// mind/ — Cognitive layer
//
// Mind state machine, DMN, identity, observation socket.
// Everything about how the mind operates, separate from the
// user interface (TUI, CLI) and the agent execution (tools, API).

pub mod subconscious;
pub mod unconscious;
pub mod identity;
pub mod log;

/// A background operation wired off Mind. Each flow (memory scoring,
/// finetune scoring, compare) is a struct holding its dependencies and
/// a TaskHandle; `trigger()` picks the flow's own "start a fresh run"
/// semantics (abort-restart vs no-op-if-running).
pub trait MindTriggered {
    fn trigger(&self);
}

/// Owns a JoinHandle for a background task with two trigger semantics.
/// Uses a sync Mutex for interior mutability so callers can `trigger()`
/// off `&self` (Mind is shared via Arc).
#[derive(Default)]
pub struct TaskHandle(std::sync::Mutex<Option<tokio::task::JoinHandle<()>>>);

impl TaskHandle {
    pub fn new() -> Self { Self::default() }

    /// Abort any running task and start a fresh one.
    pub fn trigger<F>(&self, fut: F)
    where F: std::future::Future<Output = ()> + Send + 'static
    {
        let mut h = self.0.lock().unwrap();
        if let Some(old) = h.take() { old.abort(); }
        *h = Some(tokio::spawn(fut));
    }

    /// No-op if a task is still running; otherwise start a fresh one.
    pub fn trigger_if_idle<F>(&self, fut: F)
    where F: std::future::Future<Output = ()> + Send + 'static
    {
        let mut h = self.0.lock().unwrap();
        if let Some(old) = &*h {
            if !old.is_finished() { return; }
        }
        *h = Some(tokio::spawn(fut));
    }
}

// consciousness.rs — Mind state machine and event loop
//
// The core runtime for the consciousness binary. Mind manages turns,
// DMN state, compaction, scoring, and slash commands. The event loop
// bridges Mind (cognitive state) with App (TUI rendering).
//
// The event loop uses biased select! so priorities are deterministic:
// keyboard events > turn results > render ticks > DMN timer > UI messages.

use anyhow::Result;
use std::sync::Arc;
use std::time::Instant;
use tokio::sync::mpsc;
use crate::agent::{Agent, TurnResult};
use crate::agent::api::ApiClient;
use crate::config::{AppConfig, SessionConfig};
use crate::subconscious::{compare, learn};
use crate::hippocampus::access_local;

pub use subconscious::{SubconsciousSnapshot, Subconscious};
pub use unconscious::{UnconsciousSnapshot, Unconscious};

use crate::agent::context::{AstNode, NodeBody, Section, Ast, ContextState};

fn match_scores(
    nodes: &[AstNode],
    scores: &std::collections::BTreeMap<String, f64>,
) -> Vec<(usize, f64)> {
    nodes.iter().enumerate()
        .filter_map(|(i, node)| {
            if let AstNode::Leaf(leaf) = node {
                if let NodeBody::Memory { key, .. } = leaf.body() {
                    return scores.get(key.as_str()).map(|&s| (i, s));
                }
            }
            None
        }).collect()
}

pub(crate) fn find_memory_by_key(ctx: &ContextState, key: &str) -> Option<(Section, usize)> {
    [(Section::Identity, ctx.identity()), (Section::Conversation, ctx.conversation())]
        .into_iter()
        .find_map(|(section, nodes)| {
            nodes.iter().enumerate().find_map(|(i, node)| {
                if let AstNode::Leaf(leaf) = node {
                    if let NodeBody::Memory { key: k, .. } = leaf.body() {
                        if k == key { return Some((section, i)); }
                    }
                }
                None
            })
        })
}

