Initial commit: KOC LOOP platform
This commit is contained in:
巫凤萍
315
scripts/sync-feishu-images.mjs
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315
scripts/sync-feishu-images.mjs
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@@ -0,0 +1,315 @@
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import { execFile } from "node:child_process";
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import {
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mkdir,
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readFile,
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readdir,
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stat,
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writeFile,
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} from "node:fs/promises";
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import { extname, resolve } from "node:path";
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import { promisify } from "node:util";
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const execFileAsync = promisify(execFile);
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const projectRoot = resolve(import.meta.dirname, "..");
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const snapshotPath = resolve(projectRoot, "lib/feishu-source-snapshot.json");
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const downloadDir = "/private/tmp/koc-feishu-images-954953";
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const uploadOriginArg = process.argv.find((item) =>
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item.startsWith("--upload-origin="),
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);
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const uploadOrigin = uploadOriginArg
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? uploadOriginArg.slice("--upload-origin=".length).replace(/\/$/, "")
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: "";
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const internalToken =
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process.env.KOC_ADMIN_INTERNAL_TOKEN ??
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process.env.ADMIN_INTERNAL_TOKEN ??
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"";
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const reuseDownloads = process.argv.includes("--reuse-downloads");
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const fromRowArg = process.argv.find((item) => item.startsWith("--from-row="));
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const fromRow = fromRowArg
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? Math.max(1, Number(fromRowArg.slice("--from-row=".length)) || 1)
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: 1;
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if (uploadOrigin && !internalToken) {
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throw new Error(
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"KOC_ADMIN_INTERNAL_TOKEN is required when --upload-origin is provided",
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);
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}
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const snapshot = JSON.parse(await readFile(snapshotPath, "utf8"));
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const sourceUrl =
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"https://eodzc79n5l.feishu.cn/wiki/BSzxwRbGJi5dWoksauicUjtonks?from=from_copylink&sheet=954953";
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const quietEnv = {
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...process.env,
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LARKSUITE_CLI_NO_UPDATE_NOTIFIER: "1",
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LARKSUITE_CLI_NO_SKILLS_NOTIFIER: "1",
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};
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await mkdir(downloadDir, { recursive: true });
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const { stdout: cellsStdout } = await execFileAsync(
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"lark-cli",
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[
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"sheets",
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"+cells-get",
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"--url",
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sourceUrl,
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"--sheet-id",
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snapshot.sheetId,
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"--range",
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"F2:H62",
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"--include",
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"value",
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"--max-chars",
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"500000",
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"--format",
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"json",
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],
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{
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cwd: projectRoot,
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env: quietEnv,
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maxBuffer: 2_000_000,
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},
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);
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const cellsPayload = JSON.parse(cellsStdout);
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if (!cellsPayload.ok || cellsPayload.data?.has_more) {
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throw new Error("Feishu image cells were not returned completely");
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}
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const range = cellsPayload.data?.ranges?.[0];
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if (!range || range.truncated || range.actual_range !== "F2:H62") {
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throw new Error(
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`Unexpected Feishu image range: ${range?.actual_range ?? "missing"}`,
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);
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}
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const sourceRows = new Map(
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snapshot.rows.map((row) => [Number(row.sourceRow), row]),
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);
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const assets = [];
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for (let rowIndex = 0; rowIndex < range.cells.length; rowIndex += 1) {
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const sheetRow = Number(range.row_indices[rowIndex]);
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const sourceRow = sheetRow - 1;
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if (!sourceRows.has(sourceRow)) continue;
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const cells = range.cells[rowIndex] ?? [];
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for (let columnIndex = 0; columnIndex < cells.length; columnIndex += 1) {
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const column = range.col_indices[columnIndex];
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const imageIndex = ["F", "G", "H"].indexOf(column) + 1;
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if (imageIndex < 1) continue;
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const richText = cells[columnIndex]?.rich_text ?? [];
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const image = richText.find((item) => item.type === "embed-image");
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if (!image?.image_token) continue;
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assets.push({
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sourceRow,
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imageIndex,
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token: image.image_token,
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width: Number(image.image_width) || null,
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height: Number(image.image_height) || null,
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key: `content-assets/${snapshot.sheetId}/${sourceRow}/${imageIndex}`,
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});
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}
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}
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async function downloadAsset(asset) {
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const baseName = `row-${asset.sourceRow}-image-${asset.imageIndex}`;
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if (reuseDownloads) {
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const existingName = (await readdir(downloadDir)).find((name) =>
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name.startsWith(`${baseName}.`),
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);
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if (existingName) {
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const localPath = resolve(downloadDir, existingName);
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const extension = extname(existingName).toLowerCase();
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const fileInfo = await stat(localPath);
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return {
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...asset,
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localPath,
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contentType:
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extension === ".png"
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? "image/png"
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: extension === ".webp"
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? "image/webp"
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: "image/jpeg",
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sizeBytes: fileInfo.size,
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};
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}
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}
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const { stdout } = await execFileAsync(
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"lark-cli",
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[
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"docs",
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"+media-download",
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"--token",
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asset.token,
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"--output",
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`./${baseName}`,
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"--overwrite",
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],
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{
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cwd: downloadDir,
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env: quietEnv,
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maxBuffer: 200_000,
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},
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);
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const result = JSON.parse(stdout.slice(stdout.indexOf("{")));
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if (!result.ok || !result.data?.saved_path) {
