fix(image-service): force rebuild tfjs-node for container architecture in Dockerfile

.dockerignore

@@ -1,14 +1,28 @@
 node_modules
+**/node_modules
 .next
+**/.next
 .git
 # .npmrc is allowed as it contains the registry template
 dist
+**/dist
 build
+**/build
 out
+**/out
 coverage
+**/coverage
 .vercel
+**/.vercel
 .turbo
+**/.turbo
 *.log
+**/*.log
 .DS_Store
+**/.DS_Store
 .pnpm-store
+**/.pnpm-store
 .gitea
+**/.gitea
+models
+**/models

.env

@@ -1,5 +1,5 @@
 # Project
-IMAGE_TAG=v1.8.10
+IMAGE_TAG=v1.8.12
 PROJECT_NAME=at-mintel
 PROJECT_COLOR=#82ed20
 GITEA_TOKEN=ccce002e30fe16a31a6c9d5a414740af2f72a582

.env.example

@@ -1,5 +1,5 @@
 # Project
-IMAGE_TAG=v1.8.10
+IMAGE_TAG=v1.8.12
 PROJECT_NAME=sample-website
 PROJECT_COLOR=#82ed20
 

.gitea/workflows/pipeline.yml

@@ -192,6 +192,9 @@ jobs:
           - image: directus
             file: packages/infra/docker/Dockerfile.directus
             name: Directus (Base)
+          - image: image-processor
+            file: apps/image-service/Dockerfile
+            name: Image Processor
     steps:
       - name: Checkout
         uses: actions/checkout@v4

apps/image-service/Dockerfile

@@ -0,0 +1,43 @@
+FROM node:20.18-bookworm-slim AS base
+ENV PNPM_HOME="/pnpm"
+ENV PATH="$PNPM_HOME:$PATH"
+RUN npm install -g pnpm@10.30.1
+
+FROM base AS build
+WORKDIR /app
+COPY . .
+# Note: Canvas needs build tools on Debian
+RUN apt-get update && apt-get install -y python3 make g++ libcairo2-dev libpango1.0-dev libjpeg-dev libgif-dev librsvg2-dev
+# Delete the prebuilt binary and force a clean rebuild from source for the correct container architecture
+RUN pnpm install --frozen-lockfile
+RUN for dir in $(find /app/node_modules -type d -path "*/@tensorflow/tfjs-node"); do \
+    cd $dir && \
+    rm -rf lib/napi-v8/* && \
+    npm run install -- build-addon-from-source; \
+    done
+# Generate models explicitly for Docker
+RUN ls -la packages/image-processor/scripts || true
+RUN pnpm dlx tsx packages/image-processor/scripts/download-models.ts
+RUN pnpm --filter @mintel/image-processor build
+RUN pnpm --filter image-service build
+# Generated locally for caching
+
+FROM base
+WORKDIR /app
+COPY --from=build /app/node_modules ./node_modules
+COPY --from=build /app/apps/image-service/node_modules ./apps/image-service/node_modules
+COPY --from=build /app/packages/image-processor/node_modules ./packages/image-processor/node_modules
+# Make sure directories exist to prevent COPY errors
+RUN mkdir -p /app/packages/image-processor/models /app/apps/image-service/dist
+COPY --from=build /app/apps/image-service/dist ./apps/image-service/dist
+COPY --from=build /app/apps/image-service/package.json ./apps/image-service/package.json
+COPY --from=build /app/packages/image-processor/dist ./packages/image-processor/dist
+COPY --from=build /app/packages/image-processor/package.json ./packages/image-processor/package.json
+COPY --from=build /app/packages/image-processor/models ./packages/image-processor/models
+
+# Need runtime dependencies for canvas/sharp on Debian
+RUN apt-get update && apt-get install -y libcairo2 libpango-1.0-0 libjpeg62-turbo libgif7 librsvg2-2 && rm -rf /var/lib/apt/lists/*
+
+EXPOSE 8080
+WORKDIR /app/apps/image-service
+CMD ["npm", "run", "start"]

