import { tool, type Tool, type ToolsProviderController, type ToolCallContext } from "@lmstudio/sdk";
import { z } from "zod";
import path from "path";
import fs from "fs";
import { createHash } from "crypto";
import { pathToFileURL } from "url";
import {
  syncAttachmentsToState,
  getActiveChatContext,
  resolveActiveLMStudioChatId,
  getLMStudioWorkingDir,
  readState,
  writeStateAtomic,
  generatePreviewFromBuffer,
  appendImages,
  getHealthyServerBaseUrl,
  toHttpOriginalUrl,
  toHttpPreviewUrl,
  buildAuditLogger,
  getSelfPluginIdentifier,
  VARIANT_FULL_CONFIG,
} from "../core-bundle.mjs";
import { drawBboxesOnImage } from "../helpers/drawBboxesOnImage.js";
import { injectXmpIntoBuffer, type PngAnalysisMetadata } from "../helpers/pngMetadata.js";
import {
  detectLmStudioVisionBatch,
  ensureLmStudioVisionInstanceReady,
  type VisionAnalysisItem,
  type VisionDetectionAnalyzerConfig,
  type VisionDetectionBatchResult,
} from "../services/lmStudioVisionAnalyzer.js";
import { reportToolStatus, reportToolStep } from "../helpers/toolProgress.js";
import { defaultPluginSettings, globalConfigSchematics } from "../config.js";

function isoStampCompact(): string {
  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 parsePrefixedNotation(
  s: string
): { pool: "attachment" | "image" | "variant" | "picture"; index: number } | null {
  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 };
}

function formatPluginMeta(): string {
  try {
    const cwd = process.cwd();
    const pkg = JSON.parse(fs.readFileSync(path.join(cwd, "package.json"), "utf-8"));
    const mf = JSON.parse(fs.readFileSync(path.join(cwd, "manifest.json"), "utf-8"));
    const id = mf?.owner && mf?.name ? `${mf.owner}/${mf.name}` : pkg?.name || "ceveyne/analyse-image";
    return `Plugin-Identifier: ${id}\nPlugin version: ${pkg?.version || ""}`;
  } catch {
    return "Plugin-Identifier: ceveyne/analyse-image";
  }
}

function getGlobalConfig(ctl: ToolsProviderController): any | null {
  const ctlAny = ctl as any;
  const getter = ctlAny.getGlobalPluginConfig || ctlAny.getGlobalConfig;
  if (!getter) return null;
  try {
    return getter.call(ctl, globalConfigSchematics);
  } catch {
    return null;
  }
}

function getGlobalString(gcfg: any | null, key: string, fallback: string): string {
  try {
    const value = gcfg?.get(key);
    return typeof value === "string" ? value : fallback;
  } catch {
    return fallback;
  }
}

function getGlobalNumber(gcfg: any | null, key: string, fallback: number): number {
  try {
    const value = gcfg?.get(key);
    return typeof value === "number" && Number.isFinite(value) ? value : fallback;
  } catch {
    return fallback;
  }
}

function getGlobalBoolean(gcfg: any | null, key: string, fallback: boolean): boolean {
  try {
    const value = gcfg?.get(key);
    return typeof value === "boolean" ? value : fallback;
  } catch {
    return fallback;
  }
}

/**
 * Flexible targets schema: accepts array OR comma/space-separated string.
 * Analogous to analyse_image targets — supports single or batch detection.
 */
const FlexibleTargetsList = z
  .union([
    z.string().transform((s) => (s.match(/[aivp]\d+/gi) ?? []).map((x) => x.toLowerCase())),
    z.array(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");

const DetectObjectParamsShape = {
  targets: 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, …). " +
    "Pass as a JSON array, e.g. [\"a1\", \"i3\"]. " +
    "Omit when there is exactly one image — it will be selected automatically."
  ),
  task: 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')."
    ),
} satisfies Record<string, z.ZodTypeAny>;

export function createDetectObjectTool(ctl: ToolsProviderController): Tool {
  return tool({
    name: "detect_object",
    description: `Detect objects in one or more images and draw colored bounding boxes on each result.

For each source image, returns a new annotated image (saved as iN) with bounding boxes for each detected object, plus a JSON summary with labels, coordinates, and crop percentages. Uses Qwen3-VL for detection.

Parameters:
- targets: JSON array of image notations, e.g. ["a1", "i3"]. Notation: a=attachment, i=generated image, v=variant, p=picture. Omit when there is exactly one image.
- task: 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').