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throw new Error(
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`Failed to download row ${asset.sourceRow} image ${asset.imageIndex}`,
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);
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}
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return {
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...asset,
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localPath: result.data.saved_path,
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contentType: result.data.content_type || "application/octet-stream",
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sizeBytes: Number(result.data.size_bytes) || 0,
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};
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}
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const downloaded = [];
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const queue = [...assets];
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const workers = Array.from({ length: 4 }, async () => {
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while (queue.length > 0) {
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const asset = queue.shift();
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if (!asset) return;
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downloaded.push(await downloadAsset(asset));
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}
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});
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await Promise.all(workers);
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downloaded.sort(
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(left, right) =>
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left.sourceRow - right.sourceRow || left.imageIndex - right.imageIndex,
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);
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for (const row of snapshot.rows) {
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row.images = downloaded
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.filter((asset) => asset.sourceRow === Number(row.sourceRow))
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.map((asset) => ({
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index: asset.imageIndex,
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key: asset.key,
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width: asset.width,
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height: asset.height,
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}));
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}
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await writeFile(snapshotPath, `${JSON.stringify(snapshot, null, 2)}\n`, "utf8");
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if (uploadOrigin) {
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const sharp = (await import("sharp")).default;
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const uploadConcurrency = new URL(uploadOrigin).hostname === "localhost" ? 4 : 1;
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async function prepareUpload(asset) {
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const original = await readFile(asset.localPath);
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if (original.byteLength < 800_000) {
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return {
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bytes: original,
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contentType: asset.contentType,
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extension: extname(asset.localPath) || ".bin",
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};
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}
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let quality = 86;
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let compressed = await sharp(original)
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.rotate()
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.flatten({ background: "#ffffff" })
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.resize({
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width: 1800,
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height: 2200,
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fit: "inside",
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withoutEnlargement: true,
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})
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.jpeg({ quality, mozjpeg: true })
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.toBuffer();
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while (compressed.byteLength > 800_000 && quality > 58) {
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quality -= 7;
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compressed = await sharp(original)
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.rotate()
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.flatten({ background: "#ffffff" })
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.resize({
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width: 1600,
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height: 2000,
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fit: "inside",
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withoutEnlargement: true,
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})
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.jpeg({ quality, mozjpeg: true })
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.toBuffer();
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}
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return {
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bytes: compressed,
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contentType: "image/jpeg",
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extension: ".jpg",
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};
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}
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const uploadQueue = downloaded.filter((asset) => asset.sourceRow >= fromRow);
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const uploadWorkers = Array.from({ length: uploadConcurrency }, async () => {
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while (uploadQueue.length > 0) {
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const asset = uploadQueue.shift();
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if (!asset) return;
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const prepared = await prepareUpload(asset);
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let uploaded = false;
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let lastError = "unknown error";
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for (let attempt = 1; attempt <= 3; attempt += 1) {
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const form = new FormData();
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form.append("sheetId", snapshot.sheetId);
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form.append("sourceRow", String(asset.sourceRow));
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form.append("imageIndex", String(asset.imageIndex));
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form.append(
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"file",
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new File(
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[prepared.bytes],
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`image-${asset.imageIndex}${prepared.extension}`,
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{
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type: prepared.contentType,
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},
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),
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);
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const response = await fetch(
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`${uploadOrigin}/api/content-image-upload`,
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{
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method: "POST",
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headers: {
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"X-KOC-Admin-Token": internalToken,
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},
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body: form,
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},
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);
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const responseText = await response.text();
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let result = {};
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try {
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result = JSON.parse(responseText);
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} catch {
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result = { error: responseText || `HTTP ${response.status}` };
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}
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if (response.ok) {
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uploaded = true;
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break;
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}
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lastError = result.error ?? `HTTP ${response.status}`;
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if (response.status < 500 && response.status !== 404) break;
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await new Promise((resolveDelay) =>
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setTimeout(resolveDelay, attempt * 750),
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);
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}
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if (!uploaded) {
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throw new Error(
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`Upload failed for row ${asset.sourceRow} image ${asset.imageIndex}: ${lastError}`,
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);
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}
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}
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});
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await Promise.all(uploadWorkers);
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}
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const totalBytes = downloaded.reduce(
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(sum, asset) => sum + asset.sizeBytes,
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0,
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);
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console.log(
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JSON.stringify({
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rows: snapshot.rows.length,
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images: downloaded.length,
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totalBytes,
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uploaded: Boolean(uploadOrigin),
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uploadedImages: uploadOrigin
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? downloaded.filter((asset) => asset.sourceRow >= fromRow).length
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: 0,
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downloadDir,
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}),
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);
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