"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.AnnotateImageParamsShape = exports.DetectObjectParamsShape = exports.AnalyseImageParamsShape = exports.FlexibleTargetsList = void 0;
exports.parsePrefixedNotation = parsePrefixedNotation;
exports.handleAnalyseImage = handleAnalyseImage;
exports.handleDetectObject = handleDetectObject;
exports.handleAnnotateImage = handleAnnotateImage;
const path_1 = __importDefault(require("path"));
const fs_1 = __importDefault(require("fs"));
const crypto_1 = require("crypto");
const url_1 = require("url");
const zod_1 = require("zod");
const core_bundle_mjs_1 = require("../core-bundle.mjs");
const readPngMetadata_js_1 = require("../helpers/readPngMetadata.js");
const toolProgress_js_1 = require("../helpers/toolProgress.js");
const config_js_1 = require("../config.js");
const drawBboxesOnImage_js_1 = require("../helpers/drawBboxesOnImage.js");
const pngMetadata_js_1 = require("../helpers/pngMetadata.js");
const VisionAnalyzer_js_1 = require("../services/VisionAnalyzer.js");
/** The LM-Studio-plugin entrypoint has no preset concept and only ever talks to LM Studio itself, defaults to "bionic" when VISION_API isn't set (MCP's applyMcpConfig() is the only writer of this env var). */
function resolveVisionApiFromEnv() {
    const raw = (process.env.LMSTUDIO_VISION_API || "").trim().toLowerCase();
    return raw === "generic" || raw === "unsloth" || raw === "llama-server" ? raw : "bionic";
}
// ─────────────────────────────────────────────────────────────────────────────
// Shared helpers (previously duplicated across src/tools/*.ts, verified identical)
// ─────────────────────────────────────────────────────────────────────────────
function parsePrefixedNotation(s) {
    const t = String(s || "").trim().toLowerCase();
    const m = t.match(/^([avip])(\d+)$/);
    if (!m)
        return null;
    const idx = Math.max(1, parseInt(m[2], 10));
    const pool = m[1] === "a" ? "attachment" : m[1] === "v" ? "variant" : m[1] === "i" ? "image" : "picture";
    return { pool, index: idx };
}
// A target's `id` is normally a short notation (e.g. "i1") but the MCP adapter may also pass a
// resolved absolute path (see made-for-bionic-core's resolveMcpSourcePathTarget, called from
// mcp/index.ts before this handler runs) — never embed it in a filename unsanitized.
function safeIdForFilename(id) {
    return path_1.default.basename(id).replace(/[^a-zA-Z0-9._-]/g, "_").slice(0, 80);
}
// A target that isn't aN/vN/iN/pN notation is a scratchpad basename or an absolute path (see
// generate-image's core/tools.ts for the same pattern) — deliberately local/non-shared. The MCP
// layer (src/mcp/index.ts) already resolves and containment-checks these against the bound
// scratchpad before this handler ever runs (made-for-bionic-core's resolveMcpSourceToken()) — by
// the time a target reaches here it is either aN/vN/iN/pN notation or an already-validated
// absolute path, so this is a plain existence check, not a security boundary.
async function resolveTargetPathToken(raw, baseDir) {
    const trimmed = raw.trim();
    const candidate = path_1.default.isAbsolute(trimmed)
        ? trimmed
        : baseDir && trimmed === path_1.default.basename(trimmed)
            ? path_1.default.join(baseDir, trimmed)
            : null;
    if (!candidate)
        return null;
    const exists = await fs_1.default.promises.stat(candidate).then((s) => s.isFile()).catch(() => false);
    return exists ? candidate : null;
}
// Normalizes a path-specified source buffer for the vision model, mirroring find-images'
// previewPayloadBytes policy: images already within drawthingsLimits.previewMaxSum (w + h) are
// returned untouched (no resize, no format change — a fitting PNG stays a PNG); only oversize
// originals are re-encoded to a JPEG preview capped at previewMaxSum.
async function normalizeVisionBuffer(buf) {
    const maxSum = core_bundle_mjs_1.drawthingsLimits.previewMaxSum;
    const { width, height } = await (0, core_bundle_mjs_1.getSize)(buf);
    if (width <= 0 || height <= 0 || width + height <= maxSum)
        return buf;
    const { data } = await (0, core_bundle_mjs_1.encodeJpegPreviewFromBuffer)(buf, {
        maxDim: maxSum,
        quality: core_bundle_mjs_1.drawthingsLimits.previewQuality,
        mode: "sum",
        maxSum,
    });
    return data;
}
function isoStampCompact() {
    const d = new Date();
    const year = d.getUTCFullYear();
    const month = String(d.getUTCMonth() + 1).padStart(2, "0");
    const day = String(d.getUTCDate()).padStart(2, "0");
    const hours = String(d.getUTCHours()).padStart(2, "0");
    const minutes = String(d.getUTCMinutes()).padStart(2, "0");
    const seconds = String(d.getUTCSeconds()).padStart(2, "0");
    const millis = String(d.getUTCMilliseconds()).padStart(3, "0");
    return `${year}${month}${day}T${hours}${minutes}${seconds}${millis}Z`;
}
function getGlobalConfig(ctl) {
    const ctlAny = ctl;
    const getter = ctlAny.getGlobalPluginConfig || ctlAny.getGlobalConfig;
    if (!getter)
        return null;
    try {
        return getter.call(ctl, config_js_1.globalConfigSchematics);
    }
    catch {
        return null;
    }
}
function getGlobalString(gcfg, key, fallback) {
    try {
        const value = gcfg?.get(key);
        return typeof value === "string" ? value : fallback;
    }
    catch {
        return fallback;
    }
}
function getGlobalNumber(gcfg, key, fallback) {
    try {
        const value = gcfg?.get(key);
        return typeof value === "number" && Number.isFinite(value) ? value : fallback;
    }
    catch {
        return fallback;
    }
}
function getGlobalBoolean(gcfg, key, fallback) {
    try {
        const value = gcfg?.get(key);
        return typeof value === "boolean" ? value : fallback;
    }
    catch {
        return fallback;
    }
}
/** Shared by all 3 ensureVisionModelReady() call sites -- only consumed when VISION_API="llama-server" (see ensureLlamaServerVisionModelReady). GUI setting > MCP env var > defaultPluginSettings, matching every other config field in this file. */
function resolveLlamaServerConfig(globalConfig) {
    const envPort = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_PORT || "", 10);
    const envCtxSize = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_CTX_SIZE || "", 10);
    const envModelsMax = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_MODELS_MAX || "", 10);
    const envIdleTtlMinutes = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_IDLE_TTL_MINUTES || "", 10);
    return {
        port: Math.floor(getGlobalNumber(globalConfig, "llamaServerPort", Number.isFinite(envPort) && envPort > 0 ? envPort : config_js_1.defaultPluginSettings.llamaServerPort)),
        binaryPath: getGlobalString(globalConfig, "llamaServerBinary", process.env.LMSTUDIO_LLAMA_SERVER_BINARY || config_js_1.defaultPluginSettings.llamaServerBinary),
        ctxSize: Math.floor(getGlobalNumber(globalConfig, "llamaServerCtxSize", Number.isFinite(envCtxSize) && envCtxSize > 0 ? envCtxSize : config_js_1.defaultPluginSettings.llamaServerCtxSize)),
        modelsMax: Math.floor(getGlobalNumber(globalConfig, "llamaServerModelsMax", Number.isFinite(envModelsMax) && envModelsMax > 0 ? envModelsMax : config_js_1.defaultPluginSettings.llamaServerModelsMax)),
        idleTtlMinutes: Math.floor(getGlobalNumber(globalConfig, "llamaServerIdleTtlMinutes", Number.isFinite(envIdleTtlMinutes) && envIdleTtlMinutes >= 0 ? envIdleTtlMinutes : config_js_1.defaultPluginSettings.llamaServerIdleTtlMinutes)),
    };
}
/**
 * Flexible targets schema: accepts array OR comma/space-separated string.
 * Reduces parse errors when models output "a1, v2" instead of ["a1", "v2"].
 */
exports.FlexibleTargetsList = zod_1.z
    .union([
    zod_1.z.string().transform((s) => (s.match(/[aivp]\d+/gi) ?? []).map((x) => x.toLowerCase())),
    zod_1.z.array(zod_1.z.string()),
])
    .refine((arr) => arr.length >= 1, "targets must contain at least one notation")
    .refine((arr) => arr.length <= 16, "targets must contain at most 16 notations");
// ─────────────────────────────────────────────────────────────────────────────
// analyse_image
// ─────────────────────────────────────────────────────────────────────────────
exports.AnalyseImageParamsShape = {
    targets: exports.FlexibleTargetsList.describe("One or more image notations to process. Each notation is a letter followed by a number: " +
        "a=attachment (a1, a2, …), i=generated image (i1, i2, …), v=variant (v1, v2, …), p=picture (p1, p2, …). " +
        "Also accepts a filename in the scratchpad folder or an absolute path. Pass as a JSON array, e.g. [\"a1\", \"i3\"]."),
    prompt: zod_1.z.string().optional().describe("Optional prompt for the vision model. Empty = model default."),
};
function parseTargets(targets) {
    const parsed = { a: [], v: [], i: [], p: [] };
    const invalid = [];
    for (const raw of targets) {
        const s = typeof raw === "string" ? raw.trim() : "";
        const m = /^([avip])(\d+)$/i.exec(s);
        if (!m) {
            invalid.push(String(raw));
            continue;
        }
        const kind = m[1].toLowerCase();
        const n = parseInt(m[2], 10);
        if (!Number.isFinite(n) || n <= 0) {
            invalid.push(String(raw));
            continue;
        }
        parsed[kind].push(n);
    }
    // Unique + sort for stable behavior
    Object.keys(parsed).forEach((k) => {
        parsed[k] = Array.from(new Set(parsed[k])).sort((a, b) => a - b);
    });
    return { parsed, invalid };
}
function getAvailable(state) {
    const availableA = (state.attachments || [])
        .map((x) => (typeof x?.a === "number" ? x.a : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    const availableV = (state.variants || [])
        .map((x) => (typeof x?.v === "number" ? x.v : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    const availableI = (state.images || [])
        .map((x) => (typeof x?.i === "number" ? x.i : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    const availableP = (state.pictures || [])
        .map((x) => (typeof x?.p === "number" ? x.p : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    return { availableA, availableV, availableI, availableP };
}
async function ensurePreviewExists(chatWd, previewRel) {
    const pAbs = path_1.default.join(chatWd, previewRel);
    await fs_1.default.promises.access(pAbs, fs_1.default.constants.F_OK);
}
function classifyVisionError(errMsg) {
    if (/\b503\b/.test(errMsg)) {
        return "The Vision API is reachable, but the configured vision model is not available for inference. Check the configured vision model key and loaded model state.";
    }
    if (/aborted|aborterror|timed out|timeout/i.test(errMsg)) {
        return "The Vision API request timed out before the model returned.";
    }
    if (/ECONNREFUSED|ENOTFOUND|ECONNRESET|network socket|fetch failed/i.test(errMsg)) {
        return "The Vision API is not reachable. Check the configured vision/embedding API base URL and try again.";
    }
    return /Vision API/i.test(errMsg) ? errMsg : `Vision API error: ${errMsg}`;
}
function analyzeTimeoutMs(itemCount) {
    return Math.min(600_000, Math.max(180_000, itemCount * 60_000));
}
async function handleAnalyseImage(args, ctl, statusCtx = {}, requestId, onItem) {
    const visionTmpPaths = [];
    const visionTmpIds = new Set();
    try {
        // Tolerant mode: drop invalid items instead of failing
        const strict = false;
        let targets;
        if (Array.isArray(args?.targets)) {
            targets = args.targets;
        }
        else if (typeof args?.targets === "string" && args.targets.trim()) {
            const trimmed = args.targets.trim();
            if (trimmed.startsWith("[")) {
                try {
                    const parsed = JSON.parse(trimmed);
                    targets = Array.isArray(parsed) ? parsed.map((s) => String(s).trim()).filter(Boolean) : [];
                }
                catch {
                    targets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
            }
            else {
                // A bare notation or comma/space-separated list (e.g. "i1" or "i1, i2") — the same
                // fallback detect_object/annotate_image's own string parsing already has. The MCP path
                // never re-runs this handler's args through FlexibleTargetsList's zod transform (only the
                // LM-Studio plugin path does, via @lmstudio/sdk's tool() wrapper), so this handler's own
                // string parsing must accept the same shapes on its own.
                targets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
            }
        }
        else {
            targets = [];
        }
        const prompt = typeof args?.prompt === "string" ? args.prompt : "";
        const globalConfig = getGlobalConfig(ctl);
        const configuredVisionPrompt = getGlobalString(globalConfig, "visionPrompt", process.env.VISION_PROMPT || config_js_1.defaultPluginSettings.visionPrompt);
        const effectivePrompt = (prompt || configuredVisionPrompt || "").trim();
        // Parse explicit notations (aN/vN/iN/pN)
        const { parsed, invalid: invalidRaw } = parseTargets(targets);
        // LM-Studio plugin path: ctl.getWorkingDirectory() (LM-Studio SDK method, only present when
        // ctl is a real ToolsProviderController). MCP path: ctl is `{}` and has no such method — fall
        // back to the same chat-context-bridge resolution detect_object/annotate_image already use
        // (bindChatContextToScratchpad() sets this before the MCP adapter calls this handler).
        // currentLmChatId is resolved unconditionally (also used for this call's audit log entry).
        let currentLmChatId = null;
        let boundWorkingDir = null;
        try {
            const chatCtx = await (0, core_bundle_mjs_1.getActiveChatContext)();
            if (chatCtx?.chatId)
                currentLmChatId = chatCtx.chatId;
            if (chatCtx?.workingDir)
                boundWorkingDir = chatCtx.workingDir;
        }
        catch { }
        if (!currentLmChatId) {
            try {
                const resolved = await (0, core_bundle_mjs_1.resolveActiveLMStudioChatId)();
                if (resolved?.ok)
                    currentLmChatId = resolved.chatId;
            }
            catch { }
        }
        let workingDir;
        try {
            workingDir = ctl?.getWorkingDirectory?.();
        }
        catch {
            workingDir = undefined;
        }
        if (typeof workingDir !== "string" || !workingDir.trim()) {
            workingDir = boundWorkingDir || (currentLmChatId ? (0, core_bundle_mjs_1.getLMStudioWorkingDir)(currentLmChatId) : undefined);
        }
        if (typeof workingDir !== "string" || !workingDir.trim()) {
            return "analyse_image failed: working directory not available.";
        }
        const chatWd = workingDir;
        // Sync attachments from conversation.json → chat_media_state.json.
        // maxPreviewAttachments=MAX: importAttachmentBatch generates previews for all attachments
        // and writes them to state atomically — the canonical path via draw-things-chat-core.
        try {
            await (0, core_bundle_mjs_1.syncAttachmentsToState)(chatWd, false, Number.MAX_SAFE_INTEGER);
        }
        catch (syncErr) {
            console.warn("[analyse_image] attachment sync failed (non-fatal):", syncErr?.message ?? syncErr);
        }
        const state = await (0, core_bundle_mjs_1.readState)(chatWd);
        const { availableA, availableV, availableI, availableP } = getAvailable(state);
        // If any invalid notations and strict=true, reject
        if (invalidRaw.length > 0 && strict) {
            return `analyse_image failed: invalid targets: ${invalidRaw
                .map((s) => JSON.stringify(s))
                .join(", ")}`;
        }
        // Collect analysis items (id + preview file path)
        const analysisItems = [];
        // Map from notation id → absolute path of original file (for XMP reading)
        const originalFilePaths = new Map();
        // Map from notation id → human-readable filename for the result
        const displayNames = new Map();
        const missingNotations = new Set();
        const missingDetails = [];
        // Helper: resolve & validate preview for a notation
        const addItem = async (notation, rec, previewField, originalAbsPath, displayName) => {
            if (!rec) {
                missingNotations.add(notation);
                missingDetails.push(notation);
                return false;
            }
            const previewRel = typeof rec[previewField] === "string" ? String(rec[previewField]) : "";
            if (!previewRel.trim()) {
                missingNotations.add(notation);
                missingDetails.push(`${notation} (missing preview)`);
                return false;
            }
            try {
                await ensurePreviewExists(chatWd, previewRel);
                analysisItems.push({
                    id: notation,
                    filePath: path_1.default.join(chatWd, previewRel),
                });
                if (originalAbsPath) {
                    originalFilePaths.set(notation, originalAbsPath);
                }
                const dn = displayName || (originalAbsPath ? path_1.default.basename(originalAbsPath) : undefined);
                if (dn) {
                    displayNames.set(notation, dn);
                }
                return true;
            }
            catch {
                missingNotations.add(notation);
                missingDetails.push(`${notation} (preview file missing)`);
                return false;
            }
        };
        // Process attachments (aN)
        // Preview generation + state write already handled by syncAttachmentsToState above.
        for (const n of parsed.a) {
            const rec = (state.attachments || []).find((x) => x?.a === n);
            const origAbs = rec?.originAbs ?? (rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined);
            // originalName holds the real user-visible filename (e.g. "Katze.png")
            const origName = typeof rec?.originalName === "string" && rec.originalName ? rec.originalName : undefined;
            await addItem(`a${n}`, rec, "preview", origAbs, origName);
        }
        // Process variants (vN)
        for (const n of parsed.v) {
            const rec = (state.variants || []).find((x) => x?.v === n);
            const origAbs = rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined;
            await addItem(`v${n}`, rec, "preview", origAbs);
        }
        // Process images (iN)
        for (const n of parsed.i) {
            const rec = (state.images || []).find((x) => x?.i === n);
            const origAbs = rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined;
            await addItem(`i${n}`, rec, "preview", origAbs);
        }
        // Process pictures (pN)
        for (const n of parsed.p) {
            const rec = (state.pictures || []).find((x) => x?.p === n);
            const origAbs = rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined;
            await addItem(`p${n}`, rec, "preview", origAbs);
        }
        // Entries that didn't match aN/vN/iN/pN — a scratchpad filename or an absolute path is
        // also accepted as a target, same as detect_object/annotate_image/every other Bionic MCP tool.
        for (const raw of invalidRaw) {
            const pathToken = await resolveTargetPathToken(raw, chatWd);
            if (pathToken) {
                let visionPath = pathToken;
                if (effectivePrompt) {
                    const buf = await fs_1.default.promises.readFile(pathToken);
                    const norm = await normalizeVisionBuffer(buf);
                    if (norm !== buf) {
                        const tmpPath = path_1.default.join(chatWd, `_tmp_analyse_src_${safeIdForFilename(raw)}_${Date.now()}.png`);
                        await fs_1.default.promises.writeFile(tmpPath, norm);
                        visionTmpPaths.push(tmpPath);
                        visionTmpIds.add(raw);
                        visionPath = tmpPath;
                    }
                }
                analysisItems.push({ id: raw, filePath: visionPath });
                originalFilePaths.set(raw, pathToken);
                displayNames.set(raw, path_1.default.basename(pathToken));
            }
        }
        // If strict, fail on any missing items
        if (missingDetails.length > 0 && strict) {
            const hint = `Available: ` +
                `a=[${availableA.map((x) => `a${x}`).join(", ") || "(none)"}] ` +
                `v=[${availableV.map((x) => `v${x}`).join(", ") || "(none)"}] ` +
                `i=[${availableI.map((x) => `i${x}`).join(", ") || "(none)"}] ` +
                `p=[${availableP.map((x) => `p${x}`).join(", ") || "(none)"}]`;
            return `analyse_image failed: unknown/invalid targets: ${missingDetails.join(", ")}. ${hint}`;
        }
        // If no valid items after filtering, return early
        if (analysisItems.length === 0) {
            const hint = `Available: ` +
                `a=[${availableA.map((x) => `a${x}`).join(", ") || "(none)"}] ` +
                `v=[${availableV.map((x) => `v${x}`).join(", ") || "(none)"}] ` +
                `i=[${availableI.map((x) => `i${x}`).join(", ") || "(none)"}] ` +
                `p=[${availableP.map((x) => `p${x}`).join(", ") || "(none)"}]`;
            return `analyse_image: no valid targets found. ${hint}`;
        }
        let visionError = null;
        const visionResults = new Map();
        let totalInferenceTimeMs = null;
        if (effectivePrompt) {
            const envServerMaxTokens = Number.parseInt(process.env.SERVER_MAX_TOKENS || "", 10);
            const envServerTemperature = Number.parseFloat(process.env.SERVER_TEMPERATURE || "");
            const configuredMaxTokens = Math.floor(getGlobalNumber(globalConfig, "serverMaxTokens", Number.isFinite(envServerMaxTokens) && envServerMaxTokens > 0 ? envServerMaxTokens : config_js_1.defaultPluginSettings.serverMaxTokens));
            const configuredTemperature = getGlobalNumber(globalConfig, "serverTemperature", Number.isFinite(envServerTemperature) ? envServerTemperature : config_js_1.defaultPluginSettings.serverTemperature);
            const lmStudioConfig = {
                baseUrl: getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || config_js_1.defaultPluginSettings.embeddingBaseUrl),
                apiKey: getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || config_js_1.defaultPluginSettings.embeddingApiKey),
                model: getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || config_js_1.defaultPluginSettings.qwen3VlModelPath),
                prompt: effectivePrompt,
                maxTokens: configuredMaxTokens,
                temperature: configuredTemperature,
                timeoutMs: analyzeTimeoutMs(1),
            };
            try {
                const totalSteps = analysisItems.length + 2;
                (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Analyzing ${analysisItems.length} image${analysisItems.length === 1 ? "" : "s"}...`);
                (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, totalSteps, `Preparing ${analysisItems.length} image${analysisItems.length === 1 ? "" : "s"} for visual analysis...`);
                const visionApi = resolveVisionApiFromEnv();
                const ready = await (0, VisionAnalyzer_js_1.ensureVisionModelReady)(visionApi, {
                    baseUrl: lmStudioConfig.baseUrl,
                    apiKey: lmStudioConfig.apiKey,
                    modelKey: lmStudioConfig.model || "",
                    status: (message) => { try {
                        statusCtx.status?.(message);
                    }
                    catch { } },
                    llamaServer: visionApi === "llama-server" ? resolveLlamaServerConfig(globalConfig) : undefined,
                });
                if (!ready.ok) {
                    throw new Error(ready.error);
                }
                // llama-server strand: router's own exact tagged model id/address, not the configured ones.
