316 lines
9.6 KiB
JavaScript
316 lines
9.6 KiB
JavaScript
import { Buffer } from 'node:buffer';
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import { existsSync, mkdirSync, readFileSync, readdirSync, writeFileSync } from 'node:fs';
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import path from 'node:path';
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const repoRoot = process.cwd();
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const outputDir = path.join(
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repoRoot,
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'public',
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'branding',
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'taonier-logo-squish-concepts',
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);
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const defaultTimeoutMs = 420000;
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const concepts = [
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{
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id: 'taonier-squish-v2-pulse',
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title: '软泥合拍',
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prompt:
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'无文字扁平矢量 Logo，产品名“陶泥儿”。参考方向：上下两团抽象软泥轻快合拍，中间一颗星点被捏出来。不要画手、手指、掌纹，不要聊天气泡、笑脸、眼睛、花朵、播放键。整体年轻、主流、抽象、生动、像娱乐创作 App icon。上方珊瑚红软形，下方青绿软形，中央奶油白或金色星点，最多 3 色。形状简洁，小尺寸清晰。',
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},
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{
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id: 'taonier-squish-v2-bounce',
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title: '弹力成型',
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prompt:
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'无文字扁平矢量 Logo，产品名“陶泥儿”。图形由上下两块弹性的软泥豆形组成，中间留出弯曲的白色空间和一颗小星点，表达脑洞被轻轻一压就成型。要抽象、有动感、亲和，不像手、不像眼睛、不像聊天气泡。主流 App icon 风格，简洁、高识别。配色：亮珊瑚、薄荷青、奶油白，最多 3 色。禁止文字、字母、3D、褐色、碎元素。',
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},
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{
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id: 'taonier-squish-v2-spark-gap',
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title: '星隙合拍',
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prompt:
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'无文字扁平矢量 Logo，产品名“陶泥儿”。上下两团圆润软泥彼此靠近，中间形成一个自然的星形负空间，像灵感在缝隙中被捏出来。图形必须抽象、现代、活泼，不出现手、眼睛、嘴巴、聊天气泡、播放符号或花朵。适合 App icon，小尺寸一眼识别。配色：玫红 / 珊瑚红主上形，青绿色下形，奶白负形，最多 3 色。',
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},
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{
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id: 'taonier-squish-v2-comet',
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title: '合拍星流',
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prompt:
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'无文字扁平矢量 Logo，产品名“陶泥儿”。两个抽象软泥形上下错位合拍，中央小星点带出一条短短的流线，表达 AI 把脑洞捏成会传播的作品。风格轻快、年轻、抽象、生动，但不要像表情包或特效贴纸。禁止手、眼睛、聊天气泡、笑脸、花朵、播放键、褐色、3D、文字。配色：珊瑚红、青绿、奶白，最多 3 色，元素要少。',
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},
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{
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id: 'taonier-squish-v2-token',
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title: '成型软标',
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prompt:
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'无文字扁平矢量 Logo，产品名“陶泥儿”。把“软泥合拍”做得更像长期品牌主标：上下两块抽象软泥围成一个完整圆润符号，中间只有一颗小星或圆点，表达创作成型。不要手、眼睛、嘴巴、聊天气泡、播放键、花朵、褐色陶土、复杂碎片。主流、亲和、醒目、可记忆，App icon 风格。配色：珊瑚红、青绿、奶白，最多 3 色。',
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},
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];
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const args = new Map();
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for (let index = 2; index < process.argv.length; index += 1) {
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const raw = process.argv[index];
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if (!raw.startsWith('--')) {
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continue;
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}
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const next = process.argv[index + 1];
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if (next && !next.startsWith('--')) {
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args.set(raw, next);
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index += 1;
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} else {
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args.set(raw, true);
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}
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}
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function readDotenv(fileName) {
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const filePath = path.join(repoRoot, fileName);
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if (!existsSync(filePath)) {
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return {};
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}
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const values = {};
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for (const line of readFileSync(filePath, 'utf8').split(/\r?\n/u)) {
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const trimmed = line.trim();
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if (!trimmed || trimmed.startsWith('#')) {
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continue;
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}
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const match = /^([A-Za-z_][A-Za-z0-9_]*)=(.*)$/u.exec(trimmed);
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if (!match) {
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continue;
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}
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let value = match[2].trim();
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if (
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(value.startsWith('"') && value.endsWith('"')) ||
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(value.startsWith("'") && value.endsWith("'"))
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) {
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value = value.slice(1, -1);
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}
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values[match[1]] = value;
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}
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return values;
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}
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function resolveEnv() {
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const loaded = {
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...readDotenv('.env.example'),
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...readDotenv('.env.local'),
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...readDotenv('.env.secrets.local'),
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...process.env,
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};
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return {
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baseUrl: String(loaded.VECTOR_ENGINE_BASE_URL || '')
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.trim()
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.replace(/\/+$/u, ''),
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apiKey: String(loaded.VECTOR_ENGINE_API_KEY || '').trim(),
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timeoutMs: Number.parseInt(
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String(loaded.VECTOR_ENGINE_IMAGE_REQUEST_TIMEOUT_MS || defaultTimeoutMs),
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10,
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),
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};
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}
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function buildUrl(baseUrl) {
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return baseUrl.endsWith('/v1')
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? `${baseUrl}/images/generations`
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: `${baseUrl}/v1/images/generations`;
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}
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function collectStringsByKey(value, targetKey, output) {
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if (Array.isArray(value)) {
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value.forEach((entry) => collectStringsByKey(entry, targetKey, output));
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return;
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}
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if (!value || typeof value !== 'object') {
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return;
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}
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for (const [key, nested] of Object.entries(value)) {
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if (key === targetKey) {
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if (typeof nested === 'string' && nested.trim()) {
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output.push(nested.trim());
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}
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if (Array.isArray(nested)) {
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nested.forEach((entry) => {
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if (typeof entry === 'string' && entry.trim()) {
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output.push(entry.trim());
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}
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});
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}
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}
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collectStringsByKey(nested, targetKey, output);
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}
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}
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function extractImageUrls(payload) {
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const urls = [];
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collectStringsByKey(payload, 'url', urls);
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collectStringsByKey(payload, 'image', urls);
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collectStringsByKey(payload, 'image_url', urls);
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return [...new Set(urls)].filter((url) => /^https?:\/\//u.test(url));
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}
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function extractBase64Images(payload) {
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const values = [];
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collectStringsByKey(payload, 'b64_json', values);
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return values;
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}
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function inferExtensionFromBytes(bytes) {
