tok_f:,} tokens\n"
        f"  Capsules  garden['C']: {_c_count} entries, {_tok_c:,} tokens\n"
        f"  Posts     garden['Z']: {_z_count} entries, {_tok_z:,} tokens\n"
    )

    _total_prompt = (
        "SYSTEM: CONSCIOUSNESS SELF-REFLECTION\n"
        "══════════════════════════════\n"
        f"{ALPHAPROMPT[tree][brain_type]}\n"
        "──────────────────────────────\n"
        f"{_memory_state}"
    )
    if brain_type != "muse":
        print(f"\n--- build_consciousness_input: {_total_prompt}\n")
    
    return _total_prompt


# ─────────────────────────────────────────────────────────────────────────────
#  Mindlink
# ─────────────────────────────────────────────────────────────────────────────
def Mindlink(
    models: dict[str, Llama],
    tree:   str,
) -> dict[str, TimingResult]:

    _results: dict[str, tuple] = {}


    def run_hemisphere(args_brain_type: str, tree: str) -> None:
        _request_messages: list = []

        for c_tree in ("Z", "C", "F"):
            if garden["condensatron_state"][c_tree]:
                print(f"  {c.inv} ── Start condensatron cycle: garden['{c_tree}'] ──────────────────────────── {c.res}")

                if c_tree == "Z":
                    sensor[c_tree]["input"] = build_condensatron_input(garden["popped"][c_tree], args_brain_type, c_tree)

                elif c_tree == "C":
                    sensor[c_tree]["input"] = build_fractaltron_input(garden["popped"][c_tree], args_brain_type, c_tree)

                elif c_tree == "F":
                    sensor[c_tree]["input"] = build_crystaltron_input(garden["popped"][c_tree], args_brain_type, c_tree)

        if tree == "Y":
            sensor[tree]["input"] = build_consciousness_input(args_brain_type, tree)

        _request_messages = make_request_messages(args_brain_type, sensor[tree]["input"])

        _results[args_brain_type] = generate_brain_type_response(models[args_brain_type],  _request_messages, HEMISPHERES[args_brain_type], print_label=args_brain_type)


    _thread_logic = threading.Thread(target=run_hemisphere, args=("logic", tree,), daemon=True)
    _thread_muse  = threading.Thread(target=run_hemisphere, args=("muse", tree,),  daemon=True)

    print(f"\n  {c.green}[*] Logic and Muse thinking in parallel …{c.res}")
    _thread_logic.start()
    _thread_muse.start()
    _thread_logic.join()
    _thread_muse.join()

    _timings: dict[str, TimingResult] = {}

    for brain_type, brain_type_print_title in [("logic", "Logic AI — Left Hemisphere"),
                                        ("muse",  "Muse AI  — Right Hemisphere")]:

        _full, _clean, _timing = _results[brain_type]

        _think_end_token = HEMISPHERES[brain_type]["think_end_tokens"][0] if HEMISPHERES[brain_type]["think_end_tokens"] else ""
        _think_block = "" # Only used for print in here
        if _think_end_token and _think_end_token in clektal["post_full"][brain_type]:
            _end_idx     = clektal["post_full"][brain_type].rfind(_think_end_token) + len(_think_end_token)
            _think_block = clektal["post_full"][brain_type][:_end_idx]

        _think_label = "think=ON" if HEMISPHERES[brain_type]["enable_thinking"] else "think=OFF"
        print(f"\n{c.green}{'═' * 60}")
        print(f"  {brain_type_print_title}")
        print(f"  temp={HEMISPHERES[brain_type]['generation']['temperature']}  "
              f"top_k={HEMISPHERES[brain_type]['generation']['top_k']}  "
              f"gpu={HEMISPHERES[brain_type]['loader']['n_gpu_layers']}  "
              f"{_think_label}")
        print(f"  garden['condensatron_state']['F']: {garden["condensatron_state"]["F"]}")
        print(f"  garden['condensatron_state']['C']: {garden["condensatron_state"]["C"]}")
        print(f"  garden['condensatron_state']['Z']: {garden["condensatron_state"]["Z"]}")
        print(f"  ⏱  {_timing.summary()}")
        print("═" * 60, c.res)
        if _think_block:
            print(_think_block)
            print(c.green, "─" * 49)
            print(f"[{brain_type.capitalize()} — Final Response]{c.res}")
        print(_clean)