${formatPluginMeta()}`,
    parameters: DetectObjectParamsShape,
    implementation: async (args: any, ctx: ToolCallContext) => {
      try {
        // Parse targets: accept array or comma/space-separated string (analogous to analyse_image)
        let rawTargets: string[] = [];
        if (Array.isArray(args?.targets)) {
          rawTargets = args.targets.map((s: any) => 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: any) => String(s).trim()).filter(Boolean);
              } else {
                rawTargets = trimmed.split(/[\s,]+/).map((s: string) => s.trim()).filter(Boolean);
              }
            } catch {
              rawTargets = trimmed.split(/[\s,]+/).map((s: string) => s.trim()).filter(Boolean);
            }
          } else {
            rawTargets = trimmed.split(/[\s,]+/).map((s: string) => 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: string | null = null;
        let currentLmWorkingDir: string | null = null;
        try {
          const chatCtx = await getActiveChatContext();
          if ((chatCtx as any)?.chatId) currentLmChatId = (chatCtx as any).chatId;
          if ((chatCtx as any)?.workingDir) currentLmWorkingDir = (chatCtx as any).workingDir;
        } catch {}
        if (!currentLmChatId) {
          try {
            const resolved = await resolveActiveLMStudioChatId();
            if ((resolved as any)?.ok) currentLmChatId = (resolved as any).chatId;
          } catch {}
        }

        const primaryOutDir: string | undefined =
          currentLmWorkingDir ||
          (currentLmChatId ? 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 as const,
          };
        }
        console.log("[detect_object] primaryOutDir:", primaryOutDir);
        await fs.promises.mkdir(primaryOutDir, { recursive: true }).catch(() => {});

        // Sync attachments so state is up to date
        console.log("[detect_object] syncing attachments...");
        try {
          await syncAttachmentsToState(primaryOutDir, false, Number.MAX_SAFE_INTEGER);
        } catch (e) {
          console.warn("[detect_object] attachment sync failed (non-fatal):", (e as any)?.message ?? e);
        }
        console.log("[detect_object] attachment sync done");

        console.log("[detect_object] reading state...");
        const st = await readState(primaryOutDir);
        const attachments: any[] = Array.isArray(st?.attachments) ? st.attachments : [];
        const pictures: any[] = Array.isArray(st?.pictures) ? st.pictures : [];
        const imageRecords: any[] = Array.isArray(st?.images) ? st.images : [];
        const images: Array<{ i: number; path: string }> = imageRecords
          .filter((r: any) => r && typeof r.filename === "string")
          .sort((a: any, b: any) => (a.i || 0) - (b.i || 0))
          .map((r: any) => ({ i: r.i || 1, path: path.join(primaryOutDir, r.filename) }));
        const variantRecords: any[] = Array.isArray(st?.variants) ? st.variants : [];
        const variants: Array<{ v: number; path: string }> = variantRecords
          .filter((v: any) => v && typeof v.filename === "string")
          .map((v: any) => ({ v: v.v || 1, path: path.join(primaryOutDir, v.filename) }));

        console.log("[detect_object] state:", {
          attachments: attachments.length,
          images: images.length,
          variants: variants.length,
          pictures: pictures.length,
        });

        // Resolve source buffers for each target (or auto-select if none given)
        type SourceEntry = { id: string; buf: Buffer; origBuf: Buffer };
        const sourceEntries: SourceEntry[] = [];

        async function resolveOneBuf(rawCanvas: string): Promise<Buffer> {
          const pref = parsePrefixedNotation(rawCanvas);
          if (!pref) throw new Error(`Invalid canvas notation: ${rawCanvas}`);
          if (pref.pool === "attachment") {
            const rec = attachments.find((a: any) => a?.a === pref.index);
            const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!previewRel) throw new Error(`Preview for attachment a${pref.index} not found.`);
            return fs.promises.readFile(path.join(primaryOutDir!, previewRel));
          } else if (pref.pool === "image") {
            const rec = imageRecords.find((r: any) => r?.i === pref.index);
            const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!previewRel) throw new Error(`Preview for image i${pref.index} not found.`);
            return fs.promises.readFile(path.join(primaryOutDir!, previewRel));
          } else if (pref.pool === "variant") {
            const rec = variantRecords.find((v: any) => v?.v === pref.index);
            const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!previewRel) throw new Error(`Preview for variant v${pref.index} not found.`);
            return fs.promises.readFile(path.join(primaryOutDir!, previewRel));
          } else {
            const rec = pictures.find((p: any) => 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.promises.readFile(path.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: string, previewFallback: Buffer): Promise<Buffer> {
          try {
            const pref = parsePrefixedNotation(rawCanvas);
            if (!pref) return previewFallback;
            if (pref.pool === "attachment") {
              const rec = attachments.find((a: any) => a?.a === pref.index);
              const originAbs = rec && typeof rec.originAbs === "string" ? rec.originAbs : "";
              if (!originAbs) return previewFallback;
              return await fs.promises.readFile(originAbs);
            }
            let rec: any;
            if (pref.pool === "image") rec = imageRecords.find((r: any) => r?.i === pref.index);
            else if (pref.pool === "variant") rec = variantRecords.find((v: any) => v?.v === pref.index);
            else rec = pictures.find((p: any) => p?.p === pref.index);
            const filename = rec && typeof rec.filename === "string" ? rec.filename : "";
            if (!filename) return previewFallback;
            return await fs.promises.readFile(path.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: Buffer;
            let id: string;
            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.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `a${typeof rec.a === "number" ? rec.a : 1}`;
            } else if (variantRecords.length === 1) {
              const rec = variantRecords[0];
              const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
              if (!previewRel) throw new Error("Preview for variant not found.");
              buf = await fs.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `v${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.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `i${typeof rec.i === "number" ? rec.i : 1}`;
            } else {
              const rec = pictures[0];
              const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
              if (!previewRel) throw new Error("Preview for picture not found.");
              buf = await fs.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `p${rec.p ?? 1}`;
            }
            const origBuf = await resolveOriginalBuf(id, buf);
            sourceEntries.push({ id, buf, origBuf });
          }
        } catch (e) {
          return {
            content: [{ type: "text", text: String((e as any)?.message || e) }],
            isError: true as const,
          };
        }

