# 问题 修复 文件 1 前端构建失败(引号错误) size="small type=" → size="small" type=" PosterHistoryPage.vue 2 migrate_014 ORM vs 缺失列 全部改为原始 SQL,不再引用 ORM 模型 migrate_014.py 3 cleanup 字段名错误 output_path → ppt_path cleanup.py 4 文案生成 case 越权 添加 case.user_id != user_id 校验 poster/service.py 5 存储路径未接通持久化卷 全部改用 get_storage_root()(默认 /app/api/storage/insurance) config.py, ppt/routes.py, poster/service.py, poster/tasks.py 高风险问题修复 # 问题 修复 文件 6 migrate_019 rollback 撤销成功字段 每个 ALTER 后立即 commit,失败只回滚当前语句 migrate_019.py 7 迁移锁 Windows 不兼容 + 句柄未持久化 全局变量保存锁句柄,支持 Windows msvcrt api/insurance/db/__init__.py 8 PDF 校验异常时放行 异常返回 False(文件损坏) security.py 9 健康检查始终返回成功 缺少关键资源时返回 503 + missing 列表 poster/routes.py 10 短密钥掩码泄露原值 ≤4 字符返回 **** ppt_admin_service.py 11 设置无键名白名单 添加 _ALLOWED_SETTING_KEYS 白名单 ppt_admin_service.py 12 容器重启任务永久 stuck 添加 recover_stale_tasks() 启动恢复函数 poster/tasks.py, ppt/parse_worker.py
233 lines
8.9 KiB
JavaScript
233 lines
8.9 KiB
JavaScript
#!/usr/bin/env node
|
|
/**
|
|
* API image generation fallback: renders a mock or world board with the
|
|
* user's own OpenAI key when the harness has no native image generation.
|
|
*
|
|
* context.mjs reports availability (it checks OPENAI_API_KEY); harness-native
|
|
* generation always wins when present. This uses gpt-image-2 and spends the
|
|
* user's API credit (roughly $0.05-0.25 per image at default quality), so the
|
|
* skill states that before the first call in a session.
|
|
*
|
|
* node generate-image.mjs --prompt "..." --out mock.png [--size 1536x1024] [--quality medium]
|
|
* node generate-image.mjs --prompt-file prompt.txt --out mock.png
|
|
*/
|
|
import fs from 'node:fs';
|
|
import zlib from 'node:zlib';
|
|
|
|
function arg(name, fallback = null) {
|
|
const i = process.argv.indexOf(`--${name}`);
|
|
if (i === -1) return fallback;
|
|
const v = process.argv[i + 1];
|
|
return v && !v.startsWith('--') ? v : fallback;
|
|
}
|
|
|
|
// ---------------------------------------------------------------------------
|
|
// Fake mode (IMPECCABLE_IMAGE_GEN_FAKE=1)
|
|
//
|
|
// Deterministic offline stand-in for the OpenAI call: same prompt -> identical
|
|
// bytes, no network, no key, cost line reads $0.00. Used by the new-work smoke
|
|
// suite so the concept/serve-question/image chain can run without spend. The
|
|
// output renders the prompt over a 2-3 color palette hashed from the prompt,
|
|
// plus a "SYNTHETIC COMP" corner label. SVG carries the readable text; the
|
|
// raster (.png/.webp/.jpg) fallback carries palette stripes and stows the
|
|
// prompt + marker in a PNG tEXt chunk so downstream stays a valid image.
|
|
// ---------------------------------------------------------------------------
|
|
|
|
// FNV-1a 32-bit: tiny, dependency-free, stable across runs and platforms.
|
|
function hash32(str) {
|
|
let h = 0x811c9dc5;
|
|
for (let i = 0; i < str.length; i++) {
|
|
h ^= str.charCodeAt(i);
|
|
h = Math.imul(h, 0x01000193);
|
|
}
|
|
return h >>> 0;
|
|
}
|
|
|
|
function hslToRgb(hDeg, s, l) {
|
|
const h = ((hDeg % 360) + 360) % 360 / 360;
|
|
const q = l < 0.5 ? l * (1 + s) : l + s - l * s;
|
|
const p = 2 * l - q;
|
|
const hue = (t) => {
|
|
let tt = t;
|
|
if (tt < 0) tt += 1;
|
|
if (tt > 1) tt -= 1;
|
|
if (tt < 1 / 6) return p + (q - p) * 6 * tt;
|
|
if (tt < 1 / 2) return q;
|
|
if (tt < 2 / 3) return p + (q - p) * (2 / 3 - tt) * 6;
|
|
return p;
|
|
};
|
|
return [hue(h + 1 / 3), hue(h), hue(h - 1 / 3)].map((c) => Math.round(c * 255));
|
|
}
|
|
|
|
const toHex = ([r, g, b]) =>
|
|
'#' + [r, g, b].map((c) => c.toString(16).padStart(2, '0')).join('');
|
|
|
|
// Two or three deterministic swatches derived from the prompt hash. The band
|
|
// count itself is prompt-derived, so different prompts differ in palette.
