diff --git a/.agents/rules/rules.md b/.agents/rules/rules.md
new file mode 100644
index 0000000..f4506d6
--- /dev/null
+++ b/.agents/rules/rules.md
@@ -0,0 +1,80 @@
+# FoodSnap AI — Project Rules (Anti-Hallucination Constitution)
+
+## CRITICAL: Code Quality Rules
+
+### Rule 1: No Hallucinated Imports
+- NEVER import a package, library, or module that has not been explicitly installed.
+- Before using ANY third-party package, first check if it exists in package.json (frontend) or requirements.txt (backend).
+- If it doesn't exist, add it to the dependency file AND run the install command BEFORE writing code that uses it.
+- NEVER invent package names. If you're unsure whether a package exists, say so.
+
+### Rule 2: No Hallucinated APIs
+- NEVER call an API endpoint that doesn't exist in this project.
+- NEVER assume an external API has a specific endpoint without verifying its documentation.
+- When integrating with external services (USDA, OpenAI, Supabase), always reference the ACTUAL documented API, not a guessed one.
+- If you're unsure about an API's exact endpoint or parameters, say "I need to verify this API" instead of guessing.
+
+### Rule 3: No Hallucinated File Paths
+- NEVER import from a file that doesn't exist in the project.
+- Before adding an import statement, verify the target file exists.
+- Use relative imports consistently. Don't mix relative and absolute import styles.
+
+### Rule 4: Verify Before Declaring Done
+- After writing any code, RUN it to verify it works.
+- For backend code: run the server and test with a curl command or the built-in browser.
+- For frontend code: start the dev server and visually verify in the browser.
+- NEVER say "this should work" — run it and SHOW me it works.
+
+### Rule 5: Error Handling is Mandatory
+- Every API call must have try/catch or equivalent error handling.
+- Every database query must handle connection failures.
+- Every file operation must handle missing files.
+- Every user input must be validated before processing.
+- No bare except/catch blocks — always catch specific errors.
+
+### Rule 6: No Placeholder Code
+- NEVER write `// TODO: implement this later` and move on.
+- NEVER write `pass` in Python and call a function complete.
+- If a function cannot be fully implemented, explain what's missing and what's needed.
+- Every function must either work completely or not exist.
+
+### Rule 7: Type Safety
+- Backend (Python): Use type hints on all function signatures.
+- Frontend (React/TypeScript): Use TypeScript interfaces for all props, state, and API responses.
+- Never use `any` type in TypeScript.
+- Never use untyped dictionaries in Python where a dataclass or TypedDict would work.
+
+## Architecture Rules
+
+### Tech Stack (Do Not Deviate)
+- Frontend: React Native with Expo (TypeScript)
+- Backend: Python 3.11+ with FastAPI
+- Database: PostgreSQL (via Supabase)
+- Cache: Redis (via Upstash, free tier)
+- Auth: Supabase Auth
+- Object Storage: Cloudflare R2 (free tier: 10GB)
+- AI: OpenAI GPT-4o API (primary), with fallback prompt for Gemini
+- Nutrition Data: USDA FoodData Central
+- Deployment: Backend on Render (free tier), Frontend on Expo/EAS
+
+### Code Style
+- Python: Follow PEP 8. Use Black formatter. Max line length 88.
+- TypeScript: Follow ESLint recommended. Use Prettier.
+- Naming: snake_case for Python, camelCase for TypeScript/React.
+- Files: One component per file in frontend. One router per file in backend.
+
+### Git Rules
+- Commit after every working feature (not after every file change).
+- Commit messages: imperative mood, present tense. "Add user auth endpoint" not "Added auth."
+- Never commit secrets, API keys, or .env files.
+
+### Testing Rules
+- Every backend endpoint must have at least one happy-path test and one error test.
+- Use pytest for Python. Use Jest for TypeScript.
+- Run tests before declaring any feature complete.
+
+## Communication Rules
+- When uncertain about a requirement, ASK before implementing.
+- When a task would take more than 50 lines of code, generate a PLAN first and wait for approval.
+- When you encounter an error, show the FULL error message, explain what caused it, and propose a fix.
+- Never silently swallow errors or hide failures.
diff --git a/.agents/skills/nutrition-api/SKILL.md b/.agents/skills/nutrition-api/SKILL.md
new file mode 100644
index 0000000..7132695
--- /dev/null
+++ b/.agents/skills/nutrition-api/SKILL.md
@@ -0,0 +1,32 @@
+---
+name: nutrition-api
+description: Knowledge about the USDA FoodData Central API for nutrition lookups
+---
+
+# USDA FoodData Central API
+
+## Base URL
+https://api.nal.usda.gov/fdc/v1/
+
+## Authentication
+All requests require an API key as query parameter: ?api_key=YOUR_KEY
+Get a free key at: https://fdc.nal.usda.gov/api-key-signup.html
+
+## Key Endpoints
+
+### Search Foods
+GET /foods/search?query={query}&api_key={key}
+- Returns a list of matching foods with basic nutrition info
+- Use dataType=Foundation,SR%20Legacy for best results
+- pageSize default is 50, max 200
+
+### Get Food Details
+GET /food/{fdcId}?api_key={key}
+- Returns complete nutrition profile for a specific food
+- fdcId is the numeric identifier from search results
+
+## Important Notes
+- Rate limit: 3,600 requests per hour per API key (generous)
+- Nutrient IDs: 1008=calories, 1003=protein, 1004=fat, 1005=carbs
+- Values are per 100g unless otherwise specified
+- Some foods have multiple data types; prefer "Foundation" over "Survey"
diff --git a/.agents/skills/openai-vision/SKILL.md b/.agents/skills/openai-vision/SKILL.md
new file mode 100644
index 0000000..62dd0d8
--- /dev/null
+++ b/.agents/skills/openai-vision/SKILL.md
@@ -0,0 +1,57 @@
+---
+name: openai-vision
+description: Knowledge about using OpenAI GPT-4o API for food image analysis
+---
+
+# OpenAI GPT-4o Vision API for Food Analysis
+
+## Endpoint
+POST https://api.openai.com/v1/chat/completions
+
+## Required Headers
+Authorization: Bearer YOUR_API_KEY
+Content-Type: application/json
+
+## Request Format for Image Analysis
+```json
+{
+ "model": "gpt-4o",
+ "messages": [
+ {
+ "role": "system",
+ "content": "You are a food identification system. [see full prompt in project docs]"
+ },
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "data:image/jpeg;base64,{base64_image}",
+ "detail": "low"
+ }
+ },
+ {
+ "type": "text",
+ "text": "Identify all food items in this image."