        console.log("[detect_object] sources resolved:", sourceEntries.map((s) => s.id));

        const globalConfig = getGlobalConfig(ctl);
        const embedPngMetadata = getGlobalBoolean(globalConfig, "embedPngMetadata", defaultPluginSettings.embedPngMetadata);
        const visionBaseUrl = getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || defaultPluginSettings.embeddingBaseUrl);
        const visionApiKey = getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || defaultPluginSettings.embeddingApiKey);
        const visionModelKey = getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || 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 : defaultPluginSettings.detectMaxTokens
        ));
        const configuredDetectTemperature = getGlobalNumber(
          globalConfig,
          "detectTemperature",
          Number.isFinite(envDetectTemperature) ? envDetectTemperature : defaultPluginSettings.detectTemperature
        );
        const detectionConfig: VisionDetectionAnalyzerConfig = {
          task,
          odPrompt: getGlobalString(globalConfig, "qwen3VlOdPrompt", process.env.DETECT_OD_PROMPT || defaultPluginSettings.qwen3VlOdPrompt) || undefined,
          maxTokens: configuredDetectMaxTokens,
          temperature: configuredDetectTemperature,
          timeoutMs: 120_000,
        };

        const tmpPaths: string[] = [];
        const detectionItems: VisionAnalysisItem[] = [];
        for (const entry of sourceEntries) {
          const tmpPath = path.join(primaryOutDir, `_tmp_detect_src_${entry.id}_${Date.now()}.png`);
          await fs.promises.writeFile(tmpPath, entry.buf);
          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: VisionDetectionBatchResult = {
          results: [],
          totalInferenceTimeMs: 0,
          backend: "vision-api",
        };
        try {
          reportToolStatus(ctx, `Detecting objects in ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"}...`);
          reportToolStep(ctx, 1, progressTotalSteps, `Preparing ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"} for object detection...`);
          const ready = await ensureLmStudioVisionInstanceReady({
            baseUrl: visionBaseUrl,
            apiKey: visionApiKey,
            modelKey: visionModelKey,
            status: (message) => { try { ctx.status(message); } catch {} },
          });
          if (!ready.ok) {
            throw new Error(ready.error);
          }
          for (let idx = 0; idx < detectionItems.length; idx++) {
            const item = detectionItems[idx];
            reportToolStep(ctx, idx + 2, progressTotalSteps, `Detecting objects in ${item.id} (${idx + 1}/${detectionItems.length})...`);
            const singleResult = await detectLmStudioVisionBatch([item], {
              ...detectionConfig,
              baseUrl: visionBaseUrl,
              apiKey: visionApiKey,
              model: visionModelKey,
            });
            batchResult.results.push(...singleResult.results);
            batchResult.totalInferenceTimeMs += singleResult.totalInferenceTimeMs;
            batchResult.backend = singleResult.backend;
          }
          console.log("[detect_object] detection API returned:", {
            results: batchResult.results.length,
            totalMs: batchResult.totalInferenceTimeMs,
          });
          try {
            const totalObjects = batchResult.results.reduce((s, r) => s + (r.objects?.length ?? 0), 0);
            const ms = Math.round(batchResult.totalInferenceTimeMs);
            reportToolStep(ctx, sourceEntries.length + 2, progressTotalSteps, `${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.promises.unlink(tp).catch(() => {});
        }

        if (!batchResult.results.length) {
          return {
            content: [{ type: "text", text: "detect_object: no results returned from detection API." }],
            isError: true as const,
          };
        }

