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>
46 lines
1.7 KiB
TypeScript
46 lines
1.7 KiB
TypeScript
import { NextRequest, NextResponse } from "next/server";
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import { z } from "zod";
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import { requireSession } from "@/lib/api-auth";
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import { applyRateLimit } from "@/lib/rate-limit";
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import { withAiQuota, resolveAiConfigOrError } from "@/lib/ai/ai-error";
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import { getModelConfigForUseCase } from "@/lib/ai/resolve-user-key";
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import { scanPantryPhoto } from "@/lib/ai/features/scan-pantry-photo";
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const Schema = z.object({
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imageBase64: z.string().max(14_000_000),
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mimeType: z.enum(["image/jpeg", "image/png", "image/webp"]),
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});
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export async function POST(req: NextRequest) {
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const { session, response } = await requireSession();
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if (response) return response;
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const body = await req.json() as unknown;
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const parsed = Schema.safeParse(body);
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if (!parsed.success) {
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return NextResponse.json({ error: "Validation error", issues: parsed.error.issues }, { status: 400 });
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}
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const userId = session!.user.id;
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const limited = await applyRateLimit(`rl:ai:${userId}`, 10, 60);
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if (limited) return limited;
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const configResult = await resolveAiConfigOrError(() => getModelConfigForUseCase(userId, "vision"));
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if (!configResult.ok) return configResult.response;
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const aiConfig = configResult.data;
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// Fall back to vision-capable defaults if no explicit model configured
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if (!aiConfig.model) {
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if (aiConfig.provider === "openai") aiConfig.model = "gpt-4o";
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else if (aiConfig.provider === "anthropic") aiConfig.model = "claude-sonnet-4-6";
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}
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const result = await withAiQuota(userId, session!.user.tier as "free" | "pro", () =>
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scanPantryPhoto(parsed.data.imageBase64, parsed.data.mimeType, aiConfig)
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);
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if (!result.ok) return result.response;
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return NextResponse.json(result.data);
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}
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