+ }
+ ]
+ }
+ ],
+ "max_tokens": 1000,
+ "response_format": { "type": "json_object" }
+}
+```
+
+## Cost (as of 2026)
+- Input: ~$0.0025 per image (low detail)
+- Input: ~$0.01 per image (high detail)
+- Output: ~$0.01 per 1K tokens
+- Total per analysis: $0.01-$0.03
+
+## Important Notes
+- Use detail: "low" for cost savings (512x512 resize)
+- Use detail: "high" only when identification fails on low
+- Always request JSON output format
+- The model can hallucinate food items — always cross-reference with USDA
+- Set max_tokens to 1000 — food responses are typically 200-400 tokens
diff --git a/.agents/workflows/build-section.md b/.agents/workflows/build-section.md
new file mode 100644
index 0000000..61d01cc
--- /dev/null
+++ b/.agents/workflows/build-section.md
@@ -0,0 +1,31 @@
+---
+description: Build a specific section of FoodSnap AI step by step
+---
+
+## Steps
+
+### 1. Read the requirement
+- Read the section specification from the prompt
+- List all files that will need to be created or modified
+- Identify all external dependencies needed
+
+### 2. Create implementation plan
+- Generate a numbered plan of what will be built
+- List all API endpoints, components, or functions
+- Wait for user approval before proceeding
+
+### 3. Implement
+- Write the code for each item in the plan
+- Install dependencies as needed
+- After each file: save, check for errors
+
+### 4. Verify
+- Run /verify-section workflow
+- If errors: fix them and re-verify
+- Only proceed when verification passes
+
+### 5. Report
+- List all files created/modified
+- List all new dependencies added
+- Show test results
+- Ask user: "Ready to move to the next section?"
diff --git a/.agents/workflows/verify-section.md b/.agents/workflows/verify-section.md
new file mode 100644
index 0000000..ddc5896
--- /dev/null
+++ b/.agents/workflows/verify-section.md
@@ -0,0 +1,26 @@
+---
+description: Verify that the current section/feature is fully working before proceeding
+---
+
+## Steps
+
+### 1. Check for syntax errors
+- Run the linter on all modified files
+- Fix any syntax or type errors
+
+### 2. Check for missing dependencies
+- Verify all imports reference installed packages
+- Run `pip install -r requirements.txt` or `npm install` if needed
+
+### 3. Start the service
+- For backend: start the FastAPI server and verify it responds to health check
+- For frontend: start the Expo dev server and verify it renders
+
+### 4. Run tests
+- Execute all tests related to the modified files
+- Report pass/fail results with details
+
+### 5. Manual verification
+- If the feature has a UI, open it in the browser and take a screenshot
+- If the feature is an API, make a test request and show the response
+- Confirm: "Section verified and working" or "Issues found: [list]"
diff --git a/README.md b/README.md
index b58f55c..9a77cc2 100644
--- a/README.md
+++ b/README.md
@@ -1,194 +1,108 @@
-# FoodSnap AI - Hackathon Project
+# FoodSnap AI 🥗📸
-🎉 **Welcome to the FoodSnap AI Hackathon!** 🎉
+FoodSnap AI is a modern, state-of-the-art, AI-powered health and nutrition tracking application. By combining computer vision and nutrition databases, it enables users to seamlessly analyze food images, log their daily caloric intake, monitor macronutrient distributions, track weekly stats, and calculate personalized dietary goals.
-Get ready to combine your coding skills with the fascinating world of artificial intelligence and nutrition! We're excited to see what innovative solutions you come up with.
+The ecosystem is built in two primary components:
+1. **Backend**: A robust, asynchronous FastAPI microservice backed by PostgreSQL, Supabase storage, Google Gemini 1.5 Flash (vision), and the USDA FoodData Central API.
+2. **Frontend (Mobile)**: A premium, highly responsive React Native Expo application optimized for seamless user flows, animated alerts, infinite scroll archives, and health analytics dashboards.
-**The Challenge:**
-Your mission, should you choose to accept it, is to develop a solution that can estimate the caloric content (and ideally, macronutrient breakdown – protein, carbs, fats) of a food item from an input image.
-
-## Table of Contents
+---
-1. [Project Overview](#project-overview)
-2. [Why This Project?](#why-this-project)
-3. [Scope & Implementation Freedom](#project-scope--implementation-freedom)
-4. [Key Objectives / Potential Features](#key-objectives--potential-features)
-5. [Helpful Resources & APIs](#helpful-resources--apis)
- * [Food Image Datasets](#food-image-datasets)
- * [Nutrition Information Databases & APIs](#nutrition-information-databases--apis)
- * [Machine Learning / AI Tools](#machine-learning--ai-tools)
-6. [Getting Started: Git & Submission Workflow](#getting-started-git--submission-workflow)
- * [1. Fork the Repository](#1-fork-the-repository)
- * [2. Clone Your Forked Repository](#2-clone-your-forked-repository)
- * [3. Create a New Branch](#3-create-a-new-branch)
- * [4. Develop Your Project](#4-develop-your-project)
- * [5. Push Your Branch to Your Fork](#5-push-your-branch-to-your-fork)
- * [6. Submit Your Project (Create a Pull Request)](#6-submit-your-project-create-a-pull-request)
-7. [What to Include in Your Submission](#what-to-include-in-your-submission)
-8. [Judging Criteria (General Pointers)](#judging-criteria-general-pointers)
+## 📱 Mobile Architecture & Screens
+The mobile application is structured as a tab-based navigation flow using `expo-router`. It features several production-grade views:
----
+### 1. Interactive Calorie Dashboard (`app/(tabs)/index.tsx`)
+- **Dynamic Header**: Personalizes greetings based on current local hour alongside local calendar dates.
+- **Intake Progress Engine**: Full-width calorie tracker displaying a custom progress indicator (colored green for under-goal, orange for boundary, and red for budget overruns) alongside Today's consumed vs goal calorie metrics.
+- **Macros Consolidation**: Embeds a custom `` aggregating protein, fat, and carb ratios.
+- **Today's Log stream**: Displays all meals registered today with time stamps and thumbnails. Tapping any meal directs to the detailed results.
+- **Weekly Insight Card**: Connects to the stats checker endpoint, dynamically styling backgrounds (light green, light orange, or light blue) depending on calorie goal statuses.
+- **Metrics Stats Bar**: Reflects total daily meals, average weekly calories, and an automatic consecutive logging streak.