        // Per-image: draw bboxes, save, generate preview, build state records
        const variantPreviewSpec = (VARIANT_FULL_CONFIG as any).preview;
        const stamp = isoStampCompact();
        let nextI = Math.max(1, st.counters?.nextImageI ?? 1);

        const imageRecordsForState: any[] = [];
        const resultEntries: Array<{
          id: string;
          i: number;
          detResult: typeof batchResult.results[0];
          savedPath: string;
          savedFileUrl: string;
          savedSize: number;
          preview: any | null;
          httpOriginal: string;
          httpPreview: string;
        }> = [];

        const httpBase = await 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++;
          reportToolStep(ctx, sourceEntries.length + 3 + idx, progressTotalSteps, `Drawing boxes for ${sourceId} (${idx + 1}/${batchResult.results.length})...`);

          const bboxes = detResult.objects.map((o) => o.bbox as [number, number, number, number]);
          console.log(`[detect_object] drawing ${bboxes.length} bboxes for ${sourceId}...`);
          // Draw on the original-resolution file; sourceDims carries the preview space in which
          // the detection server reported its bbox coordinates so they get scaled up correctly.
          const annotatedBuf = await drawBboxesOnImage(origBuf, bboxes, {
            sourceDims: { width: detResult.imageWidth, height: detResult.imageHeight },
            palette: true,
          });
          const analysis: PngAnalysisMetadata = {
            schema: "ceveyne.image-analysis/v1",
            tool: "detect_object",
            sourceNotation: sourceId,
            inference: {
              model: visionModelKey,
              ...(task ? { query: task } : {}),
              detectorPromptSha256: createHash("sha256").update(detectionConfig.odPrompt ?? "").digest("hex"),
              maxTokens: configuredDetectMaxTokens,
              temperature: configuredDetectTemperature,
            },
            render: { palette: true },
            detections: detResult.objects.map((object) => ({
              label: object.label,
              bbox: { x1: object.bbox[0], y1: object.bbox[1], x2: object.bbox[2], y2: object.bbox[3] },
            })),
          };
          const savedBuffer = embedPngMetadata
            ? injectXmpIntoBuffer(annotatedBuf, {
                ...(task ? { prompt: task } : {}),
                model: visionModelKey,
                mode: "object_detection",
                generatedBy: `${getSelfPluginIdentifier()}/detect_object`,
                creatorTool: `${getSelfPluginIdentifier()}/detect_object`,
                analysis,
              })
            : annotatedBuf;

          const baseName = `image-${stamp}-i${currentI}`;
          const savedPath = path.join(primaryOutDir, `${baseName}.png`);
          await fs.promises.writeFile(savedPath, savedBuffer);
          const savedFileUrl = pathToFileURL(savedPath).toString();
          const savedSize = savedBuffer.length;
          console.log(`[detect_object] annotated image written: ${savedPath} (${savedSize} bytes)`);

          let preview: any = null;
          try {
            const p = await generatePreviewFromBuffer(savedBuffer, primaryOutDir, `${baseName}.png`, variantPreviewSpec);
            preview = {
              ok: true as const,
              filePath: p.previewAbs,
              fileName: p.previewFilename,
              fileUrl: pathToFileURL(p.previewAbs).toString(),
              size_bytes: p.data.length,
              width: p.width,
              height: p.height,
              mimeType: "image/jpeg" as const,
              dataBase64: p.data.toString("base64"),
            };
          } catch (e) {
            console.warn(`[detect_object] preview generation failed for ${sourceId}:`, String(e));
          }

          const httpOriginal = httpBase
            ? toHttpOriginalUrl(`${baseName}.png`, httpBase, currentLmChatId || undefined)
            : "";
          const httpPreview = (() => {
            if (!httpBase || !currentLmChatId || !preview?.fileName) return "";
            return toHttpPreviewUrl(preview.fileName, httpBase, currentLmChatId);
          })();

          imageRecordsForState.push({
            filename: `${baseName}.png`,
            preview: preview ? `preview-${baseName}.jpg` : undefined,
            i: currentI,
            sourceTool: `${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...");
          reportToolStep(ctx, progressTotalSteps - 1, progressTotalSteps, "Updating image state and audit log...");
        try {
          const stateForUpdate = await readState(primaryOutDir);
          const appendResult = appendImages(stateForUpdate, imageRecordsForState);
          if (appendResult.changed) {
            await 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
        try {
          const audit = buildAuditLogger({ backend: "detect_object", mode: "detect_object" as any, requestId: undefined });
          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",
          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: any[] = [];
        reportToolStep(ctx, 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.",
            } as any);
          }
          // 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 as Error).message || String(error)}`,
            },
          ],
          isError: true as const,
        };
      }
    },
  });
}

