fix(image-service): resolve next.js build crash and strict TS lint warnings for ci deploy

.env

@@ -1,5 +1,5 @@
 # Project
-IMAGE_TAG=v1.8.6
+IMAGE_TAG=v1.8.11
 PROJECT_NAME=at-mintel
 PROJECT_COLOR=#82ed20
 GITEA_TOKEN=ccce002e30fe16a31a6c9d5a414740af2f72a582

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/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 },
+    );
+  }
 }

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/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 [];
     }