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=v1.8.6
+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": "1.8.6",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "scripts": {

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

@@ -1,45 +1,60 @@
-import { NextRequest, NextResponse } from 'next/server';
-import { processImageWithSmartCrop } from '@mintel/image-processor';
+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');
-    let width = parseInt(searchParams.get('w') || '800');
-    let height = parseInt(searchParams.get('h') || '600');
-    let q = parseInt(searchParams.get('q') || '80');
+  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 });
+  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 },
+      );
     }
 
-    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);
 
-        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,
-        });
+    // 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 });
-    }
+    // 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 },
+    );
+  }
 }

package.json

@@ -57,8 +57,20 @@
     "pino-pretty": "^13.1.3",
     "require-in-the-middle": "^8.0.1"
   },
-  "version": "1.8.6",
+  "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": "1.8.6",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",

packages/acquisition/package.json

@@ -1,6 +1,6 @@
 {
   "name": "acquisition",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "type": "module",
   "directus:extension": {
     "type": "endpoint",

packages/cli/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/cli",
-  "version": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "scripts": {

packages/company-manager/package.json

@@ -2,7 +2,7 @@
   "name": "company-manager",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",

packages/content-engine/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/content-engine",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",

packages/customer-manager/package.json

@@ -2,7 +2,7 @@
   "name": "customer-manager",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",

packages/directus-extension-toolkit/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/directus-extension-toolkit",
-  "version": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",

packages/gatekeeper/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/gatekeeper",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "scripts": {

packages/husky-config/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/husky-config",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/image-processor/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/image-processor",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",
@@ -18,6 +18,7 @@
     "lint": "eslint src"
   },
   "dependencies": {
+    "@tensorflow/tfjs-node": "^4.22.0",
     "@vladmandic/face-api": "^1.7.13",
     "canvas": "^2.11.2",
     "sharp": "^0.33.2"

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

@@ -1,48 +1,55 @@
-import * as fs from 'node:fs';
-import * as path from 'node:path';
-import * as https from 'node:https';
+import * as fs from "node:fs";
+import * as path from "node:path";
+import * as https from "node:https";
 
-const MODELS_DIR = path.join(process.cwd(), 'models');
-const BASE_URL = 'https://raw.githubusercontent.com/vladmandic/face-api/master/model/';
+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'
+  "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;
-    }
+  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);
+  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 });
-    }
+  if (!fs.existsSync(MODELS_DIR)) {
+    fs.mkdirSync(MODELS_DIR, { recursive: true });
+  }
 
-    for (const model of models) {
-        await downloadModel(model);
-    }
+  for (const model of models) {
+    await downloadModel(model);
+  }
 
-    console.log('All models downloaded successfully!');
+  console.log("All models downloaded successfully!");
 }
 
 main().catch(console.error);

packages/image-processor/src/processor.ts

@@ -1,139 +1,140 @@
-import * as faceapi from '@vladmandic/face-api';
+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';
+import { Canvas, Image, ImageData } from "canvas";
+import sharp from "sharp";
+import * as path from "node:path";
+import { fileURLToPath } from "node:url";
 
-// @ts-ignore
+// @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');
+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;
+  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;
+  width: number;
+  height: number;
+  format?: "webp" | "jpeg" | "png" | "avif";
+  quality?: number;
 }
 
 export async function processImageWithSmartCrop(
-    inputBuffer: Buffer,
-    options: ProcessImageOptions
+  inputBuffer: Buffer,
+  options: ProcessImageOptions,
 ): Promise<Buffer> {
-    await loadModels();
+  await loadModels();
 
-    // Load image via Canvas for face-api
-    const img = new Image();
-    img.src = inputBuffer;
+  // Load image via Canvas for face-api
+  const img = new Image();
+  img.src = inputBuffer;
 
-    // Detect faces
-    const detections = await faceapi.detectAllFaces(
-        // @ts-ignore
-        img,
-        new faceapi.TinyFaceDetectorOptions()
-    );
+  // 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();
+  const sharpImage = sharp(inputBuffer);
+  const metadata = await sharpImage.metadata();
 
-    if (!metadata.width || !metadata.height) {
-        throw new Error('Could not read image 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);
     }
 
-    // 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;
+    const faceBoxWidth = maxX - minX;
+    const faceBoxHeight = maxY - minY;
 
-        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);
-        }
+    // Calculate center of the faces
+    const centerX = Math.floor(minX + faceBoxWidth / 2);
+    const centerY = Math.floor(minY + faceBoxHeight / 2);
 
-        const faceBoxWidth = maxX - minX;
-        const faceBoxHeight = maxY - minY;
+    // 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.
 
-        // Calculate center of the faces
-        const centerX = Math.floor(minX + faceBoxWidth / 2);
-        const centerY = Math.floor(minY + faceBoxHeight / 2);
+    // 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.
 
-        // 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.
+    const targetRatio = options.width / options.height;
+    const currentRatio = metadata.width / metadata.height;
 
-        // 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.
+    let cropWidth = metadata.width;
+    let cropHeight = metadata.height;
 
-        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
-        });
+    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);
     }
 
-    // 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'
+    // 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,
     });
+  }
 
-    const format = options.format || 'webp';
-    const quality = options.quality || 80;
+  // 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",
+  });
 
-    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 });
-    }
+  const format = options.format || "webp";
+  const quality = options.quality || 80;
 
-    return finalImage.toBuffer();
+  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/infra/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/infra",
-  "version": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "version": "1.8.12",
   "private": false,
   "publishConfig": {
     "access": "public",

packages/meme-generator/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/meme-generator",
-  "version": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "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": "1.8.6",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",

packages/thumbnail-generator/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/thumbnail-generator",
-  "version": "1.8.6",
+  "version": "1.8.12",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",

packages/tsconfig/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/tsconfig",
-  "version": "1.8.6",
+  "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": "1.8.6",
+  "version": "1.8.12",
   "type": "module",
   "keywords": [
     "directus",