import { tool, type Tool, type ToolsProviderController, type ToolCallContext } from "@lmstudio/sdk";
import { z } from "zod";
import path from "path";
import fs from "fs";
import { createHash } from "crypto";
import { pathToFileURL } from "url";
import {
  syncAttachmentsToState,
  getActiveChatContext,
  resolveActiveLMStudioChatId,
  getLMStudioWorkingDir,
  readState,
  writeStateAtomic,
  generatePreviewFromBuffer,
  appendImages,
  getHealthyServerBaseUrl,
  toHttpOriginalUrl,
  toHttpPreviewUrl,
  buildAuditLogger,
  getSelfPluginIdentifier,
  VARIANT_FULL_CONFIG,
} from "../core-bundle.mjs";
import { drawBboxesOnImage } from "../helpers/drawBboxesOnImage.js";
import { injectXmpIntoBuffer, type PngAnalysisMetadata } from "../helpers/pngMetadata.js";
import {
  detectLmStudioVisionBatch,
  ensureLmStudioVisionInstanceReady,
  type VisionAnalysisItem,
  type VisionDetectionAnalyzerConfig,
  type VisionDetectionBatchResult,
} from "../services/lmStudioVisionAnalyzer.js";
import { reportToolStatus, reportToolStep } from "../helpers/toolProgress.js";
import { defaultPluginSettings, globalConfigSchematics } from "../config.js";

function isoStampCompact(): string {
  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 parsePrefixedNotation(
  s: string
): { pool: "attachment" | "image" | "variant" | "picture"; index: number } | null {
  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 };
}

function formatPluginMeta(): string {
  try {
    const cwd = process.cwd();
    const pkg = JSON.parse(fs.readFileSync(path.join(cwd, "package.json"), "utf-8"));
    const mf = JSON.parse(fs.readFileSync(path.join(cwd, "manifest.json"), "utf-8"));
    const id = mf?.owner && mf?.name ? `${mf.owner}/${mf.name}` : pkg?.name || "ceveyne/analyse-image";
    return `Plugin-Identifier: ${id}\nPlugin version: ${pkg?.version || ""}`;
  } catch {
    return "Plugin-Identifier: ceveyne/analyse-image";
  }
}

function getGlobalConfig(ctl: ToolsProviderController): any | null {
  const ctlAny = ctl as any;
  const getter = ctlAny.getGlobalPluginConfig || ctlAny.getGlobalConfig;
  if (!getter) return null;
  try {
    return getter.call(ctl, globalConfigSchematics);
  } catch {
    return null;
  }
}

function getGlobalString(gcfg: any | null, key: string, fallback: string): string {
  try {
    const value = gcfg?.get(key);
    return typeof value === "string" ? value : fallback;
  } catch {
    return fallback;
  }
}

function getGlobalNumber(gcfg: any | null, key: string, fallback: number): number {
  try {
    const value = gcfg?.get(key);
    return typeof value === "number" && Number.isFinite(value) ? value : fallback;
  } catch {
    return fallback;
  }
}

function getGlobalBoolean(gcfg: any | null, key: string, fallback: boolean): boolean {
  try {
    const value = gcfg?.get(key);
    return typeof value === "boolean" ? value : fallback;
  } catch {
    return fallback;
  }
}

/**
 * Flexible targets schema: accepts array OR comma/space-separated string.
 * Analogous to analyse_image targets — supports single or batch detection.
 */
const FlexibleTargetsList = z
  .union([
    z.string().transform((s) => (s.match(/[aivp]\d+/gi) ?? []).map((x) => x.toLowerCase())),
    z.array(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");

const DetectObjectParamsShape = {
  targets: 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, …). " +
    "Pass as a JSON array, e.g. [\"a1\", \"i3\"]. " +
    "Omit when there is exactly one image — it will be selected automatically."
  ),
  task: 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')."
    ),
} satisfies Record<string, z.ZodTypeAny>;

export function createDetectObjectTool(ctl: ToolsProviderController): Tool {
  return tool({
    name: "detect_object",
    description: `Detect objects in one or more images and draw colored bounding boxes on each result.

For each source image, returns a new annotated image (saved as iN) with bounding boxes for each detected object, plus a JSON summary with labels, coordinates, and crop percentages. Uses Qwen3-VL for detection.

Parameters:
- targets: JSON array of image notations, e.g. ["a1", "i3"]. Notation: a=attachment, i=generated image, v=variant, p=picture. Omit when there is exactly one image.
- task: 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').