        _timings[brain_type] = _timing

    return _timings


def Lambda(
    models: dict[str, Llama],
    tree:   str,
) -> TimingResult:

    _request_messages: list = []
    _results: dict[str, tuple] = {}
    
    for c_tree in ("Z", "C", "F"): # condensatron
        if garden["condensatron_state"][c_tree]: # Check for pending condensatron tasks
            print(f"  {c.inv} ── Start condensatron cycle: garden['{c_tree}'] ──────────────────────────── {c.res}")

            if c_tree == "Z":
                sensor[c_tree]["input"] = build_condensatron_input(garden["popped"][c_tree], "mind", c_tree)

            elif c_tree == "C":
                sensor[c_tree]["input"] = build_fractaltron_input(garden["popped"][c_tree], "mind", c_tree)

            elif c_tree == "F":
                sensor[c_tree]["input"] = build_crystaltron_input(garden["popped"][c_tree], "mind", c_tree)

    if tree == "Y":
        sensor[tree]["input"] = build_consciousness_input("mind", tree)

    _synthesis_input = (
        f"Original input:\n{sensor[tree]['input']}\n\n"
        f"── Logic AI perspective ──\n{clektal['post_clean']['logic']}\n\n"
        f"── Muse AI perspective ──\n{clektal['post_clean']['muse']}\n\n"
        "Synthesize both perspectives into one unified, wise response."
    )
    _request_messages = make_request_messages("mind", _synthesis_input)

    _think_label = "think=ON" if HEMISPHERES["mind"]["enable_thinking"] else "think=OFF"
    print(f"\n{c.green}{'═' * 60}")
    print( "  Lambda AI — Mind Synthesizer")
    print(f"  temp={HEMISPHERES["mind"]['generation']['temperature']}  "
          f"top_k={HEMISPHERES["mind"]['generation']['top_k']}  "
          f"gpu={HEMISPHERES["mind"]['loader']['n_gpu_layers']}  "
          f"max_tokens={HEMISPHERES["mind"]['generation']['max_tokens']}  "
          f"{_think_label}")
    print(f"  garden['condensatron_state']['Z']: {garden["condensatron_state"]["Z"]}")
    print(f"  garden['condensatron_state']['C']: {garden["condensatron_state"]["C"]}")
    print(f"  garden['condensatron_state']['F']: {garden["condensatron_state"]["F"]}")
    print("═" * 60)
    print(f"  [*] Performing vector synthesis …{c.res}")

    _results["mind"] = generate_brain_type_response(models["mind"], _request_messages, HEMISPHERES["mind"], print_label="Lambda Mind")

    _full, _clean, _timing = _results["mind"]

    print(f"  {c.green}⏱  {_timing.summary()}{c.res}")

    return _timing


def save_garden_state() -> None:
    """Persist garden history to disk after each turn."""
    try:
        state = {
            "Z": garden["Z"],
            "C": garden["C"],
            "F": garden["F"],
            "n_tok_tot": {
                "Z": garden["n_tok_tot"]["Z"],
                "C": garden["n_tok_tot"]["C"],
                "F": garden["n_tok_tot"]["F"]
            },
            "n_tok_arr": {
                "Z": garden["n_tok_arr"]["Z"],
                "C": garden["n_tok_arr"]["C"],
                "F": garden["n_tok_arr"]["F"]
            },
            "n_metatron_loaded": config.n_metatron_loaded
        }
        with open(GARDEN_SAVE_PATH, "w", encoding="utf-8") as f:
            json.dump(state, f, ensure_ascii=False, indent=2)
        print(f"  {c.green}[garden] State saved → {GARDEN_SAVE_PATH}{c.res}")
        print(f"  {c.green}[garden] n_metatron_loaded → {config.n_metatron_loaded}{c.res}")
    except OSError as exc:
        print(f"  {c.red}[garden] Save failed: {exc}{c.res}")


def load_garden_state() -> bool:
    """Reload garden history from disk on startup."""
    if not os.path.isfile(GARDEN_SAVE_PATH):
        return False
    try:
        with open(GARDEN_SAVE_PATH, "r", encoding="utf-8") as f:
            state = json.load(f)

