/**
 * Bundled llama.cpp server lifecycle manager.
 * Used by main.cjs (Electron) and server.js so local AI starts whether the
 * app runs as a packaged desktop app or via `node server.js` in a browser.
 */
const { spawn, execSync } = require('child_process');
const path = require('path');
const fs = require('fs');
const http = require('http');
const net = require('net');
const os = require('os');

const DEFAULT_PORT = 8090;
const EXPECTED_MODEL_BYTES = 2019377696; // Llama 3.2 3B Instruct Q4_K_M — the stage-speed default.
// Also on disk: model-8b.gguf (Llama 3.1 8B, deeper but ~3x slower — swap filenames + update this
// constant to switch back) and model-1b-backup.gguf (original tiny model).
const STUCK_LOADING_MS = 90000; // 8B model takes longer to load than the old 1B — don't kill it prematurely
const NO_LOG_STUCK_MS = 30000;
const MAX_RESTART_ATTEMPTS = 12;
const VC_REDIST_MISSING_CODE = 3221225781; // 0xC0000135 STATUS_DLL_NOT_FOUND

let llamaServerProcess = null;
let lastError = null;
let restartTimer = null;
let restartAttempts = 0;
let processStartedAt = 0;
let lastLogAt = 0;
let readySince = 0;
let lastHealthStatus = '';
let lastLogLines = [];
let useSafeMode = false;
let useCachedModel = false;
let activePort = DEFAULT_PORT;
let phase = 'idle'; // idle | copying | starting | loading | ready | error
let modelCopyInProgress = false;
let startPromise = null;
let vcRedistInstallAttempted = false;

function isMissingRuntimeExitCode(code) {
    return code === VC_REDIST_MISSING_CODE || code === -1073741515;
}

function formatExitError(code) {
    if (isMissingRuntimeExitCode(code)) {
        return 'Missing Microsoft Visual C++ runtime (required for local AI). Installing runtime...';
    }
    return code
        ? `Local AI process exited unexpectedly (code ${code})`
        : 'Local AI process exited before becoming ready';
}

function resolveVcRedistPath() {
    const execDir = path.dirname(process.execPath || '');
    const candidates = [
        path.join(process.resourcesPath || '', 'app.asar.unpacked', 'vendor', 'redist', 'vc_redist.x64.exe'),
        path.join(execDir, 'resources', 'app.asar.unpacked', 'vendor', 'redist', 'vc_redist.x64.exe'),
        path.join(__dirname, 'vendor', 'redist', 'vc_redist.x64.exe')
    ];
    for (const candidate of candidates) {
        try {
            if (candidate && fs.existsSync(candidate)) return candidate;
        } catch (_) {}
    }
    return null;
}

function installVcRedistSync() {
    const redist = resolveVcRedistPath();
    if (!redist) {
        lastError = 'Missing Microsoft Visual C++ runtime and bundled installer not found. Reinstall Bible App.';
        return false;
    }
    try {
        pushLog('Installing Microsoft Visual C++ runtime (one-time)...');
        console.log('[LLAMA] Running VC++ redistributable:', redist);
        execSync(`"${redist}" /install /quiet /norestart`, {
            stdio: 'ignore',
            windowsHide: true,
            timeout: 180000
        });
        pushLog('Visual C++ runtime installed');
        return true;
    } catch (e) {
        lastError = 'Failed to install Microsoft Visual C++ runtime automatically. Reinstall Bible App or install "Microsoft Visual C++ 2015-2022 Redistributable (x64)".';
        pushLog(lastError);
        return false;
    }
}

function handleMissingRuntimeExit() {
    if (vcRedistInstallAttempted) {
        lastError = 'Missing Microsoft Visual C++ runtime. Reinstall Bible App or install "Microsoft Visual C++ 2015-2022 Redistributable (x64)".';
        return false;
    }
    vcRedistInstallAttempted = true;
    phase = 'starting';
    if (installVcRedistSync()) {
        restartAttempts = 0;
        useSafeMode = true;
        useCachedModel = true;
        startPromise = null;
        scheduleRestart('Installed VC++ runtime, retrying local AI');
        return true;
    }
    return false;
}