${formatPluginMeta()}`,
    parameters: DetectObjectParamsShape,
    implementation: async (args: any, ctx: ToolCallContext) => {
      try {
        // Parse targets: accept array or comma/space-separated string (analogous to analyse_image)
        let rawTargets: string[] = [];
        if (Array.isArray(args?.targets)) {
          rawTargets = args.targets.map((s: any) => 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: any) => String(s).trim()).filter(Boolean);
              } else {
                rawTargets = trimmed.split(/[\s,]+/).map((s: string) => s.trim()).filter(Boolean);
              }
            } catch {
              rawTargets = trimmed.split(/[\s,]+/).map((s: string) => s.trim()).filter(Boolean);
            }
          } else {
            rawTargets = trimmed.split(/[\s,]+/).map((s: string) => 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: string | null = null;
        let currentLmWorkingDir: string | null = null;
        try {
          const chatCtx = await getActiveChatContext();
          if ((chatCtx as any)?.chatId) currentLmChatId = (chatCtx as any).chatId;
          if ((chatCtx as any)?.workingDir) currentLmWorkingDir = (chatCtx as any).workingDir;
        } catch {}
        if (!currentLmChatId) {
          try {
            const resolved = await resolveActiveLMStudioChatId();
            if ((resolved as any)?.ok) currentLmChatId = (resolved as any).chatId;
          } catch {}
        }

        const primaryOutDir: string | undefined =
          currentLmWorkingDir ||
          (currentLmChatId ? 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 as const,
          };
        }
        console.log("[detect_object] primaryOutDir:", primaryOutDir);
        await fs.promises.mkdir(primaryOutDir, { recursive: true }).catch(() => {});

        // Sync attachments so state is up to date
        console.log("[detect_object] syncing attachments...");
        try {
          await syncAttachmentsToState(primaryOutDir, false, Number.MAX_SAFE_INTEGER);
        } catch (e) {
          console.warn("[detect_object] attachment sync failed (non-fatal):", (e as any)?.message ?? e);
        }
        console.log("[detect_object] attachment sync done");

        console.log("[detect_object] reading state...");
        const st = await readState(primaryOutDir);
        const attachments: any[] = Array.isArray(st?.attachments) ? st.attachments : [];
        const pictures: any[] = Array.isArray(st?.pictures) ? st.pictures : [];
        const imageRecords: any[] = Array.isArray(st?.images) ? st.images : [];
        const images: Array<{ i: number; path: string }> = imageRecords
          .filter((r: any) => r && typeof r.filename === "string")
          .sort((a: any, b: any) => (a.i || 0) - (b.i || 0))
          .map((r: any) => ({ i: r.i || 1, path: path.join(primaryOutDir, r.filename) }));
        const variantRecords: any[] = Array.isArray(st?.variants) ? st.variants : [];
        const variants: Array<{ v: number; path: string }> = variantRecords
          .filter((v: any) => v && typeof v.filename === "string")
          .map((v: any) => ({ v: v.v || 1, path: path.join(primaryOutDir, v.filename) }));

        console.log("[detect_object] state:", {
          attachments: attachments.length,
          images: images.length,
          variants: variants.length,
          pictures: pictures.length,
        });

        // Resolve source buffers for each target (or auto-select if none given)
        type SourceEntry = { id: string; buf: Buffer; origBuf: Buffer };
        const sourceEntries: SourceEntry[] = [];

        async function resolveOneBuf(rawCanvas: string): Promise<Buffer> {
          const pref = parsePrefixedNotation(rawCanvas);
          if (!pref) throw new Error(`Invalid canvas notation: ${rawCanvas}`);
          if (pref.pool === "attachment") {
            const rec = attachments.find((a: any) => a?.a === pref.index);
            const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!previewRel) throw new Error(`Preview for attachment a${pref.index} not found.`);
            return fs.promises.readFile(path.join(primaryOutDir!, previewRel));
          } else if (pref.pool === "image") {
            const rec = imageRecords.find((r: any) => r?.i === pref.index);
            const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!previewRel) throw new Error(`Preview for image i${pref.index} not found.`);
            return fs.promises.readFile(path.join(primaryOutDir!, previewRel));
          } else if (pref.pool === "variant") {
            const rec = variantRecords.find((v: any) => v?.v === pref.index);
            const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
            if (!previewRel) throw new Error(`Preview for variant v${pref.index} not found.`);
            return fs.promises.readFile(path.join(primaryOutDir!, previewRel));
          } else {
            const rec = pictures.find((p: any) => 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.promises.readFile(path.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: string, previewFallback: Buffer): Promise<Buffer> {
          try {
            const pref = parsePrefixedNotation(rawCanvas);
            if (!pref) return previewFallback;
            if (pref.pool === "attachment") {
              const rec = attachments.find((a: any) => a?.a === pref.index);
              const originAbs = rec && typeof rec.originAbs === "string" ? rec.originAbs : "";
              if (!originAbs) return previewFallback;
              return await fs.promises.readFile(originAbs);
            }
            let rec: any;
            if (pref.pool === "image") rec = imageRecords.find((r: any) => r?.i === pref.index);
            else if (pref.pool === "variant") rec = variantRecords.find((v: any) => v?.v === pref.index);
            else rec = pictures.find((p: any) => p?.p === pref.index);
            const filename = rec && typeof rec.filename === "string" ? rec.filename : "";
            if (!filename) return previewFallback;
            return await fs.promises.readFile(path.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: Buffer;
            let id: string;
            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.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `a${typeof rec.a === "number" ? rec.a : 1}`;
            } else if (variantRecords.length === 1) {
              const rec = variantRecords[0];
              const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
              if (!previewRel) throw new Error("Preview for variant not found.");
              buf = await fs.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `v${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.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `i${typeof rec.i === "number" ? rec.i : 1}`;
            } else {
              const rec = pictures[0];
              const previewRel = rec && typeof rec.preview === "string" ? rec.preview : "";
              if (!previewRel) throw new Error("Preview for picture not found.");
              buf = await fs.promises.readFile(path.join(primaryOutDir!, previewRel));
              id = `p${rec.p ?? 1}`;
            }
            const origBuf = await resolveOriginalBuf(id, buf);
            sourceEntries.push({ id, buf, origBuf });
          }
        } catch (e) {
          return {
            content: [{ type: "text", text: String((e as any)?.message || e) }],
            isError: true as const,
          };
        }