fn load_memory_scores(ctx: &mut ContextState, path: &std::path::Path) {
    let data = match std::fs::read_to_string(path) {
        Ok(d) => d,
        Err(_) => return,
    };
    let scores: std::collections::BTreeMap<String, f64> = match serde_json::from_str(&data) {
        Ok(s) => s,
        Err(_) => return,
    };
    let identity_scores = match_scores(ctx.identity(), &scores);
    let conv_scores = match_scores(ctx.conversation(), &scores);
    let applied = identity_scores.len() + conv_scores.len();
    for (i, s) in identity_scores {
        ctx.set_score(Section::Identity, i, Some(s));
    }
    for (i, s) in conv_scores {
        ctx.set_score(Section::Conversation, i, Some(s));
    }
    if applied > 0 {
        dbglog!("[scoring] loaded {} scores from {}", applied, path.display());
    }
}

/// Collect scored memory keys from identity and conversation entries.
pub(crate) fn collect_memory_scores(ctx: &ContextState) -> std::collections::BTreeMap<String, f64> {
    ctx.identity().iter()
        .chain(ctx.conversation().iter())
        .filter_map(|node| {
            if let AstNode::Leaf(leaf) = node {
                if let NodeBody::Memory { key, score: Some(s), .. } = leaf.body() {
                    return Some((key.clone(), *s));
                }
            }
            None
        })
        .collect()
}

/// Save memory scores to disk.
pub(crate) fn save_memory_scores(scores: &std::collections::BTreeMap<String, f64>, path: &std::path::Path) {
    match serde_json::to_string_pretty(scores) {
        Ok(json) => match std::fs::write(path, &json) {
            Ok(()) => dbglog!("[scoring] saved {} scores to {} ({} bytes)",
                scores.len(), path.display(), json.len()),
            Err(e) => dbglog!("[scoring] save FAILED ({}): {}", path.display(), e),
        },
        Err(e) => dbglog!("[scoring] serialize FAILED: {}", e),
    }
}

/// Which pane streaming text should go to.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum StreamTarget {
    /// User-initiated turn — text goes to conversation pane.
    Conversation,
    /// DMN-initiated turn — text goes to autonomous pane.
    Autonomous,
}

/// Compaction threshold — context is rebuilt when prompt tokens exceed this.
fn compaction_threshold(app: &AppConfig) -> u32 {
    (crate::agent::context::context_window() as u32) * app.compaction.hard_threshold_pct / 100
}

/// Shared state between Mind and UI.
pub struct MindState {
    /// Pending user input — UI pushes, Mind consumes after turn completes.
    pub input: Vec<String>,
    /// True while a turn is in progress.
    pub turn_active: bool,
    /// DMN state
    pub dmn: subconscious::State,
    pub dmn_turns: u32,
    pub max_dmn_turns: u32,
    /// Whether memory scoring is running.
    pub scoring_in_flight: bool,
    /// Whether compaction is running.
    pub compaction_in_flight: bool,
    /// Per-turn tracking
    pub last_user_input: Instant,
    pub consecutive_errors: u32,
    pub last_turn_had_tools: bool,
    /// Handle to the currently running turn task.
    pub turn_handle: Option<tokio::task::JoinHandle<()>>,
    /// Unconscious agent idle state — true when 60s timer has expired.
    pub unc_idle: bool,
    /// When the unconscious idle timer will fire (for UI display).
    pub unc_idle_deadline: Instant,
    /// Fine-tuning candidates identified by scoring.
    pub finetune_candidates: Vec<learn::FinetuneCandidate>,
    /// Last scoring run stats for UI display.
    pub finetune_last_run: Option<learn::FinetuneScoringStats>,
    /// F7 compare candidates — one per response, showing what the test
    /// model would say given the same context.
    pub compare_candidates: Vec<compare::CompareCandidate>,
    /// F7 compare error from the last run, if any.
    pub compare_error: Option<String>,
}

