mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
feat(jobs)!: SnapOtter 2.0 phase 2 job spine: async queues, worker pools, object storage, admin dashboard (#217)
This commit is contained in:
SnapOtter
@@ -1,22 +1,24 @@
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import { randomUUID } from "node:crypto";
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import { writeFile } from "node:fs/promises";
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import { mkdir, rm } from "node:fs/promises";
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import { tmpdir } from "node:os";
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import { join } from "node:path";
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import { upscale } from "@snapotter/ai";
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import { getBundleForTool, TOOL_BUNDLE_MAP } from "@snapotter/shared";
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import type { FastifyInstance, FastifyReply, FastifyRequest } from "fastify";
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import sharp from "sharp";
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import { z } from "zod";
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import { registerAiJobHandler } from "../../jobs/ai-handlers.js";
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import { enqueueToolJob } from "../../jobs/enqueue.js";
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import { autoOrient } from "../../lib/auto-orient.js";
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import { formatZodErrors } from "../../lib/errors.js";
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import { formatZodErrors, stripInternalPaths } from "../../lib/errors.js";
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import { isToolInstalled } from "../../lib/feature-status.js";
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import { validateImageBuffer } from "../../lib/file-validation.js";
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import { sanitizeFilename } from "../../lib/filename.js";
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import { decodeToSharpCompat, needsCliDecode } from "../../lib/format-decoders.js";
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import { encodeJxl } from "../../lib/format-encoders.js";
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import { decodeHeic, encodeHeic } from "../../lib/heic-converter.js";
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import { getObjectBuffer, putObject } from "../../lib/object-storage.js";
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import { resolveOutputFormat } from "../../lib/output-format.js";
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import { createWorkspace } from "../../lib/workspace.js";
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import { updateSingleFileProgress } from "../progress.js";
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import { receiveUpload } from "../../lib/upload-stream.js";
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import { registerToolProcessFn } from "../tool-factory.js";
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const settingsSchema = z.object({
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@@ -28,6 +30,86 @@ const settingsSchema = z.object({
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quality: z.union([z.number(), z.string()]).transform(Number).default(95),
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});
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// ── AI job handler (runs inside the BullMQ worker) ────────────────
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registerAiJobHandler("upscale", async (input, data, ctx) => {
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const settings = settingsSchema.parse(data.settings);
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const scale = settings.scale;
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const model = settings.model;
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const faceEnhance = settings.faceEnhance;
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const denoise = settings.denoise;
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let format = settings.format;
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const outputQuality = settings.quality;
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if (format === "auto") {
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const detected = await resolveOutputFormat(input, data.filename);
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format = detected.format === "jpeg" ? "jpg" : detected.format;
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}
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const needsNodeConversion = ["heic", "heif", "avif", "jxl"].includes(format);
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const pythonFormat = needsNodeConversion ? "png" : format;
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const result = await upscale(
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input,
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ctx.scratchDir,
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{ scale, model, faceEnhance, denoise, format: pythonFormat, quality: outputQuality },
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(percent, stage) => ctx.report(percent, stage),
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);
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let outputBuffer = result.buffer;
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let finalFormat = result.format;
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if (needsNodeConversion) {
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if (format === "heic" || format === "heif") {
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outputBuffer = await encodeHeic(result.buffer, outputQuality);
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finalFormat = format;
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} else if (format === "jxl") {
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outputBuffer = await encodeJxl(result.buffer, outputQuality);
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finalFormat = "jxl";
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} else if (format === "avif") {
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outputBuffer = await sharp(result.buffer).avif({ quality: outputQuality }).toBuffer();
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finalFormat = "avif";
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}
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}
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const EXT_MAP: Record<string, string> = {
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jpeg: "jpg",
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jpg: "jpg",
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png: "png",
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webp: "webp",
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tiff: "tiff",
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gif: "gif",
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avif: "avif",
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heic: "heic",
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heif: "heif",
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jxl: "jxl",
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};
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const ext = EXT_MAP[finalFormat] || "png";
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const outputFilename = `${data.filename.replace(/\.[^.]+$/, "")}_${scale}x.${ext}`;
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const CONTENT_TYPES: Record<string, string> = {
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png: "image/png",
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jpg: "image/jpeg",
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jpeg: "image/jpeg",
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webp: "image/webp",
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tiff: "image/tiff",
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gif: "image/gif",
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avif: "image/avif",
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heic: "image/heic",
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heif: "image/heif",
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jxl: "image/jxl",
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};
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return {
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buffer: outputBuffer,
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filename: outputFilename,
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contentType: CONTENT_TYPES[finalFormat] || "image/png",
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resultPayload: {
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width: result.width,
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height: result.height,
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method: result.method,
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},
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};
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});
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/**
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* AI image upscaling route.
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* Uses Real-ESRGAN when available, falls back to Lanczos.