        console.log("[detect_object] sources resolved:", sourceEntries.map((s) => s.id));

        const globalConfig = getGlobalConfig(ctl);
        const embedPngMetadata = getGlobalBoolean(globalConfig, "embedPngMetadata", defaultPluginSettings.embedPngMetadata);
        const visionBaseUrl = getGlobalString(globalConfig, "embeddingBaseUrl", process.env.LMSTUDIO_VISION_API_BASE_URL || defaultPluginSettings.embeddingBaseUrl);
        const visionApiKey = getGlobalString(globalConfig, "embeddingApiKey", process.env.LMSTUDIO_VISION_API_KEY || defaultPluginSettings.embeddingApiKey);
        const visionModelKey = getGlobalString(globalConfig, "qwen3VlModelPath", process.env.LMSTUDIO_VISION_MODEL_KEY || 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 : defaultPluginSettings.detectMaxTokens
        ));
        const configuredDetectTemperature = getGlobalNumber(
          globalConfig,
          "detectTemperature",
          Number.isFinite(envDetectTemperature) ? envDetectTemperature : defaultPluginSettings.detectTemperature
        );
        const detectionConfig: VisionDetectionAnalyzerConfig = {
          task,
          odPrompt: getGlobalString(globalConfig, "qwen3VlOdPrompt", process.env.DETECT_OD_PROMPT || defaultPluginSettings.qwen3VlOdPrompt) || undefined,
          maxTokens: configuredDetectMaxTokens,
          temperature: configuredDetectTemperature,
          timeoutMs: 120_000,
        };

        const tmpPaths: string[] = [];
        const detectionItems: VisionAnalysisItem[] = [];
        for (const entry of sourceEntries) {
          const tmpPath = path.join(primaryOutDir, `_tmp_detect_src_${entry.id}_${Date.now()}.png`);
          await fs.promises.writeFile(tmpPath, entry.buf);
          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: VisionDetectionBatchResult = {
          results: [],
          totalInferenceTimeMs: 0,
          backend: "vision-api",
        };
        try {
          reportToolStatus(ctx, `Detecting objects in ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"}...`);
          reportToolStep(ctx, 1, progressTotalSteps, `Preparing ${sourceEntries.length} image${sourceEntries.length === 1 ? "" : "s"} for object detection...`);
          const ready = await ensureLmStudioVisionInstanceReady({
            baseUrl: visionBaseUrl,
            apiKey: visionApiKey,
            modelKey: visionModelKey,
            status: (message) => { try { ctx.status(message); } catch {} },
          });
          if (!ready.ok) {
            throw new Error(ready.error);
          }
          for (let idx = 0; idx < detectionItems.length; idx++) {
            const item = detectionItems[idx];
            reportToolStep(ctx, idx + 2, progressTotalSteps, `Detecting objects in ${item.id} (${idx + 1}/${detectionItems.length})...`);
            const singleResult = await detectLmStudioVisionBatch([item], {
              ...detectionConfig,
              baseUrl: visionBaseUrl,
              apiKey: visionApiKey,
              model: visionModelKey,
            });
            batchResult.results.push(...singleResult.results);
            batchResult.totalInferenceTimeMs += singleResult.totalInferenceTimeMs;
            batchResult.backend = singleResult.backend;
          }
          console.log("[detect_object] detection API returned:", {
            results: batchResult.results.length,
            totalMs: batchResult.totalInferenceTimeMs,
          });
          try {
            const totalObjects = batchResult.results.reduce((s, r) => s + (r.objects?.length ?? 0), 0);
            const ms = Math.round(batchResult.totalInferenceTimeMs);
            reportToolStep(ctx, sourceEntries.length + 2, progressTotalSteps, `${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.promises.unlink(tp).catch(() => {});
        }

        if (!batchResult.results.length) {
          return {
            content: [{ type: "text", text: "detect_object: no results returned from detection API." }],
            isError: true as const,
          };
        }