                if (ready.resolvedBaseUrl)
                    lmStudioConfig.baseUrl = ready.resolvedBaseUrl;
                if (ready.resolvedModelKey)
                    lmStudioConfig.model = ready.resolvedModelKey;
                let totalMs = 0;
                for (let idx = 0; idx < analysisItems.length; idx++) {
                    const item = analysisItems[idx];
                    (0, toolProgress_js_1.reportToolStep)(statusCtx, idx + 2, totalSteps, `Analyzing ${item.id} (${idx + 1}/${analysisItems.length})...`);
                    const batchResult = await (0, VisionAnalyzer_js_1.analyzeLmStudioVisionBatch)([item], lmStudioConfig);
                    for (const r of batchResult.results) {
                        visionResults.set(r.id, r.text.trim() || "(no description)");
                    }
                    totalMs += batchResult.totalInferenceTimeMs;
                }
                totalInferenceTimeMs = totalMs;
                (0, toolProgress_js_1.reportToolStep)(statusCtx, totalSteps, totalSteps, "Formatting analysis results...");
            }
            catch (e) {
                visionError = classifyVisionError(e.message || String(e));
            }
        }
        const includeGenMeta = process.env.INCLUDE_GENERATION_METADATA !== "false";
        // Build structured result
        const lines = [];
        lines.push(`Analysis results (${analysisItems.length} image${analysisItems.length !== 1 ? "s" : ""}):`);
        lines.push("");
        if (visionError) {
            lines.push(`Note: Visual analysis unavailable — ${visionError}`);
            lines.push("");
        }
        for (const item of analysisItems) {
            const { id } = item;
            const displayName = displayNames.get(id);
            const origPath = originalFilePaths.get(id);
            const header = displayName ? `${id} — ${displayName}` : id;
            lines.push(`- ${header}`);
            let visual;
            if (effectivePrompt) {
                visual = visionError ? "(not available)" : (visionResults.get(id) ?? "(no description)");
                lines.push(`  VISUAL: ${visual}`);
            }
            let metaText;
            let meta;
            let metaNote;
            if (includeGenMeta) {
                if (origPath && origPath.toLowerCase().endsWith(".png")) {
                    meta = (0, readPngMetadata_js_1.readPngGenerationMeta)(origPath) ?? undefined;
                    if (meta) {
                        metaText = (0, readPngMetadata_js_1.formatGenerationMeta)(meta);
                    }
                    else {
                        metaText = `  (No embedded generation metadata)`;
                        metaNote = "(No embedded generation metadata)";
                    }
                }
                else if (origPath && /\.jpe?g$/i.test(origPath)) {
                    const metadata = await (0, core_bundle_mjs_1.readCameraImageMetadata)(origPath);
                    metaText = Object.keys(metadata).length > 0 ? `  EXIF JSON: ${JSON.stringify(metadata)}` : `  (No embedded EXIF metadata)`;
                    metaNote = Object.keys(metadata).length > 0 ? `EXIF JSON: ${JSON.stringify(metadata)}` : "(No embedded EXIF metadata)";
                }
                else if (origPath) {
                    metaText = `  (No embedded generation metadata — not a PNG file)`;
                    metaNote = "(No embedded generation metadata — not a PNG file)";
                }
                if (metaText)
                    lines.push(metaText);
            }
            lines.push("");
            onItem?.({ id, filePath: visionTmpIds.has(id) ? (origPath ?? item.filePath) : item.filePath, displayName, visual, meta, metaNote });
        }
        if (totalInferenceTimeMs !== null) {
            lines.push(`Total inference time: ${Math.round(totalInferenceTimeMs)}ms`);
        }
        // Status update: done
        try {
            const statusSuffix = visionError ? " (vision unavailable)" : " successfully";
            statusCtx.status?.(`Analyzed ${analysisItems.length} image${analysisItems.length !== 1 ? "s" : ""}${statusSuffix}`);
        }
        catch {
            // best-effort
        }
        // Audit log — requestId is reused as-is by the MCP adapter for its HTML preview filename.
        try {
            const audit = (0, core_bundle_mjs_1.buildAuditLogger)({ backend: "analyse_image", mode: "analyse_image", requestId });
            if (currentLmChatId)
                audit.setChatId(currentLmChatId);
            audit.setUserRequest({ targets, prompt: effectivePrompt });
            audit.setOutput({ items: analysisItems.map((item) => item.id) });
            await audit.write();
        }
        catch (e) {
            console.warn("[analyse_image] audit logging failed:", String(e));
        }
        return lines.join("\n");
    }
    catch (e) {
        return `analyse_image failed: ${String(e?.message || e)}`;
    }
    finally {
        for (const tp of visionTmpPaths)
            await fs_1.default.promises.unlink(tp).catch(() => { });
    }
}
// ─────────────────────────────────────────────────────────────────────────────
// detect_object
// ─────────────────────────────────────────────────────────────────────────────
exports.DetectObjectParamsShape = {
    targets: exports.FlexibleTargetsList.optional().describe("One or more image notations to process. Each notation is a letter followed by a number: " +
        "a=attachment (a1, a2, …), i=generated image (i1, i2, …), v=variant (v1, v2, …), p=picture (p1, p2, …). " +
        "Also accepts a filename in the scratchpad folder or an absolute path. " +
        "Pass as a JSON array, e.g. [\"a1\", \"i3\"]. " +
        "Omit when there is exactly one image — it will be selected automatically."),
    task: zod_1.z
        .string()
        .optional()
        .default("")
        .describe("What to detect. Omit for full-image general object detection. " +
        "Use natural language to target specific subjects (e.g. 'all faces and hands', 'the dog', 'cars and bicycles')."),
};
async function handleDetectObject(args, ctl, statusCtx = {}, requestId) {
    try {
        // Parse targets: accept array or comma/space-separated string (analogous to analyse_image)
        let rawTargets = [];
        if (Array.isArray(args?.targets)) {
            rawTargets = args.targets.map((s) => String(s).trim()).filter(Boolean);
        }
        else if (typeof args?.targets === "string" && args.targets.trim()) {
            const trimmed = args.targets.trim();
            if (trimmed.startsWith("[")) {
                try {
                    const parsed = JSON.parse(trimmed);
                    if (Array.isArray(parsed)) {
                        rawTargets = parsed.map((s) => String(s).trim()).filter(Boolean);
                    }
                    else {
                        rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                    }
                }
                catch {
                    rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
            }
            else {
                rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
            }
        }
        const task = typeof args?.task === "string" && args.task.trim() ? args.task.trim() : "";
        console.log("[detect_object] invoked", { targets: rawTargets, task });
        let currentLmChatId = null;
        let currentLmWorkingDir = null;
        try {
            const chatCtx = await (0, core_bundle_mjs_1.getActiveChatContext)();
            if (chatCtx?.chatId)
                currentLmChatId = chatCtx.chatId;
            if (chatCtx?.workingDir)
                currentLmWorkingDir = chatCtx.workingDir;
        }
        catch { }
        if (!currentLmChatId) {
            try {
                const resolved = await (0, core_bundle_mjs_1.resolveActiveLMStudioChatId)();
                if (resolved?.ok)
                    currentLmChatId = resolved.chatId;
            }
            catch { }
        }
        const primaryOutDir = currentLmWorkingDir ||
            (currentLmChatId ? (0, core_bundle_mjs_1.getLMStudioWorkingDir)(currentLmChatId) : undefined);
        if (!primaryOutDir) {
            console.error("[detect_object] could not resolve working directory");
            return {
                content: [
                    {
                        type: "text",
                        text: "detect_object failed: could not resolve LM Studio chat working directory.",
                    },
                ],
                isError: true,
            };
        }
        console.log("[detect_object] primaryOutDir:", primaryOutDir);
        await fs_1.default.promises.mkdir(primaryOutDir, { recursive: true }).catch(() => { });
        // Sync attachments so state is up to date
        console.log("[detect_object] syncing attachments...");
        try {
            await (0, core_bundle_mjs_1.syncAttachmentsToState)(primaryOutDir, false, Number.MAX_SAFE_INTEGER);
        }
        catch (e) {
            console.warn("[detect_object] attachment sync failed (non-fatal):", e?.message ?? e);
        }
        console.log("[detect_object] attachment sync done");
        console.log("[detect_object] reading state...");
        const st = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
        const attachments = Array.isArray(st?.attachments) ? st.attachments : [];
        const pictures = Array.isArray(st?.pictures) ? st.pictures : [];
        const imageRecords = Array.isArray(st?.images) ? st.images : [];
        const images = imageRecords
            .filter((r) => r && typeof r.filename === "string")
            .sort((a, b) => (a.i || 0) - (b.i || 0))
            .map((r) => ({ i: r.i || 1, path: path_1.default.join(primaryOutDir, r.filename) }));
        const variantRecords = Array.isArray(st?.variants) ? st.variants : [];
        const variants = variantRecords
            .filter((v) => v && typeof v.filename === "string")
            .map((v) => ({ v: v.v || 1, path: path_1.default.join(primaryOutDir, v.filename) }));
        console.log("[detect_object] state:", {
            attachments: attachments.length,
            images: images.length,
            variants: variants.length,
            pictures: pictures.length,
        });
        const sourceEntries = [];
        async function resolveOneBuf(rawCanvas) {
            const pref = parsePrefixedNotation(rawCanvas);
            if (!pref) {
                const pathToken = await resolveTargetPathToken(rawCanvas, primaryOutDir);
                if (!pathToken)
                    throw new Error(`Invalid canvas notation: ${rawCanvas}`);
                return fs_1.default.promises.readFile(pathToken);
            }
            if (pref.pool === "attachment") {
                const rec = attachments.find((a) => a?.a === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for attachment a${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
            else if (pref.pool === "image") {
                const rec = imageRecords.find((r) => r?.i === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for image i${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
            else if (pref.pool === "variant") {
                const rec = variantRecords.find((v) => v?.v === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for variant v${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
            else {
                const rec = pictures.find((p) => p?.p === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for picture p${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
        }
        // Resolves the original (full-resolution) file for a canvas notation.
        // Falls back to previewFallback when the original is unavailable.
        async function resolveOriginalBuf(rawCanvas, previewFallback) {
            try {
                const pref = parsePrefixedNotation(rawCanvas);
                if (!pref) {
                    const pathToken = await resolveTargetPathToken(rawCanvas, primaryOutDir);
                    return pathToken ? await fs_1.default.promises.readFile(pathToken) : previewFallback;
                }
                if (pref.pool === "attachment") {
                    const rec = attachments.find((a) => a?.a === pref.index);
                    const originAbs = rec && typeof rec.originAbs === "string" ? rec.originAbs : "";
                    if (!originAbs)
                        return previewFallback;
                    return await fs_1.default.promises.readFile(originAbs);
                }
                let rec;
                if (pref.pool === "image")
                    rec = imageRecords.find((r) => r?.i === pref.index);
                else if (pref.pool === "variant")
                    rec = variantRecords.find((v) => v?.v === pref.index);
                else
                    rec = pictures.find((p) => p?.p === pref.index);
                const filename = rec && typeof rec.filename === "string" ? rec.filename : "";
                if (!filename)
                    return previewFallback;
                return await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, filename));
            }
            catch {
                return previewFallback;
            }
        }
        try {
            if (rawTargets.length > 0) {
                for (const t of rawTargets) {
                    const buf = await resolveOneBuf(t);
                    const origBuf = await resolveOriginalBuf(t, buf);
                    sourceEntries.push({ id: t, buf, origBuf });
                }
            }
            else {
                // Auto-select single source
                const total = attachments.length + variantRecords.length + imageRecords.length + pictures.length;
                if (total === 0)
                    throw new Error("No source image available.");
                if (total > 1)
                    throw new Error("Ambiguous source — specify targets explicitly.");
                let buf;
                let id;
                if (attachments.length === 1) {
                    const rec = attachments[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for attachment not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `a${typeof rec.a === "number" ? rec.a : 1}`;
                }
                else if (variantRecords.length === 1) {
                    const rec = variantRecords[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for variant not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `v${typeof rec.v === "number" ? rec.v : 1}`;
                }
                else if (imageRecords.length === 1) {
                    const rec = imageRecords[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for image not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `i${typeof rec.i === "number" ? rec.i : 1}`;
                }
                else {
                    const rec = pictures[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for picture not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `p${rec.p ?? 1}`;
                }
                const origBuf = await resolveOriginalBuf(id, buf);
                sourceEntries.push({ id, buf, origBuf });
            }
        }
        catch (e) {
            return {
                content: [{ type: "text", text: String(e?.message || e) }],
                isError: true,
            };
        }
        console.log("[detect_object] sources resolved:", sourceEntries.map((s) => s.id));
        const globalConfig = getGlobalConfig(ctl);
        const envEmbedPngMetadata = process.env.EMBED_PNG_METADATA;
        const embedPngMetadata = getGlobalBoolean(globalConfig, "embedPngMetadata", envEmbedPngMetadata === undefined ? config_js_1.defaultPluginSettings.embedPngMetadata : envEmbedPngMetadata !== "false");
        let visionBaseUrl = getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || config_js_1.defaultPluginSettings.embeddingBaseUrl);
        const visionApiKey = getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || config_js_1.defaultPluginSettings.embeddingApiKey);
        let visionModelKey = getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || config_js_1.defaultPluginSettings.qwen3VlModelPath);
        const envDetectMaxTokens = Number.parseInt(process.env.DETECT_MAX_TOKENS || "", 10);
        const envDetectTemperature = Number.parseFloat(process.env.DETECT_TEMPERATURE || "");
        const configuredDetectMaxTokens = Math.floor(getGlobalNumber(globalConfig, "detectMaxTokens", Number.isFinite(envDetectMaxTokens) && envDetectMaxTokens > 0 ? envDetectMaxTokens : config_js_1.defaultPluginSettings.detectMaxTokens));
        const configuredDetectTemperature = getGlobalNumber(globalConfig, "detectTemperature", Number.isFinite(envDetectTemperature) ? envDetectTemperature : config_js_1.defaultPluginSettings.detectTemperature);
        const detectionConfig = {
            task,
            odPrompt: getGlobalString(globalConfig, "qwen3VlOdPrompt", process.env.DETECT_OD_PROMPT || config_js_1.defaultPluginSettings.qwen3VlOdPrompt) || undefined,
            maxTokens: configuredDetectMaxTokens,
            temperature: configuredDetectTemperature,
            timeoutMs: 120_000,
        };
        const tmpPaths = [];
        const detectionItems = [];
        for (const entry of sourceEntries) {
            const visionBuf = await normalizeVisionBuffer(entry.buf);
            const tmpPath = path_1.default.join(primaryOutDir, `_tmp_detect_src_${safeIdForFilename(entry.id)}_${Date.now()}.png`);
            await fs_1.default.promises.writeFile(tmpPath, visionBuf);
            tmpPaths.push(tmpPath);
            detectionItems.push({ id: entry.id, filePath: tmpPath });
        }
        console.log("[detect_object] calling detection API for", detectionItems.length, "items");
        const progressTotalSteps = sourceEntries.length * 2 + 4;
        let batchResult = {
            results: [],
            totalInferenceTimeMs: 0,
            backend: "vision-api",
        };
        try {
            (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Detecting objects in ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"}...`);
            (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, progressTotalSteps, `Preparing ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"} for object detection...`);
            const visionApi = resolveVisionApiFromEnv();
            const ready = await (0, VisionAnalyzer_js_1.ensureVisionModelReady)(visionApi, {
                baseUrl: visionBaseUrl,
                apiKey: visionApiKey,
                modelKey: visionModelKey,
                status: (message) => { try {
                    statusCtx.status?.(message);
                }
                catch { } },
                llamaServer: visionApi === "llama-server" ? resolveLlamaServerConfig(globalConfig) : undefined,
            });
            if (!ready.ok) {
                throw new Error(ready.error);
            }
            // llama-server strand: router's own exact tagged model id/address, not the configured ones.
            if (ready.resolvedBaseUrl)
                visionBaseUrl = ready.resolvedBaseUrl;
            if (ready.resolvedModelKey)
                visionModelKey = ready.resolvedModelKey;
            for (let idx = 0; idx < detectionItems.length; idx++) {
                const item = detectionItems[idx];
                (0, toolProgress_js_1.reportToolStep)(statusCtx, idx + 2, progressTotalSteps, `Detecting objects in ${item.id} (${idx + 1}/${detectionItems.length})...`);
                const singleResult = await (0, VisionAnalyzer_js_1.detectLmStudioVisionBatch)([item], {
                    ...detectionConfig,
                    baseUrl: visionBaseUrl,
                    apiKey: visionApiKey,
                    model: visionModelKey,
                });
                batchResult.results.push(...singleResult.results);
                batchResult.totalInferenceTimeMs += singleResult.totalInferenceTimeMs;
                batchResult.backend = singleResult.backend;
            }
            console.log("[detect_object] detection API returned:", {
                results: batchResult.results.length,
                totalMs: batchResult.totalInferenceTimeMs,
            });
            try {
                const totalObjects = batchResult.results.reduce((s, r) => s + (r.objects?.length ?? 0), 0);
                const ms = Math.round(batchResult.totalInferenceTimeMs);
                (0, toolProgress_js_1.reportToolStep)(statusCtx, sourceEntries.length + 2, progressTotalSteps, `${totalObjects} object${totalObjects === 1 ? "" : "s"} found across ${batchResult.results.length} image${batchResult.results.length === 1 ? "" : "s"} (${ms}ms); drawing bounding boxes...`);
            }
            catch { }
        }
        finally {
            for (const tp of tmpPaths)
                await fs_1.default.promises.unlink(tp).catch(() => { });
        }
        if (!batchResult.results.length) {
            return {
                content: [{ type: "text", text: "detect_object: no results returned from detection API." }],
                isError: true,
            };
        }
        // Per-image: draw bboxes, save, generate preview, build state records
        const variantPreviewSpec = core_bundle_mjs_1.VARIANT_FULL_CONFIG.preview;
        const stamp = isoStampCompact();
        let nextI = Math.max(1, st.counters?.nextImageI ?? 1);
        const imageRecordsForState = [];
        const resultEntries = [];
        const httpBase = await (0, core_bundle_mjs_1.getHealthyServerBaseUrl)();
        for (let idx = 0; idx < batchResult.results.length; idx++) {
            const detResult = batchResult.results[idx];
            const sourceId = sourceEntries[idx]?.id ?? `canvas${idx + 1}`;
            const origBuf = sourceEntries[idx].origBuf;
            const currentI = nextI++;
            (0, toolProgress_js_1.reportToolStep)(statusCtx, sourceEntries.length + 3 + idx, progressTotalSteps, `Drawing boxes for ${sourceId} (${idx + 1}/${batchResult.results.length})...`);
            const bboxes = detResult.objects.map((o) => o.bbox);
            console.log(`[detect_object] drawing ${bboxes.length} bboxes for ${sourceId}...`);
            // Draw on the original-resolution file; sourceDims carries the preview space in which
            // the detection server reported its bbox coordinates so they get scaled up correctly.
            const annotatedBuf = await (0, drawBboxesOnImage_js_1.drawBboxesOnImage)(origBuf, bboxes, {
                sourceDims: { width: detResult.imageWidth, height: detResult.imageHeight },
                palette: true,
            });
            const analysis = {
                schema: "ceveyne.image-analysis/v1",
                tool: "detect_object",
                sourceNotation: sourceId,
                inference: {
                    model: visionModelKey,
                    ...(task ? { query: task } : {}),
                    detectorPromptSha256: (0, crypto_1.createHash)("sha256").update(detectionConfig.odPrompt ?? "").digest("hex"),
                    maxTokens: configuredDetectMaxTokens,
                    temperature: configuredDetectTemperature,
                },
                render: { palette: true },
                detections: detResult.objects.map((object) => ({
                    label: object.label,
                    bbox: { x1: object.bbox[0], y1: object.bbox[1], x2: object.bbox[2], y2: object.bbox[3] },
                })),
            };
            const savedBuffer = embedPngMetadata
                ? (0, pngMetadata_js_1.injectXmpIntoBuffer)(annotatedBuf, {
                    ...(task ? { prompt: task } : {}),
                    model: visionModelKey,
                    mode: "object_detection",
                    generatedBy: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/detect_object`,
                    creatorTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/detect_object`,
                    analysis,
                })
                : annotatedBuf;
            const baseName = `image-${stamp}-i${currentI}`;
            const savedPath = path_1.default.join(primaryOutDir, `${baseName}.png`);
            await fs_1.default.promises.writeFile(savedPath, savedBuffer);
            const savedFileUrl = (0, url_1.pathToFileURL)(savedPath).toString();
            const savedSize = savedBuffer.length;
            console.log(`[detect_object] annotated image written: ${savedPath} (${savedSize} bytes)`);
            let preview = null;
            try {
                const p = await (0, core_bundle_mjs_1.generatePreviewFromBuffer)(savedBuffer, primaryOutDir, `${baseName}.png`, variantPreviewSpec);
                preview = {
                    ok: true,
                    filePath: p.previewAbs,
                    fileName: p.previewFilename,
                    fileUrl: (0, url_1.pathToFileURL)(p.previewAbs).toString(),
                    size_bytes: p.data.length,
                    width: p.width,
                    height: p.height,
                    mimeType: "image/jpeg",
                    dataBase64: p.data.toString("base64"),
                };
            }
            catch (e) {
                console.warn(`[detect_object] preview generation failed for ${sourceId}:`, String(e));
            }
            const httpOriginal = httpBase
                ? (0, core_bundle_mjs_1.toHttpOriginalUrl)(`${baseName}.png`, httpBase, currentLmChatId || undefined)
                : "";
            const httpPreview = (() => {
                if (!httpBase || !currentLmChatId || !preview?.fileName)
                    return "";
                return (0, core_bundle_mjs_1.toHttpPreviewUrl)(preview.fileName, httpBase, currentLmChatId);
            })();
            imageRecordsForState.push({
                filename: `${baseName}.png`,
                preview: preview ? `preview-${baseName}.jpg` : undefined,
                i: currentI,
                sourceTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/detect_object`,
                detectSource: sourceId,
                task,
                imageWidth: detResult.imageWidth,
                imageHeight: detResult.imageHeight,
                analysisMetadata: analysis,
                detections: detResult.objects.map((o) => ({
                    label: o.label,
                    bbox: { x1: o.bbox[0], y1: o.bbox[1], x2: o.bbox[2], y2: o.bbox[3] },
                    crop: {
                        cropLeft: o.cropLeft,
                        cropRight: o.cropRight,
                        cropTop: o.cropTop,
                        cropBottom: o.cropBottom,
                    },
                })),
            });
            resultEntries.push({ id: sourceId, i: currentI, detResult, savedPath, savedFileUrl, savedSize, preview, httpOriginal, httpPreview });
        }
        // Update state once for all images
        console.log("[detect_object] updating state...");
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps - 1, progressTotalSteps, "Updating image state and audit log...");
        try {
            const stateForUpdate = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
            const appendResult = (0, core_bundle_mjs_1.appendImages)(stateForUpdate, imageRecordsForState);
            if (appendResult.changed) {
                await (0, core_bundle_mjs_1.writeStateAtomic)(primaryOutDir, stateForUpdate);
                console.log("[detect_object] state written, nextImageI:", stateForUpdate.counters?.nextImageI);
            }
        }
        catch (e) {
            console.warn("[detect_object] state update failed:", String(e));
        }
        // Audit log — effectiveRequestId is reused below in each summary entry, so a caller
        // (the MCP adapter) can always name its HTML report after this exact audit requestId,
        // even for a batch call whose top-level result is an array (see summaries below).