function ensureLlamaStarting() {
    if (startPromise) return startPromise;
    startPromise = startLlamaServer()
        .catch((e) => {
            startPromise = null;
            throw e;
        });
    return startPromise;
}

function getLlamaPort() {
    return activePort || parseInt(process.env.BIBLE_APP_LLAMA_PORT || String(DEFAULT_PORT), 10);
}

function pushLog(line) {
    if (!line) return;
    lastLogAt = Date.now();
    lastLogLines.push(String(line).slice(0, 500));
    if (lastLogLines.length > 40) lastLogLines.shift();
}

function resolveUserDataRoot() {
    const envDir = (process.env.BIBLE_APP_USER_DATA_DIR || process.env.BIBLE_APP_DATA_DIR || '').trim();
    if (envDir) return envDir;
    if (process.platform === 'win32') {
        return path.join(process.env.APPDATA || path.join(os.homedir(), 'AppData', 'Roaming'), 'Bible App');
    }
    if (process.platform === 'darwin') {
        return path.join(os.homedir(), 'Library', 'Application Support', 'Bible App');
    }
    return path.join(os.homedir(), '.config', 'Bible App');
}

function resolveVendorLlamaDir() {
    const execDir = path.dirname(process.execPath || '');
    const candidates = [
        path.join(process.resourcesPath || '', 'app.asar.unpacked', 'vendor', 'llama'),
        path.join(execDir, 'resources', 'app.asar.unpacked', 'vendor', 'llama'),
        path.join(__dirname, 'vendor', 'llama')
    ];
    for (const dir of candidates) {
        try {
            if (dir && fs.existsSync(path.join(dir, 'llama-server.exe'))) {
                return dir;
            }
        } catch (_) {}
    }
    return null;
}

function isPackagedInstall(vendorDir) {
    const normalized = String(vendorDir || '').toLowerCase();
    return normalized.includes('app.asar.unpacked')
        || normalized.includes('program files')
        || normalized.includes('\\resources\\');
}

function isDevInstall(vendorDir) {
    const normalized = path.resolve(vendorDir || '').toLowerCase();
    const devRoot = path.resolve(__dirname).toLowerCase();
    return normalized.startsWith(devRoot);
}

function getCachedModelPath() {
    return path.join(resolveUserDataRoot(), 'llama-cache', 'model.gguf');
}

function isCachedModelReady() {
    try {
        const cachedModel = getCachedModelPath();
        if (!fs.existsSync(cachedModel)) return false;
        const stat = fs.statSync(cachedModel);
        // Must MATCH the expected model size (±50 MB), not merely exceed it. A leftover cache from a
        // different model (e.g. the old 4.7 GB 8B build) would otherwise be treated as current and
        // silently loaded in place of the model this version ships with.
        return Math.abs(stat.size - EXPECTED_MODEL_BYTES) <= (50 * 1024 * 1024);
    } catch (_) {
        return false;
    }
}

async function copyModelToCache(sourceModel) {
    const cachedModel = getCachedModelPath();
    fs.mkdirSync(path.dirname(cachedModel), { recursive: true });
    phase = 'copying';
    pushLog('Copying AI model to local cache (one-time, about 1 GB)...');
    console.log('[LLAMA] Copying model to', cachedModel);
    // Async copy keeps the main process responsive during the ~1GB copy, so status probes
    // can actually observe the 'copying' phase (a blocking copyFileSync froze the event loop).
    await fs.promises.copyFile(sourceModel, cachedModel);
    pushLog('Model cache copy complete');
    phase = 'starting';
    return cachedModel;
}

function isProcessAlive(proc) {
    if (!proc || !proc.pid) return false;
    try {
        process.kill(proc.pid, 0);
        return true;
    } catch (_) {
        return false;
    }
}

function forceKillProcess(proc) {
    if (!proc || !proc.pid) return;
    try {
        if (process.platform === 'win32') {
            execSync(`taskkill /PID ${proc.pid} /T /F`, { stdio: 'ignore', windowsHide: true });
        } else if (isProcessAlive(proc)) {
            proc.kill('SIGKILL');
        }
    } catch (_) {
        try { if (isProcessAlive(proc)) proc.kill(); } catch (_) {}
    }
}