|
|
function palette(prompt) {
|
|
const h = hash32(prompt);
|
|
const base = h % 360;
|
|
const bands = 2 + (h >>> 9) % 2; // 2 or 3
|
|
const spread = 40 + (h >>> 3) % 120;
|
|
const out = [];
|
|
for (let i = 0; i < bands; i++) {
|
|
const hue = base + i * spread;
|
|
const light = 0.32 + ((h >>> (i * 5)) % 40) / 100; // 0.32 - 0.71
|
|
out.push(hslToRgb(hue, 0.55, light));
|
|
}
|
|
return out;
|
|
}
|
|
|
|
function svgFake(prompt, [w, h]) {
|
|
const colors = palette(prompt).map(toHex);
|
|
const stops = colors
|
|
.map((c, i) => `<stop offset="${Math.round((i / (colors.length - 1)) * 100)}%" stop-color="${c}"/>`)
|
|
.join('');
|
|
// Greedy word wrap tuned to the canvas width so the prompt stays legible.
|
|
const perLine = Math.max(12, Math.floor(w / 26));
|
|
const words = String(prompt).replace(/\s+/g, ' ').trim().split(' ');
|
|
const lines = [];
|
|
let cur = '';
|
|
for (const word of words) {
|
|
if ((cur + ' ' + word).trim().length > perLine) {
|
|
if (cur) lines.push(cur);
|
|
cur = word;
|
|
} else {
|
|
cur = (cur + ' ' + word).trim();
|
|
}
|
|
if (lines.length >= 10) break;
|
|
}
|
|
if (cur && lines.length < 11) lines.push(cur);
|
|
const escape = (s) => String(s).replace(/[&<>]/g, (c) => ({ '&': '&', '<': '<', '>': '>' }[c]));
|
|
const fontSize = Math.round(w / 24);
|
|
const startY = h / 2 - ((lines.length - 1) * fontSize * 1.3) / 2;
|
|
const text = lines
|
|
.map((line, i) => `<text x="${w / 2}" y="${Math.round(startY + i * fontSize * 1.3)}" font-family="Helvetica, Arial, sans-serif" font-size="${fontSize}" fill="#ffffff" text-anchor="middle" dominant-baseline="middle">${escape(line)}</text>`)
|
|
.join('');
|
|
return `<?xml version="1.0" encoding="UTF-8"?>
|
|
<svg xmlns="http://www.w3.org/2000/svg" width="${w}" height="${h}" viewBox="0 0 ${w} ${h}">
|
|
<defs><linearGradient id="g" x1="0" y1="0" x2="1" y2="1">${stops}</linearGradient></defs>
|
|
<rect width="${w}" height="${h}" fill="url(#g)"/>
|
|
<rect x="0" y="0" width="${w}" height="${h}" fill="#000000" fill-opacity="0.22"/>
|
|
${text}
|
|
<rect x="${w - Math.round(w / 4.2)}" y="${h - Math.round(h / 16)}" width="${Math.round(w / 4.2)}" height="${Math.round(h / 16)}" fill="#000000" fill-opacity="0.55"/>
|
|
<text x="${w - Math.round(w / 8.4)}" y="${h - Math.round(h / 32)}" font-family="Helvetica, Arial, sans-serif" font-size="${Math.round(w / 60)}" letter-spacing="2" fill="#ffffff" text-anchor="middle" dominant-baseline="middle">SYNTHETIC COMP</text>
|
|
</svg>
|
|
`;
|
|
}
|
|
|
|
// Minimal valid PNG: palette stripes plus a tEXt chunk carrying the marker and
|
|
// prompt, so a .png/.webp fake stays a decodable image and still contains the
|
|
// "SYNTHETIC" bytes downstream tools look for.
|
|
function crc32(buf) {
|
|
let c = 0xffffffff;
|
|
for (let i = 0; i < buf.length; i++) {
|
|
c ^= buf[i];
|
|
for (let k = 0; k < 8; k++) c = (c & 1) ? (0xedb88320 ^ (c >>> 1)) : (c >>> 1);
|
|
}
|
|
return (c ^ 0xffffffff) >>> 0;
|
|
}
|
|
|
|
function pngChunk(type, data) {
|
|
const typeBuf = Buffer.from(type, 'latin1');
|
|
const body = Buffer.concat([typeBuf, data]);
|
|
const len = Buffer.alloc(4);
|
|
len.writeUInt32BE(data.length, 0);
|
|
const crc = Buffer.alloc(4);
|
|
crc.writeUInt32BE(crc32(body), 0);
|
|
return Buffer.concat([len, body, crc]);
|
|
}
|
|
|
|
function pngFake(prompt, [w, h]) {
|
|
const colors = palette(prompt); // [[r,g,b], ...]