        const effectiveRequestId = requestId ?? `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`;
        try {
            const audit = (0, core_bundle_mjs_1.buildAuditLogger)({ backend: "detect_object", mode: "detect_object", requestId: effectiveRequestId });
            if (currentLmChatId)
                audit.setChatId(currentLmChatId);
            audit.setUserRequest({ targets: rawTargets, task });
            audit.setOutput({
                images: resultEntries.map((r) => ({
                    id: r.id,
                    i: r.i,
                    detections: r.detResult.objects.length,
                    path: r.savedPath,
                    url: r.savedFileUrl,
                    bytes: r.savedSize,
                    ...(r.httpOriginal ? { http_url: r.httpOriginal } : {}),
                    ...(r.preview ? { preview_path: r.preview.filePath, preview_url: r.preview.fileUrl } : {}),
                    ...(r.httpPreview ? { http_preview_url: r.httpPreview } : {}),
                })),
            });
            await audit.write();
        }
        catch (e) {
            console.warn("[detect_object] audit logging failed:", String(e));
        }
        // Assemble tool result
        const envPreviewRaw = process.env["PREVIEW_IN_CHAT"];
        const previewInChat = envPreviewRaw === undefined
            ? true
            : envPreviewRaw === "1" || envPreviewRaw.toLowerCase() === "true";
        const summaries = resultEntries.map((r) => ({
            tool: "detect_object",
            requestId: effectiveRequestId,
            source: r.id,
            i: r.i,
            imageWidth: r.detResult.imageWidth,
            imageHeight: r.detResult.imageHeight,
            inferenceTimeMs: r.detResult.inferenceTimeMs,
            detections: r.detResult.objects.map((o) => ({
                label: o.label,
                bbox: { x1: o.bbox[0], y1: o.bbox[1], x2: o.bbox[2], y2: o.bbox[3] },
                crop: {
                    left: { pct: o.cropLeft, px: Math.round((o.cropLeft / 100) * r.detResult.imageWidth) },
                    right: { pct: o.cropRight, px: Math.round((o.cropRight / 100) * r.detResult.imageWidth) },
                    top: { pct: o.cropTop, px: Math.round((o.cropTop / 100) * r.detResult.imageHeight) },
                    bottom: { pct: o.cropBottom, px: Math.round((o.cropBottom / 100) * r.detResult.imageHeight) },
                },
                crop_tool_hint: "Pass crop.left.pct as cropLeft, crop.right.pct as cropRight, crop.top.pct as cropTop, crop.bottom.pct as cropBottom to the crop tool.",
            })),
        }));
        const reviewHint = "Carefully examine the preview to make absolutely sure that the object detection matches your intent.";
        const content = [];
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps, progressTotalSteps, "Assembling detection result...");
        for (const r of resultEntries) {
            const fallbackPreviewUrl = r.preview?.fileUrl || r.savedFileUrl;
            const previewLine = `Preview i${r.i}: ${r.httpPreview ? r.httpPreview : fallbackPreviewUrl}`;
            const originalLine = `Original i${r.i}: ${r.httpOriginal ? r.httpOriginal : r.savedFileUrl}`;
            if (previewInChat && r.preview) {
                const fname = String(r.preview.fileName || "");
                content.push({
                    type: "image",
                    fileName: fname,
                    mimeType: r.preview.mimeType,
                    markdown: `![Detected Objects i${r.i} (${r.id})](./${fname})`,
                    $hint: "This is an image file. Present the image to the user by using the markdown above.",
                });
            }
            // TODO: Restore when LM Studio renders file/HTTP links again
            // content.push({ type: "text", text: previewLine });
            // content.push({ type: "text", text: originalLine });
        }
        const totalMs = Math.round(batchResult.totalInferenceTimeMs);
        if (totalMs > 0) {
            content.push({ type: "text", text: `Total inference time: ${totalMs}ms` });
        }
        content.push({
            type: "text",
            text: JSON.stringify(summaries.length === 1 ? summaries[0] : summaries),
            ...(previewInChat ? {} : { $hint: reviewHint }),
        });
        return { content };
    }
    catch (error) {
        return {
            content: [
                {
                    type: "text",
                    text: `detect_object failed: ${error.message || String(error)}`,
                },
            ],
            isError: true,
        };
    }
}
// ─────────────────────────────────────────────────────────────────────────────
// annotate_image
// ─────────────────────────────────────────────────────────────────────────────
/**
 * Expand (positive) or shrink (negative) a bbox [x1, y1, x2, y2].
 * Number → % of box diagonal. String → value + optional 'px' or '%' suffix.
 */
function applyFrameAdjust(bbox, frameAdjust, imgW, imgH) {
    const [x1, y1, x2, y2] = bbox;
    const diag = Math.hypot(x2 - x1, y2 - y1);
    let d_px;
    if (typeof frameAdjust === "string") {
        const m = String(frameAdjust).trim().match(/^([+-]?\d+(?:\.\d+)?)\s*(%|px)?$/i);
        if (!m)
            return bbox;
        const val = parseFloat(m[1]);
        d_px = m[2]?.toLowerCase() === "px" ? val : (val / 100) * diag;
    }
    else {
        d_px = (frameAdjust / 100) * diag;
    }
    return [
        Math.max(0, Math.round(x1 - d_px)),
        Math.max(0, Math.round(y1 - d_px)),
        Math.min(imgW - 1, Math.round(x2 + d_px)),
        Math.min(imgH - 1, Math.round(y2 + d_px)),
    ];
}
exports.AnnotateImageParamsShape = {
    targets: exports.FlexibleTargetsList.optional().describe("One or more image notations to process. Each notation is a letter followed by a number: " +
        "a=attachment (a1, a2, …), i=generated image (i1, i2, …), v=variant (v1, v2, …), p=picture (p1, p2, …). " +
        "Also accepts a filename in the scratchpad folder or an absolute path. " +
        "Pass via the targets field, e.g. annotate_image({\"targets\":[\"a1\", \"i3\"]}). " +
        "Omit when there is exactly one image — it will be selected automatically."),
    task: zod_1.z
        .string()
        .optional()
        .default("")
        .describe("What to detect. Omit for full-image general object detection. " +
        "Use natural language to target specific subjects (e.g. 'all faces and hands', 'the dog', 'cars and bicycles'). " +
        "Not used on correction calls — detections are loaded from state."),
    color: zod_1.z
        .string()
        .optional()
        .describe("Box color for all detected objects. CSS color name or hex, e.g. 'pink', 'red', '#FF0000'. Default: magenta."),
    lineWeight: zod_1.z
        .coerce.number().int().min(1).max(50)
        .optional()
        .describe("Line thickness in pixels for all bounding boxes. Default: 5."),
    frameAdjust: zod_1.z
        .union([zod_1.z.number(), zod_1.z.string()])
        .optional()
        .describe("Expand (positive) or shrink (negative) bounding boxes before drawing. " +
        "Number: percent of box diagonal (e.g. 5 = +5 %). " +
        "String: value + optional 'px' or '%' suffix, e.g. '10px', '-5%'. " +
        "Without detectLabel: applied to all boxes. With detectLabel: applied to the selected box only. Default: 5."),
    detectLabel: zod_1.z
        .preprocess((val) => {
        if (typeof val === "string") {
            const t = val.trim();
            if (t.startsWith("[")) {
                try {
                    const parsed = JSON.parse(t);
                    if (Array.isArray(parsed)) {
                        return parsed.map((el) => String(el).trim()).filter((s) => s.length > 0);
                    }
                }
                catch { }
            }
        }
        return val;
    }, zod_1.z.union([
        zod_1.z.string().transform((s) => s.split(/\s*,\s*/).map((x) => x.trim()).filter((x) => x.length > 0)),
        zod_1.z.array(zod_1.z.string().min(1)),
    ]))
        .optional()
        .describe("On a correction call: label(s) to match (case-insensitive). " +
        "Single string, comma-separated list ('left eye, right eye'), or JSON array. " +
        "When set, ONLY the matching detection(s) are drawn — all others are omitted. " +
        "Single label with no detectIndex: auto-expands to ALL detections for that label (Option A). " +
        "canvas may be the original source (e.g. a1) or the previous annotate_image result (e.g. i3)."),
    detectIndex: zod_1.z
        .union([
        zod_1.z.string().transform((s) => (s.match(/\d+/g) ?? []).map(Number)),
        zod_1.z.coerce.number().int().min(0).transform((n) => [n]),
        zod_1.z.array(zod_1.z.coerce.number().int().min(0)),
    ])
        .optional()
        .describe("Zero-based index or list of indices, parallel to detectLabel. " +
        "indices[li] ?? indices[0] ?? 0 for missing entries. Default: 0. " +
        "Single label + multiple indices draws that label at each specified occurrence (Option B). " +
        "Bracket notation accepted: '[2, 4, 7]'."),
    x1: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual left edge(s) in original image pixels. " +
        "Scalar: applies to all selected detections. Array (parallel to detectLabel): null = keep stored value. " +
        "E.g. x1=[null,50,null] moves only the second detection's left edge."),
    y1: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual top edge(s) in original image pixels. Scalar or array (null = keep stored). See x1."),
    x2: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual right edge(s) in original image pixels. Scalar or array (null = keep stored). See x1."),
    y2: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual bottom edge(s) in original image pixels. Scalar or array (null = keep stored). See x1."),
};
/**
 * Resolve a per-box coordinate override, analogous to resolveBoxOverride in mask.
 * override: scalar (applies to all boxes) | array (parallel, null = keep stored) | undefined.
 * Returns undefined when no override → caller uses storedValue.
 */
function resolveCoordOverride(override, index) {
    if (override === undefined)
        return undefined;
    if (Array.isArray(override)) {
        const entry = override[index];
        if (entry === null || entry === undefined)
            return undefined;
        return entry;
    }
    return override;
}
/**
 * Option A helper: count occurrences of label in detections and return [0..N-1] if N > 1, else [0].
 */
function expandDetectIndices(detections, label) {
    const lower = label.toLowerCase();
    let count = 0;
    for (const d of detections) {
        if (d.label.toLowerCase() === lower)
            count++;
    }
    return count > 1 ? Array.from({ length: count }, (_, i) => i) : [0];
}
async function handleAnnotateImage(args, ctl, statusCtx = {}, requestId) {
    try {
        // ── Parse args ─────────────────────────────────────────────────────
        let rawTargets = [];
        if (Array.isArray(args?.targets)) {
            rawTargets = args.targets.map((s) => String(s).trim()).filter(Boolean);
        }
        else if (typeof args?.targets === "string" && args.targets.trim()) {
            const trimmed = args.targets.trim();
            if (trimmed.startsWith("[")) {
                try {
                    const parsed = JSON.parse(trimmed);
                    rawTargets = Array.isArray(parsed)
                        ? parsed.map((s) => String(s).trim()).filter(Boolean)
                        : trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
                catch {
                    rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
            }
            else {
                rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
            }
        }
        const taskArg = typeof args?.task === "string" && args.task.trim() ? args.task.trim() : "";
        const globalColor = typeof args?.color === "string" && args.color.trim() ? args.color.trim() : "magenta";
        const globalLineWeight = typeof args?.lineWeight === "number"
            ? Math.max(1, Math.min(50, Math.round(args.lineWeight))) : 5;
        const globalFrameAdjust = args?.frameAdjust !== undefined ? args.frameAdjust : 5;
        // detectLabel: string → string[] via comma-split; array → as-is
        let globalLabels;
        {
            const dlRaw = args?.detectLabel;
            if (Array.isArray(dlRaw)) {
                const arr = dlRaw.map((s) => String(s).trim()).filter(Boolean);
                if (arr.length > 0)
                    globalLabels = arr;
            }
            else if (typeof dlRaw === "string" && dlRaw.trim()) {
                globalLabels = dlRaw.split(/\s*,\s*/).map((x) => x.trim()).filter(Boolean);
            }
        }
        // detectIndex: number → [n]; string → extract digit runs; array → as-is
        let globalIndices = [];
        {
            const diRaw = args?.detectIndex;
            if (Array.isArray(diRaw)) {
                globalIndices = diRaw.map((n) => typeof n === "number" ? Math.floor(n) : parseInt(String(n), 10)).filter((n) => !isNaN(n) && n >= 0);
            }
            else if (typeof diRaw === "string" && diRaw.trim()) {
                globalIndices = (diRaw.match(/\d+/g) ?? []).map(Number);
            }
            else if (typeof diRaw === "number" && diRaw >= 0) {
                globalIndices = [Math.floor(diRaw)];
            }
        }
        const rawX1 = args?.x1;
        const rawY1 = args?.y1;
        const rawX2 = args?.x2;
        const rawY2 = args?.y2;
        // Normalise: scalar number | number-or-null array | undefined
        function normaliseCoord(raw) {
            if (raw === undefined || raw === null)
                return undefined;
            if (typeof raw === "number")
                return raw;
            if (Array.isArray(raw))
                return raw.map((v) => (v === null || v === undefined) ? null : Number(v));
            if (typeof raw === "string") {
                const t = raw.trim();
                if (t.startsWith("[")) {
                    try {
                        const p = JSON.parse(t);
                        if (Array.isArray(p))
                            return p.map((v) => (v === null || v === undefined) ? null : Number(v));
                    }
                    catch { }
                }
                const n = Number(t);
                return isNaN(n) ? undefined : n;
            }
            return undefined;
        }
        const manualX1 = normaliseCoord(rawX1);
        const manualY1 = normaliseCoord(rawY1);
        const manualX2 = normaliseCoord(rawX2);
        const manualY2 = normaliseCoord(rawY2);
        const hasAnyManualCoord = manualX1 !== undefined || manualY1 !== undefined || manualX2 !== undefined || manualY2 !== undefined;
        console.log("[annotate_image] invoked", { targets: rawTargets, task: taskArg, color: globalColor, lineWeight: globalLineWeight, frameAdjust: globalFrameAdjust, detectLabels: globalLabels, detectIndices: globalIndices, manualCoords: { x1: manualX1, y1: manualY1, x2: manualX2, y2: manualY2 } });
        // ── Resolve working directory ──────────────────────────────────────
        let currentLmChatId = null;
        let currentLmWorkingDir = null;
        try {
            const chatCtx = await (0, core_bundle_mjs_1.getActiveChatContext)();
            if (chatCtx?.chatId)
                currentLmChatId = chatCtx.chatId;
            if (chatCtx?.workingDir)
                currentLmWorkingDir = chatCtx.workingDir;
        }
        catch { }
        if (!currentLmChatId) {
            try {
                const resolved = await (0, core_bundle_mjs_1.resolveActiveLMStudioChatId)();
                if (resolved?.ok)
                    currentLmChatId = resolved.chatId;
            }
            catch { }
        }
        const primaryOutDir = currentLmWorkingDir ||
            (currentLmChatId ? (0, core_bundle_mjs_1.getLMStudioWorkingDir)(currentLmChatId) : undefined);
        if (!primaryOutDir) {
            return {
                content: [{ type: "text", text: "annotate_image failed: could not resolve LM Studio chat working directory." }],
                isError: true,
            };
        }
        await fs_1.default.promises.mkdir(primaryOutDir, { recursive: true }).catch(() => { });
        // ── Sync attachments ──────────────────────────────────────────────
        try {
            await (0, core_bundle_mjs_1.syncAttachmentsToState)(primaryOutDir, false, Number.MAX_SAFE_INTEGER);
        }
        catch (e) {
            console.warn("[annotate_image] attachment sync failed (non-fatal):", e?.message ?? e);
        }
        // ── Read state ────────────────────────────────────────────────────
        const st = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
        const attachments = Array.isArray(st?.attachments) ? st.attachments : [];
        const pictures = Array.isArray(st?.pictures) ? st.pictures : [];
        const imageRecords = Array.isArray(st?.images) ? st.images : [];
        const variantRecords = Array.isArray(st?.variants) ? st.variants : [];
        // ── Buffer resolution helpers ─────────────────────────────────────
        async function resolvePreviewBuf(notation) {
            const pref = parsePrefixedNotation(notation);
            if (!pref) {
                const pathToken = await resolveTargetPathToken(notation, primaryOutDir);
                if (!pathToken)
                    throw new Error(`Invalid notation: ${notation}`);
                return fs_1.default.promises.readFile(pathToken);
            }
            if (pref.pool === "attachment") {
                const rec = attachments.find((a) => a?.a === pref.index);
                const r = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!r)
                    throw new Error(`Preview for a${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
            }
            if (pref.pool === "image") {
                const rec = imageRecords.find((r) => r?.i === pref.index);
                const r = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!r)
                    throw new Error(`Preview for i${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
            }
            if (pref.pool === "variant") {
                const rec = variantRecords.find((v) => v?.v === pref.index);
                const r = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!r)
                    throw new Error(`Preview for v${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
            }
            const rec = pictures.find((p) => p?.p === pref.index);
            const r = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!r)
                throw new Error(`Preview for p${pref.index} not found.`);
            return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
        }
        async function resolveOriginalBuf(notation, fallback) {
            try {
                const pref = parsePrefixedNotation(notation);
                if (!pref) {
                    const pathToken = await resolveTargetPathToken(notation, primaryOutDir);
                    return pathToken ? await fs_1.default.promises.readFile(pathToken) : fallback;
                }
                if (pref.pool === "attachment") {
                    const rec = attachments.find((a) => a?.a === pref.index);
                    const abs = rec && typeof rec.originAbs === "string" ? rec.originAbs : "";
                    if (!abs)
                        return fallback;
                    return await fs_1.default.promises.readFile(abs);
                }
                let rec;
                if (pref.pool === "image")
                    rec = imageRecords.find((r) => r?.i === pref.index);
                else if (pref.pool === "variant")
                    rec = variantRecords.find((v) => v?.v === pref.index);
                else
                    rec = pictures.find((p) => p?.p === pref.index);
                const fn = rec && typeof rec.filename === "string" ? rec.filename : "";
                if (!fn)
                    return fallback;
                return await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, fn));
            }
            catch {
                return fallback;
            }
        }
        // ── Resolve auto-selected source when no targets given ─────────────
        let autoId = null;
        if (rawTargets.length === 0) {
            const total = attachments.length + variantRecords.length + imageRecords.length + pictures.length;
            if (total === 0) {
                return { content: [{ type: "text", text: "No source image available." }], isError: true };
            }
            if (total > 1) {
                return { content: [{ type: "text", text: "Ambiguous source — specify targets explicitly." }], isError: true };
            }
            if (attachments.length === 1)
                autoId = `a${typeof attachments[0]?.a === "number" ? attachments[0].a : 1}`;
            else if (variantRecords.length === 1)
                autoId = `v${typeof variantRecords[0]?.v === "number" ? variantRecords[0].v : 1}`;
            else if (imageRecords.length === 1)
                autoId = `i${typeof imageRecords[0]?.i === "number" ? imageRecords[0].i : 1}`;
            else
                autoId = `p${pictures[0]?.p ?? 1}`;
            rawTargets = [autoId];
        }
        // ── Classify each target: detect or redraw ────────────────────────
        // If task is explicitly provided, always run fresh inference (ignore any prior record).