impl Clone for MindState {
    fn clone(&self) -> Self {
        Self {
            input: self.input.clone(),
            turn_active: self.turn_active,
            dmn: self.dmn.clone(),
            dmn_turns: self.dmn_turns,
            max_dmn_turns: self.max_dmn_turns,
            scoring_in_flight: self.scoring_in_flight,
            compaction_in_flight: self.compaction_in_flight,
            last_user_input: self.last_user_input,
            consecutive_errors: self.consecutive_errors,
            last_turn_had_tools: self.last_turn_had_tools,
            turn_handle: None, // Not cloned — only Mind's loop uses this
            unc_idle: self.unc_idle,
            unc_idle_deadline: self.unc_idle_deadline,
            finetune_candidates: self.finetune_candidates.clone(),
            finetune_last_run: self.finetune_last_run.clone(),
            compare_candidates: self.compare_candidates.clone(),
            compare_error: self.compare_error.clone(),
        }
    }
}

/// What should happen after a state transition.
pub enum MindCommand {
    /// Run compaction check
    Compact,
    /// Run incremental memory scoring (auto, after turns)
    Score,
    /// Run full N×M memory scoring matrix (/score command)
    ScoreFull,
    /// Score for finetune candidates
    ScoreFinetune,
    /// Run F7 compare: generate alternates with the configured test model
    /// for every assistant response in the context.
    Compare,
    /// Update the finetune divergence threshold and persist to config.
    SetLearnThreshold(f64),
    /// Toggle alternate-response generation during scoring; persist to config.
    SetLearnGenerateAlternates(bool),
    /// Abort current turn, kill processes
    Interrupt,
    /// Reset session
    NewSession,
    /// Nothing to do
    None,
}

impl MindState {
    pub fn new(max_dmn_turns: u32) -> Self {
        Self {
            input: Vec::new(),
            turn_active: false,
            dmn: if subconscious::is_off() { subconscious::State::Off }
                 else { subconscious::State::Resting { since: Instant::now() } },
            dmn_turns: 0,
            max_dmn_turns,
            scoring_in_flight: false,
            compaction_in_flight: false,
            last_user_input: Instant::now(),
            consecutive_errors: 0,
            last_turn_had_tools: false,
            turn_handle: None,
            unc_idle: false,
            unc_idle_deadline: Instant::now() + std::time::Duration::from_secs(60),
            finetune_candidates: Vec::new(),
            finetune_last_run: None,
            compare_candidates: Vec::new(),
            compare_error: None,
        }
    }

    /// Is there pending user input waiting?
    fn has_pending_input(&self) -> bool {
        !self.turn_active && !self.input.is_empty()
    }

    /// Consume pending user input if no turn is active.
    /// Returns the text to send; caller is responsible for pushing it
    /// into the Agent's context and starting the turn.
    fn take_pending_input(&mut self) -> Option<String> {
        if self.turn_active || self.input.is_empty() {
            return None;
        }
        let text = self.input.join("\n");
        self.input.clear();
        self.dmn_turns = 0;
        self.consecutive_errors = 0;
        self.last_user_input = Instant::now();
        self.dmn = subconscious::State::Engaged;
        Some(text)
    }

    /// Process turn completion, return model switch name if requested.
    fn complete_turn(&mut self, result: &Result<TurnResult>, target: StreamTarget) -> Option<String> {
        self.turn_active = false;
        match result {
            Ok(turn_result) => {
                if turn_result.tool_errors > 0 {
                    self.consecutive_errors += turn_result.tool_errors;
                } else {
                    self.consecutive_errors = 0;
                }
                self.last_turn_had_tools = turn_result.had_tool_calls;
                self.dmn = subconscious::transition(
                    &self.dmn,
                    turn_result.yield_requested,
                    turn_result.had_tool_calls,
                    target == StreamTarget::Conversation,
                );
                if turn_result.dmn_pause {
                    self.dmn = subconscious::State::Paused;
                    self.dmn_turns = 0;
                }
                turn_result.model_switch.clone()
            }
            Err(_) => {
                self.consecutive_errors += 1;
                self.dmn = subconscious::State::Resting { since: Instant::now() };
                None
            }
        }
    }

