feat: add real-time collaborative shopping list at /cospend/list

Real-time shopping list with SSE sync between multiple clients, automatic
item categorization using embedding-based classification + Bring icon
matching, and card-based UI with category grouping.

- SSE broadcast for live sync (add/check/remove items across tabs)
- Hybrid categorizer: direct catalog lookup → category-scoped embedding
  search → per-category default icons, with DB caching
- 388 Bring catalog icons matched via multilingual-e5-base embeddings
- 170+ English→German icon aliases for reliable cross-language matching
- Move cospend dashboard to /cospend/dash, /cospend redirects to list
- Shopping icon on homepage links to /cospend/list

scripts/assign-icon-categories.ts

@@ -0,0 +1,74 @@
+/**
+ * Pre-assign each Bring catalog icon to a shopping category using embeddings.
+ * This enables category-scoped icon search at runtime.
+ *
+ * Run: pnpm exec vite-node scripts/assign-icon-categories.ts
+ */
+import { pipeline } from '@huggingface/transformers';
+import { readFileSync, writeFileSync } from 'fs';
+import { resolve } from 'path';
+
+const MODEL_NAME = 'Xenova/multilingual-e5-base';
+const CATEGORY_EMBEDDINGS_PATH = resolve('src/lib/data/shoppingCategoryEmbeddings.json');
+const CATALOG_PATH = resolve('static/shopping-icons/catalog.json');
+const OUTPUT_PATH = resolve('src/lib/data/shoppingIconCategories.json');
+
+function cosineSimilarity(a: number[], b: number[]): number {
+  let dot = 0, normA = 0, normB = 0;
+  for (let i = 0; i < a.length; i++) {
+    dot += a[i] * b[i];
+    normA += a[i] * a[i];
+    normB += b[i] * b[i];
+  }
+  return dot / (Math.sqrt(normA) * Math.sqrt(normB));
+}
+
+async function main() {
+  const catData = JSON.parse(readFileSync(CATEGORY_EMBEDDINGS_PATH, 'utf-8'));
+  const catalog: Record<string, string> = JSON.parse(readFileSync(CATALOG_PATH, 'utf-8'));
+
+  console.log(`Loading model ${MODEL_NAME}...`);
+  const embedder = await pipeline('feature-extraction', MODEL_NAME, { dtype: 'q8' });
+
+  const iconNames = Object.keys(catalog);
+  console.log(`Assigning ${iconNames.length} icons to categories...`);
+
+  const assignments: Record<string, string> = {};
+
+  for (let i = 0; i < iconNames.length; i++) {
+    const name = iconNames[i];
+    const result = await embedder(`query: ${name.toLowerCase()}`, { pooling: 'mean', normalize: true });
+    const qv = Array.from(result.data as Float32Array);
+
+    let bestCategory = 'Sonstiges';
+    let bestScore = -1;
+    for (const entry of catData.entries) {
+      const score = cosineSimilarity(qv, entry.vector);
+      if (score > bestScore) {
+        bestScore = score;
+        bestCategory = entry.category;
+      }
+    }
+
+    assignments[name] = bestCategory;
+
+    if ((i + 1) % 50 === 0) {
+      console.log(`  ${i + 1}/${iconNames.length}`);
+    }
+  }
+
+  writeFileSync(OUTPUT_PATH, JSON.stringify(assignments, null, 2), 'utf-8');
+  console.log(`Written ${OUTPUT_PATH} (${iconNames.length} entries)`);
+
+  // Print summary
+  const counts: Record<string, number> = {};
+  for (const cat of Object.values(assignments)) {
+    counts[cat] = (counts[cat] || 0) + 1;
+  }
+  console.log('\nCategory distribution:');
+  for (const [cat, count] of Object.entries(counts).sort((a, b) => b[1] - a[1])) {
+    console.log(`  ${cat}: ${count}`);
+  }
+}
+
+main().catch(console.error);