        const forceDetect = taskArg.length > 0;
        const resolvedEntries = [];
        for (const rawId of rawTargets) {
            let drawSourceId = rawId;
            let stateRec = null;
            // Case A: target is an iN detection/annotation result
            const pref = parsePrefixedNotation(rawId);
            if (pref?.pool === "image") {
                const imgRec = imageRecords.find((r) => r?.i === pref.index);
                if (imgRec &&
                    Array.isArray(imgRec.detections) &&
                    imgRec.detections.length > 0 &&
                    typeof imgRec.imageWidth === "number") {
                    stateRec = imgRec;
                    drawSourceId = (typeof imgRec.detectSource === "string" && imgRec.detectSource)
                        ? imgRec.detectSource
                        : rawId;
                }
            }
            // Case B: target is an original (a1, p1, …) with a prior detection on record
            if (!stateRec) {
                const prior = [...imageRecords]
                    .reverse()
                    .find((r) => (r?.detectSource === rawId) &&
                    Array.isArray(r.detections) &&
                    r.detections.length > 0 &&
                    typeof r.imageWidth === "number");
                if (prior) {
                    stateRec = prior;
                    drawSourceId = rawId;
                }
            }
            try {
                if (stateRec && !forceDetect) {
                    const preview = await resolvePreviewBuf(drawSourceId).catch(() => null);
                    const origBuf = preview
                        ? await resolveOriginalBuf(drawSourceId, preview)
                        : Buffer.alloc(0);
                    resolvedEntries.push({
                        mode: "redraw",
                        id: drawSourceId,
                        origBuf,
                        task: typeof stateRec.task === "string" ? stateRec.task : taskArg,
                        detections: stateRec.detections,
                        imageWidth: stateRec.imageWidth,
                        imageHeight: stateRec.imageHeight,
                        analysisMetadata: stateRec.analysisMetadata,
                    });
                }
                else {
                    const previewBuf = await resolvePreviewBuf(rawId);
                    const origBuf = await resolveOriginalBuf(rawId, previewBuf);
                    resolvedEntries.push({ mode: "detect", id: rawId, previewBuf, origBuf });
                }
            }
            catch (e) {
                return {
                    content: [{ type: "text", text: String(e?.message || e) }],
                    isError: true,
                };
            }
        }
        // ── Run Qwen3-VL for detect-mode entries ───────────────────────────
        const detectEntries = resolvedEntries.filter((e) => e.mode === "detect");
        const progressTotalSteps = detectEntries.length > 0
            ? detectEntries.length + resolvedEntries.length + 4
            : resolvedEntries.length + 3;
        let batchResult = null;
        const globalConfig = getGlobalConfig(ctl);
        const envEmbedPngMetadata = process.env.EMBED_PNG_METADATA;
        const embedPngMetadata = getGlobalBoolean(globalConfig, "embedPngMetadata", envEmbedPngMetadata === undefined ? config_js_1.defaultPluginSettings.embedPngMetadata : envEmbedPngMetadata !== "false");
        let visionModelKey = "";
        let detectionConfig = null;
        if (detectEntries.length > 0) {
            let visionBaseUrl = getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || config_js_1.defaultPluginSettings.embeddingBaseUrl);
            const visionApiKey = getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || config_js_1.defaultPluginSettings.embeddingApiKey);
            visionModelKey = getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || config_js_1.defaultPluginSettings.qwen3VlModelPath);
            const envDetectMaxTokens = Number.parseInt(process.env.DETECT_MAX_TOKENS || "", 10);
            const envDetectTemperature = Number.parseFloat(process.env.DETECT_TEMPERATURE || "");
            const configuredDetectMaxTokens = Math.floor(getGlobalNumber(globalConfig, "detectMaxTokens", Number.isFinite(envDetectMaxTokens) && envDetectMaxTokens > 0 ? envDetectMaxTokens : config_js_1.defaultPluginSettings.detectMaxTokens));
            const configuredDetectTemperature = getGlobalNumber(globalConfig, "detectTemperature", Number.isFinite(envDetectTemperature) ? envDetectTemperature : config_js_1.defaultPluginSettings.detectTemperature);
            detectionConfig = {
                task: taskArg,
                odPrompt: getGlobalString(globalConfig, "qwen3VlOdPrompt", process.env.DETECT_OD_PROMPT || config_js_1.defaultPluginSettings.qwen3VlOdPrompt) || undefined,
                maxTokens: configuredDetectMaxTokens,
                temperature: configuredDetectTemperature,
                timeoutMs: 120_000,
            };
            const tmpPaths = [];
            const detectionItems = [];
            for (const entry of detectEntries) {
                const visionBuf = await normalizeVisionBuffer(entry.previewBuf);
                const tmpPath = path_1.default.join(primaryOutDir, `_tmp_annotate_src_${safeIdForFilename(entry.id)}_${Date.now()}.png`);
                await fs_1.default.promises.writeFile(tmpPath, visionBuf);
                tmpPaths.push(tmpPath);
                detectionItems.push({ id: entry.id, filePath: tmpPath });
            }
            try {
                (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Detecting objects in ${detectEntries.length} image${detectEntries.length === 1 ? "" : "s"}...`);
                (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, progressTotalSteps, `Preparing ${detectEntries.length} image${detectEntries.length === 1 ? "" : "s"} for annotation detection...`);
                const visionApi = resolveVisionApiFromEnv();
                const ready = await (0, VisionAnalyzer_js_1.ensureVisionModelReady)(visionApi, {
                    baseUrl: visionBaseUrl,
                    apiKey: visionApiKey,
                    modelKey: visionModelKey,
                    status: (message) => { try {
                        statusCtx.status?.(message);
                    }
                    catch { } },
                    llamaServer: visionApi === "llama-server" ? resolveLlamaServerConfig(globalConfig) : undefined,
                });
                if (!ready.ok) {
                    throw new Error(ready.error);
                }
                // llama-server strand: router's own exact tagged model id/address, not the configured ones.
                if (ready.resolvedBaseUrl)
                    visionBaseUrl = ready.resolvedBaseUrl;
                if (ready.resolvedModelKey)
                    visionModelKey = ready.resolvedModelKey;
                batchResult = {
                    results: [],
                    totalInferenceTimeMs: 0,
                    backend: "vision-api",
                };
                for (let idx = 0; idx < detectionItems.length; idx++) {
                    const item = detectionItems[idx];
                    (0, toolProgress_js_1.reportToolStep)(statusCtx, idx + 2, progressTotalSteps, `Detecting objects in ${item.id} (${idx + 1}/${detectionItems.length})...`);
                    const singleResult = await (0, VisionAnalyzer_js_1.detectLmStudioVisionBatch)([item], {
                        ...detectionConfig,
                        baseUrl: visionBaseUrl,
                        apiKey: visionApiKey,
                        model: visionModelKey,
                    });
                    batchResult.results.push(...singleResult.results);
                    batchResult.totalInferenceTimeMs += singleResult.totalInferenceTimeMs;
                    batchResult.backend = singleResult.backend;
                }
                try {
                    const totalObjects = batchResult.results.reduce((s, r) => s + (r.objects?.length ?? 0), 0);
                    const ms = Math.round(batchResult.totalInferenceTimeMs);
                    (0, toolProgress_js_1.reportToolStep)(statusCtx, detectEntries.length + 2, progressTotalSteps, `${totalObjects} object${totalObjects === 1 ? "" : "s"} found (${ms}ms); drawing boxes...`);
                }
                catch { }
            }
            finally {
                for (const tp of tmpPaths)
                    await fs_1.default.promises.unlink(tp).catch(() => { });
            }
            if (!batchResult || !batchResult.results.length) {
                return {
                    content: [{ type: "text", text: "annotate_image: no results returned from detection API." }],
                    isError: true,
                };
            }
        }
        else {
            (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Redrawing ${resolvedEntries.length} annotated image${resolvedEntries.length === 1 ? "" : "s"} from stored detections...`);
            (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, progressTotalSteps, `Redrawing ${resolvedEntries.length} annotated image${resolvedEntries.length === 1 ? "" : "s"} from stored detections...`);
        }
        // ── Per-entry: apply frameAdjust, draw, save ───────────────────────
        const variantPreviewSpec = core_bundle_mjs_1.VARIANT_FULL_CONFIG.preview;
        const stamp = isoStampCompact();
        let nextI = Math.max(1, st.counters?.nextImageI ?? 1);
        const imageRecordsForState = [];
        const resultEntries = [];
        const httpBase = await (0, core_bundle_mjs_1.getHealthyServerBaseUrl)();
        let detectResultIdx = 0;
        let resolvedIdx = 0;
        const drawBaseStep = detectEntries.length > 0 ? detectEntries.length + 3 : 2;
        for (const entry of resolvedEntries) {
            (0, toolProgress_js_1.reportToolStep)(statusCtx, drawBaseStep + resolvedIdx, progressTotalSteps, `Drawing annotation for ${entry.id} (${resolvedIdx + 1}/${resolvedEntries.length})...`);
            resolvedIdx++;
            let rawBboxes;
            let imgW;
            let imgH;
            let detObjects;
            let isRedraw;
            let entryTask;
            let inferenceTimeMs = 0;
            let bboxesAlreadyAdjusted = false;
            if (entry.mode === "redraw") {
                imgW = entry.imageWidth;
                imgH = entry.imageHeight;
                isRedraw = true;
                entryTask = entry.task;
                if (globalLabels !== undefined && globalLabels.length > 0) {
                    // detectLabel mode: resolve label/index pairs, draw ONLY the selected detections
                    let labels = [...globalLabels];
                    let indices = [...globalIndices];
                    // Option A: single label + no explicit index → auto-expand to all detections for that label
                    if (labels.length === 1 && indices.length === 0) {
                        const allIndices = expandDetectIndices(entry.detections, labels[0]);
                        if (allIndices.length > 1) {
                            labels = Array(allIndices.length).fill(labels[0]);
                            indices = allIndices;
                        }
                    }
                    // Option B: single label + multiple explicit indices → expand labels to match
                    if (labels.length === 1 && indices.length > 1) {
                        labels = Array(indices.length).fill(labels[0]);
                    }
                    // Pre-group detections by lowercase label once — O(1) lookup per box.
                    const detByLabel = new Map();
                    for (const d of entry.detections) {
                        const key = d.label.toLowerCase();
                        if (!detByLabel.has(key))
                            detByLabel.set(key, []);
                        detByLabel.get(key).push(d);
                    }
                    const resolvedBoxes = [];
                    for (let li = 0; li < labels.length; li++) {
                        const label = labels[li];
                        const idx = indices[li] ?? indices[0] ?? 0;
                        const selectedDet = detByLabel.get(label.toLowerCase())?.[idx];
                        if (!selectedDet) {
                            const available = [...new Set(entry.detections.map((d) => d.label))].join(", ");
                            return {
                                content: [{ type: "text", text: `annotate_image: label '${label}' (index ${idx}) not found in stored detections. Available: ${available || "(none)"}` }],
                                isError: true,
                            };
                        }
                        // Coord overrides: resolveCoordOverride per axis, fall back to stored value.
                        // Applies to all selected boxes; scalar = same for all, array = parallel (null = keep stored).
                        const ox1 = hasAnyManualCoord ? resolveCoordOverride(manualX1, li) : undefined;
                        const oy1 = hasAnyManualCoord ? resolveCoordOverride(manualY1, li) : undefined;
                        const ox2 = hasAnyManualCoord ? resolveCoordOverride(manualX2, li) : undefined;
                        const oy2 = hasAnyManualCoord ? resolveCoordOverride(manualY2, li) : undefined;
                        const bbox = [
                            ox1 ?? selectedDet.bbox.x1,
                            oy1 ?? selectedDet.bbox.y1,
                            ox2 ?? selectedDet.bbox.x2,
                            oy2 ?? selectedDet.bbox.y2,
                        ];
                        resolvedBoxes.push({ det: selectedDet, bbox });
                    }
                    rawBboxes = resolvedBoxes.map(({ bbox }) => applyFrameAdjust(bbox, globalFrameAdjust, imgW, imgH));
                    detObjects = resolvedBoxes.map(({ det, bbox }) => ({
                        ...det,
                        bbox: { x1: bbox[0], y1: bbox[1], x2: bbox[2], y2: bbox[3] },
                    }));
                    bboxesAlreadyAdjusted = true;
                }
                else if (globalIndices.length > 0) {
                    // No detectLabel but explicit detectIndex: select stored detections by position.
                    const selected = [];
                    for (const idx of globalIndices) {
                        const det = entry.detections[idx];
                        if (!det) {
                            return {
                                content: [{ type: "text", text: `annotate_image: detectIndex ${idx} out of range (${entry.detections.length} stored detections).` }],
                                isError: true,
                            };
                        }
                        selected.push(det);
                    }
                    rawBboxes = selected.map((d) => [d.bbox.x1, d.bbox.y1, d.bbox.x2, d.bbox.y2]);
                    detObjects = selected;
                    bboxesAlreadyAdjusted = false;
                }
                else {
                    // No detectLabel, no detectIndex: draw all stored boxes, apply frameAdjust to all
                    rawBboxes = entry.detections.map((d) => [d.bbox.x1, d.bbox.y1, d.bbox.x2, d.bbox.y2]);
                    detObjects = entry.detections;
                }
            }
            else {
                const detResult = batchResult.results[detectResultIdx++];
                rawBboxes = detResult.objects.map((o) => o.bbox);
                imgW = detResult.imageWidth;
                imgH = detResult.imageHeight;
                detObjects = detResult.objects.map((o) => ({
                    label: o.label,
                    bbox: { x1: o.bbox[0], y1: o.bbox[1], x2: o.bbox[2], y2: o.bbox[3] },
                    crop: { cropLeft: o.cropLeft, cropRight: o.cropRight, cropTop: o.cropTop, cropBottom: o.cropBottom },
                }));
                isRedraw = false;
                entryTask = taskArg;
                inferenceTimeMs = detResult.inferenceTimeMs ?? 0;
            }
            const adjustedBboxes = bboxesAlreadyAdjusted
                ? rawBboxes
                : rawBboxes.map((bbox) => applyFrameAdjust(bbox, globalFrameAdjust, imgW, imgH));
            const annotatedBuf = await (0, drawBboxesOnImage_js_1.drawBboxesOnImage)(entry.origBuf, adjustedBboxes, {
                sourceDims: { width: imgW, height: imgH },
                palette: false,
                color: globalColor,
                lineWeight: globalLineWeight,
            });
            const inheritedInference = entry.mode === "redraw" ? entry.analysisMetadata?.inference : undefined;
            const analysis = {
                schema: "ceveyne.image-analysis/v1",
                tool: "annotate_image",
                sourceNotation: entry.id,
                inference: entry.mode === "redraw"
                    ? inheritedInference
                        ? { ...inheritedInference, reused: true }
                        : undefined
                    : {
                        model: visionModelKey,
                        ...(entryTask ? { query: entryTask } : {}),
                        detectorPromptSha256: (0, crypto_1.createHash)("sha256").update(detectionConfig?.odPrompt ?? "").digest("hex"),
                        maxTokens: detectionConfig?.maxTokens,
                        temperature: detectionConfig?.temperature,
                    },
                render: { color: globalColor, lineWeight: globalLineWeight, frameAdjust: globalFrameAdjust },
                detections: detObjects.map((detection, index) => ({
                    label: detection.label,
                    bbox: {
                        x1: adjustedBboxes[index][0],
                        y1: adjustedBboxes[index][1],
                        x2: adjustedBboxes[index][2],
                        y2: adjustedBboxes[index][3],
                    },
                })),
            };
            const savedBuffer = embedPngMetadata
                ? (0, pngMetadata_js_1.injectXmpIntoBuffer)(annotatedBuf, {
                    ...(entryTask ? { prompt: entryTask } : {}),
                    ...(analysis.inference?.model ? { model: analysis.inference.model } : {}),
                    mode: isRedraw ? "image_annotation_redraw" : "image_annotation",
                    generatedBy: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/annotate_image`,
                    creatorTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/annotate_image`,
                    analysis,
                })
                : annotatedBuf;
            const currentI = nextI++;
            const baseName = `image-${stamp}-i${currentI}`;
            const savedPath = path_1.default.join(primaryOutDir, `${baseName}.png`);
            await fs_1.default.promises.writeFile(savedPath, savedBuffer);
            const savedFileUrl = (0, url_1.pathToFileURL)(savedPath).toString();
            const savedSize = savedBuffer.length;
            let preview = null;
            try {
                const p = await (0, core_bundle_mjs_1.generatePreviewFromBuffer)(savedBuffer, primaryOutDir, `${baseName}.png`, variantPreviewSpec);
                preview = {
                    ok: true,
                    filePath: p.previewAbs,
                    fileName: p.previewFilename,
                    fileUrl: (0, url_1.pathToFileURL)(p.previewAbs).toString(),
                    size_bytes: p.data.length,
                    width: p.width,
                    height: p.height,
                    mimeType: "image/jpeg",
                    dataBase64: p.data.toString("base64"),
                };
            }
            catch (e) {
                console.warn(`[annotate_image] preview generation failed for ${entry.id}:`, String(e));
            }
            const httpOriginal = httpBase
                ? (0, core_bundle_mjs_1.toHttpOriginalUrl)(`${baseName}.png`, httpBase, currentLmChatId || undefined) : "";
            const httpPreview = (() => {
                if (!httpBase || !currentLmChatId || !preview?.fileName)
                    return "";
                return (0, core_bundle_mjs_1.toHttpPreviewUrl)(preview.fileName, httpBase, currentLmChatId);
            })();
            imageRecordsForState.push({
                filename: `${baseName}.png`,
                preview: preview ? `preview-${baseName}.jpg` : undefined,
                i: currentI,
                sourceTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/annotate_image`,
                detectSource: entry.id,
                task: entryTask,
                annotateColor: globalColor,
                annotateLineWeight: globalLineWeight,
                annotateFrameAdjust: globalFrameAdjust,
                imageWidth: imgW,
                imageHeight: imgH,
                analysisMetadata: analysis,
                detections: detObjects.map((d) => ({
                    label: d.label,
                    bbox: { x1: d.bbox.x1, y1: d.bbox.y1, x2: d.bbox.x2, y2: d.bbox.y2 },
                    crop: d.crop ?? {},
                })),
            });
            resultEntries.push({
                id: entry.id,
                i: currentI,
                isRedraw,
                task: entryTask,
                detObjects,
                imageWidth: imgW,
                imageHeight: imgH,
                savedPath,
                savedFileUrl,
                savedSize,
                preview,
                httpOriginal,
                httpPreview,
                inferenceTimeMs,
            });
        }
        // ── Update state ──────────────────────────────────────────────────
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps - 1, progressTotalSteps, "Updating image state and audit log...");
        try {
            const stateForUpdate = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
            const appendResult = (0, core_bundle_mjs_1.appendImages)(stateForUpdate, imageRecordsForState);
            if (appendResult.changed) {
                await (0, core_bundle_mjs_1.writeStateAtomic)(primaryOutDir, stateForUpdate);
            }
        }
        catch (e) {
            console.warn("[annotate_image] state update failed:", String(e));
        }
        // ── Audit log ─────────────────────────────────────────────────────
        // effectiveRequestId is reused below in each summary entry, so a caller (the MCP adapter)
        // can always name its HTML report after this exact audit requestId, even for a batch call
        // whose top-level result is an array (see summaries below).
        const effectiveRequestId = requestId ?? `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`;
        try {
            const audit = (0, core_bundle_mjs_1.buildAuditLogger)({ backend: "annotate_image", mode: "annotate_image", requestId: effectiveRequestId });
            if (currentLmChatId)
                audit.setChatId(currentLmChatId);
            audit.setUserRequest({ targets: rawTargets, task: taskArg, color: globalColor, lineWeight: globalLineWeight, frameAdjust: globalFrameAdjust });
            audit.setOutput({
                images: resultEntries.map((r) => ({
                    id: r.id,
                    i: r.i,
                    redraw: r.isRedraw,
                    detections: r.detObjects.length,
                    path: r.savedPath,
                    url: r.savedFileUrl,
                    bytes: r.savedSize,
                    ...(r.httpOriginal ? { http_url: r.httpOriginal } : {}),
                    ...(r.preview ? { preview_path: r.preview.filePath, preview_url: r.preview.fileUrl } : {}),
                    ...(r.httpPreview ? { http_preview_url: r.httpPreview } : {}),
                })),
            });
            await audit.write();
        }
        catch { }
        // ── Assemble result ───────────────────────────────────────────────
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps, progressTotalSteps, "Assembling annotation result...");
        const summaries = resultEntries.map((r) => ({
            tool: "annotate_image",
            requestId: effectiveRequestId,
            source: r.id,
            i: r.i,
            redraw: r.isRedraw,
            color: globalColor,
            lineWeight: globalLineWeight,
            frameAdjust: globalFrameAdjust,
            ...(r.inferenceTimeMs > 0 ? { inferenceTimeMs: r.inferenceTimeMs } : {}),
            detections: r.detObjects.map((d) => ({
                label: d.label,
                bbox: { x1: d.bbox.x1, y1: d.bbox.y1, x2: d.bbox.x2, y2: d.bbox.y2 },
            })),
        }));
        const envPreviewRaw = process.env["PREVIEW_IN_CHAT"];
        const previewInChat = envPreviewRaw === undefined
            ? true
            : envPreviewRaw === "1" || envPreviewRaw.toLowerCase() === "true";
        // Build target notations for hints (e.g. ["i9", "i10"])
        const resultNotations = resultEntries.map((r) => `i${r.i}`);
        const targetsJson = JSON.stringify(resultNotations);
        const reviewHintFalse = `Carefully examine the preview to make absolutely sure that the object detection matches your intent. Registered as ${resultNotations.join(", ")}. Use review_image({"targets":${targetsJson}}) to review, or annotate_image({"targets":${targetsJson}}) to apply corrections.`;
        const reviewHintTrue = `Carefully examine the preview to make absolutely sure that the object detection matches your intent. This is an image file. Present the image to the user by using the markdown above. Registered as ${resultNotations.join(", ")}. Use review_image({"targets":${targetsJson}}) to review, or annotate_image({"targets":${targetsJson}}) to apply corrections.`;
        const content = [];
        for (const r of resultEntries) {
            const fallbackPreviewUrl = r.preview?.fileUrl || r.savedFileUrl;
            if (previewInChat && r.preview) {
                const fname = String(r.preview.fileName || "");
                content.push({
                    type: "image",
                    fileName: fname,
                    mimeType: r.preview.mimeType,
                    markdown: `![Annotated i${r.i} (${r.id})](./${fname})`,
                    $hint: reviewHintTrue,
                });
            }
            // TODO: Restore when LM Studio renders file/HTTP links again
            // content.push({ type: "text", text: `Preview i${r.i}: ${r.httpPreview || fallbackPreviewUrl}` });
            // content.push({ type: "text", text: `Original i${r.i}: ${r.httpOriginal || r.savedFileUrl}` });
        }
        if (batchResult && batchResult.totalInferenceTimeMs > 0) {
            content.push({ type: "text", text: `Total inference time: ${Math.round(batchResult.totalInferenceTimeMs)}ms` });
        }
        content.push({
            type: "text",
            text: JSON.stringify(summaries.length === 1 ? summaries[0] : summaries),
            ...(previewInChat ? {} : { $hint: reviewHintFalse }),
        });
        return { content };
    }
    catch (error) {
        return {
            content: [{ type: "text", text: `annotate_image failed: ${error.message || String(error)}` }],
            isError: true,
        };
    }
}

"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.AnnotateImageParamsShape = exports.DetectObjectParamsShape = exports.AnalyseImageParamsShape = exports.FlexibleTargetsList = void 0;
exports.parsePrefixedNotation = parsePrefixedNotation;
exports.handleAnalyseImage = handleAnalyseImage;
exports.handleDetectObject = handleDetectObject;
exports.handleAnnotateImage = handleAnnotateImage;
const path_1 = __importDefault(require("path"));
const fs_1 = __importDefault(require("fs"));
const crypto_1 = require("crypto");
const url_1 = require("url");
const zod_1 = require("zod");
const core_bundle_mjs_1 = require("../core-bundle.mjs");
const readPngMetadata_js_1 = require("../helpers/readPngMetadata.js");
const toolProgress_js_1 = require("../helpers/toolProgress.js");
const config_js_1 = require("../config.js");
const drawBboxesOnImage_js_1 = require("../helpers/drawBboxesOnImage.js");
const pngMetadata_js_1 = require("../helpers/pngMetadata.js");
const VisionAnalyzer_js_1 = require("../services/VisionAnalyzer.js");
/** The LM-Studio-plugin entrypoint has no preset concept and only ever talks to LM Studio itself, defaults to "bionic" when VISION_API isn't set (MCP's applyMcpConfig() is the only writer of this env var). */
function resolveVisionApiFromEnv() {
    const raw = (process.env.LMSTUDIO_VISION_API || "").trim().toLowerCase();
    return raw === "generic" || raw === "unsloth" || raw === "llama-server" ? raw : "bionic";
}
// ─────────────────────────────────────────────────────────────────────────────
// Shared helpers (previously duplicated across src/tools/*.ts, verified identical)
// ─────────────────────────────────────────────────────────────────────────────
function parsePrefixedNotation(s) {
    const t = String(s || "").trim().toLowerCase();
    const m = t.match(/^([avip])(\d+)$/);
    if (!m)
        return null;
    const idx = Math.max(1, parseInt(m[2], 10));
    const pool = m[1] === "a" ? "attachment" : m[1] === "v" ? "variant" : m[1] === "i" ? "image" : "picture";
    return { pool, index: idx };
}
// A target's `id` is normally a short notation (e.g. "i1") but the MCP adapter may also pass a
// resolved absolute path (see made-for-bionic-core's resolveMcpSourcePathTarget, called from
// mcp/index.ts before this handler runs) — never embed it in a filename unsanitized.