apps/image-service/package.json

@@ -0,0 +1,23 @@
+{
+  "name": "image-service",
+  "version": "1.8.12",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "dev": "tsx watch src/index.ts",
+    "build": "tsc",
+    "start": "node dist/index.js",
+    "lint": "eslint src"
+  },
+  "dependencies": {
+    "@mintel/image-processor": "workspace:*",
+    "fastify": "^4.26.2"
+  },
+  "devDependencies": {
+    "@mintel/eslint-config": "workspace:*",
+    "@mintel/tsconfig": "workspace:*",
+    "@types/node": "^20.0.0",
+    "tsx": "^4.7.1",
+    "typescript": "^5.0.0"
+  }
+}

apps/image-service/src/index.ts

@@ -0,0 +1,80 @@
+import Fastify from "fastify";
+import { processImageWithSmartCrop } from "@mintel/image-processor";
+
+const fastify = Fastify({
+  logger: true,
+});
+
+fastify.get("/unsafe/:options/:urlSafeB64", async (request, reply) => {
+  // Compatibility endpoint for old imgproxy calls (optional, but requested by some systems sometimes)
+  // For now, replacing logic in clients is preferred. So we just redirect or error.
+  return reply
+    .status(400)
+    .send({ error: "Legacy imgproxy API not supported. Use /process" });
+});
+
+fastify.get("/process", async (request, reply) => {
+  const query = request.query as {
+    url?: string;
+    w?: string;
+    h?: string;
+    q?: string;
+    format?: string;
+  };
+
+  const { url } = query;
+  const width = parseInt(query.w || "800", 10);
+  const height = parseInt(query.h || "600", 10);
+  const quality = parseInt(query.q || "80", 10);
+  const format = (query.format || "webp") as "webp" | "jpeg" | "png" | "avif";
+
+  if (!url) {
+    return reply.status(400).send({ error: 'Parameter "url" is required' });
+  }
+
+  try {
+    const response = await fetch(url);
+    if (!response.ok) {
+      return reply
+        .status(response.status)
+        .send({
+          error: `Failed to fetch source image: ${response.statusText}`,
+        });
+    }
+
+    const arrayBuffer = await response.arrayBuffer();
+    const buffer = Buffer.from(arrayBuffer);
+
+    const processedBuffer = await processImageWithSmartCrop(buffer, {
+      width,
+      height,
+      format,
+      quality,
+    });
+
+    reply.header("Content-Type", `image/${format}`);
+    reply.header("Cache-Control", "public, max-age=31536000, immutable");
+    return reply.send(processedBuffer);
+  } catch (err) {
+    fastify.log.error(err);
+    return reply
+      .status(500)
+      .send({ error: "Internal Server Error processing image" });
+  }
+});
+
+fastify.get("/health", async () => {
+  return { status: "ok" };
+});
+
+const start = async () => {
+  try {
+    await fastify.listen({ port: 8080, host: "0.0.0.0" });
+    console.log(`Server listening on 8080`);
+  } catch (err) {
+    fastify.log.error(err);
+    process.exit(1);
+  }
+};
+
+start();

apps/image-service/tsconfig.json

@@ -0,0 +1,11 @@
+{
+  "extends": "@mintel/tsconfig/base.json",
+  "compilerOptions": {
+    "outDir": "dist",
+    "rootDir": "src",
+    "module": "NodeNext",
+    "moduleResolution": "NodeNext",
+    "noEmit": false
+  },
+  "include": ["src/**/*"]
+}

apps/sample-website/next.config.ts

@@ -1,6 +1,13 @@
 import mintelNextConfig from "@mintel/next-config";
 
 /** @type {import('next').NextConfig} */
-const nextConfig = {};
+const nextConfig = {
+  serverExternalPackages: [
+    "@mintel/image-processor",
+    "@tensorflow/tfjs-node",
+    "sharp",
+    "canvas",
+  ],
+};
 
 export default mintelNextConfig(nextConfig);

apps/sample-website/package.json

@@ -1,6 +1,6 @@
 {
   "name": "sample-website",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "scripts": {
@@ -18,6 +18,7 @@
     "@mintel/next-utils": "workspace:*",
     "@mintel/observability": "workspace:*",
     "@mintel/next-observability": "workspace:*",
+    "@mintel/image-processor": "workspace:*",
     "@sentry/nextjs": "10.38.0",
     "next": "16.1.6",
     "next-intl": "^4.8.2",