        // Per-image: draw bboxes, save, generate preview, build state records
        const variantPreviewSpec = (VARIANT_FULL_CONFIG as any).preview;
        const stamp = isoStampCompact();
        let nextI = Math.max(1, st.counters?.nextImageI ?? 1);

        const imageRecordsForState: any[] = [];
        const resultEntries: Array<{
          id: string;
          i: number;
          detResult: typeof batchResult.results[0];
          savedPath: string;
          savedFileUrl: string;
          savedSize: number;
          preview: any | null;
          httpOriginal: string;
          httpPreview: string;
        }> = [];

        const httpBase = await 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++;
          reportToolStep(ctx, sourceEntries.length + 3 + idx, progressTotalSteps, `Drawing boxes for ${sourceId} (${idx + 1}/${batchResult.results.length})...`);

          const bboxes = detResult.objects.map((o) => o.bbox as [number, number, number, number]);
          console.log(`[detect_object] drawing ${bboxes.length} bboxes for ${sourceId}...`);
          // Draw on the original-resolution file; sourceDims carries the preview space in which
          // the detection server reported its bbox coordinates so they get scaled up correctly.
          const annotatedBuf = await drawBboxesOnImage(origBuf, bboxes, {
            sourceDims: { width: detResult.imageWidth, height: detResult.imageHeight },
            palette: true,
          });
          const analysis: PngAnalysisMetadata = {
            schema: "ceveyne.image-analysis/v1",
            tool: "detect_object",
            sourceNotation: sourceId,
            inference: {
              model: visionModelKey,
              ...(task ? { query: task } : {}),
              detectorPromptSha256: createHash("sha256").update(detectionConfig.odPrompt ?? "").digest("hex"),
              maxTokens: configuredDetectMaxTokens,
              temperature: configuredDetectTemperature,
            },
            render: { palette: true },
            detections: detResult.objects.map((object) => ({
              label: object.label,
              bbox: { x1: object.bbox[0], y1: object.bbox[1], x2: object.bbox[2], y2: object.bbox[3] },
            })),
          };
          const savedBuffer = embedPngMetadata
            ? injectXmpIntoBuffer(annotatedBuf, {
                ...(task ? { prompt: task } : {}),
                model: visionModelKey,
                mode: "object_detection",
                generatedBy: `${getSelfPluginIdentifier()}/detect_object`,
                creatorTool: `${getSelfPluginIdentifier()}/detect_object`,
                analysis,
              })
            : annotatedBuf;

          const baseName = `image-${stamp}-i${currentI}`;
          const savedPath = path.join(primaryOutDir, `${baseName}.png`);
          await fs.promises.writeFile(savedPath, savedBuffer);
          const savedFileUrl = pathToFileURL(savedPath).toString();
          const savedSize = savedBuffer.length;
          console.log(`[detect_object] annotated image written: ${savedPath} (${savedSize} bytes)`);

          let preview: any = null;
          try {
            const p = await generatePreviewFromBuffer(savedBuffer, primaryOutDir, `${baseName}.png`, variantPreviewSpec);
            preview = {
              ok: true as const,
              filePath: p.previewAbs,
              fileName: p.previewFilename,
              fileUrl: pathToFileURL(p.previewAbs).toString(),
              size_bytes: p.data.length,
              width: p.width,
              height: p.height,
              mimeType: "image/jpeg" as const,
              dataBase64: p.data.toString("base64"),
            };
          } catch (e) {
            console.warn(`[detect_object] preview generation failed for ${sourceId}:`, String(e));
          }

          const httpOriginal = httpBase
            ? toHttpOriginalUrl(`${baseName}.png`, httpBase, currentLmChatId || undefined)
            : "";
          const httpPreview = (() => {
            if (!httpBase || !currentLmChatId || !preview?.fileName) return "";
            return toHttpPreviewUrl(preview.fileName, httpBase, currentLmChatId);
          })();

          imageRecordsForState.push({
            filename: `${baseName}.png`,
            preview: preview ? `preview-${baseName}.jpg` : undefined,
            i: currentI,
            sourceTool: `${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...");
          reportToolStep(ctx, progressTotalSteps - 1, progressTotalSteps, "Updating image state and audit log...");
        try {
          const stateForUpdate = await readState(primaryOutDir);
          const appendResult = appendImages(stateForUpdate, imageRecordsForState);
          if (appendResult.changed) {
            await 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
        try {
          const audit = buildAuditLogger({ backend: "detect_object", mode: "detect_object" as any, requestId: undefined });
          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",
          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: any[] = [];
        reportToolStep(ctx, 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.",
            } as any);
          }
          // 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 as Error).message || String(error)}`,
            },
          ],
          isError: true as const,
        };
      }
    },
  });
}