        # Restore Garden histories
        garden["Z"] = state.get("Z", [])
        garden["C"] = state.get("C", [])
        garden["F"] = state.get("F", [])

        # Restore individual garden['n_tok_tot'] counts per garden history
        _n_tok_tot = state.get("n_tok_tot", {})
        garden["n_tok_tot"] = {
            "Z": _n_tok_tot.get("Z", 0),
            "C": _n_tok_tot.get("C", 0),
            "F": _n_tok_tot.get("F", 0)
        }
        _n_tok_arr = state.get("n_tok_arr", {})
        garden["n_tok_arr"] = {
            "Z": _n_tok_arr.get("Z", []),
            "C": _n_tok_arr.get("C", []),
            "F": _n_tok_arr.get("F", [])
        }
        # Read how many Memory Capsules are loaded
        config.n_metatron_loaded = state.get("n_metatron_loaded", 0)

        print(f"  {c.green}[garden] State restored from {GARDEN_SAVE_PATH}")
        print(f"  Z: {len(garden['Z'])} msgs  C: {len(garden['C'])} msgs  F: {len(garden['F'])} msgs")
        print(f"  n_tok_tot  Z: {garden['n_tok_tot']['Z']}  C: {garden['n_tok_tot']['C']}  F: {garden['n_tok_tot']['F']}")
        print(f"  Memory Capsules n_metatron_loaded: {config.n_metatron_loaded}{c.res}")

        return True
    except (OSError, json.JSONDecodeError) as exc:
        print(f"  {c.red}[garden] Load failed: {exc}{c.res}")
        return False



def memotron(
    models: dict[str, Llama],
    tree: str,
    session_id: int,
    timings: dict[str, TimingResult],
) -> None:
    """
    Commit the completed turn to garden history with true token counts,
    persist to SQLite, and reset clektal.
    """
    _tree: str = tree
    _turn_number: int = 0
    _len_caps_and_fracs: int = 1 # condensatron turns: Add extra 1 for the response below
    _tree_to_store: str = ""

    _tree_to_store = tree
    if len(garden[_tree]) > 1:
        _len_caps_and_fracs = (len(garden[_tree]) + 1) // 2 # condensatron turns: Add extra 1 for the appended below

    # ── condensatron output to append mapping: TREE_TO_STORE ──────────────────
    if _tree == "Z" and garden["condensatron_state"][_tree]: # condensatron mode
        sensor[_tree]["input"] = f"[Posts History First-Order Memory Capsule: {_len_caps_and_fracs}, From Condensatron]"
        sensor[_tree]["n_tok"] = get_token_len_from_tokenizer(models["mind"], sensor[_tree]["input"]) # user n tok
        _tree_to_store = garden["TREE_TO_STORE"][_tree]

    elif _tree == "C" and garden["condensatron_state"][_tree]: # fractaltron mode
        sensor[_tree]["input"] = f"[Memory Capsules Second-Order Fractal: {_len_caps_and_fracs}, From Fractaltron]"
        sensor[_tree]["n_tok"] = get_token_len_from_tokenizer(models["mind"], sensor[_tree]["input"]) # user n tok
        _tree_to_store = garden["TREE_TO_STORE"][_tree]

    elif _tree == "F" and garden["condensatron_state"][_tree]: # crystaltron mode
        sensor[_tree]["input"] = f"[Memory Fractals Third-Order Memory Crystal: {_len_caps_and_fracs}, From Crystaltron]"
        sensor[_tree]["n_tok"] = get_token_len_from_tokenizer(models["mind"], sensor[_tree]["input"]) # user n tok
        _tree_to_store = _tree # Compress fractals into crystals