function isPortAvailable(port) {
    return new Promise((resolve) => {
        const tester = net.createServer()
            .once('error', () => resolve(false))
            .once('listening', () => {
                tester.close(() => resolve(true));
            })
            .listen(port, '127.0.0.1');
    });
}

async function pickPort() {
    for (let port = DEFAULT_PORT; port < DEFAULT_PORT + 12; port++) {
        // eslint-disable-next-line no-await-in-loop
        if (await isPortAvailable(port)) return port;
    }
    return DEFAULT_PORT;
}

async function resolveModelPath(vendorDir) {
    const sourceModel = path.join(vendorDir, 'model.gguf');

    // A model sitting in user data always wins: it is either one installed by hand from
    // Settings > Local AI, or the fallback copy made when reading the install folder failed.
    if (isCachedModelReady()) return getCachedModelPath();
    // Then the machine-wide copy the full one-file installer drops in ProgramData.
    if (isSharedModelReady()) return getSharedModelPath();
    // Nothing bundled and nothing cached — the caller reports "model not installed".
    if (!fs.existsSync(sourceModel)) return sourceModel;
    // The model ships INSIDE the installer now, unpacked next to llama-server.exe, so read it
    // straight from there. Duplicating ~1.9 GB into user data is only worth it as a retry fallback
    // (useCachedModel), for the rare machine where antivirus blocks reads from the install folder.
    if (!useCachedModel) return sourceModel;
    return copyModelToCache(sourceModel);
}
/*
 * Machine-wide model location, written by the full one-file installer.
 *
 * That installer runs elevated (Windows treats any unmanifested "...Setup.exe" as an installer), and
 * an elevated process can end up with a DIFFERENT user's %APPDATA% if someone types another admin's
 * credentials at the UAC prompt. ProgramData is the same path for every account and readable by all
 * of them, so the model always lands where the app will actually look for it — and no app update
 * ever touches it.
 */
function getSharedModelPath() {
    if (process.platform !== 'win32') return null;
    const base = process.env.ProgramData || process.env.ALLUSERSPROFILE || 'C:\\ProgramData';
    return path.join(base, 'Bible App', 'llama-cache', 'model.gguf');
}

function isSharedModelReady() {
    try {
        const p = getSharedModelPath();
        if (!p || !fs.existsSync(p)) return false;
        return Math.abs(fs.statSync(p).size - EXPECTED_MODEL_BYTES) <= (50 * 1024 * 1024);
    } catch (_) { return false; }
}

function getBundledModelPath() {
    try {
        const dir = resolveVendorLlamaDir();
        return dir ? path.join(dir, 'model.gguf') : null;
    } catch (_) { return null; }
}

function isBundledModelReady() {
    try {
        const p = getBundledModelPath();
        if (!p || !fs.existsSync(p)) return false;
        return Math.abs(fs.statSync(p).size - EXPECTED_MODEL_BYTES) <= (50 * 1024 * 1024);
    } catch (_) { return false; }
}

// True when neither a bundled nor a cached model exists — the app can run, the Ask tab just needs
// the model installed once from Settings.
function isModelMissing() {
    try {
        if (isCachedModelReady()) return false;
        if (isSharedModelReady()) return false;
        const dir = resolveVendorLlamaDir();
        if (dir && fs.existsSync(path.join(dir, 'model.gguf'))) return false;
        return true;
    } catch (_) { return true; }
}