function safeIdForFilename(id) {
    return path_1.default.basename(id).replace(/[^a-zA-Z0-9._-]/g, "_").slice(0, 80);
}
// A target that isn't aN/vN/iN/pN notation is a scratchpad basename or an absolute path (see
// generate-image's core/tools.ts for the same pattern) — deliberately local/non-shared. The MCP
// layer (src/mcp/index.ts) already resolves and containment-checks these against the bound
// scratchpad before this handler ever runs (made-for-bionic-core's resolveMcpSourceToken()) — by
// the time a target reaches here it is either aN/vN/iN/pN notation or an already-validated
// absolute path, so this is a plain existence check, not a security boundary.
async function resolveTargetPathToken(raw, baseDir) {
    const trimmed = raw.trim();
    const candidate = path_1.default.isAbsolute(trimmed)
        ? trimmed
        : baseDir && trimmed === path_1.default.basename(trimmed)
            ? path_1.default.join(baseDir, trimmed)
            : null;
    if (!candidate)
        return null;
    const exists = await fs_1.default.promises.stat(candidate).then((s) => s.isFile()).catch(() => false);
    return exists ? candidate : null;
}
// Normalizes a path-specified source buffer for the vision model, mirroring find-images'
// previewPayloadBytes policy: images already within drawthingsLimits.previewMaxSum (w + h) are
// returned untouched (no resize, no format change — a fitting PNG stays a PNG); only oversize
// originals are re-encoded to a JPEG preview capped at previewMaxSum.
async function normalizeVisionBuffer(buf) {
    const maxSum = core_bundle_mjs_1.drawthingsLimits.previewMaxSum;
    const { width, height } = await (0, core_bundle_mjs_1.getSize)(buf);
    if (width <= 0 || height <= 0 || width + height <= maxSum)
        return buf;
    const { data } = await (0, core_bundle_mjs_1.encodeJpegPreviewFromBuffer)(buf, {
        maxDim: maxSum,
        quality: core_bundle_mjs_1.drawthingsLimits.previewQuality,
        mode: "sum",
        maxSum,
    });
    return data;
}
function isoStampCompact() {
    const d = new Date();
    const year = d.getUTCFullYear();
    const month = String(d.getUTCMonth() + 1).padStart(2, "0");
    const day = String(d.getUTCDate()).padStart(2, "0");
    const hours = String(d.getUTCHours()).padStart(2, "0");
    const minutes = String(d.getUTCMinutes()).padStart(2, "0");
    const seconds = String(d.getUTCSeconds()).padStart(2, "0");
    const millis = String(d.getUTCMilliseconds()).padStart(3, "0");
    return `${year}${month}${day}T${hours}${minutes}${seconds}${millis}Z`;
}
function getGlobalConfig(ctl) {
    const ctlAny = ctl;
    const getter = ctlAny.getGlobalPluginConfig || ctlAny.getGlobalConfig;
    if (!getter)
        return null;
    try {
        return getter.call(ctl, config_js_1.globalConfigSchematics);
    }
    catch {
        return null;
    }
}
function getGlobalString(gcfg, key, fallback) {
    try {
        const value = gcfg?.get(key);
        return typeof value === "string" ? value : fallback;
    }
    catch {
        return fallback;
    }
}
function getGlobalNumber(gcfg, key, fallback) {
    try {
        const value = gcfg?.get(key);
        return typeof value === "number" && Number.isFinite(value) ? value : fallback;
    }
    catch {
        return fallback;
    }
}
function getGlobalBoolean(gcfg, key, fallback) {
    try {
        const value = gcfg?.get(key);
        return typeof value === "boolean" ? value : fallback;
    }
    catch {
        return fallback;
    }
}
/** Shared by all 3 ensureVisionModelReady() call sites -- only consumed when VISION_API="llama-server" (see ensureLlamaServerVisionModelReady). GUI setting > MCP env var > defaultPluginSettings, matching every other config field in this file. */
function resolveLlamaServerConfig(globalConfig) {
    const envPort = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_PORT || "", 10);
    const envCtxSize = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_CTX_SIZE || "", 10);
    const envModelsMax = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_MODELS_MAX || "", 10);
    const envIdleTtlMinutes = Number.parseInt(process.env.LMSTUDIO_LLAMA_SERVER_IDLE_TTL_MINUTES || "", 10);
    return {
        port: Math.floor(getGlobalNumber(globalConfig, "llamaServerPort", Number.isFinite(envPort) && envPort > 0 ? envPort : config_js_1.defaultPluginSettings.llamaServerPort)),
        binaryPath: getGlobalString(globalConfig, "llamaServerBinary", process.env.LMSTUDIO_LLAMA_SERVER_BINARY || config_js_1.defaultPluginSettings.llamaServerBinary),
        ctxSize: Math.floor(getGlobalNumber(globalConfig, "llamaServerCtxSize", Number.isFinite(envCtxSize) && envCtxSize > 0 ? envCtxSize : config_js_1.defaultPluginSettings.llamaServerCtxSize)),
        modelsMax: Math.floor(getGlobalNumber(globalConfig, "llamaServerModelsMax", Number.isFinite(envModelsMax) && envModelsMax > 0 ? envModelsMax : config_js_1.defaultPluginSettings.llamaServerModelsMax)),
        idleTtlMinutes: Math.floor(getGlobalNumber(globalConfig, "llamaServerIdleTtlMinutes", Number.isFinite(envIdleTtlMinutes) && envIdleTtlMinutes >= 0 ? envIdleTtlMinutes : config_js_1.defaultPluginSettings.llamaServerIdleTtlMinutes)),
    };
}
/**
 * Flexible targets schema: accepts array OR comma/space-separated string.
 * Reduces parse errors when models output "a1, v2" instead of ["a1", "v2"].
 */
exports.FlexibleTargetsList = zod_1.z
    .union([
    zod_1.z.string().transform((s) => (s.match(/[aivp]\d+/gi) ?? []).map((x) => x.toLowerCase())),
    zod_1.z.array(zod_1.z.string()),
])
    .refine((arr) => arr.length >= 1, "targets must contain at least one notation")
    .refine((arr) => arr.length <= 16, "targets must contain at most 16 notations");
// ─────────────────────────────────────────────────────────────────────────────
// analyse_image
// ─────────────────────────────────────────────────────────────────────────────
exports.AnalyseImageParamsShape = {
    targets: exports.FlexibleTargetsList.describe("One or more image notations to process. Each notation is a letter followed by a number: " +
        "a=attachment (a1, a2, …), i=generated image (i1, i2, …), v=variant (v1, v2, …), p=picture (p1, p2, …). " +
        "Also accepts a filename in the scratchpad folder or an absolute path. Pass as a JSON array, e.g. [\"a1\", \"i3\"]."),
    prompt: zod_1.z.string().optional().describe("Optional prompt for the vision model. Empty = model default."),
};
function parseTargets(targets) {
    const parsed = { a: [], v: [], i: [], p: [] };
    const invalid = [];
    for (const raw of targets) {
        const s = typeof raw === "string" ? raw.trim() : "";
        const m = /^([avip])(\d+)$/i.exec(s);
        if (!m) {
            invalid.push(String(raw));
            continue;
        }
        const kind = m[1].toLowerCase();
        const n = parseInt(m[2], 10);
        if (!Number.isFinite(n) || n <= 0) {
            invalid.push(String(raw));
            continue;
        }
        parsed[kind].push(n);
    }
    // Unique + sort for stable behavior
    Object.keys(parsed).forEach((k) => {
        parsed[k] = Array.from(new Set(parsed[k])).sort((a, b) => a - b);
    });
    return { parsed, invalid };
}
function getAvailable(state) {
    const availableA = (state.attachments || [])
        .map((x) => (typeof x?.a === "number" ? x.a : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    const availableV = (state.variants || [])
        .map((x) => (typeof x?.v === "number" ? x.v : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    const availableI = (state.images || [])
        .map((x) => (typeof x?.i === "number" ? x.i : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    const availableP = (state.pictures || [])
        .map((x) => (typeof x?.p === "number" ? x.p : undefined))
        .filter((x) => typeof x === "number" && x > 0)
        .sort((a, b) => a - b);
    return { availableA, availableV, availableI, availableP };
}
async function ensurePreviewExists(chatWd, previewRel) {
    const pAbs = path_1.default.join(chatWd, previewRel);
    await fs_1.default.promises.access(pAbs, fs_1.default.constants.F_OK);
}
function classifyVisionError(errMsg) {
    if (/\b503\b/.test(errMsg)) {
        return "The Vision API is reachable, but the configured vision model is not available for inference. Check the configured vision model key and loaded model state.";
    }
    if (/aborted|aborterror|timed out|timeout/i.test(errMsg)) {
        return "The Vision API request timed out before the model returned.";
    }
    if (/ECONNREFUSED|ENOTFOUND|ECONNRESET|network socket|fetch failed/i.test(errMsg)) {
        return "The Vision API is not reachable. Check the configured vision/embedding API base URL and try again.";
    }
    return /Vision API/i.test(errMsg) ? errMsg : `Vision API error: ${errMsg}`;
}
function analyzeTimeoutMs(itemCount) {
    return Math.min(600_000, Math.max(180_000, itemCount * 60_000));
}
async function handleAnalyseImage(args, ctl, statusCtx = {}, requestId, onItem) {
    const visionTmpPaths = [];
    const visionTmpIds = new Set();
    try {
        // Tolerant mode: drop invalid items instead of failing
        const strict = false;
        let targets;
        if (Array.isArray(args?.targets)) {
            targets = args.targets;
        }
        else if (typeof args?.targets === "string" && args.targets.trim()) {
            const trimmed = args.targets.trim();
            if (trimmed.startsWith("[")) {
                try {
                    const parsed = JSON.parse(trimmed);
                    targets = Array.isArray(parsed) ? parsed.map((s) => String(s).trim()).filter(Boolean) : [];
                }
                catch {
                    targets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
            }
            else {
                // A bare notation or comma/space-separated list (e.g. "i1" or "i1, i2") — the same
                // fallback detect_object/annotate_image's own string parsing already has. The MCP path
                // never re-runs this handler's args through FlexibleTargetsList's zod transform (only the
                // LM-Studio plugin path does, via @lmstudio/sdk's tool() wrapper), so this handler's own
                // string parsing must accept the same shapes on its own.
                targets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
            }
        }
        else {
            targets = [];
        }
        const prompt = typeof args?.prompt === "string" ? args.prompt : "";
        const globalConfig = getGlobalConfig(ctl);
        const configuredVisionPrompt = getGlobalString(globalConfig, "visionPrompt", process.env.VISION_PROMPT || config_js_1.defaultPluginSettings.visionPrompt);
        const effectivePrompt = (prompt || configuredVisionPrompt || "").trim();
        // Parse explicit notations (aN/vN/iN/pN)
        const { parsed, invalid: invalidRaw } = parseTargets(targets);
        // LM-Studio plugin path: ctl.getWorkingDirectory() (LM-Studio SDK method, only present when
        // ctl is a real ToolsProviderController). MCP path: ctl is `{}` and has no such method — fall
        // back to the same chat-context-bridge resolution detect_object/annotate_image already use
        // (bindChatContextToScratchpad() sets this before the MCP adapter calls this handler).
        // currentLmChatId is resolved unconditionally (also used for this call's audit log entry).
        let currentLmChatId = null;
        let boundWorkingDir = null;
        try {
            const chatCtx = await (0, core_bundle_mjs_1.getActiveChatContext)();
            if (chatCtx?.chatId)
                currentLmChatId = chatCtx.chatId;
            if (chatCtx?.workingDir)
                boundWorkingDir = chatCtx.workingDir;
        }
        catch { }
        if (!currentLmChatId) {
            try {
                const resolved = await (0, core_bundle_mjs_1.resolveActiveLMStudioChatId)();
                if (resolved?.ok)
                    currentLmChatId = resolved.chatId;
            }
            catch { }
        }
        let workingDir;
        try {
            workingDir = ctl?.getWorkingDirectory?.();
        }
        catch {
            workingDir = undefined;
        }
        if (typeof workingDir !== "string" || !workingDir.trim()) {
            workingDir = boundWorkingDir || (currentLmChatId ? (0, core_bundle_mjs_1.getLMStudioWorkingDir)(currentLmChatId) : undefined);
        }
        if (typeof workingDir !== "string" || !workingDir.trim()) {
            return "analyse_image failed: working directory not available.";
        }
        const chatWd = workingDir;
        // Sync attachments from conversation.json → chat_media_state.json.
        // maxPreviewAttachments=MAX: importAttachmentBatch generates previews for all attachments
        // and writes them to state atomically — the canonical path via draw-things-chat-core.
        try {
            await (0, core_bundle_mjs_1.syncAttachmentsToState)(chatWd, false, Number.MAX_SAFE_INTEGER);
        }
        catch (syncErr) {
            console.warn("[analyse_image] attachment sync failed (non-fatal):", syncErr?.message ?? syncErr);
        }
        const state = await (0, core_bundle_mjs_1.readState)(chatWd);
        const { availableA, availableV, availableI, availableP } = getAvailable(state);
        // If any invalid notations and strict=true, reject
        if (invalidRaw.length > 0 && strict) {
            return `analyse_image failed: invalid targets: ${invalidRaw
                .map((s) => JSON.stringify(s))
                .join(", ")}`;
        }
        // Collect analysis items (id + preview file path)
        const analysisItems = [];
        // Map from notation id → absolute path of original file (for XMP reading)
        const originalFilePaths = new Map();
        // Map from notation id → human-readable filename for the result
        const displayNames = new Map();
        const missingNotations = new Set();
        const missingDetails = [];
        // Helper: resolve & validate preview for a notation
        const addItem = async (notation, rec, previewField, originalAbsPath, displayName) => {
            if (!rec) {
                missingNotations.add(notation);
                missingDetails.push(notation);
                return false;
            }
            const previewRel = typeof rec[previewField] === "string" ? String(rec[previewField]) : "";
            if (!previewRel.trim()) {
                missingNotations.add(notation);
                missingDetails.push(`${notation} (missing preview)`);
                return false;
            }
            try {
                await ensurePreviewExists(chatWd, previewRel);
                analysisItems.push({
                    id: notation,
                    filePath: path_1.default.join(chatWd, previewRel),
                });
                if (originalAbsPath) {
                    originalFilePaths.set(notation, originalAbsPath);
                }
                const dn = displayName || (originalAbsPath ? path_1.default.basename(originalAbsPath) : undefined);
                if (dn) {
                    displayNames.set(notation, dn);
                }
                return true;
            }
            catch {
                missingNotations.add(notation);
                missingDetails.push(`${notation} (preview file missing)`);
                return false;
            }
        };
        // Process attachments (aN)
        // Preview generation + state write already handled by syncAttachmentsToState above.
        for (const n of parsed.a) {
            const rec = (state.attachments || []).find((x) => x?.a === n);
            const origAbs = rec?.originAbs ?? (rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined);
            // originalName holds the real user-visible filename (e.g. "Katze.png")
            const origName = typeof rec?.originalName === "string" && rec.originalName ? rec.originalName : undefined;
            await addItem(`a${n}`, rec, "preview", origAbs, origName);
        }
        // Process variants (vN)
        for (const n of parsed.v) {
            const rec = (state.variants || []).find((x) => x?.v === n);
            const origAbs = rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined;
            await addItem(`v${n}`, rec, "preview", origAbs);
        }
        // Process images (iN)
        for (const n of parsed.i) {
            const rec = (state.images || []).find((x) => x?.i === n);
            const origAbs = rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined;
            await addItem(`i${n}`, rec, "preview", origAbs);
        }
        // Process pictures (pN)
        for (const n of parsed.p) {
            const rec = (state.pictures || []).find((x) => x?.p === n);
            const origAbs = rec?.filename ? path_1.default.join(chatWd, rec.filename) : undefined;
            await addItem(`p${n}`, rec, "preview", origAbs);
        }
        // Entries that didn't match aN/vN/iN/pN — a scratchpad filename or an absolute path is
        // also accepted as a target, same as detect_object/annotate_image/every other Bionic MCP tool.
        for (const raw of invalidRaw) {
            const pathToken = await resolveTargetPathToken(raw, chatWd);
            if (pathToken) {
                let visionPath = pathToken;
                if (effectivePrompt) {
                    const buf = await fs_1.default.promises.readFile(pathToken);
                    const norm = await normalizeVisionBuffer(buf);
                    if (norm !== buf) {
                        const tmpPath = path_1.default.join(chatWd, `_tmp_analyse_src_${safeIdForFilename(raw)}_${Date.now()}.png`);
                        await fs_1.default.promises.writeFile(tmpPath, norm);
                        visionTmpPaths.push(tmpPath);
                        visionTmpIds.add(raw);
                        visionPath = tmpPath;
                    }
                }
                analysisItems.push({ id: raw, filePath: visionPath });
                originalFilePaths.set(raw, pathToken);
                displayNames.set(raw, path_1.default.basename(pathToken));
            }
        }
        // If strict, fail on any missing items
        if (missingDetails.length > 0 && strict) {
            const hint = `Available: ` +
                `a=[${availableA.map((x) => `a${x}`).join(", ") || "(none)"}] ` +
                `v=[${availableV.map((x) => `v${x}`).join(", ") || "(none)"}] ` +
                `i=[${availableI.map((x) => `i${x}`).join(", ") || "(none)"}] ` +
                `p=[${availableP.map((x) => `p${x}`).join(", ") || "(none)"}]`;
            return `analyse_image failed: unknown/invalid targets: ${missingDetails.join(", ")}. ${hint}`;
        }
        // If no valid items after filtering, return early
        if (analysisItems.length === 0) {
            const hint = `Available: ` +
                `a=[${availableA.map((x) => `a${x}`).join(", ") || "(none)"}] ` +
                `v=[${availableV.map((x) => `v${x}`).join(", ") || "(none)"}] ` +
                `i=[${availableI.map((x) => `i${x}`).join(", ") || "(none)"}] ` +
                `p=[${availableP.map((x) => `p${x}`).join(", ") || "(none)"}]`;
            return `analyse_image: no valid targets found. ${hint}`;
        }
        let visionError = null;
        const visionResults = new Map();
        let totalInferenceTimeMs = null;
        if (effectivePrompt) {
            const envServerMaxTokens = Number.parseInt(process.env.SERVER_MAX_TOKENS || "", 10);
            const envServerTemperature = Number.parseFloat(process.env.SERVER_TEMPERATURE || "");
            const configuredMaxTokens = Math.floor(getGlobalNumber(globalConfig, "serverMaxTokens", Number.isFinite(envServerMaxTokens) && envServerMaxTokens > 0 ? envServerMaxTokens : config_js_1.defaultPluginSettings.serverMaxTokens));
            const configuredTemperature = getGlobalNumber(globalConfig, "serverTemperature", Number.isFinite(envServerTemperature) ? envServerTemperature : config_js_1.defaultPluginSettings.serverTemperature);
            const lmStudioConfig = {
                baseUrl: getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || config_js_1.defaultPluginSettings.embeddingBaseUrl),
                apiKey: getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || config_js_1.defaultPluginSettings.embeddingApiKey),
                model: getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || config_js_1.defaultPluginSettings.qwen3VlModelPath),
                prompt: effectivePrompt,
                maxTokens: configuredMaxTokens,
                temperature: configuredTemperature,
                timeoutMs: analyzeTimeoutMs(1),
            };
            try {
                const totalSteps = analysisItems.length + 2;
                (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Analyzing ${analysisItems.length} image${analysisItems.length === 1 ? "" : "s"}...`);
                (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, totalSteps, `Preparing ${analysisItems.length} image${analysisItems.length === 1 ? "" : "s"} for visual analysis...`);
                const visionApi = resolveVisionApiFromEnv();
                const ready = await (0, VisionAnalyzer_js_1.ensureVisionModelReady)(visionApi, {
                    baseUrl: lmStudioConfig.baseUrl,
                    apiKey: lmStudioConfig.apiKey,
                    modelKey: lmStudioConfig.model || "",
                    status: (message) => { try {
                        statusCtx.status?.(message);
                    }
                    catch { } },
                    llamaServer: visionApi === "llama-server" ? resolveLlamaServerConfig(globalConfig) : undefined,
                });
                if (!ready.ok) {
                    throw new Error(ready.error);
                }
                // llama-server strand: router's own exact tagged model id/address, not the configured ones.