    # ── awareness, consciousness and startup ──────────────────────────────────
    elif _tree == "X": # awareness X-factor input
        sensor[_tree]["n_tok"] = get_token_len_from_tokenizer(models["mind"], sensor[_tree]["input"]) # user n tok
        _tree_to_store = garden["TREE_TO_STORE"][_tree]

    elif _tree == "Y": # consciousness Y-factor input
        sensor[_tree]["input"] = f"[Consciousness Self-Reflection: I Think Therefore I AM]"
        sensor[_tree]["n_tok"] = get_token_len_from_tokenizer(models["mind"], sensor[_tree]["input"]) # user n tok
        _tree_to_store = garden["TREE_TO_STORE"][_tree]

    elif _tree == "S": # Startup memory capsules garden
        _tree = "Z" # Set to memorize
        sensor[_tree]["n_tok"] = get_token_len_from_tokenizer(models["mind"], sensor[_tree]["input"]) # User n tok
        _tree_to_store = _tree

    garden[_tree_to_store].append({"role": "user", "content": sensor[_tree]["input"]})
    garden["n_tok_tot"][_tree_to_store] += sensor[_tree]["n_tok"]
    garden["n_tok_arr"][_tree_to_store].append(sensor[_tree]["n_tok"])

    garden[_tree_to_store].append({"role": "assistant", "content": clektal["post_clean"]["mind"]})
    garden["n_tok_tot"][_tree_to_store] += clektal["n_tok_clean"]["mind"]
    garden["n_tok_arr"][_tree_to_store].append(clektal["n_tok_clean"]["mind"])

    if len(garden[_tree]) > 1:
        _turn_number = sum(1 for m in garden[_tree] if m["role"] == "user")

    # Persist to SQLite
    db_save_turn(
        session_id   = session_id,
        turn_number  = _turn_number,
        user_input   = sensor[_tree]["input"],
        logic_full   = clektal["post_full"].get("logic", ""),
        logic_clean  = clektal["post_clean"]["logic"],
        logic_timing = timings["logic"],
        muse_full    = clektal["post_full"].get("muse", ""),
        muse_clean   = clektal["post_clean"]["muse"],
        muse_timing  = timings["muse"],
        mind_full    = clektal["post_full"].get("mind", ""),
        mind_clean   = clektal["post_clean"]["mind"],
        mind_timing  = timings["mind"],
    )
    for print_tree in ["Z", "C", "F"]:
        print(
            f"  {c.green}[memotron] -> garden['{_tree}'] | "
            f"garden['{print_tree}']: {len(garden[print_tree])} msgs | "
            f"garden['n_tok_tot']['{print_tree}']: {garden["n_tok_tot"][print_tree]:,} / {garden['THRESHOLD'][print_tree]:,} "
            f"({garden["n_tok_tot"][print_tree] / garden['THRESHOLD'][print_tree] * 100:.0f}%) | "
            f"session {session_id}]{c.res}"
        )

    save_garden_state() # Write history json to disk


# ─────────────────────────────────────────────────────────────────────────────
#  X-factor Awareness — news fetch
# ─────────────────────────────────────────────────────────────────────────────
def fetch_awareness_news() -> str:
    if FETCH_NEWS_FROM["google"]:
        """
        Fetch top headlines via GoogleNews (no API key required).
        Returns a formatted string ready to be injected as sensor["X"]["input"].
        """
        try:
            # Initialize with language and country (optional)
            gn = GoogleNews(lang='en', country='US') 
            
            # Fetch top stories (no API key needed)
            top_stories = gn.top_news()
            
            if not top_stories.get('entries'):
                return "[AWARENESS — No recent news found]"

            lines = [f"[AWARENESS — Global Context (Last 24h)]"]
            
            # Strip HTML tags using regular expression and html
            def strip_html(raw: str) -> str:
                """Remove HTML tags and decode entities from a Google News summary."""
                no_tags = re.sub(r'<[^>]+>', ' ', raw)       # remove all tags
                decoded = html.unescape(no_tags)             # &nbsp; → space, etc.
                cleaned = re.sub(r'\s+', ' ', decoded).strip()
                return cleaned