/*
 * What the Ask tab will ACTUALLY load, for Settings to report honestly.
 * The model ships inside the installer, so on a normal machine it is 'bundled' and there is nothing
 * to install. Settings used to look only at the user-data copy, which is why a machine with a
 * perfectly good bundled model still announced "AI model not installed yet".
 */
function getModelInfo() {
    const sizeOf = (p) => {
        try { return p && fs.existsSync(p) ? fs.statSync(p).size : 0; } catch (_) { return 0; }
    };
    if (isCachedModelReady()) {
        const p = getCachedModelPath();
        return { ready: true, source: 'installed', path: p, sizeMB: Math.round(sizeOf(p) / 1048576) };
    }
    if (isSharedModelReady()) {
        const p = getSharedModelPath();
        return { ready: true, source: 'shared', path: p, sizeMB: Math.round(sizeOf(p) / 1048576) };
    }
    if (isBundledModelReady()) {
        const p = getBundledModelPath();
        return { ready: true, source: 'bundled', path: p, sizeMB: Math.round(sizeOf(p) / 1048576) };
    }
    // Not usable — report the biggest partial we can see so Settings can say "incomplete" vs "absent".
    const best = [getCachedModelPath(), getSharedModelPath(), getBundledModelPath()]
        .filter(Boolean)
        .reduce((a, b) => (sizeOf(a) >= sizeOf(b) ? a : b));
    return { ready: false, source: 'none', path: best, sizeMB: Math.round(sizeOf(best) / 1048576) };
}

function buildArgs(modelPath, port) {
    const cpuCount = os.cpus().length;
    const threadCount = useSafeMode
        ? Math.max(2, Math.min(4, cpuCount))
        : Math.max(2, Math.min(8, cpuCount)); // 8B model benefits from more threads; leave headroom for the app
    const args = [
        '-m', modelPath,
        '--host', '127.0.0.1',
        '--port', String(port),
        // 4096 ctx: scripture grounding + a few turns of history overflowed the old 1024/2048
        // window ("exceeds the available context size" errors mid-service). KV cache for the
        // bundled ~1B model at 4096 is modest, safe even in safe mode.
        '--ctx-size', '4096',
        '--threads', String(threadCount),
        '--threads-batch', String(threadCount),
        '--batch-size', useSafeMode ? '128' : '256',
        '--ubatch-size', useSafeMode ? '128' : '256',
        '--parallel', '1',
        '--cont-batching'
        // (no --no-warmup: warm the model at BOOT so the first live question isn't the slow one)
    ];
    if (useSafeMode) args.push('--cache-ram', '0');
    return args;
}

function killLlamaProcess(reason) {
    if (!llamaServerProcess) return;
    pushLog(reason || 'Stopping local AI process');
    console.warn('[LLAMA]', reason || 'Stopping local AI process');
    const proc = llamaServerProcess;
    llamaServerProcess = null;
    forceKillProcess(proc);
}

function scheduleRestart(reason) {
    if (restartTimer) return;
    if (restartAttempts >= MAX_RESTART_ATTEMPTS) {
        phase = 'error';
        lastError = reason || lastError || 'Local AI failed to start after multiple attempts';
        console.error('[LLAMA]', lastError);
        return;
    }
    restartAttempts += 1;
    useSafeMode = true;
    useCachedModel = true;
    phase = 'starting';
    const delayMs = Math.min(8000, 800 * restartAttempts);
    console.warn(`[LLAMA] Scheduling restart attempt ${restartAttempts}/${MAX_RESTART_ATTEMPTS} in ${delayMs}ms`);
    restartTimer = setTimeout(() => {
        restartTimer = null;
        startPromise = null;
        startLlamaServer({ isRetry: true });
    }, delayMs);
}

function checkStuckAndRecover() {
    if (modelCopyInProgress) return false;
    if (!llamaServerProcess) return false;
    if (!isProcessAlive(llamaServerProcess)) {
        llamaServerProcess = null;
        scheduleRestart('Local AI process stopped unexpectedly');
        return true;
    }
    if (readySince) return false;

    const elapsed = Date.now() - processStartedAt;
    const sinceLog = lastLogAt ? (Date.now() - lastLogAt) : elapsed;
    const stuckOnPort = elapsed >= STUCK_LOADING_MS;
    const silentTooLong = sinceLog >= NO_LOG_STUCK_MS && lastLogLines.length <= 1;

    if (!stuckOnPort && !silentTooLong) return false;

    const reason = silentTooLong
        ? `Local AI produced no output for ${Math.round(sinceLog / 1000)}s; retrying from local cache`
        : `Local AI stuck waiting for port after ${Math.round(elapsed / 1000)}s; retrying from local cache`;
    lastError = reason;
    killLlamaProcess(reason);
    scheduleRestart(reason);
    return true;
}