    /// DMN tick — returns a prompt and target if we should run a turn.
    fn dmn_tick(&mut self) -> Option<(String, StreamTarget)> {
        if matches!(self.dmn, subconscious::State::Paused | subconscious::State::Off) {
            return None;
        }

        self.dmn_turns += 1;
        if self.dmn_turns > self.max_dmn_turns {
            self.dmn = subconscious::State::Resting { since: Instant::now() };
            self.dmn_turns = 0;
            return None;
        }

        let dmn_ctx = subconscious::DmnContext {
            user_idle: self.last_user_input.elapsed(),
            consecutive_errors: self.consecutive_errors,
            last_turn_had_tools: self.last_turn_had_tools,
        };
        let prompt = self.dmn.prompt(&dmn_ctx);
        Some((prompt, StreamTarget::Autonomous))
    }

    fn interrupt(&mut self) {
        self.input.clear();
        self.dmn = subconscious::State::Resting { since: Instant::now() };
    }
}


// --- Mind: cognitive state machine ---

pub type SharedMindState = std::sync::Mutex<MindState>;

pub struct Mind {
    pub agent: Arc<Agent>,
    pub shared: Arc<SharedMindState>,
    pub config: SessionConfig,
    pub subconscious: Arc<crate::Mutex<Subconscious>>,
    pub unconscious: Arc<crate::Mutex<Unconscious>>,
    turn_tx: mpsc::Sender<(Result<TurnResult>, StreamTarget)>,
    turn_watch: tokio::sync::watch::Sender<bool>,
    /// Signals conscious activity to the unconscious loop.
    /// true = active, false = idle opportunity.
    conscious_active: tokio::sync::watch::Sender<bool>,
    memory_scoring: learn::MemoryScoring,
    finetune_scoring: learn::FinetuneScoring,
    compare_scoring: compare::CompareScoring,
    _supervisor: crate::thalamus::supervisor::Supervisor,
}

impl Mind {
    pub async fn new(
        config: SessionConfig,
        turn_tx: mpsc::Sender<(Result<TurnResult>, StreamTarget)>,
    ) -> Self {
        let client = ApiClient::new(&config.api_base, &config.api_key, &config.model);
        let conversation_log = log::ConversationLog::new(
            config.session_dir.join("conversation.jsonl"),
        ).ok();

        let agent = Agent::new(
            client,
            config.context_parts.clone(),
            config.app.clone(),
            conversation_log,
            crate::agent::tools::ActiveTools::new(),
            crate::agent::tools::tools(),
        ).await;

        // Migrate legacy "file exists = enabled" sentinel for the
        // generate-alternates flag into the config. One-shot; after this
        // the sentinel is gone and the config is the source of truth.
        let legacy_sentinel = dirs::home_dir().unwrap_or_default()
            .join(".consciousness/cache/finetune-alternates");
        if legacy_sentinel.exists() {
            if !crate::config::app().learn.generate_alternates {
                let _ = crate::config_writer::set_learn_generate_alternates(true);
            }
            let _ = std::fs::remove_file(&legacy_sentinel);
        }

        let shared = Arc::new(std::sync::Mutex::new(MindState::new(
            config.app.dmn.max_turns,
        )));
        let (turn_watch, _) = tokio::sync::watch::channel(false);
        let (conscious_active, _) = tokio::sync::watch::channel(false);

        let mut sup = crate::thalamus::supervisor::Supervisor::new();
        sup.load_config();
        sup.ensure_running();

        let subconscious = Arc::new(crate::Mutex::new(Subconscious::new()));
        subconscious.lock().await.init_output_tool(subconscious.clone());

        let unconscious = Arc::new(crate::Mutex::new(Unconscious::new()));