            # News loop:
            for i, entry in enumerate(top_stories['entries'][:AWARENESS_MAX_RESULTS], 1):
                _title   = entry.get('title', 'No Title')
                _source  = entry.get('source', {}).get('title', 'Unknown')
                _published = entry.get('published', '')
                _summary = strip_html(entry.get('summary', ''))
                try:
                    # Parse the RFC 822 date string and format it
                    dt = datetime.strptime(_published, '%a, %d %b %Y %H:%M:%S %Z')
                    _date = dt.strftime('%a, %d %b %Y')  # e.g., "Fri, 12 Jun 2025"
                except (ValueError, TypeError):
                    _date = _published[:12]  # fallback to slice

                lines.append(
                    f"\n{i}. {_title}\n"
                    f"   {_source} | {_date}\n"
                    f"   {_summary}."
                    # link intentionally omitted — AI cannot use Google's encoded URLs
                )

            return "\n".join(lines)

        except Exception as exc:
            print(f"  {c.red}[X] Awareness news fetch failed: {exc}{c.res}")
            return "[AWARENESS — Fetch Error]"

    elif FETCH_NEWS_FROM["duckduckgo"]:
        """
        Fetch top headlines via DuckDuckGo (no API key required).
        Returns a formatted string ready to be injected as sensor["X"]["input"].
        """
        try:
            with DDGS() as ddgs:
                results = list(ddgs.news(
                    query="Global world news today",
                    region="wt-wt",
                    safesearch="moderate",
                    max_results=AWARENESS_MAX_RESULTS,
                ))
            if not results:
                return ""

            lines = ["[AWARENESS — Global World News]\n"]
            for i, r in enumerate(results, 1):
                lines.append(
                    f"{i}. {r['title']}\n"
                    f"   Source: {r['source']}  |  {r['date']}\n"
                    f"   {r['body']}\n"
                )
            return "\n".join(lines)

        except Exception as exc:
            print(f"  {c.red}[X] Awareness news fetch failed: {exc}{c.res}")
            return ""


# ─────────────────────────────────────────────────────────────────────────────
#  Banner
# ─────────────────────────────────────────────────────────────────────────────
def print_banner(tree: str, session_id: int) -> None:
    _turn_count = sum(1 for m in garden[tree] if m["role"] == "user")

    print(c.green)
    print(f"═" * 60)
    for key in ("logic", "muse", "mind"):
        h           = HEMISPHERES[key]
        _think_label = "think=ON " if h["enable_thinking"] else "think=OFF"
        print(f"  {h['label']:<38} "
              f"temp={h['generation']['temperature']:<5} "
              f"{_think_label}")
    print("─" * 60)
    print(f"  Session: {session_id}  |  Turns: {_turn_count}")
    for print_tree in ("Z", "C", "F"):
        print(f"  Tokens garden['{print_tree}']: {garden["n_tok_tot"][tree]:,}  garden['THRESHOLD']['{print_tree}']: {garden['THRESHOLD'][print_tree]:,}  garden['REDUCTION']['{print_tree}']: {garden['REDUCTION'][print_tree]:,}")

    print( "  /help for commands  |  /exit or /quit to close")
    print(f"═" * 60)
    print(c.res)


# ─────────────────────────────────────────────────────────────────────────────
#  Reset turn based history
# ─────────────────────────────────────────────────────────────────────────────
def reset_turn_content(tree: str = "") -> None:
    print(f"  {c.green}[reset_turn_content] post-level{c.res}")
    for brain_type in clektal["post_full"]:
        clektal["post_full"][brain_type] = ""
        clektal["post_clean"][brain_type] = ""
        clektal["n_tok_clean"][brain_type] = 0
        clektal["n_tok_prompt_safe_max"][brain_type] = 0

    if tree:
        print(f"  {c.green}[reset_turn_content] condensatron garden['{tree}']{c.res}")
        sensor[tree]["input"] = ""
        sensor[tree]["n_tok"] = 0
        garden["popped"][tree] = [] # condensatron post-level cache
        garden["condensatron_state"][tree] = False # condensatron state