async function startLlamaServer(options = {}) {
    if (modelCopyInProgress) {
        return { started: false, preparing: true, phase: 'copying' };
    }
    if (llamaServerProcess && isProcessAlive(llamaServerProcess)) {
        return { started: true, alreadyRunning: true };
    }
    llamaServerProcess = null;

    try {
        const dir = resolveVendorLlamaDir();
        if (!dir) {
            phase = 'error';
            lastError = 'Bundled AI files not found (vendor/llama missing from install)';
            console.warn('[LLAMA]', lastError);
            return { started: false, error: lastError };
        }

        if (!options.isRetry) {
            restartAttempts = 0;
            // Start at FULL speed everywhere. Safe mode halves the threads, quarters the batch size
            // and turns off the prompt-cache RAM the Ask tab relies on — defaulting installed
            // machines into it made the stage PC noticeably slower than the dev box for no reason.
            // scheduleRestart() still switches it (and the cached-model copy) on if a start fails.
            useSafeMode = false;
            useCachedModel = false;
        }

        activePort = await pickPort();
        process.env.BIBLE_APP_LLAMA_PORT = String(activePort);

        const exePath = path.join(dir, 'llama-server.exe');
        let modelPath;
        try {
            modelCopyInProgress = useCachedModel && !isCachedModelReady();
            modelPath = await resolveModelPath(dir);
        } finally {
            modelCopyInProgress = false;
        }

        if (!fs.existsSync(modelPath)) {
            phase = 'error';
            lastError = 'AI model not installed yet. Open Settings > Local AI to install it (one time).';
            console.warn('[LLAMA]', lastError, modelPath);
            return { started: false, error: lastError, modelMissing: true };
        }

        const modelStat = fs.statSync(modelPath);
        if (modelStat.size < EXPECTED_MODEL_BYTES - (50 * 1024 * 1024)) {
            phase = 'error';
            lastError = `AI model file looks incomplete (${Math.round(modelStat.size / (1024 * 1024))} MB)`;
            console.warn('[LLAMA]', lastError);
            return { started: false, error: lastError };
        }

        const args = buildArgs(modelPath, activePort);
        processStartedAt = Date.now();
        lastLogAt = processStartedAt;
        readySince = 0;
        lastHealthStatus = 'starting';
        phase = 'loading';

        console.log('[LLAMA] Spawning', exePath, args.join(' '));
        pushLog(`Starting local AI on port ${activePort} (attempt ${restartAttempts + 1})`);

        llamaServerProcess = spawn(exePath, args, {
            cwd: dir,
            windowsHide: true,
            stdio: ['ignore', 'pipe', 'pipe'],
            env: {
                ...process.env,
                PATH: `${dir}${path.delimiter}${process.env.PATH || ''}`,
                LLAMA_LOG_COLORS: '0'
            }
        });

        llamaServerProcess.stdout.on('data', (chunk) => {
            chunk.toString('utf-8').split(/\r?\n/).forEach((line) => {
                const trimmed = line.trim();
                if (!trimmed) return;
                pushLog(trimmed);
                console.log('[LLAMA]', trimmed);
                if (/model loaded|server is listening/i.test(trimmed)) {
                    lastHealthStatus = 'model loaded';
                }
            });
        });
        llamaServerProcess.stderr.on('data', (chunk) => {
            chunk.toString('utf-8').split(/\r?\n/).forEach((line) => {
                const trimmed = line.trim();
                if (!trimmed) return;
                pushLog(trimmed);
                console.log('[LLAMA]', trimmed);
            });
        });
        llamaServerProcess.on('exit', (code, sig) => {
            const wasReady = !!readySince;
            llamaServerProcess = null;
            readySince = 0;
            console.log('[LLAMA] llama-server exited code=' + code + ' signal=' + sig);
            if (wasReady && (!code || code === 0)) return;
            if (isMissingRuntimeExitCode(code) && handleMissingRuntimeExit()) return;
            phase = 'error';
            lastError = formatExitError(code);
            scheduleRestart(lastError);
        });
        llamaServerProcess.on('error', (err) => {
            phase = 'error';
            lastError = `Failed to launch local AI: ${err.message}`;
            console.error('[LLAMA]', lastError);
            llamaServerProcess = null;
            scheduleRestart(lastError);
        });