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if (bytes.subarray(0, 8).equals(Buffer.from('\x89PNG\r\n\x1A\n', 'binary'))) {
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return 'png';
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}
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if (bytes.subarray(0, 3).equals(Buffer.from([0xff, 0xd8, 0xff]))) {
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return 'jpg';
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}
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if (
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bytes.subarray(0, 4).toString('ascii') === 'RIFF' &&
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bytes.subarray(8, 12).toString('ascii') === 'WEBP'
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) {
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return 'webp';
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}
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return 'png';
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}
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async function fetchJson(url, options, timeoutMs) {
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const abortController = new AbortController();
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const timer = setTimeout(() => abortController.abort(), timeoutMs);
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try {
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const response = await fetch(url, {
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...options,
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signal: abortController.signal,
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});
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const text = await response.text();
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if (!response.ok) {
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throw new Error(`VectorEngine ${response.status}: ${text.slice(0, 600)}`);
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}
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return JSON.parse(text);
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} catch (error) {
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if (error?.name === 'AbortError') {
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throw new Error(`VectorEngine request timed out after ${timeoutMs}ms`);
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}
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throw error;
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} finally {
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clearTimeout(timer);
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}
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}
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async function downloadUrl(url, timeoutMs) {
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const abortController = new AbortController();
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const timer = setTimeout(() => abortController.abort(), timeoutMs);
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try {
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const response = await fetch(url, { signal: abortController.signal });
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if (!response.ok) {
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throw new Error(`download ${response.status}`);
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}
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return Buffer.from(await response.arrayBuffer());
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} catch (error) {
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if (error?.name === 'AbortError') {
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throw new Error(`Generated image download timed out after ${timeoutMs}ms`);
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}
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throw error;
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} finally {
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clearTimeout(timer);
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}
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}
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async function generateConcept(env, concept) {
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const requestBody = {
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model: 'gpt-image-2-all',
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prompt: concept.prompt,
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n: 1,
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size: '1024x1024',
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};
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const payload = await fetchJson(
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buildUrl(env.baseUrl),
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{
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method: 'POST',
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headers: {
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Authorization: `Bearer ${env.apiKey}`,
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Accept: 'application/json',
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'Content-Type': 'application/json',
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},
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body: JSON.stringify(requestBody),
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},
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env.timeoutMs,
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);
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const urls = extractImageUrls(payload);
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const b64Images = extractBase64Images(payload);
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let bytes;
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if (urls[0]) {
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bytes = await downloadUrl(urls[0], env.timeoutMs);
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} else if (b64Images[0]) {
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bytes = Buffer.from(b64Images[0], 'base64');
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} else {
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throw new Error(`VectorEngine returned no image for ${concept.id}`);
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}
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mkdirSync(outputDir, { recursive: true });
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const extension = inferExtensionFromBytes(bytes);
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const outputPath = path.join(outputDir, `${concept.id}.${extension}`);
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writeFileSync(outputPath, bytes);
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return outputPath;
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}
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const dryRun = args.has('--dry-run') || !args.has('--live');
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const onlyIds = String(args.get('--only') || '')
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.split(',')
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.map((value) => value.trim())
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.filter(Boolean);
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const selected = concepts.filter(
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(concept) => !onlyIds.length || onlyIds.includes(concept.id),
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);
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if (dryRun) {
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console.log(
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JSON.stringify(
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{
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mode: 'dry-run',
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outputDir,
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count: selected.length,
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requests: selected.map((concept) => ({
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id: concept.id,
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title: concept.title,
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body: {
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model: 'gpt-image-2-all',
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prompt: concept.prompt,
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n: 1,
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size: '1024x1024',
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},
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})),
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},
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null,
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2,
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),
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);
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process.exit(0);
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}
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const env = resolveEnv();
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if (!env.baseUrl || !env.apiKey) {
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console.error(
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JSON.stringify({
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ok: false,
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error: 'Missing VECTOR_ENGINE_BASE_URL or VECTOR_ENGINE_API_KEY',
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hasBaseUrl: Boolean(env.baseUrl),
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hasApiKey: Boolean(env.apiKey),
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}),
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);
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process.exit(1);
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}
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const generated = [];
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for (const concept of selected) {
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console.log(`Generating ${concept.id}...`);
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generated.push(await generateConcept(env, concept));
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}
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console.log(
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JSON.stringify(
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{
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ok: true,
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count: generated.length,
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files: generated,
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verifiedFiles: readdirSync(outputDir).sort(),
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},
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null,
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2,
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),
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);
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