-## Project Overview
+### 2. Food Log History Archive (`app/(tabs)/history.tsx`)
+- **Interactive Day Filter**: Allows users to filter logs instantly using "All" or a calendar day in the last week.
+- **Infinite Lazy Scroll**: Loads 20 historical meals per page using a optimized, debounced `FlatList` component.
+- **Long-Press Ejection**: Users can long-press rows to show native alerts and trigger `deleteFoodLog`, safely deleting database items and ejecting them locally.
-**Project Name:** FoodSnap AI
+### 3. Demographics Profile & Settings (`app/(tabs)/profile.tsx`)
+- **Demographics Inputs**: Simple metric-aligned row entries for Height (cm), Weight (kg), and Age.
+- **Mifflin-St Jeor Targeter**: Calculates TDEE (Total Daily Energy Expenditure) goals automatically from the backend when all demographics are saved.
+- **Weight Goal Selector**: Inline goal pill tabs allowing users to toggle targets (`Lose Weight`, `Maintain`, `Gain Muscle`).
+- **Activity Descriptor Sheets**: Overlay modal selecting activity thresholds with integrated descriptors.
+- **Allergen Sync Chips**: Interactive multi-select chips supporting allergen flags (e.g. Vegetarian, Gluten-Free, Halal) linked with the profile.
+- **Logout Trigger**: Cleans active secure stores and logs out.
-**The Challenge:**
-Develop a solution that can estimate the caloric content (and ideally, macronutrient breakdown – protein, carbs, fats) of a food item from an input image.
+### 4. Global Animation Toasts (`src/components/Toast.tsx`)
+- Application-wide React Context `ToastProvider` and a light hook `useToast()`.
+- Renders slide/fade cards absolute-positioned at the top with colored borders indicating `Success` (Green), `Error` (Red), or `Info` (Blue) states.
-## Why This Project?
+---
-Understanding calorie intake is crucial for health and fitness. Manually logging food can be tedious. FoodSnap AI aims to simplify this process, making nutritional awareness more accessible to everyone. Imagine snapping a photo of your meal and instantly getting its nutritional information!
+## ⚙️ Backend Architecture
-## Project Scope & Implementation Freedom
+The API microservice coordinates model orchestration and databases:
+- **FastAPI**: Handles high-performance asynchronous API endpoints.
+- **Google Gemini 1.5 Flash**: Processes uploaded food images, identifies components, estimates weight portions, and formats structured outputs.
+- **USDA FoodData Central Cache**: Resolves matching food items from cached repositories or queries USDA indexes to populate precise protein, fat, and carb calculations.
+- **PostgreSQL Database**: Stores user demographic states, food log relations, and caching tables.
+- **Supabase Storage**: Manages secure CDN hosting for food photographs.
-You have complete freedom in how you bring FoodSnap AI to life!
-
-* **Platform:** Mobile App (iOS, Android, cross-platform), Web App, Website, or even a Command Line Interface (CLI) tool.
-* **Technology Stack:** Use any programming languages, frameworks, libraries, or APIs you prefer. Python, JavaScript, Java, Swift, Kotlin, Ruby, Go – the choice is yours!
-* **Approach:** You can use pre-trained machine learning models, train your own, leverage existing food recognition APIs, or come up with a completely novel approach.
-
-## Key Objectives / Potential Features
-
-1. **Image Input:** The system must accept an image of food as input.
-2. **Food Identification (Implicit or Explicit):** The system needs to identify the food item(s) in the image. This could be a direct output or an internal step.
-3. **Calorie Estimation:** Based on the identified food, estimate its total calories.
-4. **Macronutrient Breakdown (Bonus):** If possible, also estimate protein, carbohydrates, and fats.
-5. **Portion Size Consideration (Advanced Bonus):** Accurately estimating portion size from an image is challenging but would be a significant enhancement.
-6. **User Interface (for non-CLI):** If you're building an app or website, make it user-friendly and intuitive.
-7. **Multiple Food Items (Advanced Bonus):** Can your solution handle an image with multiple food items on a plate?
-
-## Helpful Resources & APIs
-
-To get you started, here are some resources that might be useful. You are **not limited** to these and are encouraged to explore!
-
-### Food Image Datasets
-*(For training/inspiration if you go the custom ML route)*
-
-* **Food-101:** [https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) (101 food categories, 101,000 images)
-* **UECFood-100 / UECFood-256:** [http://foodcam.mobi/dataset.html](http://foodcam.mobi/dataset.html) (Japanese food primarily, good for object detection)
-* **Recipe1M+:** [http://pic2recipe.csail.mit.edu/](http://pic2recipe.csail.mit.edu/) (Images and recipes)
-* **Google Images / Flickr:** Can be used for scraping specific food images (be mindful of terms of service).
-
-### Nutrition Information Databases & APIs
-*(For calorie/macro lookup)*
-
-* **USDA FoodData Central API:** [https://fdc.nal.usda.gov/api-guide.html](https://fdc.nal.usda.gov/api-guide.html) (Comprehensive US food composition database)
-* **Edamam Food Database API:** [https://developer.edamam.com/food-database-api](https://developer.edamam.com/food-database-api) (Offers free tier for recipe analysis and food database lookup)
-* **Spoonacular API:** [https://spoonacular.com/food-api](https://spoonacular.com/food-api) (Nutrition, recipes, food products, free tier available)
-* **MyFitnessPal / CalorieKing / FatSecret:** While direct API access might be limited or paid, these websites are excellent sources for manual data collection or understanding how nutritional information is presented. Web scraping *could* be an option, but always respect `robots.txt` and terms of service.
-* **Open Food Facts:** [https://world.openfoodfacts.org/](https://world.openfoodfacts.org/) (A collaborative, free, and open database of food products from around the world. They have an API.)
-
-### Machine Learning / AI Tools
-
-* **TensorFlow / Keras:** For building and training custom models.
-* **PyTorch:** Another popular deep learning framework.
-* **OpenCV:** For image processing tasks.
-* **Pre-trained Image Recognition Models:** (e.g., MobileNet, ResNet, InceptionV3 available via TensorFlow Hub, PyTorch Hub, etc.) These can often be fine-tuned for food recognition.
-* **Cloud AI Services:** Google Cloud Vision AI, AWS Rekognition, Azure Computer Vision (these often have free tiers for experimentation and can perform object/food recognition out-of-the-box).
-
-## Getting Started: Git & Submission Workflow
-
-We will be using GitHub for version control and submission. Please follow these steps carefully.