apps/sample-website/src/app/api/image/route.ts

@@ -0,0 +1,60 @@
+import { NextRequest, NextResponse } from "next/server";
+
+export const dynamic = "force-dynamic";
+export const runtime = "nodejs";
+
+export async function GET(request: NextRequest) {
+  const { searchParams } = new URL(request.url);
+  const url = searchParams.get("url");
+  const width = parseInt(searchParams.get("w") || "800");
+  const height = parseInt(searchParams.get("h") || "600");
+  const q = parseInt(searchParams.get("q") || "80");
+
+  if (!url) {
+    return NextResponse.json(
+      { error: "Missing url parameter" },
+      { status: 400 },
+    );
+  }
+
+  try {
+    // 1. Fetch image from original URL
+    const response = await fetch(url);
+    if (!response.ok) {
+      return NextResponse.json(
+        { error: "Failed to fetch original image" },
+        { status: response.status },
+      );
+    }
+
+    const arrayBuffer = await response.arrayBuffer();
+    const buffer = Buffer.from(arrayBuffer);
+
+    // Dynamically import to prevent Next.js from trying to bundle tfjs-node/sharp locally at build time
+    const { processImageWithSmartCrop } =
+      await import("@mintel/image-processor");
+
+    // 2. Process image with Face-API and Sharp
+    const processedBuffer = await processImageWithSmartCrop(buffer, {
+      width,
+      height,
+      format: "webp",
+      quality: q,
+    });
+
+    // 3. Return the processed image
+    return new NextResponse(new Uint8Array(processedBuffer), {
+      status: 200,
+      headers: {
+        "Content-Type": "image/webp",
+        "Cache-Control": "public, max-age=31536000, immutable",
+      },
+    });
+  } catch (error) {
+    console.error("Image Processing Error:", error);
+    return NextResponse.json(
+      { error: "Failed to process image" },
+      { status: 500 },
+    );
+  }
+}

models/tiny_face_detector_model-shard1

@@ -0,0 +1 @@
+404: Not Found

models/tiny_face_detector_model-weights_manifest.json

@@ -0,0 +1,30 @@
+[
+  {
+      "weights":
+      [
+          {"name":"conv0/filters","shape":[3,3,3,16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009007044399485869,"min":-1.2069439495311063}},
+          {"name":"conv0/bias","shape":[16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005263455241334205,"min":-0.9211046672334858}},
+          {"name":"conv1/depthwise_filter","shape":[3,3,16,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004001977630690033,"min":-0.5042491814669441}},
+          {"name":"conv1/pointwise_filter","shape":[1,1,16,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013836609615999109,"min":-1.411334180831909}},
+          {"name":"conv1/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0015159862590771096,"min":-0.30926119685173037}},
+          {"name":"conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002666276225856706,"min":-0.317286870876948}},
+          {"name":"conv2/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015265831292844286,"min":-1.6792414422128714}},
+          {"name":"conv2/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0020280554598453,"min":-0.37113414915168985}},
+          {"name":"conv3/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006100742489683862,"min":-0.8907084034938438}},
+          {"name":"conv3/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.016276211832083907,"min":-2.0508026908425725}},
+          {"name":"conv3/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003394414279975143,"min":-0.7637432129944072}},
+          {"name":"conv4/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006716050119961009,"min":-0.8059260143953211}},
+          {"name":"conv4/pointwise_filter","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.021875603993733724,"min":-2.8875797271728514}},
+          {"name":"conv4/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0041141652009066415,"min":-0.8187188749804216}},
+          {"name":"conv5/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008423839597141042,"min":-0.9013508368940915}},
+          {"name":"conv5/pointwise_filter","shape":[1,1,256,512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.030007277283014035,"min":-3.8709387695088107}},
+          {"name":"conv5/bias","shape":[512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008402082966823203,"min":-1.4871686851277068}},
+          {"name":"conv8/filters","shape":[1,1,512,25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.028336129469030042,"min":-4.675461362389957}},
+          {"name":"conv8/bias","shape":[25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002268134028303857,"min":-0.41053225912299807}}
+      ],
+      "paths":
+      [
+          "tiny_face_detector_model.bin"
+      ]
+  }
+]

