chore: sync versions to v1.8.20

.env

@@ -1,5 +1,5 @@
 # Project
-IMAGE_TAG=v1.8.6
+IMAGE_TAG=v1.8.19
 PROJECT_NAME=at-mintel
 PROJECT_COLOR=#82ed20
 GITEA_TOKEN=ccce002e30fe16a31a6c9d5a414740af2f72a582

.env.example

@@ -1,5 +1,5 @@
 # Project
-IMAGE_TAG=v1.8.11
+IMAGE_TAG=v1.8.20
 PROJECT_NAME=sample-website
 PROJECT_COLOR=#82ed20
 

apps/image-service/Dockerfile

@@ -6,34 +6,21 @@ 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
+# We only need standard pnpm install now, no C++ tools needed for basic Sharp
 RUN pnpm install --frozen-lockfile
-# Force tfjs-node to build the native addon from source so it compiles for arm64 (bypassing pnpm quirks)
-RUN for f in $(find /app/node_modules/.pnpm -path "*/@tensorflow/tfjs-node/scripts/install.js"); do cd $(dirname $(dirname $f)) && npm run install -- build-addon-from-source; done
-RUN pnpm install --frozen-lockfile
-# 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
+RUN mkdir -p /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

apps/image-service/package.json

@@ -1,6 +1,6 @@
 {
   "name": "image-service",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "private": true,
   "type": "module",
   "scripts": {

apps/image-service/src/index.ts

@@ -1,18 +1,75 @@
 import Fastify from "fastify";
-import { processImageWithSmartCrop } from "@mintel/image-processor";
+import {
+  processImageWithSmartCrop,
+  parseImgproxyOptions,
+  mapUrl,
+} 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" });
+  const { options, urlSafeB64 } = request.params as {
+    options: string;
+    urlSafeB64: string;
+  };
+
+  // urlSafeB64 might be "plain/http://..." or a Base64 string
+  let url = "";
+  if (urlSafeB64.startsWith("plain/")) {
+    url = urlSafeB64.substring(6);
+  } else {
+    try {
+      url = Buffer.from(urlSafeB64, "base64").toString("utf-8");
+    } catch (e) {
+      return reply.status(400).send({ error: "Invalid Base64 URL" });
+    }
+  }
+
+  const parsedOptions = parseImgproxyOptions(options);
+  const mappedUrl = mapUrl(url, process.env.IMGPROXY_URL_MAPPING);
+
+  return handleProcessing(mappedUrl, parsedOptions, reply);
 });
 
+// Helper to avoid duplication
+async function handleProcessing(url: string, options: any, reply: any) {
+  const width = options.width || 800;
+  const height = options.height || 600;
+  const quality = options.quality || 80;
+  const format = options.format || "webp";
+
+  try {
+    const response = await fetch(url);
+    if (!response.ok) {
+      return reply.status(response.status).send({
+        error: `Failed to fetch source image: ${response.statusText}`,
+      });
+    }
+
+    const arrayBuffer = await response.arrayBuffer();
+    const buffer = Buffer.from(arrayBuffer);
+
+    const processedBuffer = await processImageWithSmartCrop(buffer, {
+      width,
+      height,
+      format,
+      quality,
+      openRouterApiKey: process.env.OPENROUTER_API_KEY,
+    });
+
+    reply.header("Content-Type", `image/${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("/process", async (request, reply) => {
   const query = request.query as {
     url?: string;
@@ -26,41 +83,14 @@ fastify.get("/process", async (request, reply) => {
   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";
+  const format = (query.format || "webp") as any;
 
   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" });
-  }
+  const mappedUrl = mapUrl(url, process.env.IMGPROXY_URL_MAPPING);
+  return handleProcessing(mappedUrl, { width, height, quality, format }, reply);
 });
 
 fastify.get("/health", async () => {

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": "1.8.11",
+  "version": "1.8.20",
   "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