-
-**This Repository (Main Project):** `https://github.com/WeCode-Community-Dev/foodsnap-ai`
-
-### 1. Fork the Repository
-* Go to the main project repository: `https://github.com/WeCode-Community-Dev/foodsnap-ai`
-* In the top-right corner of the page, click the "**Fork**" button.
-* This will create a copy of the repository under your own GitHub account (e.g., `https://github.com/YOUR_USERNAME/foodsnap-ai`). This is *your* personal remote copy.
-
-### 2. Clone Your Forked Repository
-* On your GitHub page for *your forked repository* (`https://github.com/YOUR_USERNAME/foodsnap-ai`), click the green "**Code**" button.
-* Copy the HTTPS or SSH URL.
-* Open your terminal or Git client and run:
- ```bash
- git clone https://github.com/YOUR_USERNAME/foodsnap-ai.git
- cd foodsnap-ai
- ```
- (Replace `YOUR_USERNAME` with your actual GitHub username.)
-
-### 3. Create a New Branch
-* It's crucial to work on a new branch rather than directly on `main` or `master`.
-* Choose a descriptive branch name, for example, `feature/your-team-name` or `solution-john-doe`.
-* In your terminal, inside the `foodsnap-ai` directory, run:
- ```bash
- git checkout -b feature/your-team-name
- ```
- (e.g., `git checkout -b feature/awesome-coders` or `git checkout -b solution-jane-doe`)
-* You are now on your new branch. Verify by running `git branch`.
-
-### 4. Develop Your Project
-* Start coding! Add your files, write your logic, and build your FoodSnap AI solution.
-* Commit your changes frequently with clear commit messages:
- ```bash
- # Stage all new and modified files
- git add .
- # Or stage specific files
- # git add path/to/your/file.py path/to/another/file.js
-
- # Commit your changes
- git commit -m "feat: Implement image upload functionality"
- # Example commit types: feat, fix, docs, style, refactor, test, chore
- ```
-
-### 5. Push Your Branch to Your Fork
-* When you're ready to save your progress to *your remote fork on GitHub*, push your branch:
- ```bash
- git push origin feature/your-team-name
- ```
- (Replace `feature/your-team-name` with your actual branch name.)
-* If it's the first time pushing this branch, Git might suggest a command like `git push --set-upstream origin feature/your-team-name`. Use that command.
-
-### 6. Submit Your Project (Create a Pull Request)
-* Once your project is complete (or at a submittable stage), go to *your* forked repository on GitHub (`https://github.com/YOUR_USERNAME/foodsnap-ai`).
-* You should see a prompt saying "`feature/your-team-name` had recent pushes". Click the "**Compare & pull request**" button.
-* If you don't see the prompt, go to the "**Pull requests**" tab and click "**New pull request**".
-* **Crucially, ensure the settings are:**
- * **Base repository:** `WeCode-Community-Dev/foodsnap-ai`
- * **Base branch:** `main` (or `master`, whichever is the default for this repository)
- * **Head repository:** `YOUR_USERNAME/foodsnap-ai`
- * **Compare branch:** `feature/your-team-name` (your development branch)
-* Write a clear title and a detailed description for your Pull Request (PR). Include:
- * A brief overview of your solution.
- * Technologies used.
- * How to run/test your project (setup, commands, etc.).
- * Any known issues or limitations.
- * A link to a live demo if applicable (e.g., Heroku, Netlify, GitHub Pages).
- * Screenshots or a short video showcasing your project can be very helpful!
-* Click "**Create pull request**".
-
-## What to Include in Your Submission
-*(In your project's directory, pushed to your branch and included in the PR)*
-
-* **Source Code:** All the code for your project.
-* **`README.md` (Your Project's README):** This is very important! Your project's `README.md` (different from this main hackathon `README.md`) should include:
- * Project Title & Team Name/Members (if applicable).
- * A brief description of your FoodSnap AI solution.
- * What features you implemented.
- * Tech stack used.
- * **Clear instructions on how to set up and run *your specific project* locally.** This includes dependencies, environment variables, build steps, and run commands.
- * Any API keys or environment variables needed (explain how to get them, but **DO NOT COMMIT ACTUAL KEYS** to the repository). Use a `.env.example` file to show what's needed.
- * Link to a live demo (if any).
-* **(Optional but Recommended)** A short demo video or presentation slides (you can link these in your PR description or in your project's README).
-
-## Judging Criteria (General Pointers)
-
-While specific criteria might be announced, generally projects are evaluated on:
-
-* **Functionality:** Does it work as intended? Does it achieve the core goal of calorie estimation from an image?
-* **Accuracy:** How close are the calorie/macro estimations? (We understand this is complex!)
-* **Innovation & Creativity:** Did you come up with a unique approach or an interesting feature?
-* **Technical Implementation:** Quality of code, choice of technology, and complexity handled.
-* **User Experience (UX/UI):** If applicable, is the application easy and pleasant to use?
-* **Presentation/Demo:** How well you explain and showcase your project.
-* **Adherence to Submission Guidelines:** Including a good project `README.md` and following the Git workflow.
+---
+## 🛠️ Local Development & Installation
+
+### Prerequisite Environment Variables
+Create a `.env` in the `backend/` directory:
+```env
+DATABASE_URL=postgresql+asyncpg://:@/
+SUPABASE_URL=your_supabase_project_url
+SUPABASE_KEY=your_supabase_anon_key
+GEMINI_API_KEY=your_gemini_api_key
+USDA_API_KEY=your_usda_data_api_key
+JWT_SECRET=your_jwt_signing_key
+```
+
+Configure `mobile/src/api/config.ts` base URLs:
+```typescript
+export const API_BASE_URL = __DEV__
+ ? "http://YOUR_LOCAL_IP:8000"
+ : "https://your-production-app.onrender.com";
+```
+
+### 1. Launching the Backend
+```bash
+cd backend
+python -m venv venv
+source venv/bin/activate # On Windows: .\venv\Scripts\activate
+pip install -r requirements.txt
+uvicorn app.main:app --reload --port 8000
+```
+
+### 2. Launching the Mobile App
+Ensure you have the Expo Go app installed on your physical device:
+```bash
+cd mobile
+npm install
+npx expo start --tunnel
+```
+Scan the QR code printed in the terminal using your phone camera.
---
-Good luck, innovators! We can't wait to see your FoodSnap AI creations. Remember to have fun, learn, and collaborate!
