Arnaud
b0849c3989
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>
44 lines
1.3 KiB
TypeScript
44 lines
1.3 KiB
TypeScript
import { generateObject } from "ai";
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import { z } from "zod";
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import { resolveModel, type AiConfig } from "../factory";
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const PantryScanSchema = z.object({
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items: z.array(
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z.object({
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rawName: z.string(),
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estimatedQuantity: z.string().optional(),
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unit: z.string().optional(),
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})
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).max(30),
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});
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export type PantryScanResult = z.infer<typeof PantryScanSchema>;
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export async function scanPantryPhoto(
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imageBase64: string,
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mimeType: "image/jpeg" | "image/png" | "image/webp",
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config?: AiConfig,
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): Promise<PantryScanResult> {
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const model = resolveModel(config);
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const { object } = await generateObject({
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model,
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schema: PantryScanSchema,
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system:
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"You identify food items visible in a photo of a pantry, fridge, or shelf, or a single unbarcoded item (e.g. fresh produce). " +
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"List each distinct item once with a short, common ingredient name. Only include an estimated quantity or unit when it can be " +
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"reasonably judged from the image (e.g. '3' apples, '1' bunch). Do not guess brand names or nutrition facts.",
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messages: [
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{
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role: "user",
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content: [
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{ type: "image", image: imageBase64, mediaType: mimeType },
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{ type: "text", text: "Identify the food items in this photo." },
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],
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},
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],
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});
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return object;
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}
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