feat: cooking history/gallery, unit conversion, nutrition diary, pantry scan, digest cron, nutrition-targeted meal plans

Six M-sized items from HANDOFF.md's new-features backlog:

- Profile tabs: cooking-history stats (total cooked, last-cooked, streak)
  and a "cooked it" photo gallery, both owner-only
- Display-time unit conversion (metric<->imperial) for recipe ingredients,
  respecting each user's unitPref; original value always shown alongside
  the conversion
- Nutrition daily diary: per-day macro totals computed from cooking history
  x recipe nutritionData, compared against user goals
- Pantry scan: real barcode lookup (zxing + Open Food Facts, no API key)
  with an AI-vision fallback for unbarcoded items, always confirm-before-
  insert, both paths tier/rate-limited like other AI features
- Weekly digest email: new followers/comments/ratings + trending recipes,
  sent via a new `cron` Docker stage (alpine+crond+curl) and `digest-cron`
  compose service hitting a bearer-token-protected internal route
- Meal-plan generation can now target a user's nutrition goals as a
  prompt-level nudge (recipes are AI-invented, not DB-sourced, so this
  can't be a hard macro constraint)

Caught a real deploy-breaking issue while adding the cron stage: appending
it after `runner` silently changed the Dockerfile's default build target,
and `web`'s compose config didn't pin one — fixed by pinning `target:
runner` explicitly. Verified with typecheck, lint, and three separate
`docker build --target` runs (runner/cron/migrator) plus `docker compose
config` validation.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

apps/web/lib/ai/features/scan-pantry-photo.ts

@@ -0,0 +1,43 @@
+import { generateObject } from "ai";
+import { z } from "zod";
+import { resolveModel, type AiConfig } from "../factory";
+
+const PantryScanSchema = z.object({
+  items: z.array(
+    z.object({
+      rawName: z.string(),
+      estimatedQuantity: z.string().optional(),
+      unit: z.string().optional(),
+    })
+  ).max(30),
+});
+
+export type PantryScanResult = z.infer<typeof PantryScanSchema>;
+
+export async function scanPantryPhoto(
+  imageBase64: string,
+  mimeType: "image/jpeg" | "image/png" | "image/webp",
+  config?: AiConfig,
+): Promise<PantryScanResult> {
+  const model = resolveModel(config);
+
+  const { object } = await generateObject({
+    model,
+    schema: PantryScanSchema,
+    system:
+      "You identify food items visible in a photo of a pantry, fridge, or shelf, or a single unbarcoded item (e.g. fresh produce). " +
+      "List each distinct item once with a short, common ingredient name. Only include an estimated quantity or unit when it can be " +
+      "reasonably judged from the image (e.g. '3' apples, '1' bunch). Do not guess brand names or nutrition facts.",
+    messages: [
+      {
+        role: "user",
+        content: [
+          { type: "image", image: imageBase64, mediaType: mimeType },
+          { type: "text", text: "Identify the food items in this photo." },
+        ],
+      },
+    ],
+  });
+
+  return object;
+}