package.json

@@ -57,11 +57,23 @@
     "pino-pretty": "^13.1.3",
     "require-in-the-middle": "^8.0.1"
   },
-  "version": "1.8.10",
+  "version": "1.8.12",
   "pnpm": {
+    "onlyBuiltDependencies": [
+      "@parcel/watcher",
+      "@sentry/cli",
+      "@swc/core",
+      "@tensorflow/tfjs-node",
+      "canvas",
+      "core-js",
+      "esbuild",
+      "sharp",
+      "unrs-resolver",
+      "vue-demi"
+    ],
     "overrides": {
       "next": "16.1.6",
       "@sentry/nextjs": "10.38.0"
     }
   }
-}
+}

packages/acquisition-manager/package.json

@@ -2,7 +2,7 @@
   "name": "acquisition-manager",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",
@@ -27,4 +27,4 @@
     "@directus/extensions-sdk": "11.0.2",
     "vue": "^3.4.0"
   }
-}
+}

packages/acquisition/package.json

@@ -1,6 +1,6 @@
 {
   "name": "acquisition",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "directus:extension": {
     "type": "endpoint",
@@ -24,4 +24,4 @@
     "react": "^19.2.4",
     "react-dom": "^19.2.4"
   }
-}
+}

packages/cli/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/cli",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/cloner-library/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/cloner",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "main": "dist/index.js",
   "module": "dist/index.js",

packages/cms-infra/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/cms-infra",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "scripts": {
@@ -15,4 +15,4 @@
     "sync:push": "../../scripts/sync-directus.sh push infra",
     "sync:pull": "../../scripts/sync-directus.sh pull infra"
   }
-}
+}

packages/company-manager/package.json

@@ -2,7 +2,7 @@
   "name": "company-manager",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",
@@ -27,4 +27,4 @@
     "@directus/extensions-sdk": "11.0.2",
     "vue": "^3.4.0"
   }
-}
+}

packages/content-engine/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/content-engine",
-  "version": "1.0.0",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",

packages/content-engine/src/orchestrator.ts

@@ -475,6 +475,7 @@ BLOG POST BEST PRACTICES (MANDATORY):
 - Agiere als hochprofessioneller Digital Architect und entferne alte MDX-Metadaten im Body.
 - Fazit: Schließe JEDEN Artikel ZWINGEND mit einem starken, klaren 'Fazit' ab.
 - ORIGINAL LANGUAGE QUOTES: Übersetze NIEMALS Zitate (z.B. in ArticleQuote). Behalte das Original (z.B. Englisch), wenn du Studien von Deloitte, McKinsey oder Aussagen von CEOs zitierst. Das erhöht die Authentizität im B2B-Mittelstand.
+- CONTENT PRUNING: Wenn das dir übergebene MDX bereits interaktive Komponenten (z.B. \`<YouTubeEmbed>\`) enthält, die **nicht** oder **nicht mehr** zum inhaltlichen Fokus passen (z.B. irrelevante Videos oder platzhalter-ähnliche Snippets), MUSST du diese radikal **entfernen**. Behalte keine halluzinierten oder unpassenden Medien, nur weil sie schon da waren.
 
 STRICT MDX OUTPUT RULES:
 1. ONLY use the exact components defined above.

packages/customer-manager/package.json

@@ -2,7 +2,7 @@
   "name": "customer-manager",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",
@@ -27,4 +27,4 @@
     "@directus/extensions-sdk": "11.0.2",
     "vue": "^3.4.0"
   }
-}
+}

packages/directus-extension-toolkit/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/directus-extension-toolkit",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "description": "Shared toolkit for Directus extensions in the Mintel ecosystem",
   "type": "module",
   "main": "./dist/index.js",

packages/eslint-config/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/eslint-config",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/feedback-commander/package.json