|
|
const bandH = Math.ceil(h / colors.length);
|
|
// Raw image: each scanline prefixed with a 0 filter byte, RGB pixels.
|
|
const stride = w * 3;
|
|
const raw = Buffer.alloc(h * (stride + 1));
|
|
for (let y = 0; y < h; y++) {
|
|
const rowStart = y * (stride + 1);
|
|
raw[rowStart] = 0;
|
|
const [r, g, b] = colors[Math.min(colors.length - 1, Math.floor(y / bandH))];
|
|
for (let x = 0; x < w; x++) {
|
|
const p = rowStart + 1 + x * 3;
|
|
raw[p] = r;
|
|
raw[p + 1] = g;
|
|
raw[p + 2] = b;
|
|
}
|
|
}
|
|
const ihdr = Buffer.alloc(13);
|
|
ihdr.writeUInt32BE(w, 0);
|
|
ihdr.writeUInt32BE(h, 4);
|
|
ihdr[8] = 8; // bit depth
|
|
ihdr[9] = 2; // color type: truecolor RGB
|
|
const idat = zlib.deflateSync(raw, { level: 9 });
|
|
const textData = Buffer.concat([
|
|
Buffer.from('Comment', 'latin1'),
|
|
Buffer.from([0]),
|
|
Buffer.from(`SYNTHETIC COMP: ${String(prompt).replace(/\s+/g, ' ').trim()}`, 'latin1'),
|
|
]);
|
|
return Buffer.concat([
|
|
Buffer.from([0x89, 0x50, 0x4e, 0x47, 0x0d, 0x0a, 0x1a, 0x0a]),
|
|
pngChunk('IHDR', ihdr),
|
|
pngChunk('tEXt', textData),
|
|
pngChunk('IDAT', idat),
|
|
pngChunk('IEND', Buffer.alloc(0)),
|
|
]);
|
|
}
|
|
|
|
function parseSize(sizeStr) {
|
|
const m = String(sizeStr).match(/^(\d+)x(\d+)$/);
|
|
if (!m) return [1536, 1024];
|
|
return [Number(m[1]), Number(m[2])];
|
|
}
|
|
|
|
if (process.env.IMPECCABLE_IMAGE_GEN_FAKE) {
|
|
const fakePromptFile = arg('prompt-file');
|
|
const fakePrompt = fakePromptFile ? fs.readFileSync(fakePromptFile, 'utf8') : arg('prompt');
|
|
const fakeOut = arg('out');
|
|
if (!fakePrompt || !fakeOut) {
|
|
console.error('generate-image: --prompt (or --prompt-file) and --out are required.');
|
|
process.exit(1);
|
|
}
|
|
const dims = parseSize(arg('size', '1536x1024'));
|
|
const bytes = fakeOut.endsWith('.svg')
|
|
? Buffer.from(svgFake(fakePrompt, dims), 'utf8')
|
|
: pngFake(fakePrompt, dims);
|
|
fs.writeFileSync(fakeOut, bytes);
|
|
console.log(`IMAGE: ${fakeOut} (${dims[0]}x${dims[1]}, fake synthetic comp, $0.00, no API call)`);
|
|
process.exit(0);
|
|
}
|
|
|
|
const key = process.env.OPENAI_API_KEY;
|
|
if (!key) {
|
|
console.error('generate-image: OPENAI_API_KEY is not set; use the harness-native image tool instead.');
|
|
process.exit(1);
|
|
}
|
|
const promptFile = arg('prompt-file');
|
|
const prompt = promptFile ? fs.readFileSync(promptFile, 'utf8') : arg('prompt');
|
|
const out = arg('out');
|
|
if (!prompt || !out) {
|
|
console.error('generate-image: --prompt (or --prompt-file) and --out are required.');
|
|
process.exit(1);
|
|
}
|
|
const size = arg('size', '1536x1024');
|
|
const quality = arg('quality', 'medium');
|
|
|
|
const response = await fetch('https://api.openai.com/v1/images/generations', {
|
|
method: 'POST',
|
|
headers: { Authorization: `Bearer ${key}`, 'content-type': 'application/json' },
|
|
body: JSON.stringify({ model: 'gpt-image-2', prompt, size, quality, n: 1 }),
|
|
});
|
|
if (!response.ok) {
|
|
console.error(`generate-image: API error ${response.status}: ${(await response.text()).slice(0, 300)}`);
|
|
process.exit(1);
|
|
}
|
|
const json = await response.json();
|
|
const b64 = json?.data?.[0]?.b64_json;
|
|
if (!b64) {
|
|
console.error('generate-image: no image in response');
|
|
process.exit(1);
|
|
}
|
|
fs.writeFileSync(out, Buffer.from(b64, 'base64'));
|
|
console.log(`IMAGE: ${out} (${size}, ${quality}, gpt-image-2, billed to your OpenAI key)`);
|