@@ -57,7 +57,7 @@
     "pino-pretty": "^13.1.3",
     "require-in-the-middle": "^8.0.1"
   },
-  "version": "1.8.11",
+  "version": "1.8.20",
   "pnpm": {
     "onlyBuiltDependencies": [
       "@parcel/watcher",

packages/acquisition-manager/package.json

@@ -2,7 +2,7 @@
   "name": "acquisition-manager",
   "description": "Custom High-Fidelity Management for Directus",
   "icon": "extension",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "keywords": [
     "directus",

packages/acquisition/package.json

@@ -1,6 +1,6 @@
 {
   "name": "acquisition",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "directus:extension": {
     "type": "endpoint",

packages/cli/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/cli",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/cloner-library/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/cloner",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "main": "dist/index.js",
   "module": "dist/index.js",

packages/cms-infra/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/cms-infra",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "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": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "keywords": [
     "directus",

packages/content-engine/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@mintel/content-engine",
-  "version": "1.8.11",
-  "private": true,
+  "version": "1.8.20",
+  "private": false,
   "type": "module",
   "main": "./dist/index.js",
   "module": "./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": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "keywords": [
     "directus",

packages/directus-extension-toolkit/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/directus-extension-toolkit",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "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": "1.8.11",
+  "version": "1.8.20",
   "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": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "keywords": [
     "directus",

packages/gatekeeper/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/gatekeeper",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "private": true,
   "type": "module",
   "scripts": {

packages/husky-config/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/husky-config",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/image-processor/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/image-processor",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "private": true,
   "type": "module",
   "main": "./dist/index.js",
@@ -18,9 +18,6 @@
     "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": {

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

@@ -1,55 +0,0 @@
-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/processor.ts

@@ -1,51 +1,174 @@
-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;
+  openRouterApiKey?: string;
+}
+
+/**
+ * Maps a URL based on the IMGPROXY_URL_MAPPING environment variable.
+ * Format: "match1:replace1,match2:replace2"
+ */
+export function mapUrl(url: string, mappingString?: string): string {
+  if (!mappingString) return url;
+
+  const mappings = mappingString.split(",").map((m) => {
+    if (m.includes("|")) {
+      return m.split("|");
+    }
+
+    // Legacy support for simple "host:target" or cases where one side might have a protocol
+    // We try to find the split point that isn't part of a protocol "://"
+    const colonIndices = [];
+    for (let i = 0; i < m.length; i++) {
+      if (m[i] === ":") {
+        // Check if this colon is part of "://"
+        if (!(m[i + 1] === "/" && m[i + 2] === "/")) {
+          colonIndices.push(i);
+        }
+      }
+    }
+
+    if (colonIndices.length === 0) return [m];
+
+    // In legacy mode with colons, we take the LAST non-protocol colon as the separator
+    // This handles "http://host:port" or "host:http://target" better
+    const lastColon = colonIndices[colonIndices.length - 1];
+    return [m.substring(0, lastColon), m.substring(lastColon + 1)];
+  });
+
+  let mappedUrl = url;
+
+  for (const [match, replace] of mappings) {
+    if (match && replace && url.includes(match)) {
+      mappedUrl = url.replace(match, replace);
+    }
+  }
+
+  return mappedUrl;
+}
+
+/**
+ * Parses legacy imgproxy options string.
+ * Example: rs:fill:300:400/q:80
+ */
+export function parseImgproxyOptions(
+  optionsStr: string,
+): Partial<ProcessImageOptions> {
+  const parts = optionsStr.split("/");
+  const options: Partial<ProcessImageOptions> = {};
+
+  for (const part of parts) {
+    if (part.startsWith("rs:")) {
+      const [, , w, h] = part.split(":");
+      if (w) options.width = parseInt(w, 10);
+      if (h) options.height = parseInt(h, 10);
+    } else if (part.startsWith("q:")) {
+      const q = part.split(":")[1];
+      if (q) options.quality = parseInt(q, 10);
+    } else if (part.startsWith("ext:")) {
+      const ext = part.split(":")[1] as any;
+      if (["webp", "jpeg", "png", "avif"].includes(ext)) {
+        options.format = ext;
+      }
+    }
+  }
+
+  return options;
+}
+
+interface FaceDetection {
+  x: number;
+  y: number;
+  width: number;
+  height: number;
+}
+
+/**
+ * Detects faces using OpenRouter Vision API.
+ * Uses a small preview to save bandwidth and tokens.
+ */
+async function detectFacesWithCloud(
+  inputBuffer: Buffer,
+  apiKey: string,
+): Promise<FaceDetection[]> {
+  try {
+    // Generate a small preview for vision API (max 512px)
+    const preview = await sharp(inputBuffer)
+      .resize(512, 512, { fit: "inside" })
+      .jpeg({ quality: 60 })
+      .toBuffer();
+
+    const base64Image = preview.toString("base64");
+
+    const response = await fetch(
+      "https://openrouter.ai/api/v1/chat/completions",
+      {
+        method: "POST",
+        headers: {
+          Authorization: `Bearer ${apiKey}`,
+          "Content-Type": "application/json",
+          "HTTP-Referer": "https://mintel.me",
+          "X-Title": "Mintel Image Service",
+        },
+        body: JSON.stringify({
+          model: "google/gemini-3-flash-preview", // Fast, cheap, and supports vision
+          messages: [
+            {
+              role: "user",
+              content: [
+                {
+                  type: "text",
+                  text: 'Detect all human faces in this image. Return ONLY a JSON array of bounding boxes like: [{"x": 0.1, "y": 0.2, "width": 0.05, "height": 0.05}]. Coordinates must be normalized (0 to 1). If no faces, return [].',
+                },
+                {
+                  type: "image_url",
+                  image_url: {
+                    url: `data:image/jpeg;base64,${base64Image}`,
+                  },
+                },
+              ],
+            },
+          ],
+          response_format: { type: "json_object" },
+        }),
+      },
+    );
+
+    if (!response.ok) {
+      throw new Error(`OpenRouter API error: ${response.statusText}`);
+    }
+
+    const data = (await response.json()) as any;
+    const content = data.choices[0]?.message?.content;
+
+    if (!content) return [];
+
+    // The model might return directly or wrapped in a json field
+    const parsed = typeof content === "string" ? JSON.parse(content) : content;
+    const detections = (parsed.faces || parsed.detections || parsed) as any[];
+
+    if (!Array.isArray(detections)) return [];
+
+    return detections.map((d) => ({
+      x: d.x,
+      y: d.y,
+      width: d.width,
+      height: d.height,
+    }));
+  } catch (error) {
+    console.error("Cloud face detection failed:", error);
+    return [];
+  }
 }
 