@@ -2,7 +2,7 @@
   "name": "feedback-commander",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",
@@ -24,4 +24,4 @@
     "@directus/extensions-sdk": "11.0.2",
     "vue": "^3.4.0"
   }
-}
+}

packages/gatekeeper/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/gatekeeper",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "scripts": {

packages/husky-config/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/husky-config",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/image-processor/package.json

@@ -0,0 +1,33 @@
+{
+  "name": "@mintel/image-processor",
+  "version": "1.8.12",
+  "private": true,
+  "type": "module",
+  "main": "./dist/index.js",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": {
+    ".": {
+      "types": "./dist/index.d.ts",
+      "import": "./dist/index.js"
+    }
+  },
+  "scripts": {
+    "build": "tsup src/index.ts --format esm --dts --clean",
+    "dev": "tsup src/index.ts --format esm --watch --dts",
+    "lint": "eslint src"
+  },
+  "dependencies": {
+    "@tensorflow/tfjs-node": "^4.22.0",
+    "@vladmandic/face-api": "^1.7.13",
+    "canvas": "^2.11.2",
+    "sharp": "^0.33.2"
+  },
+  "devDependencies": {
+    "@mintel/eslint-config": "workspace:*",
+    "@mintel/tsconfig": "workspace:*",
+    "@types/node": "^20.0.0",
+    "tsup": "^8.3.5",
+    "typescript": "^5.0.0"
+  }
+}

packages/image-processor/scripts/download-models.ts

@@ -0,0 +1,55 @@
+import * as fs from "node:fs";
+import * as path from "node:path";
+import * as https from "node:https";
+
+import { fileURLToPath } from "node:url";
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const MODELS_DIR = path.join(__dirname, "..", "models");
+const BASE_URL =
+  "https://raw.githubusercontent.com/vladmandic/face-api/master/model/";
+
+const models = [
+  "tiny_face_detector_model-weights_manifest.json",
+  "tiny_face_detector_model-shard1",
+];
+
+async function downloadModel(filename: string) {
+  const destPath = path.join(MODELS_DIR, filename);
+  if (fs.existsSync(destPath)) {
+    console.log(`Model ${filename} already exists.`);
+    return;
+  }
+
+  return new Promise((resolve, reject) => {
+    console.log(`Downloading ${filename}...`);
+    const file = fs.createWriteStream(destPath);
+    https
+      .get(BASE_URL + filename, (response) => {
+        response.pipe(file);
+        file.on("finish", () => {
+          file.close();
+          resolve(true);
+        });
+      })
+      .on("error", (err) => {
+        fs.unlinkSync(destPath);
+        reject(err);
+      });
+  });
+}
+
+async function main() {
+  if (!fs.existsSync(MODELS_DIR)) {
+    fs.mkdirSync(MODELS_DIR, { recursive: true });
+  }
+
+  for (const model of models) {
+    await downloadModel(model);
+  }
+
+  console.log("All models downloaded successfully!");
+}
+
+main().catch(console.error);

packages/image-processor/src/index.ts

@@ -0,0 +1 @@
+export * from './processor.js';