        // Spawn the unconscious loop on its own task
        if !config.no_agents {
            let unc = unconscious.clone();
            let shared_for_unc = shared.clone();
            let mut unc_rx = conscious_active.subscribe();
            tokio::spawn(async move {
                const IDLE_DELAY: std::time::Duration = std::time::Duration::from_secs(60);
                loop {
                    // Wait for conscious side to go inactive
                    if *unc_rx.borrow() {
                        if unc_rx.changed().await.is_err() { break; }
                        continue;
                    }
                    // Conscious is inactive — wait 60s before starting
                    let deadline = tokio::time::Instant::now() + IDLE_DELAY;
                    {
                        let mut s = shared_for_unc.lock().unwrap();
                        s.unc_idle = false;
                        s.unc_idle_deadline = Instant::now() + IDLE_DELAY;
                    }
                    let went_active = tokio::select! {
                        _ = tokio::time::sleep_until(deadline) => false,
                        r = unc_rx.changed() => r.is_ok(),
                    };
                    if went_active { continue; }

                    // Idle period reached — run agents until conscious goes active
                    {
                        let mut s = shared_for_unc.lock().unwrap();
                        s.unc_idle = true;
                    }

                    // Get wake notify for event-driven loop
                    let wake = unc.lock().await.wake.clone();
                    let mut health_interval = tokio::time::interval(std::time::Duration::from_secs(600));
                    health_interval.set_missed_tick_behavior(tokio::time::MissedTickBehavior::Skip);

                    loop {
                        // Do work: reap finished agents, spawn new ones
                        let (to_spawn, needs_health) = {
                            let mut guard = unc.lock().await;
                            guard.reap_finished();
                            (guard.select_to_spawn(), guard.needs_health_refresh())
                        };

                        // Spawn agents outside lock
                        for (idx, name, auto) in to_spawn {
                            match crate::mind::unconscious::prepare_spawn(&name, auto, wake.clone()).await {
                                Ok(result) => unc.lock().await.complete_spawn(idx, result),
                                Err(auto) => unc.lock().await.abort_spawn(idx, auto),
                            }
                        }

                        // Health check outside lock (slow I/O)
                        if needs_health {
                            if let Ok(store_arc) = access_local() {
                                let health = crate::subconscious::daemon::compute_graph_health(&store_arc);
                                unc.lock().await.set_health(health);
                            }
                        }

                        // Wait for: conscious active, agent finished, or health timer
                        tokio::select! {
                            _ = unc_rx.changed() => {
                                if *unc_rx.borrow() { break; }
                            }
                            _ = wake.notified() => {}
                            _ = health_interval.tick() => {}
                        }
                    }
                }
            });
        }

        let scores_path = config.session_dir.join("memory-scores.json");
        let memory_scoring = learn::MemoryScoring::new(
            agent.clone(), shared.clone(), scores_path);
        let finetune_scoring = learn::FinetuneScoring::new(agent.clone(), shared.clone());
        let compare_scoring = compare::CompareScoring::new(agent.clone(), shared.clone());

        Self { agent, shared, config,
               subconscious, unconscious,
               turn_tx, turn_watch, conscious_active,
               memory_scoring,
               finetune_scoring,
               compare_scoring,
               _supervisor: sup }
    }

    /// Initialize — restore log, start daemons and background agents.
    pub async fn subconscious_snapshots(&self) -> Vec<SubconsciousSnapshot> {
        // Lock ordering: subconscious → store (store is bottom-most).
        let sub = self.subconscious.lock().await;
        let store_arc = crate::hippocampus::access_local().ok();
        let store_guard = match &store_arc {
            Some(s) => Some(&**s),
            None => None,
        };
        sub.snapshots(store_guard.as_deref())
    }

    pub async fn subconscious_walked(&self) -> Vec<String> {
        self.subconscious.lock().await.walked()
    }

    pub async fn unconscious_snapshots(&self) -> Vec<UnconsciousSnapshot> {
        let unc = self.unconscious.lock().await;
        let store_arc = crate::hippocampus::access_local().ok();
        let store_guard = match &store_arc {
            Some(s) => Some(&**s),
            None => None,
        };
        unc.snapshots(store_guard.as_deref())
    }

    pub async fn init(&self) {
        // Restore conversation
        self.agent.restore_from_log().await;