                if (ready.resolvedBaseUrl)
                    lmStudioConfig.baseUrl = ready.resolvedBaseUrl;
                if (ready.resolvedModelKey)
                    lmStudioConfig.model = ready.resolvedModelKey;
                let totalMs = 0;
                for (let idx = 0; idx < analysisItems.length; idx++) {
                    const item = analysisItems[idx];
                    (0, toolProgress_js_1.reportToolStep)(statusCtx, idx + 2, totalSteps, `Analyzing ${item.id} (${idx + 1}/${analysisItems.length})...`);
                    const batchResult = await (0, VisionAnalyzer_js_1.analyzeLmStudioVisionBatch)([item], lmStudioConfig);
                    for (const r of batchResult.results) {
                        visionResults.set(r.id, r.text.trim() || "(no description)");
                    }
                    totalMs += batchResult.totalInferenceTimeMs;
                }
                totalInferenceTimeMs = totalMs;
                (0, toolProgress_js_1.reportToolStep)(statusCtx, totalSteps, totalSteps, "Formatting analysis results...");
            }
            catch (e) {
                visionError = classifyVisionError(e.message || String(e));
            }
        }
        const includeGenMeta = process.env.INCLUDE_GENERATION_METADATA !== "false";
        // Build structured result
        const lines = [];
        lines.push(`Analysis results (${analysisItems.length} image${analysisItems.length !== 1 ? "s" : ""}):`);
        lines.push("");
        if (visionError) {
            lines.push(`Note: Visual analysis unavailable — ${visionError}`);
            lines.push("");
        }
        for (const item of analysisItems) {
            const { id } = item;
            const displayName = displayNames.get(id);
            const origPath = originalFilePaths.get(id);
            const header = displayName ? `${id} — ${displayName}` : id;
            lines.push(`- ${header}`);
            let visual;
            if (effectivePrompt) {
                visual = visionError ? "(not available)" : (visionResults.get(id) ?? "(no description)");
                lines.push(`  VISUAL: ${visual}`);
            }
            let metaText;
            let meta;
            let metaNote;
            if (includeGenMeta) {
                if (origPath && origPath.toLowerCase().endsWith(".png")) {
                    meta = (0, readPngMetadata_js_1.readPngGenerationMeta)(origPath) ?? undefined;
                    if (meta) {
                        metaText = (0, readPngMetadata_js_1.formatGenerationMeta)(meta);
                    }
                    else {
                        metaText = `  (No embedded generation metadata)`;
                        metaNote = "(No embedded generation metadata)";
                    }
                }
                else if (origPath && /\.jpe?g$/i.test(origPath)) {
                    const metadata = await (0, core_bundle_mjs_1.readCameraImageMetadata)(origPath);
                    metaText = Object.keys(metadata).length > 0 ? `  EXIF JSON: ${JSON.stringify(metadata)}` : `  (No embedded EXIF metadata)`;
                    metaNote = Object.keys(metadata).length > 0 ? `EXIF JSON: ${JSON.stringify(metadata)}` : "(No embedded EXIF metadata)";
                }
                else if (origPath) {
                    metaText = `  (No embedded generation metadata — not a PNG file)`;
                    metaNote = "(No embedded generation metadata — not a PNG file)";
                }
                if (metaText)
                    lines.push(metaText);
            }
            lines.push("");
            onItem?.({ id, filePath: visionTmpIds.has(id) ? (origPath ?? item.filePath) : item.filePath, displayName, visual, meta, metaNote });
        }
        if (totalInferenceTimeMs !== null) {
            lines.push(`Total inference time: ${Math.round(totalInferenceTimeMs)}ms`);
        }
        // Status update: done
        try {
            const statusSuffix = visionError ? " (vision unavailable)" : " successfully";
            statusCtx.status?.(`Analyzed ${analysisItems.length} image${analysisItems.length !== 1 ? "s" : ""}${statusSuffix}`);
        }
        catch {
            // best-effort
        }
        // Audit log — requestId is reused as-is by the MCP adapter for its HTML preview filename.
        try {
            const audit = (0, core_bundle_mjs_1.buildAuditLogger)({ backend: "analyse_image", mode: "analyse_image", requestId });
            if (currentLmChatId)
                audit.setChatId(currentLmChatId);
            audit.setUserRequest({ targets, prompt: effectivePrompt });
            audit.setOutput({ items: analysisItems.map((item) => item.id) });
            await audit.write();
        }
        catch (e) {
            console.warn("[analyse_image] audit logging failed:", String(e));
        }
        return lines.join("\n");
    }
    catch (e) {
        return `analyse_image failed: ${String(e?.message || e)}`;
    }
    finally {
        for (const tp of visionTmpPaths)
            await fs_1.default.promises.unlink(tp).catch(() => { });
    }
}
// ─────────────────────────────────────────────────────────────────────────────
// detect_object
// ─────────────────────────────────────────────────────────────────────────────
exports.DetectObjectParamsShape = {
    targets: exports.FlexibleTargetsList.optional().describe("One or more image notations to process. Each notation is a letter followed by a number: " +
        "a=attachment (a1, a2, …), i=generated image (i1, i2, …), v=variant (v1, v2, …), p=picture (p1, p2, …). " +
        "Also accepts a filename in the scratchpad folder or an absolute path. " +
        "Pass as a JSON array, e.g. [\"a1\", \"i3\"]. " +
        "Omit when there is exactly one image — it will be selected automatically."),
    task: zod_1.z
        .string()
        .optional()
        .default("")
        .describe("What to detect. Omit for full-image general object detection. " +
        "Use natural language to target specific subjects (e.g. 'all faces and hands', 'the dog', 'cars and bicycles')."),
};
async function handleDetectObject(args, ctl, statusCtx = {}, requestId) {
    try {
        // Parse targets: accept array or comma/space-separated string (analogous to analyse_image)
        let rawTargets = [];
        if (Array.isArray(args?.targets)) {
            rawTargets = args.targets.map((s) => String(s).trim()).filter(Boolean);
        }
        else if (typeof args?.targets === "string" && args.targets.trim()) {
            const trimmed = args.targets.trim();
            if (trimmed.startsWith("[")) {
                try {
                    const parsed = JSON.parse(trimmed);
                    if (Array.isArray(parsed)) {
                        rawTargets = parsed.map((s) => String(s).trim()).filter(Boolean);
                    }
                    else {
                        rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                    }
                }
                catch {
                    rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
            }
            else {
                rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
            }
        }
        const task = typeof args?.task === "string" && args.task.trim() ? args.task.trim() : "";
        console.log("[detect_object] invoked", { targets: rawTargets, task });
        let currentLmChatId = null;
        let currentLmWorkingDir = null;
        try {
            const chatCtx = await (0, core_bundle_mjs_1.getActiveChatContext)();
            if (chatCtx?.chatId)
                currentLmChatId = chatCtx.chatId;
            if (chatCtx?.workingDir)
                currentLmWorkingDir = chatCtx.workingDir;
        }
        catch { }
        if (!currentLmChatId) {
            try {
                const resolved = await (0, core_bundle_mjs_1.resolveActiveLMStudioChatId)();
                if (resolved?.ok)
                    currentLmChatId = resolved.chatId;
            }
            catch { }
        }
        const primaryOutDir = currentLmWorkingDir ||
            (currentLmChatId ? (0, core_bundle_mjs_1.getLMStudioWorkingDir)(currentLmChatId) : undefined);
        if (!primaryOutDir) {
            console.error("[detect_object] could not resolve working directory");
            return {
                content: [
                    {
                        type: "text",
                        text: "detect_object failed: could not resolve LM Studio chat working directory.",
                    },
                ],
                isError: true,
            };
        }
        console.log("[detect_object] primaryOutDir:", primaryOutDir);
        await fs_1.default.promises.mkdir(primaryOutDir, { recursive: true }).catch(() => { });
        // Sync attachments so state is up to date
        console.log("[detect_object] syncing attachments...");
        try {
            await (0, core_bundle_mjs_1.syncAttachmentsToState)(primaryOutDir, false, Number.MAX_SAFE_INTEGER);
        }
        catch (e) {
            console.warn("[detect_object] attachment sync failed (non-fatal):", e?.message ?? e);
        }
        console.log("[detect_object] attachment sync done");
        console.log("[detect_object] reading state...");
        const st = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
        const attachments = Array.isArray(st?.attachments) ? st.attachments : [];
        const pictures = Array.isArray(st?.pictures) ? st.pictures : [];
        const imageRecords = Array.isArray(st?.images) ? st.images : [];
        const images = imageRecords
            .filter((r) => r && typeof r.filename === "string")
            .sort((a, b) => (a.i || 0) - (b.i || 0))
            .map((r) => ({ i: r.i || 1, path: path_1.default.join(primaryOutDir, r.filename) }));
        const variantRecords = Array.isArray(st?.variants) ? st.variants : [];
        const variants = variantRecords
            .filter((v) => v && typeof v.filename === "string")
            .map((v) => ({ v: v.v || 1, path: path_1.default.join(primaryOutDir, v.filename) }));
        console.log("[detect_object] state:", {
            attachments: attachments.length,
            images: images.length,
            variants: variants.length,
            pictures: pictures.length,
        });
        const sourceEntries = [];
        async function resolveOneBuf(rawCanvas) {
            const pref = parsePrefixedNotation(rawCanvas);
            if (!pref) {
                const pathToken = await resolveTargetPathToken(rawCanvas, primaryOutDir);
                if (!pathToken)
                    throw new Error(`Invalid canvas notation: ${rawCanvas}`);
                return fs_1.default.promises.readFile(pathToken);
            }
            if (pref.pool === "attachment") {
                const rec = attachments.find((a) => a?.a === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for attachment a${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
            else if (pref.pool === "image") {
                const rec = imageRecords.find((r) => r?.i === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for image i${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
            else if (pref.pool === "variant") {
                const rec = variantRecords.find((v) => v?.v === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for variant v${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
            else {
                const rec = pictures.find((p) => p?.p === pref.index);
                const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!previewRel)
                    throw new Error(`Preview for picture p${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
            }
        }
        // Resolves the original (full-resolution) file for a canvas notation.
        // Falls back to previewFallback when the original is unavailable.
        async function resolveOriginalBuf(rawCanvas, previewFallback) {
            try {
                const pref = parsePrefixedNotation(rawCanvas);
                if (!pref) {
                    const pathToken = await resolveTargetPathToken(rawCanvas, primaryOutDir);
                    return pathToken ? await fs_1.default.promises.readFile(pathToken) : previewFallback;
                }
                if (pref.pool === "attachment") {
                    const rec = attachments.find((a) => a?.a === pref.index);
                    const originAbs = rec && typeof rec.originAbs === "string" ? rec.originAbs : "";
                    if (!originAbs)
                        return previewFallback;
                    return await fs_1.default.promises.readFile(originAbs);
                }
                let rec;
                if (pref.pool === "image")
                    rec = imageRecords.find((r) => r?.i === pref.index);
                else if (pref.pool === "variant")
                    rec = variantRecords.find((v) => v?.v === pref.index);
                else
                    rec = pictures.find((p) => p?.p === pref.index);
                const filename = rec && typeof rec.filename === "string" ? rec.filename : "";
                if (!filename)
                    return previewFallback;
                return await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, filename));
            }
            catch {
                return previewFallback;
            }
        }
        try {
            if (rawTargets.length > 0) {
                for (const t of rawTargets) {
                    const buf = await resolveOneBuf(t);
                    const origBuf = await resolveOriginalBuf(t, buf);
                    sourceEntries.push({ id: t, buf, origBuf });
                }
            }
            else {
                // Auto-select single source
                const total = attachments.length + variantRecords.length + imageRecords.length + pictures.length;
                if (total === 0)
                    throw new Error("No source image available.");
                if (total > 1)
                    throw new Error("Ambiguous source — specify targets explicitly.");
                let buf;
                let id;
                if (attachments.length === 1) {
                    const rec = attachments[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for attachment not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `a${typeof rec.a === "number" ? rec.a : 1}`;
                }
                else if (variantRecords.length === 1) {
                    const rec = variantRecords[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for variant not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `v${typeof rec.v === "number" ? rec.v : 1}`;
                }
                else if (imageRecords.length === 1) {
                    const rec = imageRecords[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for image not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `i${typeof rec.i === "number" ? rec.i : 1}`;
                }
                else {
                    const rec = pictures[0];
                    const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
                    if (!previewRel)
                        throw new Error("Preview for picture not found.");
                    buf = await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, previewRel));
                    id = `p${rec.p ?? 1}`;
                }
                const origBuf = await resolveOriginalBuf(id, buf);
                sourceEntries.push({ id, buf, origBuf });
            }
        }
        catch (e) {
            return {
                content: [{ type: "text", text: String(e?.message || e) }],
                isError: true,
            };
        }
        console.log("[detect_object] sources resolved:", sourceEntries.map((s) => s.id));
        const globalConfig = getGlobalConfig(ctl);
        const envEmbedPngMetadata = process.env.EMBED_PNG_METADATA;
        const embedPngMetadata = getGlobalBoolean(globalConfig, "embedPngMetadata", envEmbedPngMetadata === undefined ? config_js_1.defaultPluginSettings.embedPngMetadata : envEmbedPngMetadata !== "false");
        let visionBaseUrl = getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || config_js_1.defaultPluginSettings.embeddingBaseUrl);
        const visionApiKey = getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || config_js_1.defaultPluginSettings.embeddingApiKey);
        let visionModelKey = getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || config_js_1.defaultPluginSettings.qwen3VlModelPath);
        const envDetectMaxTokens = Number.parseInt(process.env.DETECT_MAX_TOKENS || "", 10);
        const envDetectTemperature = Number.parseFloat(process.env.DETECT_TEMPERATURE || "");
        const configuredDetectMaxTokens = Math.floor(getGlobalNumber(globalConfig, "detectMaxTokens", Number.isFinite(envDetectMaxTokens) && envDetectMaxTokens > 0 ? envDetectMaxTokens : config_js_1.defaultPluginSettings.detectMaxTokens));
        const configuredDetectTemperature = getGlobalNumber(globalConfig, "detectTemperature", Number.isFinite(envDetectTemperature) ? envDetectTemperature : config_js_1.defaultPluginSettings.detectTemperature);
        const detectionConfig = {
            task,
            odPrompt: getGlobalString(globalConfig, "qwen3VlOdPrompt", process.env.DETECT_OD_PROMPT || config_js_1.defaultPluginSettings.qwen3VlOdPrompt) || undefined,
            maxTokens: configuredDetectMaxTokens,
            temperature: configuredDetectTemperature,
            timeoutMs: 120_000,
        };
        const tmpPaths = [];
        const detectionItems = [];
        for (const entry of sourceEntries) {
            const visionBuf = await normalizeVisionBuffer(entry.buf);
            const tmpPath = path_1.default.join(primaryOutDir, `_tmp_detect_src_${safeIdForFilename(entry.id)}_${Date.now()}.png`);
            await fs_1.default.promises.writeFile(tmpPath, visionBuf);
            tmpPaths.push(tmpPath);
            detectionItems.push({ id: entry.id, filePath: tmpPath });
        }
        console.log("[detect_object] calling detection API for", detectionItems.length, "items");
        const progressTotalSteps = sourceEntries.length * 2 + 4;
        let batchResult = {
            results: [],
            totalInferenceTimeMs: 0,
            backend: "vision-api",
        };
        try {
            (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Detecting objects in ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"}...`);
            (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, progressTotalSteps, `Preparing ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"} for object detection...`);
            const visionApi = resolveVisionApiFromEnv();
            const ready = await (0, VisionAnalyzer_js_1.ensureVisionModelReady)(visionApi, {
                baseUrl: visionBaseUrl,
                apiKey: visionApiKey,
                modelKey: visionModelKey,
                status: (message) => { try {
                    statusCtx.status?.(message);
                }
                catch { } },
                llamaServer: visionApi === "llama-server" ? resolveLlamaServerConfig(globalConfig) : undefined,
            });
            if (!ready.ok) {
                throw new Error(ready.error);
            }
            // llama-server strand: router's own exact tagged model id/address, not the configured ones.
            if (ready.resolvedBaseUrl)
                visionBaseUrl = ready.resolvedBaseUrl;
            if (ready.resolvedModelKey)
                visionModelKey = ready.resolvedModelKey;
            for (let idx = 0; idx < detectionItems.length; idx++) {
                const item = detectionItems[idx];
                (0, toolProgress_js_1.reportToolStep)(statusCtx, idx + 2, progressTotalSteps, `Detecting objects in ${item.id} (${idx + 1}/${detectionItems.length})...`);
                const singleResult = await (0, VisionAnalyzer_js_1.detectLmStudioVisionBatch)([item], {
                    ...detectionConfig,
                    baseUrl: visionBaseUrl,
                    apiKey: visionApiKey,
                    model: visionModelKey,
                });
                batchResult.results.push(...singleResult.results);
                batchResult.totalInferenceTimeMs += singleResult.totalInferenceTimeMs;
                batchResult.backend = singleResult.backend;
            }
            console.log("[detect_object] detection API returned:", {
                results: batchResult.results.length,
                totalMs: batchResult.totalInferenceTimeMs,
            });
            try {
                const totalObjects = batchResult.results.reduce((s, r) => s + (r.objects?.length ?? 0), 0);
                const ms = Math.round(batchResult.totalInferenceTimeMs);
                (0, toolProgress_js_1.reportToolStep)(statusCtx, sourceEntries.length + 2, progressTotalSteps, `${totalObjects} object${totalObjects === 1 ? "" : "s"} found across ${batchResult.results.length} image${batchResult.results.length === 1 ? "" : "s"} (${ms}ms); drawing bounding boxes...`);
            }
            catch { }
        }
        finally {
            for (const tp of tmpPaths)
                await fs_1.default.promises.unlink(tp).catch(() => { });
        }
        if (!batchResult.results.length) {
            return {
                content: [{ type: "text", text: "detect_object: no results returned from detection API." }],
                isError: true,
            };
        }
        // Per-image: draw bboxes, save, generate preview, build state records
        const variantPreviewSpec = core_bundle_mjs_1.VARIANT_FULL_CONFIG.preview;
        const stamp = isoStampCompact();
        let nextI = Math.max(1, st.counters?.nextImageI ?? 1);
        const imageRecordsForState = [];
        const resultEntries = [];
        const httpBase = await (0, core_bundle_mjs_1.getHealthyServerBaseUrl)();
        for (let idx = 0; idx < batchResult.results.length; idx++) {
            const detResult = batchResult.results[idx];
            const sourceId = sourceEntries[idx]?.id ?? `canvas${idx + 1}`;
            const origBuf = sourceEntries[idx].origBuf;
            const currentI = nextI++;
            (0, toolProgress_js_1.reportToolStep)(statusCtx, sourceEntries.length + 3 + idx, progressTotalSteps, `Drawing boxes for ${sourceId} (${idx + 1}/${batchResult.results.length})...`);
            const bboxes = detResult.objects.map((o) => o.bbox);
            console.log(`[detect_object] drawing ${bboxes.length} bboxes for ${sourceId}...`);
            // Draw on the original-resolution file; sourceDims carries the preview space in which
            // the detection server reported its bbox coordinates so they get scaled up correctly.
            const annotatedBuf = await (0, drawBboxesOnImage_js_1.drawBboxesOnImage)(origBuf, bboxes, {
                sourceDims: { width: detResult.imageWidth, height: detResult.imageHeight },
                palette: true,
            });
            const analysis = {
                schema: "ceveyne.image-analysis/v1",
                tool: "detect_object",
                sourceNotation: sourceId,
                inference: {
                    model: visionModelKey,
                    ...(task ? { query: task } : {}),
                    detectorPromptSha256: (0, crypto_1.createHash)("sha256").update(detectionConfig.odPrompt ?? "").digest("hex"),
                    maxTokens: configuredDetectMaxTokens,
                    temperature: configuredDetectTemperature,
                },
                render: { palette: true },
                detections: detResult.objects.map((object) => ({
                    label: object.label,
                    bbox: { x1: object.bbox[0], y1: object.bbox[1], x2: object.bbox[2], y2: object.bbox[3] },
                })),
            };
            const savedBuffer = embedPngMetadata
                ? (0, pngMetadata_js_1.injectXmpIntoBuffer)(annotatedBuf, {
                    ...(task ? { prompt: task } : {}),
                    model: visionModelKey,
                    mode: "object_detection",
                    generatedBy: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/detect_object`,
                    creatorTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/detect_object`,
                    analysis,
                })
                : annotatedBuf;
            const baseName = `image-${stamp}-i${currentI}`;
            const savedPath = path_1.default.join(primaryOutDir, `${baseName}.png`);
            await fs_1.default.promises.writeFile(savedPath, savedBuffer);
            const savedFileUrl = (0, url_1.pathToFileURL)(savedPath).toString();
            const savedSize = savedBuffer.length;
            console.log(`[detect_object] annotated image written: ${savedPath} (${savedSize} bytes)`);
            let preview = null;
            try {
                const p = await (0, core_bundle_mjs_1.generatePreviewFromBuffer)(savedBuffer, primaryOutDir, `${baseName}.png`, variantPreviewSpec);
                preview = {
                    ok: true,
                    filePath: p.previewAbs,
                    fileName: p.previewFilename,
                    fileUrl: (0, url_1.pathToFileURL)(p.previewAbs).toString(),
                    size_bytes: p.data.length,
                    width: p.width,
                    height: p.height,
                    mimeType: "image/jpeg",
                    dataBase64: p.data.toString("base64"),
                };
            }
            catch (e) {
                console.warn(`[detect_object] preview generation failed for ${sourceId}:`, String(e));
            }
            const httpOriginal = httpBase
                ? (0, core_bundle_mjs_1.toHttpOriginalUrl)(`${baseName}.png`, httpBase, currentLmChatId || undefined)
                : "";
            const httpPreview = (() => {
                if (!httpBase || !currentLmChatId || !preview?.fileName)
                    return "";
                return (0, core_bundle_mjs_1.toHttpPreviewUrl)(preview.fileName, httpBase, currentLmChatId);
            })();
            imageRecordsForState.push({
                filename: `${baseName}.png`,
                preview: preview ? `preview-${baseName}.jpg` : undefined,
                i: currentI,
                sourceTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/detect_object`,
                detectSource: sourceId,
                task,
                imageWidth: detResult.imageWidth,
                imageHeight: detResult.imageHeight,
                analysisMetadata: analysis,
                detections: detResult.objects.map((o) => ({
                    label: o.label,
                    bbox: { x1: o.bbox[0], y1: o.bbox[1], x2: o.bbox[2], y2: o.bbox[3] },
                    crop: {
                        cropLeft: o.cropLeft,
                        cropRight: o.cropRight,
                        cropTop: o.cropTop,
                        cropBottom: o.cropBottom,
                    },
                })),
            });
            resultEntries.push({ id: sourceId, i: currentI, detResult, savedPath, savedFileUrl, savedSize, preview, httpOriginal, httpPreview });
        }
        // Update state once for all images
        console.log("[detect_object] updating state...");
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps - 1, progressTotalSteps, "Updating image state and audit log...");
        try {
            const stateForUpdate = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
            const appendResult = (0, core_bundle_mjs_1.appendImages)(stateForUpdate, imageRecordsForState);
            if (appendResult.changed) {
                await (0, core_bundle_mjs_1.writeStateAtomic)(primaryOutDir, stateForUpdate);
                console.log("[detect_object] state written, nextImageI:", stateForUpdate.counters?.nextImageI);
            }
        }
        catch (e) {
            console.warn("[detect_object] state update failed:", String(e));
        }
        // Audit log — effectiveRequestId is reused below in each summary entry, so a caller
        // (the MCP adapter) can always name its HTML report after this exact audit requestId,
        // even for a batch call whose top-level result is an array (see summaries below).