packages/image-processor/src/processor.ts

@@ -0,0 +1,140 @@
+import * as faceapi from "@vladmandic/face-api";
+// Provide Canvas fallback for face-api in Node.js
+import { Canvas, Image, ImageData } from "canvas";
+import sharp from "sharp";
+import * as path from "node:path";
+import { fileURLToPath } from "node:url";
+
+// @ts-expect-error FaceAPI does not have type definitions for monkeyPatch
+faceapi.env.monkeyPatch({ Canvas, Image, ImageData });
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+
+// Path to the downloaded models
+const MODELS_PATH = path.join(__dirname, "..", "models");
+
+let isModelsLoaded = false;
+
+async function loadModels() {
+  if (isModelsLoaded) return;
+  await faceapi.nets.tinyFaceDetector.loadFromDisk(MODELS_PATH);
+  isModelsLoaded = true;
+}
+
+export interface ProcessImageOptions {
+  width: number;
+  height: number;
+  format?: "webp" | "jpeg" | "png" | "avif";
+  quality?: number;
+}
+
+export async function processImageWithSmartCrop(
+  inputBuffer: Buffer,
+  options: ProcessImageOptions,
+): Promise<Buffer> {
+  await loadModels();
+
+  // Load image via Canvas for face-api
+  const img = new Image();
+  img.src = inputBuffer;
+
+  // Detect faces
+  const detections = await faceapi.detectAllFaces(
+    // @ts-expect-error FaceAPI does not have type definitions for monkeyPatch
+    img,
+    new faceapi.TinyFaceDetectorOptions(),
+  );
+
+  const sharpImage = sharp(inputBuffer);
+  const metadata = await sharpImage.metadata();
+
+  if (!metadata.width || !metadata.height) {
+    throw new Error("Could not read image metadata");
+  }
+
+  // If faces are found, calculate the bounding box containing all faces
+  if (detections.length > 0) {
+    let minX = metadata.width;
+    let minY = metadata.height;
+    let maxX = 0;
+    let maxY = 0;
+
+    for (const det of detections) {
+      const { x, y, width, height } = det.box;
+      if (x < minX) minX = Math.max(0, x);
+      if (y < minY) minY = Math.max(0, y);
+      if (x + width > maxX) maxX = Math.min(metadata.width, x + width);
+      if (y + height > maxY) maxY = Math.min(metadata.height, y + height);
+    }
+
+    const faceBoxWidth = maxX - minX;
+    const faceBoxHeight = maxY - minY;
+
+    // Calculate center of the faces
+    const centerX = Math.floor(minX + faceBoxWidth / 2);
+    const centerY = Math.floor(minY + faceBoxHeight / 2);
+
+    // Provide this as a focus point for sharp's extract or resize
+    // We can use sharp's resize with `position` focusing on crop options,
+    // or calculate an exact bounding box. However, extracting an exact bounding box
+    // and then resizing usually yields the best results when focusing on a specific coordinate.
+
+    // A simpler approach is to crop a rectangle with the target aspect ratio
+    // centered on the faces, then resize. Let's calculate the crop box.
+
+    const targetRatio = options.width / options.height;
+    const currentRatio = metadata.width / metadata.height;
+
+    let cropWidth = metadata.width;
+    let cropHeight = metadata.height;
+
+    if (currentRatio > targetRatio) {
+      // Image is wider than target, calculate new width
+      cropWidth = Math.floor(metadata.height * targetRatio);
+    } else {
+      // Image is taller than target, calculate new height
+      cropHeight = Math.floor(metadata.width / targetRatio);
+    }
+
+    // Try to center the crop box around the faces
+    let cropX = Math.floor(centerX - cropWidth / 2);
+    let cropY = Math.floor(centerY - cropHeight / 2);
+
+    // Keep crop box within image bounds
+    if (cropX < 0) cropX = 0;
+    if (cropY < 0) cropY = 0;
+    if (cropX + cropWidth > metadata.width) cropX = metadata.width - cropWidth;
+    if (cropY + cropHeight > metadata.height)
+      cropY = metadata.height - cropHeight;
+
+    sharpImage.extract({
+      left: cropX,
+      top: cropY,
+      width: cropWidth,
+      height: cropHeight,
+    });
+  }
+
+  // Finally, resize to the requested dimensions and format
+  let finalImage = sharpImage.resize(options.width, options.height, {
+    // If faces weren't found, default to entropy/attention based cropping as fallback
+    fit: "cover",
+    position: detections.length > 0 ? "center" : "attention",
+  });
+
+  const format = options.format || "webp";
+  const quality = options.quality || 80;
+
+  if (format === "webp") {
+    finalImage = finalImage.webp({ quality });
+  } else if (format === "jpeg") {
+    finalImage = finalImage.jpeg({ quality });
+  } else if (format === "png") {
+    finalImage = finalImage.png({ quality });
+  } else if (format === "avif") {
+    finalImage = finalImage.avif({ quality });
+  }
+
+  return finalImage.toBuffer();
+}

packages/image-processor/tsconfig.json

@@ -0,0 +1,19 @@
+{
+    "extends": "@mintel/tsconfig/base.json",
+    "compilerOptions": {
+        "outDir": "dist",
+        "rootDir": "src",
+        "allowJs": true,
+        "esModuleInterop": true,
+        "module": "NodeNext",
+        "moduleResolution": "NodeNext"
+    },
+    "include": [
+        "src/**/*"
+    ],
+    "exclude": [
+        "node_modules",
+        "dist",
+        "**/*.test.ts"
+    ]
+}

