consciousness/src/mind/mod.rs

// 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 dmn;
pub mod identity;
pub mod log;

// 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::learn;

pub use dmn::{SubconsciousSnapshot, Subconscious};

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

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 mut applied = 0;
    for i in 0..ctx.conversation().len() {
        if let AstNode::Leaf(leaf) = &ctx.conversation()[i] {
            if let NodeBody::Memory { key, .. } = leaf.body() {
                if let Some(&s) = scores.get(key.as_str()) {
                    ctx.set_score(Section::Conversation, i, Some(s));
                    applied += 1;
                }
            }
        }
    }
    if applied > 0 {
        dbglog!("[scoring] loaded {} scores from {}", applied, path.display());
    }
}

/// Collect scored memory keys from conversation entries.
fn collect_memory_scores(ctx: &ContextState) -> std::collections::BTreeMap<String, f64> {
    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.
fn save_memory_scores(scores: &std::collections::BTreeMap<String, f64>, path: &std::path::Path) {
    if let Ok(json) = serde_json::to_string_pretty(scores) {
        let _ = std::fs::write(path, json);
        dbglog!("[scoring] saved {} scores to {}", scores.len(), path.display());
    }
}

/// 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: dmn::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<()>>,
}

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
        }
    }
}

/// What should happen after a state transition.
pub enum MindCommand {
    /// Run compaction check
    Compact,
    /// Run memory scoring
    Score,
    /// 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 dmn::is_off() { dmn::State::Off }
                 else { dmn::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,
        }
    }

    /// 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 = dmn::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 = dmn::transition(
                    &self.dmn,
                    turn_result.yield_requested,
                    turn_result.had_tool_calls,
                    target == StreamTarget::Conversation,
                );
                if turn_result.dmn_pause {
                    self.dmn = dmn::State::Paused;
                    self.dmn_turns = 0;
                }
                turn_result.model_switch.clone()
            }
            Err(_) => {
                self.consecutive_errors += 1;
                self.dmn = dmn::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, dmn::State::Paused | dmn::State::Off) {
            return None;
        }

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

        let dmn_ctx = dmn::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 = dmn::State::Resting { since: Instant::now() };
    }
}

/// Background task completion events.
enum BgEvent {
    ScoringDone,
}

// --- 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,
    subconscious: Arc<tokio::sync::Mutex<Subconscious>>,
    turn_tx: mpsc::Sender<(Result<TurnResult>, StreamTarget)>,
    turn_watch: tokio::sync::watch::Sender<bool>,
    bg_tx: mpsc::UnboundedSender<BgEvent>,
    bg_rx: std::sync::Mutex<Option<mpsc::UnboundedReceiver<BgEvent>>>,
    _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.system_prompt.clone(),
            config.context_parts.clone(),
            config.app.clone(),
            config.prompt_file.clone(),
            conversation_log,
            crate::agent::tools::ActiveTools::new(),
        ).await;

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

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

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

        Self { agent, shared, config,
               subconscious, turn_tx, turn_watch, bg_tx,
               bg_rx: std::sync::Mutex::new(Some(bg_rx)), _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 = crate::store::Store::cached().await.ok();
        let store_guard = match &store {
            Some(s) => Some(s.lock().await),
            None => None,
        };
        sub.snapshots(store_guard.as_deref())
    }

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

    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);
    }

    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 => {
                    let mut s = self.shared.lock().unwrap();
                    if !s.scoring_in_flight {
                        s.scoring_in_flight = true;
                        drop(s);
                        self.start_memory_scoring();
                    }
                }
                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 = dmn::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);
                    }
                    {
                        let mut st = self.agent.state.lock().await;
                        st.conversation_log = new_log;
                        st.generation += 1;
                        st.last_prompt_tokens = 0;
                    }
                    self.agent.compact().await;
                }
            }
        }
    }

    pub fn start_memory_scoring(&self) {
        let agent = self.agent.clone();
        let bg_tx = self.bg_tx.clone();
        let scores_path = self.config.session_dir.join("memory-scores.json");
        let cfg = crate::config::get();
        let max_age = cfg.scoring_interval_secs;
        let response_window = cfg.scoring_response_window;
        tokio::spawn(async move {
            let (context, client) = {
                let mut st = agent.state.lock().await;
                if st.memory_scoring_in_flight { return; }
                st.memory_scoring_in_flight = true;
                drop(st);
                let ctx = agent.context.lock().await.clone();
                (ctx, agent.client.clone())
            };
            let _result = learn::score_memories_incremental(
                &context, max_age as i64, response_window, &client, &agent,
                |key: String, score: f64| {
                    let agent = agent.clone();
                    let path = scores_path.clone();
                    async move {
                        let scores_snapshot = {
                            let mut ctx = agent.context.lock().await;
                            for i in 0..ctx.conversation().len() {
                                if let AstNode::Leaf(leaf) = &ctx.conversation()[i] {
                                    if let NodeBody::Memory { key: k, .. } = leaf.body() {
                                        if *k == key {
                                            ctx.set_score(Section::Conversation, i, Some(score));
                                        }
                                    }
                                }
                            }
                            let snapshot = collect_memory_scores(&ctx);
                            drop(ctx);
                            agent.state.lock().await.changed.notify_one();
                            snapshot
                        };
                        save_memory_scores(&scores_snapshot, &path);
                    }
                },
            ).await;
            {
                agent.state.lock().await.memory_scoring_in_flight = false;
            }
            let _ = bg_tx.send(BgEvent::ScoringDone);
        });
    }

    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 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)>,
    ) {
        let mut bg_rx = self.bg_rx.lock().unwrap().take()
            .expect("Mind::run() called twice");
        loop {
            let timeout = self.shared.lock().unwrap().dmn.interval();
            let turn_active = self.shared.lock().unwrap().turn_active;

            let mut cmds = Vec::new();

            tokio::select! {
                biased;

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

                Some(bg) = bg_rx.recv() => {
                    match bg {
                        BgEvent::ScoringDone => {
                            self.shared.lock().unwrap().scoring_in_flight = false;
                        }
                    }
                }

                Some((result, target)) = turn_rx.recv() => {
                    self.shared.lock().unwrap().turn_handle = None;
                    let model_switch = self.shared.lock().unwrap().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);
                    }
                }

                _ = tokio::time::sleep(timeout), if !turn_active => {
                    let tick = self.shared.lock().unwrap().dmn_tick();
                    if let Some((prompt, target)) = tick {
                        self.start_turn(&prompt, target).await;
                    }
                }
            }

            // Subconscious: collect finished results, trigger due agents
            if !self.config.no_agents {
                let mut sub = self.subconscious.lock().await;
                sub.collect_results(&self.agent).await;
                sub.trigger(&self.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;
            }

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