 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();
 
@@ -53,35 +176,36 @@ export async function processImageWithSmartCrop(
     throw new Error("Could not read image metadata");
   }
 
+  const detections = options.openRouterApiKey
+    ? await detectFacesWithCloud(inputBuffer, options.openRouterApiKey)
+    : [];
+
   // If faces are found, calculate the bounding box containing all faces
   if (detections.length > 0) {
+    // Map normalized coordinates back to pixels
+    const pixelDetections = detections.map((d) => ({
+      x: d.x * (metadata.width || 0),
+      y: d.y * (metadata.height || 0),
+      width: d.width * (metadata.width || 0),
+      height: d.height * (metadata.height || 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);
+    for (const det of pixelDetections) {
+      if (det.x < minX) minX = Math.max(0, det.x);
+      if (det.y < minY) minY = Math.max(0, det.y);
+      if (det.x + det.width > maxX)
+        maxX = Math.min(metadata.width, det.x + det.width);
+      if (det.y + det.height > maxY)
+        maxY = Math.min(metadata.height, det.y + det.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 centerX = Math.floor(minX + (maxX - minX) / 2);
+    const centerY = Math.floor(minY + (maxY - minY) / 2);
 
     const targetRatio = options.width / options.height;
     const currentRatio = metadata.width / metadata.height;
@@ -90,18 +214,14 @@ export async function processImageWithSmartCrop(
     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;
@@ -116,9 +236,7 @@ export async function processImageWithSmartCrop(
     });
   }
 
-  // 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",
   });

packages/infra/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/infra",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/journaling/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/journaling",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "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": "1.8.11",
+  "version": "1.8.20",
   "private": false,
   "publishConfig": {
     "access": "public",

packages/meme-generator/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@mintel/meme-generator",
-  "version": "1.8.11",
-  "private": true,
+  "version": "1.8.20",
+  "private": false,
   "type": "module",
   "main": "./dist/index.js",
   "module": "./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": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/next-feedback/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/next-feedback",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/next-observability/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/next-observability",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/next-utils/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/next-utils",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/observability/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/observability",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "publishConfig": {
     "access": "public",
     "registry": "https://npm.infra.mintel.me"

packages/pdf-library/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/pdf",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "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": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "keywords": [
     "directus",

packages/thumbnail-generator/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@mintel/thumbnail-generator",
-  "version": "1.8.11",
-  "private": true,
+  "version": "1.8.20",
+  "private": false,
   "type": "module",
   "main": "./dist/index.js",
   "module": "./dist/index.js",

packages/tsconfig/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mintel/tsconfig",
-  "version": "1.8.11",
+  "version": "1.8.20",
   "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": "1.8.11",
+  "version": "1.8.20",
   "type": "module",
   "keywords": [
     "directus",