        const effectiveRequestId = requestId ?? `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`;
        try {
            const audit = (0, core_bundle_mjs_1.buildAuditLogger)({ backend: "detect_object", mode: "detect_object", requestId: effectiveRequestId });
            if (currentLmChatId)
                audit.setChatId(currentLmChatId);
            audit.setUserRequest({ targets: rawTargets, task });
            audit.setOutput({
                images: resultEntries.map((r) => ({
                    id: r.id,
                    i: r.i,
                    detections: r.detResult.objects.length,
                    path: r.savedPath,
                    url: r.savedFileUrl,
                    bytes: r.savedSize,
                    ...(r.httpOriginal ? { http_url: r.httpOriginal } : {}),
                    ...(r.preview ? { preview_path: r.preview.filePath, preview_url: r.preview.fileUrl } : {}),
                    ...(r.httpPreview ? { http_preview_url: r.httpPreview } : {}),
                })),
            });
            await audit.write();
        }
        catch (e) {
            console.warn("[detect_object] audit logging failed:", String(e));
        }
        // Assemble tool result
        const envPreviewRaw = process.env["PREVIEW_IN_CHAT"];
        const previewInChat = envPreviewRaw === undefined
            ? true
            : envPreviewRaw === "1" || envPreviewRaw.toLowerCase() === "true";
        const summaries = resultEntries.map((r) => ({
            tool: "detect_object",
            requestId: effectiveRequestId,
            source: r.id,
            i: r.i,
            imageWidth: r.detResult.imageWidth,
            imageHeight: r.detResult.imageHeight,
            inferenceTimeMs: r.detResult.inferenceTimeMs,
            detections: r.detResult.objects.map((o) => ({
                label: o.label,
                bbox: { x1: o.bbox[0], y1: o.bbox[1], x2: o.bbox[2], y2: o.bbox[3] },
                crop: {
                    left: { pct: o.cropLeft, px: Math.round((o.cropLeft / 100) * r.detResult.imageWidth) },
                    right: { pct: o.cropRight, px: Math.round((o.cropRight / 100) * r.detResult.imageWidth) },
                    top: { pct: o.cropTop, px: Math.round((o.cropTop / 100) * r.detResult.imageHeight) },
                    bottom: { pct: o.cropBottom, px: Math.round((o.cropBottom / 100) * r.detResult.imageHeight) },
                },
                crop_tool_hint: "Pass crop.left.pct as cropLeft, crop.right.pct as cropRight, crop.top.pct as cropTop, crop.bottom.pct as cropBottom to the crop tool.",
            })),
        }));
        const reviewHint = "Carefully examine the preview to make absolutely sure that the object detection matches your intent.";
        const content = [];
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps, progressTotalSteps, "Assembling detection result...");
        for (const r of resultEntries) {
            const fallbackPreviewUrl = r.preview?.fileUrl || r.savedFileUrl;
            const previewLine = `Preview i${r.i}: ${r.httpPreview ? r.httpPreview : fallbackPreviewUrl}`;
            const originalLine = `Original i${r.i}: ${r.httpOriginal ? r.httpOriginal : r.savedFileUrl}`;
            if (previewInChat && r.preview) {
                const fname = String(r.preview.fileName || "");
                content.push({
                    type: "image",
                    fileName: fname,
                    mimeType: r.preview.mimeType,
                    markdown: `![Detected Objects i${r.i} (${r.id})](./${fname})`,
                    $hint: "This is an image file. Present the image to the user by using the markdown above.",
                });
            }
            // TODO: Restore when LM Studio renders file/HTTP links again
            // content.push({ type: "text", text: previewLine });
            // content.push({ type: "text", text: originalLine });
        }
        const totalMs = Math.round(batchResult.totalInferenceTimeMs);
        if (totalMs > 0) {
            content.push({ type: "text", text: `Total inference time: ${totalMs}ms` });
        }
        content.push({
            type: "text",
            text: JSON.stringify(summaries.length === 1 ? summaries[0] : summaries),
            ...(previewInChat ? {} : { $hint: reviewHint }),
        });
        return { content };
    }
    catch (error) {
        return {
            content: [
                {
                    type: "text",
                    text: `detect_object failed: ${error.message || String(error)}`,
                },
            ],
            isError: true,
        };
    }
}
// ─────────────────────────────────────────────────────────────────────────────
// annotate_image
// ─────────────────────────────────────────────────────────────────────────────
/**
 * Expand (positive) or shrink (negative) a bbox [x1, y1, x2, y2].
 * Number → % of box diagonal. String → value + optional 'px' or '%' suffix.
 */
function applyFrameAdjust(bbox, frameAdjust, imgW, imgH) {
    const [x1, y1, x2, y2] = bbox;
    const diag = Math.hypot(x2 - x1, y2 - y1);
    let d_px;
    if (typeof frameAdjust === "string") {
        const m = String(frameAdjust).trim().match(/^([+-]?\d+(?:\.\d+)?)\s*(%|px)?$/i);
        if (!m)
            return bbox;
        const val = parseFloat(m[1]);
        d_px = m[2]?.toLowerCase() === "px" ? val : (val / 100) * diag;
    }
    else {
        d_px = (frameAdjust / 100) * diag;
    }
    return [
        Math.max(0, Math.round(x1 - d_px)),
        Math.max(0, Math.round(y1 - d_px)),
        Math.min(imgW - 1, Math.round(x2 + d_px)),
        Math.min(imgH - 1, Math.round(y2 + d_px)),
    ];
}
exports.AnnotateImageParamsShape = {
    targets: exports.FlexibleTargetsList.optional().describe("One or more image notations to process. Each notation is a letter followed by a number: " +
        "a=attachment (a1, a2, …), i=generated image (i1, i2, …), v=variant (v1, v2, …), p=picture (p1, p2, …). " +
        "Also accepts a filename in the scratchpad folder or an absolute path. " +
        "Pass via the targets field, e.g. annotate_image({\"targets\":[\"a1\", \"i3\"]}). " +
        "Omit when there is exactly one image — it will be selected automatically."),
    task: zod_1.z
        .string()
        .optional()
        .default("")
        .describe("What to detect. Omit for full-image general object detection. " +
        "Use natural language to target specific subjects (e.g. 'all faces and hands', 'the dog', 'cars and bicycles'). " +
        "Not used on correction calls — detections are loaded from state."),
    color: zod_1.z
        .string()
        .optional()
        .describe("Box color for all detected objects. CSS color name or hex, e.g. 'pink', 'red', '#FF0000'. Default: magenta."),
    lineWeight: zod_1.z
        .coerce.number().int().min(1).max(50)
        .optional()
        .describe("Line thickness in pixels for all bounding boxes. Default: 5."),
    frameAdjust: zod_1.z
        .union([zod_1.z.number(), zod_1.z.string()])
        .optional()
        .describe("Expand (positive) or shrink (negative) bounding boxes before drawing. " +
        "Number: percent of box diagonal (e.g. 5 = +5 %). " +
        "String: value + optional 'px' or '%' suffix, e.g. '10px', '-5%'. " +
        "Without detectLabel: applied to all boxes. With detectLabel: applied to the selected box only. Default: 5."),
    detectLabel: zod_1.z
        .preprocess((val) => {
        if (typeof val === "string") {
            const t = val.trim();
            if (t.startsWith("[")) {
                try {
                    const parsed = JSON.parse(t);
                    if (Array.isArray(parsed)) {
                        return parsed.map((el) => String(el).trim()).filter((s) => s.length > 0);
                    }
                }
                catch { }
            }
        }
        return val;
    }, zod_1.z.union([
        zod_1.z.string().transform((s) => s.split(/\s*,\s*/).map((x) => x.trim()).filter((x) => x.length > 0)),
        zod_1.z.array(zod_1.z.string().min(1)),
    ]))
        .optional()
        .describe("On a correction call: label(s) to match (case-insensitive). " +
        "Single string, comma-separated list ('left eye, right eye'), or JSON array. " +
        "When set, ONLY the matching detection(s) are drawn — all others are omitted. " +
        "Single label with no detectIndex: auto-expands to ALL detections for that label (Option A). " +
        "canvas may be the original source (e.g. a1) or the previous annotate_image result (e.g. i3)."),
    detectIndex: zod_1.z
        .union([
        zod_1.z.string().transform((s) => (s.match(/\d+/g) ?? []).map(Number)),
        zod_1.z.coerce.number().int().min(0).transform((n) => [n]),
        zod_1.z.array(zod_1.z.coerce.number().int().min(0)),
    ])
        .optional()
        .describe("Zero-based index or list of indices, parallel to detectLabel. " +
        "indices[li] ?? indices[0] ?? 0 for missing entries. Default: 0. " +
        "Single label + multiple indices draws that label at each specified occurrence (Option B). " +
        "Bracket notation accepted: '[2, 4, 7]'."),
    x1: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual left edge(s) in original image pixels. " +
        "Scalar: applies to all selected detections. Array (parallel to detectLabel): null = keep stored value. " +
        "E.g. x1=[null,50,null] moves only the second detection's left edge."),
    y1: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual top edge(s) in original image pixels. Scalar or array (null = keep stored). See x1."),
    x2: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual right edge(s) in original image pixels. Scalar or array (null = keep stored). See x1."),
    y2: zod_1.z.preprocess((val) => { if (typeof val === "string" && val.trim().startsWith("[")) {
        try {
            return JSON.parse(val);
        }
        catch { }
    } return val; }, zod_1.z.union([zod_1.z.number(), zod_1.z.array(zod_1.z.union([zod_1.z.number(), zod_1.z.null()]))])).optional().describe("Manual bottom edge(s) in original image pixels. Scalar or array (null = keep stored). See x1."),
};
/**
 * Resolve a per-box coordinate override, analogous to resolveBoxOverride in mask.
 * override: scalar (applies to all boxes) | array (parallel, null = keep stored) | undefined.
 * Returns undefined when no override → caller uses storedValue.
 */
function resolveCoordOverride(override, index) {
    if (override === undefined)
        return undefined;
    if (Array.isArray(override)) {
        const entry = override[index];
        if (entry === null || entry === undefined)
            return undefined;
        return entry;
    }
    return override;
}
/**
 * Option A helper: count occurrences of label in detections and return [0..N-1] if N > 1, else [0].
 */
function expandDetectIndices(detections, label) {
    const lower = label.toLowerCase();
    let count = 0;
    for (const d of detections) {
        if (d.label.toLowerCase() === lower)
            count++;
    }
    return count > 1 ? Array.from({ length: count }, (_, i) => i) : [0];
}
async function handleAnnotateImage(args, ctl, statusCtx = {}, requestId) {
    try {
        // ── Parse args ─────────────────────────────────────────────────────
        let rawTargets = [];
        if (Array.isArray(args?.targets)) {
            rawTargets = args.targets.map((s) => String(s).trim()).filter(Boolean);
        }
        else if (typeof args?.targets === "string" && args.targets.trim()) {
            const trimmed = args.targets.trim();
            if (trimmed.startsWith("[")) {
                try {
                    const parsed = JSON.parse(trimmed);
                    rawTargets = Array.isArray(parsed)
                        ? parsed.map((s) => String(s).trim()).filter(Boolean)
                        : trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
                catch {
                    rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
                }
            }
            else {
                rawTargets = trimmed.split(/[\s,]+/).map((s) => s.trim()).filter(Boolean);
            }
        }
        const taskArg = typeof args?.task === "string" && args.task.trim() ? args.task.trim() : "";
        const globalColor = typeof args?.color === "string" && args.color.trim() ? args.color.trim() : "magenta";
        const globalLineWeight = typeof args?.lineWeight === "number"
            ? Math.max(1, Math.min(50, Math.round(args.lineWeight))) : 5;
        const globalFrameAdjust = args?.frameAdjust !== undefined ? args.frameAdjust : 5;
        // detectLabel: string → string[] via comma-split; array → as-is
        let globalLabels;
        {
            const dlRaw = args?.detectLabel;
            if (Array.isArray(dlRaw)) {
                const arr = dlRaw.map((s) => String(s).trim()).filter(Boolean);
                if (arr.length > 0)
                    globalLabels = arr;
            }
            else if (typeof dlRaw === "string" && dlRaw.trim()) {
                globalLabels = dlRaw.split(/\s*,\s*/).map((x) => x.trim()).filter(Boolean);
            }
        }
        // detectIndex: number → [n]; string → extract digit runs; array → as-is
        let globalIndices = [];
        {
            const diRaw = args?.detectIndex;
            if (Array.isArray(diRaw)) {
                globalIndices = diRaw.map((n) => typeof n === "number" ? Math.floor(n) : parseInt(String(n), 10)).filter((n) => !isNaN(n) && n >= 0);
            }
            else if (typeof diRaw === "string" && diRaw.trim()) {
                globalIndices = (diRaw.match(/\d+/g) ?? []).map(Number);
            }
            else if (typeof diRaw === "number" && diRaw >= 0) {
                globalIndices = [Math.floor(diRaw)];
            }
        }
        const rawX1 = args?.x1;
        const rawY1 = args?.y1;
        const rawX2 = args?.x2;
        const rawY2 = args?.y2;
        // Normalise: scalar number | number-or-null array | undefined
        function normaliseCoord(raw) {
            if (raw === undefined || raw === null)
                return undefined;
            if (typeof raw === "number")
                return raw;
            if (Array.isArray(raw))
                return raw.map((v) => (v === null || v === undefined) ? null : Number(v));
            if (typeof raw === "string") {
                const t = raw.trim();
                if (t.startsWith("[")) {
                    try {
                        const p = JSON.parse(t);
                        if (Array.isArray(p))
                            return p.map((v) => (v === null || v === undefined) ? null : Number(v));
                    }
                    catch { }
                }
                const n = Number(t);
                return isNaN(n) ? undefined : n;
            }
            return undefined;
        }
        const manualX1 = normaliseCoord(rawX1);
        const manualY1 = normaliseCoord(rawY1);
        const manualX2 = normaliseCoord(rawX2);
        const manualY2 = normaliseCoord(rawY2);
        const hasAnyManualCoord = manualX1 !== undefined || manualY1 !== undefined || manualX2 !== undefined || manualY2 !== undefined;
        console.log("[annotate_image] invoked", { targets: rawTargets, task: taskArg, color: globalColor, lineWeight: globalLineWeight, frameAdjust: globalFrameAdjust, detectLabels: globalLabels, detectIndices: globalIndices, manualCoords: { x1: manualX1, y1: manualY1, x2: manualX2, y2: manualY2 } });
        // ── Resolve working directory ──────────────────────────────────────
        let currentLmChatId = null;
        let currentLmWorkingDir = null;
        try {
            const chatCtx = await (0, core_bundle_mjs_1.getActiveChatContext)();
            if (chatCtx?.chatId)
                currentLmChatId = chatCtx.chatId;
            if (chatCtx?.workingDir)
                currentLmWorkingDir = chatCtx.workingDir;
        }
        catch { }
        if (!currentLmChatId) {
            try {
                const resolved = await (0, core_bundle_mjs_1.resolveActiveLMStudioChatId)();
                if (resolved?.ok)
                    currentLmChatId = resolved.chatId;
            }
            catch { }
        }
        const primaryOutDir = currentLmWorkingDir ||
            (currentLmChatId ? (0, core_bundle_mjs_1.getLMStudioWorkingDir)(currentLmChatId) : undefined);
        if (!primaryOutDir) {
            return {
                content: [{ type: "text", text: "annotate_image failed: could not resolve LM Studio chat working directory." }],
                isError: true,
            };
        }
        await fs_1.default.promises.mkdir(primaryOutDir, { recursive: true }).catch(() => { });
        // ── Sync attachments ──────────────────────────────────────────────
        try {
            await (0, core_bundle_mjs_1.syncAttachmentsToState)(primaryOutDir, false, Number.MAX_SAFE_INTEGER);
        }
        catch (e) {
            console.warn("[annotate_image] attachment sync failed (non-fatal):", e?.message ?? e);
        }
        // ── Read state ────────────────────────────────────────────────────
        const st = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
        const attachments = Array.isArray(st?.attachments) ? st.attachments : [];
        const pictures = Array.isArray(st?.pictures) ? st.pictures : [];
        const imageRecords = Array.isArray(st?.images) ? st.images : [];
        const variantRecords = Array.isArray(st?.variants) ? st.variants : [];
        // ── Buffer resolution helpers ─────────────────────────────────────
        async function resolvePreviewBuf(notation) {
            const pref = parsePrefixedNotation(notation);
            if (!pref) {
                const pathToken = await resolveTargetPathToken(notation, primaryOutDir);
                if (!pathToken)
                    throw new Error(`Invalid notation: ${notation}`);
                return fs_1.default.promises.readFile(pathToken);
            }
            if (pref.pool === "attachment") {
                const rec = attachments.find((a) => a?.a === pref.index);
                const r = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!r)
                    throw new Error(`Preview for a${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
            }
            if (pref.pool === "image") {
                const rec = imageRecords.find((r) => r?.i === pref.index);
                const r = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!r)
                    throw new Error(`Preview for i${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
            }
            if (pref.pool === "variant") {
                const rec = variantRecords.find((v) => v?.v === pref.index);
                const r = rec && typeof rec.preview === "string" ? rec.preview : "";
                if (!r)
                    throw new Error(`Preview for v${pref.index} not found.`);
                return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
            }
            const rec = pictures.find((p) => p?.p === pref.index);
            const r = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!r)
                throw new Error(`Preview for p${pref.index} not found.`);
            return fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, r));
        }
        async function resolveOriginalBuf(notation, fallback) {
            try {
                const pref = parsePrefixedNotation(notation);
                if (!pref) {
                    const pathToken = await resolveTargetPathToken(notation, primaryOutDir);
                    return pathToken ? await fs_1.default.promises.readFile(pathToken) : fallback;
                }
                if (pref.pool === "attachment") {
                    const rec = attachments.find((a) => a?.a === pref.index);
                    const abs = rec && typeof rec.originAbs === "string" ? rec.originAbs : "";
                    if (!abs)
                        return fallback;
                    return await fs_1.default.promises.readFile(abs);
                }
                let rec;
                if (pref.pool === "image")
                    rec = imageRecords.find((r) => r?.i === pref.index);
                else if (pref.pool === "variant")
                    rec = variantRecords.find((v) => v?.v === pref.index);
                else
                    rec = pictures.find((p) => p?.p === pref.index);
                const fn = rec && typeof rec.filename === "string" ? rec.filename : "";
                if (!fn)
                    return fallback;
                return await fs_1.default.promises.readFile(path_1.default.join(primaryOutDir, fn));
            }
            catch {
                return fallback;
            }
        }
        // ── Resolve auto-selected source when no targets given ─────────────
        let autoId = null;
        if (rawTargets.length === 0) {
            const total = attachments.length + variantRecords.length + imageRecords.length + pictures.length;
            if (total === 0) {
                return { content: [{ type: "text", text: "No source image available." }], isError: true };
            }
            if (total > 1) {
                return { content: [{ type: "text", text: "Ambiguous source — specify targets explicitly." }], isError: true };
            }
            if (attachments.length === 1)
                autoId = `a${typeof attachments[0]?.a === "number" ? attachments[0].a : 1}`;
            else if (variantRecords.length === 1)
                autoId = `v${typeof variantRecords[0]?.v === "number" ? variantRecords[0].v : 1}`;
            else if (imageRecords.length === 1)
                autoId = `i${typeof imageRecords[0]?.i === "number" ? imageRecords[0].i : 1}`;
            else
                autoId = `p${pictures[0]?.p ?? 1}`;
            rawTargets = [autoId];
        }
        // ── Classify each target: detect or redraw ────────────────────────
        // If task is explicitly provided, always run fresh inference (ignore any prior record).