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@@ -46,21 +128,20 @@ export function registerUpscale(app: FastifyInstance) {
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});
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}
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const jobId = randomUUID();
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let fileBuffer: Buffer | null = null;
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let filename = "image";
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let settingsRaw: string | null = null;
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let clientJobId: string | null = null;
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let inputKey: string | null = null;
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try {
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const parts = request.parts();
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for await (const part of parts) {
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if (part.type === "file") {
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const chunks: Buffer[] = [];
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for await (const chunk of part.file) {
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chunks.push(chunk);
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}
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fileBuffer = Buffer.concat(chunks);
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filename = sanitizeFilename(part.filename ?? "image");
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const upload = await receiveUpload(part, jobId);
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inputKey = upload.key;
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filename = upload.filename;
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} else if (part.fieldname === "settings") {
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settingsRaw = part.value as string;
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} else if (part.fieldname === "clientJobId") {
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@@ -73,16 +154,19 @@ export function registerUpscale(app: FastifyInstance) {
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} catch (err) {
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return reply.status(400).send({
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error: "Failed to parse multipart request",
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details: err instanceof Error ? err.message : String(err),
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details: stripInternalPaths(err instanceof Error ? err.message : String(err)),
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});
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}
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if (!fileBuffer || fileBuffer.length === 0) {
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if (!inputKey) {
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return reply.status(400).send({ error: "No image file provided" });
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}
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fileBuffer = await getObjectBuffer(inputKey);
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const validation = await validateImageBuffer(fileBuffer, filename);
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if (!validation.valid) {
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// Orphaned uploads/<jobId>/ dir will be cleaned by T10 TTL sweeper
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return reply.status(400).send({ error: `Invalid image: ${validation.reason}` });
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}
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@@ -100,169 +184,46 @@ export function registerUpscale(app: FastifyInstance) {
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return reply.status(400).send({ error: "Settings must be valid JSON" });
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}
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const scale = settings.scale;
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const model = settings.model;
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const faceEnhance = settings.faceEnhance;
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const denoise = settings.denoise;
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let format = settings.format;
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const outputQuality = settings.quality;
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try {
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if (format === "auto") {
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const detected = await resolveOutputFormat(fileBuffer, filename);
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format = detected.format === "jpeg" ? "jpg" : detected.format;
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}
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// Decode HEIC/HEIF input via system decoder
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if (validation.format === "heif") {
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fileBuffer = await decodeHeic(fileBuffer);
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}
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// Decode CLI-decoded formats (RAW, TGA, PSD, EXR, HDR)
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if (needsCliDecode(validation.format)) {
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fileBuffer = await decodeToSharpCompat(fileBuffer, validation.format);
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}
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// Auto-orient to fix EXIF rotation before upscaling
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fileBuffer = await autoOrient(fileBuffer);
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} catch (err) {
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request.log.error({ err, toolId: "upscale" }, "Input decoding failed");
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return reply.status(422).send({
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error: "Upscaling failed",
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details: err instanceof Error ? err.message : "Unknown error",
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details: stripInternalPaths(err instanceof Error ? err.message : "Unknown error"),
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});
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}
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const originalSize = fileBuffer.length;
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const jobId = randomUUID();
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// Write decoded input for the worker
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const decodedKey = `uploads/${jobId}/${filename}`;
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if (decodedKey !== inputKey) {
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await putObject(decodedKey, fileBuffer);
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inputKey = decodedKey;
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} else {
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await putObject(inputKey, fileBuffer);
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}
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const progressJobId = clientJobId || jobId;
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let workspacePath: string;
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try {
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workspacePath = await createWorkspace(jobId);
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const inputPath = join(workspacePath, "input", filename);
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await writeFile(inputPath, fileBuffer);
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} catch (err) {
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request.log.error({ err, toolId: "upscale" }, "Workspace creation failed");
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return reply.status(422).send({
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error: "Upscaling failed",
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details: err instanceof Error ? err.message : "Unknown error",
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});
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}
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const log = request.log;
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log.info(
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{ toolId: "upscale", imageSize: originalSize, scale, model, format },
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"Starting upscale",
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);
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// Reply immediately so the HTTP connection closes within proxy timeout limits.
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// The result will be delivered via the SSE progress channel.