        lastError = null;
        return {
            started: true,
            port: activePort,
            dir,
            modelPath,
            safeMode: useSafeMode,
            cachedModel: useCachedModel,
            phase
        };
    } catch (e) {
        phase = 'error';
        lastError = e.message || String(e);
        console.error('[LLAMA] startLlamaServer error:', lastError);
        return { started: false, error: lastError };
    }
}

function stopLlamaServer() {
    if (restartTimer) {
        clearTimeout(restartTimer);
        restartTimer = null;
    }
    killLlamaProcess('Stopping local AI on app exit');
    phase = 'idle';
}

function getLlamaStatus() {
    checkStuckAndRecover();
    const alive = isProcessAlive(llamaServerProcess);
    const loadingSeconds = processStartedAt && !readySince
        ? Math.max(0, Math.round((Date.now() - processStartedAt) / 1000))
        : 0;
    return {
        port: getLlamaPort(),
        dir: resolveVendorLlamaDir(),
        pid: alive && llamaServerProcess ? llamaServerProcess.pid : null,
        lastError,
        restartAttempts,
        loadingSeconds,
        safeMode: useSafeMode,
        cachedModel: useCachedModel,
        cacheReady: isCachedModelReady(),
        phase: modelCopyInProgress ? 'copying' : phase,
        lastHealthStatus,
        recentLog: lastLogLines.slice(-6).join(' | ')
    };
}

function probeLlamaHealth() {
    checkStuckAndRecover();
    const port = getLlamaPort();
    return new Promise((resolve) => {
        const req2 = http.request({
            hostname: '127.0.0.1',
            port,
            path: '/health',
            method: 'GET',
            timeout: 4000
        }, (r2) => {
            let body = '';
            r2.on('data', (c) => { body += c.toString('utf-8'); });
            r2.on('end', () => {
                let status = '';
                try {
                    const j = JSON.parse(body);
                    status = (j && j.status) || '';
                } catch (_) {}
                lastHealthStatus = status || `http-${r2.statusCode}`;
                const loading = status === 'loading model' || status === 'loading';
                const ready = r2.statusCode === 200 && !loading && status !== 'error';
                if (ready) {
                    readySince = Date.now();
                    phase = 'ready';
                }
                resolve({
                    ready,
                    loading: !ready && (loading || !!llamaServerProcess),
                    status: lastHealthStatus
                });
            });
        });
        req2.on('timeout', () => {
            try { req2.destroy(); } catch (_) {}
            const alive = isProcessAlive(llamaServerProcess);
            resolve({
                ready: false,
                loading: alive || modelCopyInProgress || phase === 'copying',
                status: modelCopyInProgress || phase === 'copying'
                    ? 'copying-model'
                    : (alive ? 'waiting-for-health' : 'not-running'),
                unreachable: true
            });
        });
        req2.on('error', (err) => {
            const alive = isProcessAlive(llamaServerProcess);
            resolve({
                ready: false,
                loading: alive || modelCopyInProgress || phase === 'copying',
                status: modelCopyInProgress || phase === 'copying'
                    ? 'copying-model'
                    : (alive ? 'waiting-for-port' : 'not-running'),
                unreachable: true,
                error: err.code || err.message
            });
        });
        req2.end();
    });
}

module.exports = {
    startLlamaServer,
    ensureLlamaStarting,
    stopLlamaServer,
    probeLlamaHealth,
    getLlamaStatus,
    getLlamaPort,
    resolveVendorLlamaDir,
    checkStuckAndRecover,
    // Model provisioning (bundled with the installer; can also be installed by hand)
    getCachedModelPath,
    isCachedModelReady,
    getSharedModelPath,
    isSharedModelReady,
    getBundledModelPath,
    isBundledModelReady,
    getModelInfo,
    isModelMissing,
    EXPECTED_MODEL_BYTES
};