feat: initial implementation — all 35 requirements across phases 1-3

Backend (Spring Boot 3.2 / Java 21 / PostgreSQL): - JWT auth with BCrypt password hashing - User profile + Mifflin-St Jeor BMR calculator - Food search + barcode via OpenFoodFacts API with local cache - Meal CRUD with user data isolation and ownership checks - AI photo analysis (OpenAI Vision) with confidence intervals - AI correction feedback loop for personalisation - Flyway DB migrations + RFC-7807 error responses Mobile (React Native / TypeScript): - Full navigation stack (Auth → Tabs → Home stack) - Design tokens (WCAG 2.2 AA colours, 8px grid, 48px touch targets) - 10 screens: Login, Register, Home, Search, Camera, AI Result, Edit Meal, Daily Details, History, Profile - Confidence-aware calorie display (kcal ± range) - Repeat last meal shortcut + macro tracking Docs: - docs/PLAN-AND-REQUIREMENTS.md - docs/traceability.csv (35 requirements, all Implemented)
2026-05-18 21:56:13 +03:00
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+Perfect — this is exactly the kind of product where a **clean MVP design = 80% of success**.
+
+I’ll give you **copy‑paste ready PRD, data model, APIs, and UI flows** — structured so you could literally start building (or drop into Notion / repo).
+
+***
+
+# 🧾 1. MVP PRD (Feature Set)
+
+## 🎯 Product Vision
+
+> “The easiest way to track calories with minimal effort and acceptable accuracy using AI + smart defaults.”
+
+***
+
+## 👤 Target User
+
+* Busy professionals (like you 😄)
+* Eat mixed: home + restaurant + packaged food
+* Want **low friction**, not perfect precision
+
+***
+
+## ✅ MVP Scope (STRICT — avoid scope creep)
+
+### Core Features
+
+### 1. Meal Logging (3 methods)
+
+#### ✅ A. Quick Add (manual)
+
+* Search food
+* Select portion (grams / default servings)
+* Add to day
+
+#### ✅ B. Barcode scan
+
+* Scan product → auto-fill nutrition
+
+#### ✅ C. Photo (AI assist, NOT full auto)
+
+* Take picture
+* App suggests:
+  * detected food(s)
+  * estimated portions
+* User must confirm/edit
+
+👉 Important: **User confirmation required (trust + accuracy)**
+
+***
+
+### 2. Daily Tracking
+
+* Calories consumed (main KPI)
+* Optional:
+  * protein / carbs / fat
+* Remaining calories (based on goal)
+
+***
+
+### 3. User Profile
+
+* Age, weight, height
+* Goal:
+  * lose / maintain / gain
+* Daily calorie target (calculated)
+
+(BMR-based baseline — like MyFitnessPal approach)
+
+***
+
+### 4. History & Reuse
+
+* Recent foods
+* Repeat last meal (1 tap)
+
+***
+
+### 5. Correction Loop (THIS IS YOUR SECRET WEAPON)
+
+* User edits AI result
+* Store correction
+* Improve next suggestions
+
+***
+
+## ❌ NOT in MVP (important discipline)
+
+* No social features
+* No meal plans
+* No wearable integrations
+* No deep health analytics
+
+***
+
+# 🧠 2. Data Model (clean + scalable)
+
+Use something like **Postgres (or Firestore if you go fast)**.
+
+***
+
+## Core Entities
+
+### User
+
+```json
+{
+  "id": "uuid",
+  "email": "string",
+  "createdAt": "timestamp",
+  "profile": {
+    "age": 30,
+    "weightKg": 80,
+    "heightCm": 180,
+    "goal": "lose|maintain|gain",
+    "dailyCaloriesTarget": 2200
+  }
+}
+```
+
+***
+
+### FoodItem (normalized DB)
+
+```json
+{
+  "id": "uuid",
+  "name": "Chicken breast",
+  "source": "openfoodfacts|custom|ai",
+  "caloriesPer100g": 165,
+  "macros": {
+    "protein": 31,
+    "fat": 3.6,
+    "carbs": 0
+  }
+}
+```
+
+***
+
+### MealEntry
+
+```json
+{
+  "id": "uuid",
+  "userId": "uuid",
+  "date": "2026-05-16",
+  "items": [
+    {
+      "foodItemId": "uuid",
+      "quantityGrams": 200,
+      "calories": 330
+    }
+  ],
+  "source": "manual|barcode|photo",
+  "confidence": 0.82
+}
+```
+
+***
+
+### PhotoAnalysis (AI trace — VERY IMPORTANT)
+
+```json
+{
+  "id": "uuid",