-
-**Happy Hacking!**
-The WeCode Community Dev Team
+## 🧪 Production Verification
+
+### Type Safety Checks
+Verify that all mobile app files compile cleanly:
+```bash
+cd mobile
+npx tsc --noEmit
+```
+
+### Android APK Build
+Build a lightweight Android APK preview using EAS CLI:
+```bash
+cd mobile
+npm install -g eas-cli
+eas build --platform android --profile preview
+```
diff --git a/backend/.env.example b/backend/.env.example
new file mode 100644
index 0000000..4a29869
--- /dev/null
+++ b/backend/.env.example
@@ -0,0 +1,16 @@
+# ─── FoodSnap AI Environment Variables ───────────────────────────────────────
+# Copy this file to .env and fill in the values.
+
+# Database connection string (e.g. PostgreSQL via Supabase)
+DATABASE_URL=
+
+# OpenAI API key for GPT-4o Vision food analysis
+OPENAI_API_KEY=
+# Supabase project URL and anonymous/service key
+SUPABASE_URL=
+SUPABASE_KEY=
+SUPABASE_SERVICE_ROLE_KEY=
+SUPABASE_STORAGE_BUCKET=foodsnap-images
+
+# Cloudflare R2 bucket URL for image storage
+R2_BUCKET_URL=
diff --git a/backend/.gitignore b/backend/.gitignore
new file mode 100644
index 0000000..9df6eda
--- /dev/null
+++ b/backend/.gitignore
@@ -0,0 +1,42 @@
+# ─── Python ──────────────────────────────────────────────────────────────────
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+*.egg
+dist/
+build/
+*.whl
+
+# ─── Virtual Environments ────────────────────────────────────────────────────
+venv/
+.venv/
+env/
+
+# ─── Environment Variables ───────────────────────────────────────────────────
+.env
+
+# ─── IDE / Editor ────────────────────────────────────────────────────────────
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# ─── OS ──────────────────────────────────────────────────────────────────────
+.DS_Store
+Thumbs.db
+
+# ─── Testing / Coverage ─────────────────────────────────────────────────────
+.pytest_cache/
+htmlcov/
+.coverage
+coverage.xml
+
+# ─── Misc ────────────────────────────────────────────────────────────────────
+*.log
+
+# ─── Security / Scratch ──────────────────────────────────────────────────────
+scratch/
+test_token.txt
+new_token.txt
+*.txt
diff --git a/backend/Dockerfile b/backend/Dockerfile
new file mode 100644
index 0000000..b24baa5
--- /dev/null
+++ b/backend/Dockerfile
@@ -0,0 +1,37 @@
+# ═══════════════════════════════════════════════════════════════════════════════
+# FoodSnap AI — Multi-stage production Dockerfile
+# ═══════════════════════════════════════════════════════════════════════════════
+
+# ── Stage 1: Builder ─────────────────────────────────────────────────────────
+FROM python:3.11-slim AS builder
+
+WORKDIR /build
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir --prefix=/install -r requirements.txt
+
+# ── Stage 2: Runtime ─────────────────────────────────────────────────────────
+FROM python:3.11-slim AS runtime
+
+# Security: run as non-root user
+RUN groupadd --system appgroup && \
+ useradd --system --gid appgroup --create-home appuser
+
+WORKDIR /app
+
+# Copy only installed packages from builder
+COPY --from=builder /install /usr/local
+
+# Copy application code
+COPY . .
+
+# Drop privileges
+USER appuser
+
+EXPOSE 8000
+
+# Health check for orchestrators (Docker Compose, ECS, K8s)
+HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \
+ CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/backend/alembic.ini b/backend/alembic.ini
new file mode 100644
index 0000000..68dbe58
--- /dev/null
+++ b/backend/alembic.ini
@@ -0,0 +1,117 @@
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts.
+# Use forward slashes (/) also on windows to provide an os agnostic path
+script_location = alembic
+
+# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
+# Uncomment the line below if you want the files to be prepended with date and time
+# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
+
+# sys.path path, will be prepended to sys.path if present.
+# defaults to the current working directory.
+prepend_sys_path = .
+
+# timezone to use when rendering the date within the migration file
+# as well as the filename.
+# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.
+# Any required deps can installed by adding `alembic[tz]` to the pip requirements
+# string value is passed to ZoneInfo()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version location specification; This defaults
+# to alembic/versions. When using multiple version
+# directories, initial revisions must be specified with --version-path.
+# The path separator used here should be the separator specified by "version_path_separator" below.
+# version_locations = %(here)s/bar:%(here)s/bat:alembic/versions
+
+# version path separator; As mentioned above, this is the character used to split
+# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
+# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
+# Valid values for version_path_separator are:
+#
+# version_path_separator = :
+# version_path_separator = ;
+# version_path_separator = space
+# version_path_separator = newline
+#
+# Use os.pathsep. Default configuration used for new projects.
+version_path_separator = os
+
+# set to 'true' to search source files recursively
+# in each "version_locations" directory
+# new in Alembic version 1.10
+# recursive_version_locations = false
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+sqlalchemy.url = driver://user:pass@localhost/dbname
+
+
+[post_write_hooks]
+# post_write_hooks defines scripts or Python functions that are run
+# on newly generated revision scripts. See the documentation for further
+# detail and examples
+
+# format using "black" - use the console_scripts runner, against the "black" entrypoint
+# hooks = black
+# black.type = console_scripts
+# black.entrypoint = black
+# black.options = -l 79 REVISION_SCRIPT_FILENAME
+
+# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
+# hooks = ruff
+# ruff.type = exec
+# ruff.executable = %(here)s/.venv/bin/ruff
+# ruff.options = --fix REVISION_SCRIPT_FILENAME
+
+# Logging configuration
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARNING
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARNING
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S
diff --git a/backend/alembic/README b/backend/alembic/README
new file mode 100644
index 0000000..e0d0858
--- /dev/null
+++ b/backend/alembic/README
@@ -0,0 +1 @@
+Generic single-database configuration with an async dbapi.
\ No newline at end of file
diff --git a/backend/alembic/env.py b/backend/alembic/env.py
new file mode 100644
index 0000000..c5f165e
--- /dev/null
+++ b/backend/alembic/env.py
@@ -0,0 +1,92 @@
+import asyncio
+from logging.config import fileConfig
+
+from sqlalchemy import pool
+from sqlalchemy.engine import Connection
+from sqlalchemy.ext.asyncio import async_engine_from_config
+
+from alembic import context
+
+# this is the Alembic Config object, which provides
+# access to the values within the .ini file in use.
