Cal AI

Build a self-hosted photo calorie logger as a mobile-first PWA. No accounts, no cloud, no telemetry, single user. Stack, no substitutions: - Next.js 15 App Router, TypeScript, Tailwind. Server actions and route handlers only, no separate API service. - SQLite via better-sqlite3, file at ./data/food.db, schema created on boot if missing. - OpenAI-compatible chat completions with image input for the estimate. Read OPENAI_API_KEY and MODEL from .env. Commit a .env.example, never a real key. Core loop: 1. Home screen shows today: total kcal, protein, carbs, fat, plus a list of logged entries with thumbnails and a delete button on each. 2. A big camera button uses an input type="file" with accept="image/*" and capture="environment". Resize client side to max 1024px on the long edge, JPEG quality 0.8, before upload. 3. Server action stores the image under ./data/uploads, then sends it to the model with a strict instruction: identify each distinct food item, estimate portion in grams, return JSON only matching { items: [{ name, grams, kcal, protein_g, carbs_g, fat_g, confidence }], notes }. Use JSON response format and validate with zod. On parse failure, retry once, then surface an error, do not fake numbers. 4. Show a confirmation screen listing detected items with editable grams. Editing grams rescales that item's macros linearly. Save writes one row per item plus a parent meal row. 5. Manual entry form as a fallback: name, grams, kcal, macros. No barcode scanning, no external food database. Also include: - A daily kcal and protein target stored in a settings table, shown as progress bars. - A /history page: last 30 days, one row per day with totals, and a tiny inline bar chart drawn with divs, no chart library. - Export button that dumps all entries as CSV. - PWA manifest and an icon so it can be added to a phone home screen. Offline is out of scope, say so in the README. Explicitly out of scope: auth, multi-user, HealthKit or Google Fit sync, push notifications, streaks, recipes, native apps. Deliver a README with setup, .env vars, how to run on a LAN so the phone can reach it, and one blunt paragraph noting that portion estimates from a photo are rough and the edit-grams step is not optional if you care about the numbers.