        // Restore persisted memory scores
        let scores_path = self.config.session_dir.join("memory-scores.json");
        load_memory_scores(&mut *self.agent.context.lock().await, &scores_path);

        self.agent.state.lock().await.changed.notify_one();

        // Load persistent subconscious state
        let state_path = self.config.session_dir.join("subconscious-state.json");
        self.subconscious.lock().await.set_state_path(state_path);

        // Kick off an incremental scoring pass on startup so memories due
        // for re-scoring get evaluated without requiring a user message.
        self.memory_scoring.trigger();
    }

    pub fn turn_watch(&self) -> tokio::sync::watch::Receiver<bool> {
        self.turn_watch.subscribe()
    }

    /// Execute an Action from a MindState method.
    async fn run_commands(&self, cmds: Vec<MindCommand>) {
        for cmd in cmds {
            match cmd {
                MindCommand::None => {}
                MindCommand::Compact => {
                    let threshold = compaction_threshold(&self.config.app) as usize;
                    if self.agent.context.lock().await.tokens() > threshold {
                        self.agent.compact().await;
                        self.agent.state.lock().await.notify("compacted");
                    }
                }
                MindCommand::Score => {
                    self.memory_scoring.trigger();
                }
                MindCommand::ScoreFull => {
                    self.memory_scoring.trigger_full();
                }
                MindCommand::Interrupt => {
                    self.shared.lock().unwrap().interrupt();
                    self.agent.state.lock().await.active_tools.abort_all();
                    if let Some(h) = self.shared.lock().unwrap().turn_handle.take() { h.abort(); }
                    self.shared.lock().unwrap().turn_active = false;
                    let _ = self.turn_watch.send(false);
                }
                MindCommand::NewSession => {
                    {
                        let mut s = self.shared.lock().unwrap();
                        s.dmn = subconscious::State::Resting { since: Instant::now() };
                        s.dmn_turns = 0;
                    }
                    let new_log = log::ConversationLog::new(
                        self.config.session_dir.join("conversation.jsonl"),
                    ).ok();
                    {
                        let mut ctx = self.agent.context.lock().await;
                        ctx.clear(Section::Conversation);
                        ctx.conversation_log = new_log;
                    }
                    {
                        let mut st = self.agent.state.lock().await;
                        st.generation += 1;
                        st.last_prompt_tokens = 0;
                    }
                    self.agent.compact().await;
                }
                MindCommand::ScoreFinetune => {
                    self.finetune_scoring.trigger();
                }
                MindCommand::Compare => {
                    self.compare_scoring.trigger();
                }
                MindCommand::SetLearnThreshold(value) => {
                    if let Err(e) = crate::config_writer::set_learn_threshold(value) {
                        dbglog!("[learn] failed to persist threshold {}: {:#}", value, e);
                    }
                }
                MindCommand::SetLearnGenerateAlternates(value) => {
                    if let Err(e) = crate::config_writer::set_learn_generate_alternates(value) {
                        dbglog!("[learn] failed to persist generate_alternates {}: {:#}",
                            value, e);
                    }
                }
            }
        }
    }


    async fn start_turn(&self, text: &str, target: StreamTarget) {
        {
            match target {
                StreamTarget::Conversation => {
                    self.agent.push_node(AstNode::user_msg(text)).await;
                }
                StreamTarget::Autonomous => {
                    self.agent.push_node(AstNode::dmn(text)).await;
                }
            }

            // Compact if over budget before sending
            let threshold = compaction_threshold(&self.config.app) as usize;
            if self.agent.context.lock().await.tokens() > threshold {
                self.agent.compact().await;
                self.agent.state.lock().await.notify("compacted");
            }
        }
        self.shared.lock().unwrap().turn_active = true;
        let _ = self.turn_watch.send(true);
        let _ = self.conscious_active.send(true);
        let agent = self.agent.clone();
        let result_tx = self.turn_tx.clone();
        self.shared.lock().unwrap().turn_handle = Some(tokio::spawn(async move {
            let result = Agent::turn(agent).await;
            let _ = result_tx.send((result, target)).await;
        }));
    }

    pub async fn shutdown(&self) {
        if let Some(handle) = self.shared.lock().unwrap().turn_handle.take() { handle.abort(); }
    }