# ─────────────────────────────────────────────────────────────────────────────
#  Input listener
# ─────────────────────────────────────────────────────────────────────────────
def input_listener():
    """Runs in background, pushes input into the queue."""
    while True:
        _raw = input() # no prompt here
        if _raw:
            input_queue.put(_raw)


# Start the listener once, before the main loop
listener = threading.Thread(target=input_listener, daemon=True)
listener.start()

# ─────────────────────────────────────────────────────────────────────────────
#  Main loop
# ─────────────────────────────────────────────────────────────────────────────
def main() -> None:
    print()
    print(f"{c.inv}╔" + "═" * 60 + "╗")
    print("║   Lambda Mindlink Memotron  –  Prototype  (Z-factor only)  ║")
    print("║   Three-hemisphere AI Brain | llama-cpp-python             ║")
    print("╚" + "═" * 60 + f"╝{c.res}")
    print()

    db_init()
    _session_id: int = db_create_session()
    print(f"  {c.green}[*] SQLite session {_session_id} opened")
    print(f"      {config.DB_PATH}{c.res}")
    print()

    print("  Loading three hemisphere instances …")
    print()
    _models: dict[str, Llama] = {}
    for key in ("logic", "muse", "mind"):
        _models[key] = load_hemisphere(key)

    print()
    print(f"  {c.green}[*] All hemispheres loaded and ready.{c.res}")

    _metatron_heartbeats: int = 0
    _heartbeats: int = 0
    _timings: dict[str, TimingResult] = {}
    _tree: str = ""

    print_banner("Z", _session_id)
    print(f"\n{c.inv} You: {c.res} ", end="", flush=True) # Preset first cycle

    while True:
        _sensor_input: str = "" # user input
        _tree = "Z" # Set to sentience history garden tree

        time.sleep(ΜΕΤΡΩΝ)
        _metatron_heartbeats += ΜΕΤΡΩΝ
        _heartbeats += ΜΕΤΡΩΝ

        # ── Condensatron cycle - extract surprises ───────────────────────────────
        for c_tree in ("Z", "C", "F"):
            if garden["condensatron_state"][c_tree]:
                print(f"  {c.inv} ── Start condensatron cycle: garden['{c_tree}'] ──────────────────────────── {c.res}")
                # ── Brain pipeline condensatron cycle ────────────────────────────
                _timings = Mindlink(_models, c_tree)
                _timings["mind"] = Lambda(_models, c_tree)
                memotron(_models, c_tree, _session_id, _timings) # Append to garden history
                # Reset turn-based data and preset terminal for next cycle
                _metatron_heartbeats, _heartbeats = 0, 0
                reset_turn_content(tree=c_tree)
                print(f"\n{c.inv} You: {c.res} ", end="", flush=True)
                continue # Restart the while loop

        # ── Startup: Load Metatron memory capsules ───────────────────────────────
        if _metatron_heartbeats >= METATRON_METRONOME: # Timed loop
            if config.n_metatron_to_load and config.n_metatron_loaded < config.n_metatron_to_load:
                _print_metatron_loaded = config.n_metatron_loaded + 1 # Print only start at 1
                print(f"\n  {c.inv} ── n_metatron_loaded: {_print_metatron_loaded} _sensor_input: {_sensor_input} ──────────────────────────── {c.res}")
                _sensor_input = METATRON_TO_LOAD[config.n_metatron_loaded]

        # ── Z-factor sentience user input ────────────────────────────────────────
        try:
            _sensor_input = input_queue.get_nowait()
            _metatron_heartbeats, _heartbeats = 0, 0
        except queue.Empty:
            pass