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reply.status(202).send({ jobId: progressJobId, async: true });
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const needsNodeConversion = ["heic", "heif", "avif", "jxl"].includes(format);
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const pythonFormat = needsNodeConversion ? "png" : format;
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const onProgress = (percent: number, stage: string) => {
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updateSingleFileProgress({
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jobId: progressJobId,
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phase: "processing",
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stage,
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percent,
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});
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};
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// Fire-and-forget: processing happens after the response is sent
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(async () => {
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const result = await upscale(
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fileBuffer,
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join(workspacePath, "output"),
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{ scale, model, faceEnhance, denoise, format: pythonFormat, quality: outputQuality },
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onProgress,
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);
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let outputBuffer = result.buffer;
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let finalFormat = result.format;
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if (needsNodeConversion) {
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if (format === "heic" || format === "heif") {
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outputBuffer = await encodeHeic(result.buffer, outputQuality);
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finalFormat = format;
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} else if (format === "jxl") {
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outputBuffer = await encodeJxl(result.buffer, outputQuality);
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finalFormat = "jxl";
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} else if (format === "avif") {
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outputBuffer = await sharp(result.buffer).avif({ quality: outputQuality }).toBuffer();
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finalFormat = "avif";
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}
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}
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const EXT_MAP: Record<string, string> = {
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jpeg: "jpg",
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jpg: "jpg",
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png: "png",
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webp: "webp",
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tiff: "tiff",
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gif: "gif",
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avif: "avif",
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heic: "heic",
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heif: "heif",
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jxl: "jxl",
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};
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const ext = EXT_MAP[finalFormat] || "png";
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const outputFilename = `${filename.replace(/\.[^.]+$/, "")}_${scale}x.${ext}`;
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const outputPath = join(workspacePath, "output", outputFilename);
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await writeFile(outputPath, outputBuffer);
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const BROWSER_PREVIEWABLE = new Set(["png", "jpg", "jpeg", "webp", "gif", "avif", "bmp"]);
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let previewUrl: string | undefined;
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if (!BROWSER_PREVIEWABLE.has(finalFormat)) {
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try {
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const previewInput =
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finalFormat === "heic" || finalFormat === "heif"
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? await decodeHeic(outputBuffer)
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: outputBuffer;
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const previewBuffer = await sharp(previewInput).webp({ quality: 80 }).toBuffer();
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const previewPath = join(workspacePath, "output", "preview.webp");
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await writeFile(previewPath, previewBuffer);
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previewUrl = `/api/v1/download/${jobId}/preview.webp`;
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} catch {
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// Non-fatal
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}
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}
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if (model !== "auto" && result.method !== model) {
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log.warn(
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{ toolId: "upscale", requested: model, actual: result.method },
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`Upscale model mismatch: requested ${model} but used ${result.method}`,
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);
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}
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const downloadUrl = `/api/v1/download/${jobId}/${encodeURIComponent(outputFilename)}`;
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updateSingleFileProgress({
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jobId: progressJobId,
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phase: "complete",
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percent: 100,
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result: {
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jobId,
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downloadUrl,
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previewUrl,
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originalSize,
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processedSize: outputBuffer.length,
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width: result.width,
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height: result.height,
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method: result.method,
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},
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});
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log.info({ toolId: "upscale", jobId, downloadUrl }, "Upscale complete");
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})().catch((err) => {
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log.error({ err, toolId: "upscale" }, "Upscaling failed");
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updateSingleFileProgress({
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jobId: progressJobId,
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phase: "failed",
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percent: 0,
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error: err instanceof Error ? err.message : "Upscale failed",
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});
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await enqueueToolJob({
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jobId,
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toolId,
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userId: null,
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pool: "ai",
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inputRefs: [inputKey],
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filename,
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settings,
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clientJobId: clientJobId ?? undefined,
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kind: "ai-tool",
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});
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return reply.status(202).send({ jobId: progressJobId, async: true });
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});
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// Register in the pipeline/batch registry so this tool can be used
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@@ -272,26 +233,31 @@ export function registerUpscale(app: FastifyInstance) {
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settingsSchema: z.object({
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scale: z.union([z.number(), z.string()]).transform(Number).default(2),
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}),
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process: async (inputBuffer, settings, filename) => {
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process: async (inputBuffer, settings, filename, ctx) => {
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const scale = Number((settings as { scale?: number }).scale) || 2;
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const orientedBuffer = await autoOrient(inputBuffer);
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const jobId = randomUUID();
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const workspacePath = await createWorkspace(jobId);
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const result = await upscale(orientedBuffer, join(workspacePath, "output"), { scale });
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const outputFormat = await resolveOutputFormat(inputBuffer, filename);
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let outputBuffer = result.buffer;
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if (outputFormat.format !== "png") {
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outputBuffer = await sharp(result.buffer)
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.toFormat(outputFormat.format, { quality: outputFormat.quality })
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.toBuffer();
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const scratchDir = ctx?.scratchDir ?? join(tmpdir(), "snapotter-scratch", randomUUID());
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const needsCleanup = !ctx?.scratchDir;
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if (needsCleanup) await mkdir(scratchDir, { recursive: true });
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try {
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const result = await upscale(orientedBuffer, scratchDir, { scale });
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const outputFormat = await resolveOutputFormat(inputBuffer, filename);
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let outputBuffer = result.buffer;
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if (outputFormat.format !== "png") {
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outputBuffer = await sharp(result.buffer)
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.toFormat(outputFormat.format, { quality: outputFormat.quality })
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.toBuffer();
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}
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const ext = outputFormat.format === "jpeg" ? "jpg" : outputFormat.format;
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const outputFilename = `${filename.replace(/\.[^.]+$/, "")}_${scale}x.${ext}`;
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return {
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buffer: outputBuffer,
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filename: outputFilename,
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contentType: outputFormat.contentType,
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};
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} finally {
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if (needsCleanup) await rm(scratchDir, { recursive: true, force: true }).catch(() => {});
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}
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const ext = outputFormat.format === "jpeg" ? "jpg" : outputFormat.format;
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const outputFilename = `${filename.replace(/\.[^.]+$/, "")}_${scale}x.${ext}`;
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return {
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buffer: outputBuffer,
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filename: outputFilename,
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contentType: outputFormat.contentType,
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};
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},
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});
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}
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