packages/infra/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/infra",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/journaling/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/journaling",
-  "version": "1.0.0",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",

packages/journaling/src/agent.ts

@@ -1,7 +1,7 @@
 import OpenAI from "openai";
 import { DataCommonsClient } from "./clients/data-commons";
 import { TrendsClient } from "./clients/trends";
-import { SerperClient, type SerperVideoResult } from "./clients/serper";
+import { SerperClient } from "./clients/serper";
 
 export interface Fact {
   statement: string;
@@ -54,7 +54,6 @@ export class ResearchAgent {
         if (data.length > 0) {
           // Analyze trend
           const latest = data[data.length - 1];
-          const max = Math.max(...data.map((d) => d.value));
           facts.push({
             statement: `Interest in "${kw}" is currently at ${latest.value}% of peak popularity.`,
             source: "Google Trends",
@@ -246,7 +245,7 @@ Return a JSON object with a single string field "query". Example: {"query": "cor
       const evalPrompt = `You are a strict technical evaluator. You must select the MOST RELEVANT educational tech video from the list below based on this core article context: "${topic.slice(0, 800)}..."
 
 Videos:
-${ytVideos.map((v, i) => `[ID: ${i}] Title: "${v.title}" | Channel: "${v.channel}" | Snippet: "${v.snippet || 'none'}"`).join("\n")}
+${ytVideos.map((v, i) => `[ID: ${i}] Title: "${v.title}" | Channel: "${v.channel}" | Snippet: "${v.snippet || "none"}"`).join("\n")}
 
 RULES:
 1. The video MUST be highly relevant to the EXACT technical topic of the context.
@@ -268,7 +267,7 @@ Return ONLY a JSON object: {"bestVideoId": number}`;
           evalResponse.choices[0].message.content || '{"bestVideoId": -1}',
         );
         bestIdx = evalParsed.bestVideoId;
-      } catch (e) {
+      } catch {
         console.warn("Failed to parse video evaluation response");
       }
 
@@ -343,7 +342,7 @@ CRITICAL: Do NOT provide more than 2 trendsKeywords. Keep it extremely focused.`
     try {
       let parsed = JSON.parse(
         response.choices[0].message.content ||
-        '{"trendsKeywords": [], "dcVariables": []}',
+          '{"trendsKeywords": [], "dcVariables": []}',
       );
       if (Array.isArray(parsed)) {
         parsed = parsed[0] || { trendsKeywords: [], dcVariables: [] };

packages/mail/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/mail",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "private": false,
   "publishConfig": {
     "access": "public",

packages/meme-generator/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/meme-generator",
-  "version": "1.0.0",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",

packages/meme-generator/src/index.ts

@@ -123,7 +123,7 @@ IMPORTANT: Return ONLY the JSON object. No markdown wrappers.`,
     let result;
     try {
       result = JSON.parse(body);
-    } catch (e) {
+    } catch {
       console.error("Failed to parse AI response", body);
       return [];
     }

packages/next-config/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/next-config",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/next-feedback/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/next-feedback",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/next-observability/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/next-observability",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/next-utils/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/next-utils",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/observability/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/observability",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/pdf-library/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/pdf",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "main": "dist/index.js",
   "module": "dist/index.js",

packages/people-manager/package.json

@@ -2,7 +2,7 @@
   "name": "people-manager",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",
@@ -27,4 +27,4 @@
     "@directus/extensions-sdk": "11.0.2",
     "vue": "^3.4.0"
   }
-}
+}

packages/thumbnail-generator/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/thumbnail-generator",
-  "version": "1.0.0",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",

packages/tsconfig/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/tsconfig",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/unified-dashboard/package.json

@@ -2,7 +2,7 @@
   "name": "unified-dashboard",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.10",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",
@@ -24,4 +24,4 @@
     "@directus/extensions-sdk": "11.0.2",
     "vue": "^3.4.0"
   }
-}
+}