+config = context.config
+
+# Interpret the config file for Python logging.
+# This line sets up loggers basically.
+if config.config_file_name is not None:
+ fileConfig(config.config_file_name)
+
+# add your model's MetaData object here
+# for 'autogenerate' support
+from app.database import Base, ASYNC_DATABASE_URL
+from app.models import * # ensure models are imported
+
+config.set_main_option("sqlalchemy.url", ASYNC_DATABASE_URL)
+target_metadata = Base.metadata
+
+# other values from the config, defined by the needs of env.py,
+# can be acquired:
+# my_important_option = config.get_main_option("my_important_option")
+# ... etc.
+
+
+def run_migrations_offline() -> None:
+ """Run migrations in 'offline' mode.
+
+ This configures the context with just a URL
+ and not an Engine, though an Engine is acceptable
+ here as well. By skipping the Engine creation
+ we don't even need a DBAPI to be available.
+
+ Calls to context.execute() here emit the given string to the
+ script output.
+
+ """
+ url = config.get_main_option("sqlalchemy.url")
+ context.configure(
+ url=url,
+ target_metadata=target_metadata,
+ literal_binds=True,
+ dialect_opts={"paramstyle": "named"},
+ )
+
+ with context.begin_transaction():
+ context.run_migrations()
+
+
+def do_run_migrations(connection: Connection) -> None:
+ context.configure(connection=connection, target_metadata=target_metadata)
+
+ with context.begin_transaction():
+ context.run_migrations()
+
+
+async def run_async_migrations() -> None:
+ """In this scenario we need to create an Engine
+ and associate a connection with the context.
+
+ """
+
+ connectable = async_engine_from_config(
+ config.get_section(config.config_ini_section, {}),
+ prefix="sqlalchemy.",
+ poolclass=pool.NullPool,
+ connect_args={"statement_cache_size": 0},
+ )
+
+ async with connectable.connect() as connection:
+ await connection.run_sync(do_run_migrations)
+
+ await connectable.dispose()
+
+
+def run_migrations_online() -> None:
+ """Run migrations in 'online' mode."""
+
+ asyncio.run(run_async_migrations())
+
+
+if context.is_offline_mode():
+ run_migrations_offline()
+else:
+ run_migrations_online()
diff --git a/backend/alembic/script.py.mako b/backend/alembic/script.py.mako
new file mode 100644
index 0000000..fbc4b07
--- /dev/null
+++ b/backend/alembic/script.py.mako
@@ -0,0 +1,26 @@
+"""${message}
+
+Revision ID: ${up_revision}
+Revises: ${down_revision | comma,n}
+Create Date: ${create_date}
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+${imports if imports else ""}
+
+# revision identifiers, used by Alembic.
+revision: str = ${repr(up_revision)}
+down_revision: Union[str, None] = ${repr(down_revision)}
+branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
+depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
+
+
+def upgrade() -> None:
+ ${upgrades if upgrades else "pass"}
+
+
+def downgrade() -> None:
+ ${downgrades if downgrades else "pass"}
diff --git a/backend/alembic/versions/5e80acf77ff7_add_user_health_profile_fields.py b/backend/alembic/versions/5e80acf77ff7_add_user_health_profile_fields.py
new file mode 100644
index 0000000..e52c053
--- /dev/null
+++ b/backend/alembic/versions/5e80acf77ff7_add_user_health_profile_fields.py
@@ -0,0 +1,40 @@
+"""add user health profile fields
+
+Revision ID: 5e80acf77ff7
+Revises: e3fe67d48e2b
+Create Date: 2026-05-16 10:00:43.097462
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '5e80acf77ff7'
+down_revision: Union[str, None] = 'e3fe67d48e2b'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+ # ### commands auto generated by Alembic - please adjust! ###
+ op.drop_column('food_items', 'matched')
+ op.add_column('users', sa.Column('height_cm', sa.Numeric(precision=5, scale=1), nullable=True))
+ op.add_column('users', sa.Column('weight_kg', sa.Numeric(precision=5, scale=1), nullable=True))
+ op.add_column('users', sa.Column('age', sa.Integer(), nullable=True))
+ op.add_column('users', sa.Column('activity_level', sa.String(length=20), nullable=True))
+ op.add_column('users', sa.Column('goal', sa.String(length=20), nullable=True))
+ # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+ # ### commands auto generated by Alembic - please adjust! ###
+ op.drop_column('users', 'goal')
+ op.drop_column('users', 'activity_level')
+ op.drop_column('users', 'age')
+ op.drop_column('users', 'weight_kg')
+ op.drop_column('users', 'height_cm')
+ op.add_column('food_items', sa.Column('matched', sa.BOOLEAN(), server_default=sa.text('true'), autoincrement=False, nullable=True))
+ # ### end Alembic commands ###
diff --git a/backend/alembic/versions/e3fe67d48e2b_initial_tables.py b/backend/alembic/versions/e3fe67d48e2b_initial_tables.py
new file mode 100644
index 0000000..cff45c1
--- /dev/null
+++ b/backend/alembic/versions/e3fe67d48e2b_initial_tables.py
@@ -0,0 +1,96 @@
+"""initial tables
+
+Revision ID: e3fe67d48e2b
+Revises:
+Create Date: 2026-05-15 21:53:57.874383
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'e3fe67d48e2b'
+down_revision: Union[str, None] = None
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+ # ### commands auto generated by Alembic - please adjust! ###
+ op.create_table('food_aliases',
+ sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
+ sa.Column('ai_name', sa.String(length=200), nullable=False),
+ sa.Column('correct_name', sa.String(length=200), nullable=False),
+ sa.Column('usda_id', sa.Integer(), nullable=True),
+ sa.Column('frequency', sa.Integer(), server_default=sa.text('1'), nullable=False),
+ sa.PrimaryKeyConstraint('id'),
+ sa.UniqueConstraint('ai_name', 'correct_name', name='uq_ai_correct_name')
+ )
+ op.create_table('nutrition_cache',
+ sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
+ sa.Column('food_name_normalized', sa.String(length=200), nullable=False),
+ sa.Column('usda_id', sa.Integer(), nullable=False),
+ sa.Column('calories_per_100g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('protein_per_100g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('fat_per_100g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('carbs_per_100g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('last_verified', sa.DateTime(timezone=True), server_default=sa.text('(now())'), nullable=False),
+ sa.PrimaryKeyConstraint('id'),
+ sa.UniqueConstraint('food_name_normalized')