+  "userId": "uuid",
+  "imageUrl": "string",
+  "detectedItems": [
+    {
+      "name": "rice",
+      "estimatedGrams": 150,
+      "confidence": 0.76
+    }
+  ],
+  "userCorrections": [
+    {
+      "name": "rice",
+      "correctedGrams": 180
+    }
+  ]
+}
+```
+
+***
+
+### UserFoodMemory (optimization layer)
+
+```json
+{
+  "userId": "uuid",
+  "foodName": "coffee with milk",
+  "avgPortionGrams": 250,
+  "lastUsed": "timestamp"
+}
+```
+
+👉 This enables:
+
+* auto-fill frequent meals
+* personalization
+
+***
+
+# 🔌 3. API Design (clean + realistic)
+
+Assume REST (simple for MVP)
+
+***
+
+## Auth
+
+```
+POST /auth/register
+POST /auth/login
+```
+
+***
+
+## User
+
+```
+GET /user/profile
+PUT /user/profile
+```
+
+***
+
+## Food Search
+
+```
+GET /foods?query=chicken
+```
+
+Response:
+
+```json
+[
+  {
+    "id": "uuid",
+    "name": "Chicken breast",
+    "caloriesPer100g": 165
+  }
+]
+```
+
+***
+
+## Barcode
+
+```
+GET /foods/barcode/{code}
+```
+
+***
+
+## Meal Logging
+
+```
+POST /meals
+```
+
+```json
+{
+  "date": "2026-05-16",
+  "items": [
+    {
+      "foodItemId": "uuid",
+      "grams": 200
+    }
+  ],
+  "source": "manual"
+}
+```
+
+***
+
+## Daily Overview
+
+```
+GET /meals/daily?date=2026-05-16
+```
+
+Response:
+
+```json
+{
+  "totalCalories": 1800,
+  "target": 2200,
+  "remaining": 400,
+  "meals": [...]
+}
+```
+
+***
+
+## Photo Analysis (AI entry point)
+
+```
+POST /ai/analyze-meal
+```
+
+Request:
+
+* image
+
+Response:
+
+```json
+{
+  "suggestions": [
+    {
+      "name": "pasta",
+      "grams": 250,
+      "confidence": 0.78
+    }
+  ]
+}
+```
+
+***
+
+## Feedback Loop
+
+```
+POST /ai/correction
+```
+
+```json
+{
+  "analysisId": "uuid",
+  "corrections": [...]
+}
+```
+
+***
+
+# 📱 4. UI Flows (VERY IMPORTANT — UX is everything)
+
+I’ll give you **clear flows you can directly translate into screens**
+
+***
+
+## 🏠 Home Screen (Daily Dashboard)
+
+```
+------------------------------------
+Calories: 1800 / 2200
+Remaining: 400
+
+[ + Add Meal ]
+
+Today:
+- Breakfast (450 kcal)
+- Lunch (800 kcal)
+- Dinner (550 kcal)
+------------------------------------
+```
+
+***
+
+## ➕ Add Meal (entry selector)
+
+```
+Choose how to log:
+
+[ 📷 Take Photo ]
+[ 🔍 Search Food ]
+[ 📦 Scan Barcode ]
+```
+
+👉 Always start here — reduces friction.
+
+***
+
+## 📷 Photo Flow
+
+### Step 1 — Capture
+
+```
+[ Camera View ]
+[ Snap ]
+```
+
+***
+
+### Step 2 — AI Suggestion (critical UX moment)
+
+```
+We detected:
+
+- Rice (150g)
+- Chicken (120g)
+
+[ Edit ]   [ Confirm ]
+```
+
+***
+
+### Step 3 — Edit
+
+```
+Rice: 150g → [ adjust slider ]
+Chicken: 120g → [ adjust ]
+[ + Add item ]
+
+[ Save Meal ]
+```
+
+***
+
+## 🔍 Manual Search Flow
+
+```
+Search: "chicken"
+
+Results:
+- Chicken breast
+- Chicken thigh
+
+→ Select
+→ Choose grams
+→ Add
+```
+
+***
+
+## 📊 Daily Details Screen
+
+```
+Total: 1800 kcal
+
+Macros:
+Protein: 120g
+Carbs: 180g
+Fat: 60g
+
+Meals:
+- Breakfast
+- Lunch
+- Dinner
+```
+
+***
+
+## 🔁 Repeat Meal (killer UX shortcut)
+
+On Home:
+
+```
+[ Repeat yesterday's lunch ]
+```
+
+***
+
+# 🎯 5. Architecture Decisions (important for you)
+
+Given your background:
+
+### Backend
+
+* FastAPI or Spring Boot (you’re Java → maybe Spring Boot)
+
+### Frontend
+
+* React Native (fast MVP)
+
+### AI
+
+* Start with API-based (no custom model)
+* Later optimize
+
+***
+
+# 🚀 6. What makes YOUR app better (MVP differentiation)
+
+Focus on:
+
+✅ Minimal friction  
+✅ Smart defaults  
+✅ Learning user habits
+
+NOT:
+❌ Better AI model  
+❌ More features
+
+***
+
+# ✅ Next logical step
+
+If you want, I can next:
+
+👉 Turn this into **actual repo structure + starter code (backend + mobile)**  
+👉 Or design **clickable UI wireframes (Figma-style)**
+
+Just tell me which direction 👍