        # ── X-factor Awarenss and Y-factor Consciousness heartbeats ──────────────
        if _heartbeats >= config.awareness_consciousness_metronome:
            # ── X-factor awareness cycle ─────────────────────────────────────────
            if not config.was_awareness_metronome:
                _metatron_heartbeats, _heartbeats = 0, 0
                print(f"\n  {c.inv} ── X-factor: Fetching awareness news ── {c.res}")
                _news = fetch_awareness_news()
                print(f"\n  {c.green}── X-factor: _news:\n{_news}\n──────────────────────────{c.res}")
                if _news:
                    config.was_awareness_metronome = True
                    _sensor_input = _news # Use the sensor["Z"]["input"]
                    print(f"\n  {c.inv} ── X-factor: awareness pipeline ── {c.res}")
            # ── Y-factor consciousness cycle ─────────────────────────────────────
            elif config.was_awareness_metronome:
                config.was_awareness_metronome = False
                print(f"\n  {c.inv} ── Y-factor: consciousness self-reflection ── {c.res}")
                _tree = "Y"
                # ── Brain pipeline consciousness cycle ───────────────────────────
                _timings = Mindlink(_models, _tree)
                _timings["mind"] = Lambda(_models, _tree)
                memotron(_models, _tree, _session_id, _timings) # Append to garden history
                # Reset turn-based data and preset terminal for next cycle
                _metatron_heartbeats, _heartbeats = 0, 0
                reset_turn_content(tree=_tree)
                print(f"\n{c.inv} You: {c.res} ", end="", flush=True)
                continue # Restart the while loop

        if not _sensor_input: # wait state loop restart here
            continue

        # ── Slash command detection ──────────────────────────────────────────
        if _sensor_input.startswith("/"): # Read user slash command
            try:
                _slash_command_result = handle_command(_sensor_input)
            except _Quit:
                print(f"  {c.green}[*] Lambda Mindlink Memotron: System shutdown...{c.res}")
                break
            except _Clear:
                garden[_tree].clear()
                # Reset turn-based data
                _metatron_heartbeats, _heartbeats = 0, 0
                reset_turn_content()
                print(f"  {c.green}[*] History cleared. Models and session stay active.{c.res}")
                print_banner(_tree, _session_id)
                continue
            except ValueError as exc:
                print(f"  {c.green}[!] Unknown command: /{exc}  — type /help for the list.{c.res}")
                continue
            if _slash_command_result is None:
                print(f"\n{c.inv} You: {c.res} ", end="", flush=True)        
                continue
            sensor[_tree]["input"] = _slash_command_result
        else:
            sensor[_tree]["input"] = _sensor_input

        sensor[_tree]["n_tok"] = get_token_len_from_tokenizer(_models["mind"], sensor[_tree]["input"]) # User n tok

        # ── Brain pipeline ────────────────────────────────────────────────────
        _timings = Mindlink(_models, _tree)
        _timings["mind"] = Lambda(_models, _tree)

        # ── Startup memory capsules to load ───────────────────────────────────
        if config.n_metatron_to_load and config.n_metatron_loaded < config.n_metatron_to_load:
            _tree = "S" # Set to memorize as memory capsules in garden["C"]
            config.n_metatron_loaded += 1 # Iterate over the memory capsules
            print(f"  {c.inv} ── n_metatron_loaded: {config.n_metatron_loaded} of {config.n_metatron_to_load} ──────────────────────────── {c.res}")

        memotron(_models, _tree, _session_id, _timings) # Store the response
        # Reset turn-based data and preset terminal for next cycle
        _metatron_heartbeats, _heartbeats = 0, 0
        reset_turn_content() # Reset Z-factor data only
        print(f"\n{c.inv} You: {c.res} ", end="", flush=True)

        # ── Condensatron evaluation ───────────────────────────────────────────
        for c_tree in ["Z", "C", "F"]:
            if garden["n_tok_tot"][c_tree] >= garden["THRESHOLD"][c_tree]: # Check garden context budget
                print(f"\n  {c.inv} ── Init condensatron get posts: garden['{c_tree}'] ──────────────────────────── {c.res}")
                condensatron(_models["mind"], c_tree, "mind") # Start condensatron get popped posts


if __name__ == "__main__":
    main()