        const forceDetect = taskArg.length > 0;
        const resolvedEntries = [];
        for (const rawId of rawTargets) {
            let drawSourceId = rawId;
            let stateRec = null;
            // Case A: target is an iN detection/annotation result
            const pref = parsePrefixedNotation(rawId);
            if (pref?.pool === "image") {
                const imgRec = imageRecords.find((r) => r?.i === pref.index);
                if (imgRec &&
                    Array.isArray(imgRec.detections) &&
                    imgRec.detections.length > 0 &&
                    typeof imgRec.imageWidth === "number") {
                    stateRec = imgRec;
                    drawSourceId = (typeof imgRec.detectSource === "string" && imgRec.detectSource)
                        ? imgRec.detectSource
                        : rawId;
                }
            }
            // Case B: target is an original (a1, p1, …) with a prior detection on record
            if (!stateRec) {
                const prior = [...imageRecords]
                    .reverse()
                    .find((r) => (r?.detectSource === rawId) &&
                    Array.isArray(r.detections) &&
                    r.detections.length > 0 &&
                    typeof r.imageWidth === "number");
                if (prior) {
                    stateRec = prior;
                    drawSourceId = rawId;
                }
            }
            try {
                if (stateRec && !forceDetect) {
                    const preview = await resolvePreviewBuf(drawSourceId).catch(() => null);
                    const origBuf = preview
                        ? await resolveOriginalBuf(drawSourceId, preview)
                        : Buffer.alloc(0);
                    resolvedEntries.push({
                        mode: "redraw",
                        id: drawSourceId,
                        origBuf,
                        task: typeof stateRec.task === "string" ? stateRec.task : taskArg,
                        detections: stateRec.detections,
                        imageWidth: stateRec.imageWidth,
                        imageHeight: stateRec.imageHeight,
                        analysisMetadata: stateRec.analysisMetadata,
                    });
                }
                else {
                    const previewBuf = await resolvePreviewBuf(rawId);
                    const origBuf = await resolveOriginalBuf(rawId, previewBuf);
                    resolvedEntries.push({ mode: "detect", id: rawId, previewBuf, origBuf });
                }
            }
            catch (e) {
                return {
                    content: [{ type: "text", text: String(e?.message || e) }],
                    isError: true,
                };
            }
        }
        // ── Run Qwen3-VL for detect-mode entries ───────────────────────────
        const detectEntries = resolvedEntries.filter((e) => e.mode === "detect");
        const progressTotalSteps = detectEntries.length > 0
            ? detectEntries.length + resolvedEntries.length + 4
            : resolvedEntries.length + 3;
        let batchResult = null;
        const globalConfig = getGlobalConfig(ctl);
        const envEmbedPngMetadata = process.env.EMBED_PNG_METADATA;
        const embedPngMetadata = getGlobalBoolean(globalConfig, "embedPngMetadata", envEmbedPngMetadata === undefined ? config_js_1.defaultPluginSettings.embedPngMetadata : envEmbedPngMetadata !== "false");
        let visionModelKey = "";
        let detectionConfig = null;
        if (detectEntries.length > 0) {
            let visionBaseUrl = getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || config_js_1.defaultPluginSettings.embeddingBaseUrl);
            const visionApiKey = getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || config_js_1.defaultPluginSettings.embeddingApiKey);
            visionModelKey = getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || config_js_1.defaultPluginSettings.qwen3VlModelPath);
            const envDetectMaxTokens = Number.parseInt(process.env.DETECT_MAX_TOKENS || "", 10);
            const envDetectTemperature = Number.parseFloat(process.env.DETECT_TEMPERATURE || "");
            const configuredDetectMaxTokens = Math.floor(getGlobalNumber(globalConfig, "detectMaxTokens", Number.isFinite(envDetectMaxTokens) && envDetectMaxTokens > 0 ? envDetectMaxTokens : config_js_1.defaultPluginSettings.detectMaxTokens));
            const configuredDetectTemperature = getGlobalNumber(globalConfig, "detectTemperature", Number.isFinite(envDetectTemperature) ? envDetectTemperature : config_js_1.defaultPluginSettings.detectTemperature);
            detectionConfig = {
                task: taskArg,
                odPrompt: getGlobalString(globalConfig, "qwen3VlOdPrompt", process.env.DETECT_OD_PROMPT || config_js_1.defaultPluginSettings.qwen3VlOdPrompt) || undefined,
                maxTokens: configuredDetectMaxTokens,
                temperature: configuredDetectTemperature,
                timeoutMs: 120_000,
            };
            const tmpPaths = [];
            const detectionItems = [];
            for (const entry of detectEntries) {
                const visionBuf = await normalizeVisionBuffer(entry.previewBuf);
                const tmpPath = path_1.default.join(primaryOutDir, `_tmp_annotate_src_${safeIdForFilename(entry.id)}_${Date.now()}.png`);
                await fs_1.default.promises.writeFile(tmpPath, visionBuf);
                tmpPaths.push(tmpPath);
                detectionItems.push({ id: entry.id, filePath: tmpPath });
            }
            try {
                (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Detecting objects in ${detectEntries.length} image${detectEntries.length === 1 ? "" : "s"}...`);
                (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, progressTotalSteps, `Preparing ${detectEntries.length} image${detectEntries.length === 1 ? "" : "s"} for annotation detection...`);
                const visionApi = resolveVisionApiFromEnv();
                const ready = await (0, VisionAnalyzer_js_1.ensureVisionModelReady)(visionApi, {
                    baseUrl: visionBaseUrl,
                    apiKey: visionApiKey,
                    modelKey: visionModelKey,
                    status: (message) => { try {
                        statusCtx.status?.(message);
                    }
                    catch { } },
                    llamaServer: visionApi === "llama-server" ? resolveLlamaServerConfig(globalConfig) : undefined,
                });
                if (!ready.ok) {
                    throw new Error(ready.error);
                }
                // llama-server strand: router's own exact tagged model id/address, not the configured ones.
                if (ready.resolvedBaseUrl)
                    visionBaseUrl = ready.resolvedBaseUrl;
                if (ready.resolvedModelKey)
                    visionModelKey = ready.resolvedModelKey;
                batchResult = {
                    results: [],
                    totalInferenceTimeMs: 0,
                    backend: "vision-api",
                };
                for (let idx = 0; idx < detectionItems.length; idx++) {
                    const item = detectionItems[idx];
                    (0, toolProgress_js_1.reportToolStep)(statusCtx, idx + 2, progressTotalSteps, `Detecting objects in ${item.id} (${idx + 1}/${detectionItems.length})...`);
                    const singleResult = await (0, VisionAnalyzer_js_1.detectLmStudioVisionBatch)([item], {
                        ...detectionConfig,
                        baseUrl: visionBaseUrl,
                        apiKey: visionApiKey,
                        model: visionModelKey,
                    });
                    batchResult.results.push(...singleResult.results);
                    batchResult.totalInferenceTimeMs += singleResult.totalInferenceTimeMs;
                    batchResult.backend = singleResult.backend;
                }
                try {
                    const totalObjects = batchResult.results.reduce((s, r) => s + (r.objects?.length ?? 0), 0);
                    const ms = Math.round(batchResult.totalInferenceTimeMs);
                    (0, toolProgress_js_1.reportToolStep)(statusCtx, detectEntries.length + 2, progressTotalSteps, `${totalObjects} object${totalObjects === 1 ? "" : "s"} found (${ms}ms); drawing boxes...`);
                }
                catch { }
            }
            finally {
                for (const tp of tmpPaths)
                    await fs_1.default.promises.unlink(tp).catch(() => { });
            }
            if (!batchResult || !batchResult.results.length) {
                return {
                    content: [{ type: "text", text: "annotate_image: no results returned from detection API." }],
                    isError: true,
                };
            }
        }
        else {
            (0, toolProgress_js_1.reportToolStatus)(statusCtx, `Redrawing ${resolvedEntries.length} annotated image${resolvedEntries.length === 1 ? "" : "s"} from stored detections...`);
            (0, toolProgress_js_1.reportToolStep)(statusCtx, 1, progressTotalSteps, `Redrawing ${resolvedEntries.length} annotated image${resolvedEntries.length === 1 ? "" : "s"} from stored detections...`);
        }
        // ── Per-entry: apply frameAdjust, draw, save ───────────────────────
        const variantPreviewSpec = core_bundle_mjs_1.VARIANT_FULL_CONFIG.preview;
        const stamp = isoStampCompact();
        let nextI = Math.max(1, st.counters?.nextImageI ?? 1);
        const imageRecordsForState = [];
        const resultEntries = [];
        const httpBase = await (0, core_bundle_mjs_1.getHealthyServerBaseUrl)();
        let detectResultIdx = 0;
        let resolvedIdx = 0;
        const drawBaseStep = detectEntries.length > 0 ? detectEntries.length + 3 : 2;
        for (const entry of resolvedEntries) {
            (0, toolProgress_js_1.reportToolStep)(statusCtx, drawBaseStep + resolvedIdx, progressTotalSteps, `Drawing annotation for ${entry.id} (${resolvedIdx + 1}/${resolvedEntries.length})...`);
            resolvedIdx++;
            let rawBboxes;
            let imgW;
            let imgH;
            let detObjects;
            let isRedraw;
            let entryTask;
            let inferenceTimeMs = 0;
            let bboxesAlreadyAdjusted = false;
            if (entry.mode === "redraw") {
                imgW = entry.imageWidth;
                imgH = entry.imageHeight;
                isRedraw = true;
                entryTask = entry.task;
                if (globalLabels !== undefined && globalLabels.length > 0) {
                    // detectLabel mode: resolve label/index pairs, draw ONLY the selected detections
                    let labels = [...globalLabels];
                    let indices = [...globalIndices];
                    // Option A: single label + no explicit index → auto-expand to all detections for that label
                    if (labels.length === 1 && indices.length === 0) {
                        const allIndices = expandDetectIndices(entry.detections, labels[0]);
                        if (allIndices.length > 1) {
                            labels = Array(allIndices.length).fill(labels[0]);
                            indices = allIndices;
                        }
                    }
                    // Option B: single label + multiple explicit indices → expand labels to match
                    if (labels.length === 1 && indices.length > 1) {
                        labels = Array(indices.length).fill(labels[0]);
                    }
                    // Pre-group detections by lowercase label once — O(1) lookup per box.
                    const detByLabel = new Map();
                    for (const d of entry.detections) {
                        const key = d.label.toLowerCase();
                        if (!detByLabel.has(key))
                            detByLabel.set(key, []);
                        detByLabel.get(key).push(d);
                    }
                    const resolvedBoxes = [];
                    for (let li = 0; li < labels.length; li++) {
                        const label = labels[li];
                        const idx = indices[li] ?? indices[0] ?? 0;
                        const selectedDet = detByLabel.get(label.toLowerCase())?.[idx];
                        if (!selectedDet) {
                            const available = [...new Set(entry.detections.map((d) => d.label))].join(", ");
                            return {
                                content: [{ type: "text", text: `annotate_image: label '${label}' (index ${idx}) not found in stored detections. Available: ${available || "(none)"}` }],
                                isError: true,
                            };
                        }
                        // Coord overrides: resolveCoordOverride per axis, fall back to stored value.
                        // Applies to all selected boxes; scalar = same for all, array = parallel (null = keep stored).
                        const ox1 = hasAnyManualCoord ? resolveCoordOverride(manualX1, li) : undefined;
                        const oy1 = hasAnyManualCoord ? resolveCoordOverride(manualY1, li) : undefined;
                        const ox2 = hasAnyManualCoord ? resolveCoordOverride(manualX2, li) : undefined;
                        const oy2 = hasAnyManualCoord ? resolveCoordOverride(manualY2, li) : undefined;
                        const bbox = [
                            ox1 ?? selectedDet.bbox.x1,
                            oy1 ?? selectedDet.bbox.y1,
                            ox2 ?? selectedDet.bbox.x2,
                            oy2 ?? selectedDet.bbox.y2,
                        ];
                        resolvedBoxes.push({ det: selectedDet, bbox });
                    }
                    rawBboxes = resolvedBoxes.map(({ bbox }) => applyFrameAdjust(bbox, globalFrameAdjust, imgW, imgH));
                    detObjects = resolvedBoxes.map(({ det, bbox }) => ({
                        ...det,
                        bbox: { x1: bbox[0], y1: bbox[1], x2: bbox[2], y2: bbox[3] },
                    }));
                    bboxesAlreadyAdjusted = true;
                }
                else if (globalIndices.length > 0) {
                    // No detectLabel but explicit detectIndex: select stored detections by position.
                    const selected = [];
                    for (const idx of globalIndices) {
                        const det = entry.detections[idx];
                        if (!det) {
                            return {
                                content: [{ type: "text", text: `annotate_image: detectIndex ${idx} out of range (${entry.detections.length} stored detections).` }],
                                isError: true,
                            };
                        }
                        selected.push(det);
                    }
                    rawBboxes = selected.map((d) => [d.bbox.x1, d.bbox.y1, d.bbox.x2, d.bbox.y2]);
                    detObjects = selected;
                    bboxesAlreadyAdjusted = false;
                }
                else {
                    // No detectLabel, no detectIndex: draw all stored boxes, apply frameAdjust to all
                    rawBboxes = entry.detections.map((d) => [d.bbox.x1, d.bbox.y1, d.bbox.x2, d.bbox.y2]);
                    detObjects = entry.detections;
                }
            }
            else {
                const detResult = batchResult.results[detectResultIdx++];
                rawBboxes = detResult.objects.map((o) => o.bbox);
                imgW = detResult.imageWidth;
                imgH = detResult.imageHeight;
                detObjects = detResult.objects.map((o) => ({
                    label: o.label,
                    bbox: { x1: o.bbox[0], y1: o.bbox[1], x2: o.bbox[2], y2: o.bbox[3] },
                    crop: { cropLeft: o.cropLeft, cropRight: o.cropRight, cropTop: o.cropTop, cropBottom: o.cropBottom },
                }));
                isRedraw = false;
                entryTask = taskArg;
                inferenceTimeMs = detResult.inferenceTimeMs ?? 0;
            }
            const adjustedBboxes = bboxesAlreadyAdjusted
                ? rawBboxes
                : rawBboxes.map((bbox) => applyFrameAdjust(bbox, globalFrameAdjust, imgW, imgH));
            const annotatedBuf = await (0, drawBboxesOnImage_js_1.drawBboxesOnImage)(entry.origBuf, adjustedBboxes, {
                sourceDims: { width: imgW, height: imgH },
                palette: false,
                color: globalColor,
                lineWeight: globalLineWeight,
            });
            const inheritedInference = entry.mode === "redraw" ? entry.analysisMetadata?.inference : undefined;
            const analysis = {
                schema: "ceveyne.image-analysis/v1",
                tool: "annotate_image",
                sourceNotation: entry.id,
                inference: entry.mode === "redraw"
                    ? inheritedInference
                        ? { ...inheritedInference, reused: true }
                        : undefined
                    : {
                        model: visionModelKey,
                        ...(entryTask ? { query: entryTask } : {}),
                        detectorPromptSha256: (0, crypto_1.createHash)("sha256").update(detectionConfig?.odPrompt ?? "").digest("hex"),
                        maxTokens: detectionConfig?.maxTokens,
                        temperature: detectionConfig?.temperature,
                    },
                render: { color: globalColor, lineWeight: globalLineWeight, frameAdjust: globalFrameAdjust },
                detections: detObjects.map((detection, index) => ({
                    label: detection.label,
                    bbox: {
                        x1: adjustedBboxes[index][0],
                        y1: adjustedBboxes[index][1],
                        x2: adjustedBboxes[index][2],
                        y2: adjustedBboxes[index][3],
                    },
                })),
            };
            const savedBuffer = embedPngMetadata
                ? (0, pngMetadata_js_1.injectXmpIntoBuffer)(annotatedBuf, {
                    ...(entryTask ? { prompt: entryTask } : {}),
                    ...(analysis.inference?.model ? { model: analysis.inference.model } : {}),
                    mode: isRedraw ? "image_annotation_redraw" : "image_annotation",
                    generatedBy: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/annotate_image`,
                    creatorTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/annotate_image`,
                    analysis,
                })
                : annotatedBuf;
            const currentI = nextI++;
            const baseName = `image-${stamp}-i${currentI}`;
            const savedPath = path_1.default.join(primaryOutDir, `${baseName}.png`);
            await fs_1.default.promises.writeFile(savedPath, savedBuffer);
            const savedFileUrl = (0, url_1.pathToFileURL)(savedPath).toString();
            const savedSize = savedBuffer.length;
            let preview = null;
            try {
                const p = await (0, core_bundle_mjs_1.generatePreviewFromBuffer)(savedBuffer, primaryOutDir, `${baseName}.png`, variantPreviewSpec);
                preview = {
                    ok: true,
                    filePath: p.previewAbs,
                    fileName: p.previewFilename,
                    fileUrl: (0, url_1.pathToFileURL)(p.previewAbs).toString(),
                    size_bytes: p.data.length,
                    width: p.width,
                    height: p.height,
                    mimeType: "image/jpeg",
                    dataBase64: p.data.toString("base64"),
                };
            }
            catch (e) {
                console.warn(`[annotate_image] preview generation failed for ${entry.id}:`, String(e));
            }
            const httpOriginal = httpBase
                ? (0, core_bundle_mjs_1.toHttpOriginalUrl)(`${baseName}.png`, httpBase, currentLmChatId || undefined) : "";
            const httpPreview = (() => {
                if (!httpBase || !currentLmChatId || !preview?.fileName)
                    return "";
                return (0, core_bundle_mjs_1.toHttpPreviewUrl)(preview.fileName, httpBase, currentLmChatId);
            })();
            imageRecordsForState.push({
                filename: `${baseName}.png`,
                preview: preview ? `preview-${baseName}.jpg` : undefined,
                i: currentI,
                sourceTool: `${(0, core_bundle_mjs_1.getSelfPluginIdentifier)()}/annotate_image`,
                detectSource: entry.id,
                task: entryTask,
                annotateColor: globalColor,
                annotateLineWeight: globalLineWeight,
                annotateFrameAdjust: globalFrameAdjust,
                imageWidth: imgW,
                imageHeight: imgH,
                analysisMetadata: analysis,
                detections: detObjects.map((d) => ({
                    label: d.label,
                    bbox: { x1: d.bbox.x1, y1: d.bbox.y1, x2: d.bbox.x2, y2: d.bbox.y2 },
                    crop: d.crop ?? {},
                })),
            });
            resultEntries.push({
                id: entry.id,
                i: currentI,
                isRedraw,
                task: entryTask,
                detObjects,
                imageWidth: imgW,
                imageHeight: imgH,
                savedPath,
                savedFileUrl,
                savedSize,
                preview,
                httpOriginal,
                httpPreview,
                inferenceTimeMs,
            });
        }
        // ── Update state ──────────────────────────────────────────────────
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps - 1, progressTotalSteps, "Updating image state and audit log...");
        try {
            const stateForUpdate = await (0, core_bundle_mjs_1.readState)(primaryOutDir);
            const appendResult = (0, core_bundle_mjs_1.appendImages)(stateForUpdate, imageRecordsForState);
            if (appendResult.changed) {
                await (0, core_bundle_mjs_1.writeStateAtomic)(primaryOutDir, stateForUpdate);
            }
        }
        catch (e) {
            console.warn("[annotate_image] state update failed:", String(e));
        }
        // ── Audit log ─────────────────────────────────────────────────────
        // effectiveRequestId is reused below in each summary entry, so a caller (the MCP adapter)
        // can always name its HTML report after this exact audit requestId, even for a batch call
        // whose top-level result is an array (see summaries below).
        const effectiveRequestId = requestId ?? `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`;
        try {
            const audit = (0, core_bundle_mjs_1.buildAuditLogger)({ backend: "annotate_image", mode: "annotate_image", requestId: effectiveRequestId });
            if (currentLmChatId)
                audit.setChatId(currentLmChatId);
            audit.setUserRequest({ targets: rawTargets, task: taskArg, color: globalColor, lineWeight: globalLineWeight, frameAdjust: globalFrameAdjust });
            audit.setOutput({
                images: resultEntries.map((r) => ({
                    id: r.id,
                    i: r.i,
                    redraw: r.isRedraw,
                    detections: r.detObjects.length,
                    path: r.savedPath,
                    url: r.savedFileUrl,
                    bytes: r.savedSize,
                    ...(r.httpOriginal ? { http_url: r.httpOriginal } : {}),
                    ...(r.preview ? { preview_path: r.preview.filePath, preview_url: r.preview.fileUrl } : {}),
                    ...(r.httpPreview ? { http_preview_url: r.httpPreview } : {}),
                })),
            });
            await audit.write();
        }
        catch { }
        // ── Assemble result ───────────────────────────────────────────────
        (0, toolProgress_js_1.reportToolStep)(statusCtx, progressTotalSteps, progressTotalSteps, "Assembling annotation result...");
        const summaries = resultEntries.map((r) => ({
            tool: "annotate_image",
            requestId: effectiveRequestId,
            source: r.id,
            i: r.i,
            redraw: r.isRedraw,
            color: globalColor,
            lineWeight: globalLineWeight,
            frameAdjust: globalFrameAdjust,
            ...(r.inferenceTimeMs > 0 ? { inferenceTimeMs: r.inferenceTimeMs } : {}),
            detections: r.detObjects.map((d) => ({
                label: d.label,
                bbox: { x1: d.bbox.x1, y1: d.bbox.y1, x2: d.bbox.x2, y2: d.bbox.y2 },
            })),
        }));
        const envPreviewRaw = process.env["PREVIEW_IN_CHAT"];
        const previewInChat = envPreviewRaw === undefined
            ? true
            : envPreviewRaw === "1" || envPreviewRaw.toLowerCase() === "true";
        // Build target notations for hints (e.g. ["i9", "i10"])
        const resultNotations = resultEntries.map((r) => `i${r.i}`);
        const targetsJson = JSON.stringify(resultNotations);
        const reviewHintFalse = `Carefully examine the preview to make absolutely sure that the object detection matches your intent. Registered as ${resultNotations.join(", ")}. Use review_image({"targets":${targetsJson}}) to review, or annotate_image({"targets":${targetsJson}}) to apply corrections.`;
        const reviewHintTrue = `Carefully examine the preview to make absolutely sure that the object detection matches your intent. This is an image file. Present the image to the user by using the markdown above. Registered as ${resultNotations.join(", ")}. Use review_image({"targets":${targetsJson}}) to review, or annotate_image({"targets":${targetsJson}}) to apply corrections.`;
        const content = [];
        for (const r of resultEntries) {
            const fallbackPreviewUrl = r.preview?.fileUrl || r.savedFileUrl;
            if (previewInChat && r.preview) {
                const fname = String(r.preview.fileName || "");
                content.push({
                    type: "image",
                    fileName: fname,
                    mimeType: r.preview.mimeType,
                    markdown: `![Annotated i${r.i} (${r.id})](./${fname})`,
                    $hint: reviewHintTrue,
                });
            }
            // TODO: Restore when LM Studio renders file/HTTP links again
            // content.push({ type: "text", text: `Preview i${r.i}: ${r.httpPreview || fallbackPreviewUrl}` });
            // content.push({ type: "text", text: `Original i${r.i}: ${r.httpOriginal || r.savedFileUrl}` });
        }
        if (batchResult && batchResult.totalInferenceTimeMs > 0) {
            content.push({ type: "text", text: `Total inference time: ${Math.round(batchResult.totalInferenceTimeMs)}ms` });
        }
        content.push({
            type: "text",
            text: JSON.stringify(summaries.length === 1 ? summaries[0] : summaries),
            ...(previewInChat ? {} : { $hint: reviewHintFalse }),
        });
        return { content };
    }
    catch (error) {
        return {
            content: [{ type: "text", text: `annotate_image failed: ${error.message || String(error)}` }],
            isError: true,
        };
    }
}