+ )
+ op.create_table('users',
+ sa.Column('id', sa.UUID(), server_default=sa.text('(gen_random_uuid())'), nullable=False),
+ sa.Column('email', sa.String(length=255), nullable=False),
+ sa.Column('display_name', sa.String(length=100), nullable=True),
+ sa.Column('daily_calorie_goal', sa.Integer(), server_default=sa.text('(2000)'), nullable=False),
+ sa.Column('dietary_restrictions', sa.JSON(), server_default=sa.text("('[]'::json)"), nullable=False),
+ sa.Column('preferred_units', sa.String(length=10), server_default=sa.text("'metric'"), nullable=False),
+ sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('(now())'), nullable=False),
+ sa.Column('updated_at', sa.DateTime(timezone=True), server_default=sa.text('(now())'), nullable=False),
+ sa.PrimaryKeyConstraint('id'),
+ sa.UniqueConstraint('email')
+ )
+ op.create_table('food_logs',
+ sa.Column('id', sa.UUID(), server_default=sa.text('(gen_random_uuid())'), nullable=False),
+ sa.Column('user_id', sa.UUID(), nullable=False),
+ sa.Column('image_url', sa.String(length=500), nullable=False),
+ sa.Column('meal_type', sa.String(length=20), server_default=sa.text("'unspecified'"), nullable=False),
+ sa.Column('total_calories', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('total_protein_g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('total_fat_g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('total_carbs_g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('analyzed_at', sa.DateTime(timezone=True), server_default=sa.text('(now())'), nullable=False),
+ sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+ sa.PrimaryKeyConstraint('id')
+ )
+ op.create_table('food_items',
+ sa.Column('id', sa.UUID(), server_default=sa.text('(gen_random_uuid())'), nullable=False),
+ sa.Column('food_log_id', sa.UUID(), nullable=False),
+ sa.Column('name', sa.String(length=200), nullable=False),
+ sa.Column('usda_id', sa.Integer(), nullable=True),
+ sa.Column('usda_match_name', sa.String(length=200), nullable=True),
+ sa.Column('confidence', sa.Numeric(precision=3, scale=2), nullable=True),
+ sa.Column('weight_grams', sa.Numeric(precision=8, scale=2), nullable=False),
+ sa.Column('calories', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('protein_g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('fat_g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('carbs_g', sa.Numeric(precision=8, scale=2), nullable=True),
+ sa.Column('is_user_corrected', sa.Boolean(), server_default=sa.text('(false)'), nullable=False),
+ sa.Column('original_ai_name', sa.String(length=200), nullable=True),
+ sa.ForeignKeyConstraint(['food_log_id'], ['food_logs.id'], ondelete='CASCADE'),
+ sa.PrimaryKeyConstraint('id')
+ )
+ # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+ # ### commands auto generated by Alembic - please adjust! ###
+ op.drop_table('food_items')
+ op.drop_table('food_logs')
+ op.drop_table('users')
+ op.drop_table('nutrition_cache')
+ op.drop_table('food_aliases')
+ # ### end Alembic commands ###
diff --git a/backend/app/FoodSnap AI.code-workspace b/backend/app/FoodSnap AI.code-workspace
new file mode 100644
index 0000000..0844037
--- /dev/null
+++ b/backend/app/FoodSnap AI.code-workspace
@@ -0,0 +1,7 @@
+{
+ "folders": [
+ {
+ "path": "../.."
+ }
+ ]
+}
\ No newline at end of file
diff --git a/backend/app/__init__.py b/backend/app/__init__.py
new file mode 100644
index 0000000..449f180
--- /dev/null
+++ b/backend/app/__init__.py
@@ -0,0 +1 @@
+# FoodSnap AI Backend Application
diff --git a/backend/app/config.py b/backend/app/config.py
new file mode 100644
index 0000000..9ae5f98
--- /dev/null
+++ b/backend/app/config.py
@@ -0,0 +1,43 @@
+"""
+Application configuration loaded from environment variables using pydantic-settings.
+"""
+
+from pydantic_settings import BaseSettings
+
+
+class Settings(BaseSettings):
+ """Application settings loaded from .env file and environment variables."""
+
+ # Application
+ APP_NAME: str = "FoodSnap AI"
+ APP_VERSION: str = "0.1.0"
+ DEBUG: bool = False
+
+ # Database
+ DATABASE_URL: str = ""
+
+ # OpenAI
+ OPENAI_API_KEY: str = ""
+
+ # Google Gemini
+ GOOGLE_API_KEY: str = ""
+
+ # USDA FoodData Central
+ USDA_API_KEY: str = ""
+
+ # Supabase
+ SUPABASE_URL: str = ""
+ SUPABASE_KEY: str = ""
+ SUPABASE_SERVICE_ROLE_KEY: str = ""
+ STORAGE_BUCKET: str = "foodsnap-images"
+
+ model_config = {
+ "env_file": ".env",
+ "env_file_encoding": "utf-8",
+ "case_sensitive": True,
+ "extra": "ignore",
+ }
+
+
+# Singleton settings instance
+settings = Settings()
diff --git a/backend/app/database.py b/backend/app/database.py
new file mode 100644
index 0000000..b0e7cd0
--- /dev/null
+++ b/backend/app/database.py
@@ -0,0 +1,62 @@
+"""
+Async SQLAlchemy database engine, session factory, and FastAPI dependency.
+"""
+
+from typing import AsyncGenerator
+
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+from sqlalchemy.orm import DeclarativeBase
+from sqlalchemy.pool import NullPool
+
+from app.config import settings
+
+# ---------------------------------------------------------------------------
+# Convert the DATABASE_URL to use the asyncpg driver
+# Supabase provides: postgresql://... → we need: postgresql+asyncpg://...
+# ---------------------------------------------------------------------------
+_raw_url = settings.DATABASE_URL
+if _raw_url.startswith("postgresql://"):
+ ASYNC_DATABASE_URL = _raw_url.replace("postgresql://", "postgresql+asyncpg://", 1)
+elif _raw_url.startswith("postgres://"):
+ ASYNC_DATABASE_URL = _raw_url.replace("postgres://", "postgresql+asyncpg://", 1)
+else:
+ ASYNC_DATABASE_URL = _raw_url
+
+# ---------------------------------------------------------------------------
+# Engine & Session
+# ---------------------------------------------------------------------------
+engine = create_async_engine(
+ ASYNC_DATABASE_URL,
+ echo=settings.DEBUG,
+ poolclass=NullPool,
+ connect_args={"statement_cache_size": 0},
+)
+
+async_session_factory = async_sessionmaker(
+ bind=engine,
+ class_=AsyncSession,
+ expire_on_commit=False,
+)
+
+
+# ---------------------------------------------------------------------------
+# Declarative Base
+# ---------------------------------------------------------------------------
+class Base(DeclarativeBase):
+ """Base class for all SQLAlchemy ORM models."""