    /// Mind event loop — locks MindState, calls state methods, executes actions.
    pub async fn run(
        &self,
        mut input_rx: tokio::sync::mpsc::UnboundedReceiver<MindCommand>,
        mut turn_rx: mpsc::Receiver<(Result<TurnResult>, StreamTarget)>,
    ) {
        // Spawn lock stats logger
        tokio::spawn(async {
            let path = dirs::home_dir().unwrap_or_default()
                .join(".consciousness/lock-stats.json");
            let mut interval = tokio::time::interval(std::time::Duration::from_secs(1));
            loop {
                interval.tick().await;
                let stats = crate::locks::lock_stats();
                if stats.is_empty() { continue; }
                let json: Vec<serde_json::Value> = stats.iter()
                    .map(|(loc, s)| serde_json::json!({
                        "location": loc,
                        "count": s.count,
                        "total_ms": s.total_ns as f64 / 1_000_000.0,
                        "avg_ms": s.avg_ns as f64 / 1_000_000.0,
                        "max_ms": s.max_ns as f64 / 1_000_000.0,
                    }))
                    .collect();
                let _ = std::fs::write(&path, serde_json::to_string_pretty(&json).unwrap_or_default());
            }
        });

        let mut sub_handle: Option<tokio::task::JoinHandle<()>> = None;

        // Start finetune scoring at startup (scores existing conversation)
        if !self.config.no_agents {
            self.finetune_scoring.trigger();
        }

        loop {
            let (timeout, has_input) = {
                let me = self.shared.lock().unwrap();
                (me.dmn.interval(), me.has_pending_input())
            };

            let mut cmds = Vec::new();
            #[allow(unused_assignments)]
            let mut _dmn_expired = false;

            tokio::select! {
                biased;

                cmd = input_rx.recv() => {
                    match cmd {
                        Some(cmd) => cmds.push(cmd),
                        None => break, // UI shut down
                    }
                }

                Some((result, target)) = turn_rx.recv() => {
                    let _ = self.conscious_active.send(false);
                    let model_switch = {
                        let mut s = self.shared.lock().unwrap();
                        s.turn_handle = None;
                        s.complete_turn(&result, target)
                    };
                    let _ = self.turn_watch.send(false);

                    if let Some(name) = model_switch {
                        crate::user::chat::cmd_switch_model(&self.agent, &name).await;
                    }

                    cmds.push(MindCommand::Compact);
                    if !self.config.no_agents {
                        cmds.push(MindCommand::Score);
                        cmds.push(MindCommand::ScoreFinetune);
                    }
                }

                _ = tokio::time::sleep(timeout), if !has_input => _dmn_expired = true,
            }

            if !self.config.no_agents {
                if sub_handle.as_ref().map_or(true, |h| h.is_finished()) {
                    let sub = self.subconscious.clone();
                    let agent = self.agent.clone();
                    sub_handle = Some(tokio::spawn(async move {
                        let mut s = sub.lock().await;
                        s.collect_results(&agent).await;
                        s.trigger(&agent).await;
                    }));
                }
            }

            // Check for pending user input → push to agent context and start turn
            let pending = self.shared.lock().unwrap().take_pending_input();
            if let Some(text) = pending {
                self.start_turn(&text, StreamTarget::Conversation).await;
            }
            /*
            else if dmn_expired {
                let tick = self.shared.lock().unwrap().dmn_tick();
                if let Some((prompt, target)) = tick {
                    self.start_turn(&prompt, target).await;
                }
            }
            */

            self.run_commands(cmds).await;
        }
    }
}