+
+ pass
+
+
+# ---------------------------------------------------------------------------
+# FastAPI Dependency
+# ---------------------------------------------------------------------------
+async def get_db() -> AsyncGenerator[AsyncSession, None]:
+ """Yield an async database session, rolling back on error."""
+ async with async_session_factory() as session:
+ try:
+ yield session
+ await session.commit()
+ except Exception:
+ await session.rollback()
+ raise
diff --git a/backend/app/dependencies.py b/backend/app/dependencies.py
new file mode 100644
index 0000000..f679ace
--- /dev/null
+++ b/backend/app/dependencies.py
@@ -0,0 +1,56 @@
+from fastapi import Depends, HTTPException, status
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.future import select
+from app.database import get_db
+from app.services import auth_service
+from app.models.users import User
+from uuid import UUID
+import logging
+
+logger = logging.getLogger(__name__)
+security = HTTPBearer()
+
+async def get_current_user(
+ credentials: HTTPAuthorizationCredentials = Depends(security),
+ db: AsyncSession = Depends(get_db)
+) -> UUID:
+ """
+ Dependency that extracts the Bearer token, verifies it with Supabase,
+ and ensures the user exists in the local database (lazy sync).
+ Returns the user's UUID.
+ """
+ token = credentials.credentials
+ user_info = await auth_service.get_current_user_info(token)
+
+ try:
+ user_id = UUID(user_info.id)
+ except (ValueError, AttributeError):
+ raise HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED,
+ detail="Invalid user identification"
+ )
+
+ # Lazy Sync: Check if user exists in local database
+ result = await db.execute(select(User).where(User.id == user_id))
+ user = result.scalars().first()
+
+ if not user:
+ logger.info(f"User {user_id} not found in local DB. Performing lazy sync...")
+ try:
+ new_user = User(
+ id=user_id,
+ email=user_info.email,
+ display_name=user_info.email.split("@")[0]
+ )
+ db.add(new_user)
+ await db.flush()
+ await db.refresh(new_user)
+ logger.info(f"Lazy sync successful for user {user_id}")
+ except Exception as e:
+ logger.error(f"Failed to perform lazy sync for user {user_id}: {e}")
+ # We don't fail the request here, as they ARE authenticated in Supabase
+ # But subsequent DB operations might fail if they rely on the user existing
+ await db.rollback()
+
+ return user_id
diff --git a/backend/app/main.py b/backend/app/main.py
new file mode 100644
index 0000000..bd26788
--- /dev/null
+++ b/backend/app/main.py
@@ -0,0 +1,45 @@
+"""
+FoodSnap AI — FastAPI application entry point.
+"""
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from app.config import settings
+from app.routes.health import router as health_router
+from app.routes.auth import router as auth_router
+from app.routes.admin import router as admin_router
+from app.routes.upload import router as upload_router
+from app.routes.analyze import router as analyze_router
+from app.routes.logs import router as logs_router
+from app.routes.users import router as users_router
+from app.routes.stats import router as stats_router
+
+app = FastAPI(
+ title=settings.APP_NAME,
+ version=settings.APP_VERSION,
+ description="AI-powered food calorie estimation API",
+)
+
+# ---------------------------------------------------------------------------
+# CORS — allow all origins during development
+# ---------------------------------------------------------------------------
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=["*"],
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+)
+
+# ---------------------------------------------------------------------------
+# Routers
+# ---------------------------------------------------------------------------
+app.include_router(health_router)
+app.include_router(auth_router)
+app.include_router(admin_router)
+app.include_router(upload_router)
+app.include_router(analyze_router)
+app.include_router(logs_router, prefix="/api")
+app.include_router(users_router)
+app.include_router(stats_router)
diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py
new file mode 100644
index 0000000..322d8af
--- /dev/null
+++ b/backend/app/models/__init__.py
@@ -0,0 +1,11 @@
+"""
+SQLAlchemy ORM models — import all models here so Alembic can discover them.
+"""
+
+from app.models.food_aliases import FoodAlias
+from app.models.food_items import FoodItem
+from app.models.food_logs import FoodLog
+from app.models.nutrition_cache import NutritionCache
+from app.models.users import User
+
+__all__ = ["User", "FoodLog", "FoodItem", "NutritionCache", "FoodAlias"]
diff --git a/backend/app/models/food_aliases.py b/backend/app/models/food_aliases.py
new file mode 100644
index 0000000..a23fc83
--- /dev/null
+++ b/backend/app/models/food_aliases.py
@@ -0,0 +1,27 @@
+"""
+FoodAlias model — learns from user corrections to improve AI accuracy over time.
+"""
+
+from sqlalchemy import Integer, String, UniqueConstraint, text
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.database import Base
+
+
+class FoodAlias(Base):
+ __tablename__ = "food_aliases"
+
+ __table_args__ = (
+ UniqueConstraint("ai_name", "correct_name", name="uq_ai_correct_name"),
+ )
+
+ id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+ ai_name: Mapped[str] = mapped_column(String(200), nullable=False)
+ correct_name: Mapped[str] = mapped_column(String(200), nullable=False)
+ usda_id: Mapped[int | None] = mapped_column(Integer, nullable=True)
+ frequency: Mapped[int] = mapped_column(
+ Integer, default=1, server_default=text("1")
+ )
+
+ def __repr__(self) -> str:
+ return f""
diff --git a/backend/app/models/food_items.py b/backend/app/models/food_items.py
new file mode 100644
index 0000000..af903d6
--- /dev/null
+++ b/backend/app/models/food_items.py
@@ -0,0 +1,45 @@
+"""
+FoodItem model — individual food detected within a FoodLog.
+"""
+
+import uuid
+
+from sqlalchemy import Boolean, ForeignKey, Integer, Numeric, String, text
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from app.database import Base
+
+
+class FoodItem(Base):
+ __tablename__ = "food_items"
+
+ id: Mapped[uuid.UUID] = mapped_column(
+ UUID(as_uuid=True),
+ primary_key=True,
+ server_default=text("gen_random_uuid()"),
+ )
+ food_log_id: Mapped[uuid.UUID] = mapped_column(
+ UUID(as_uuid=True),
+ ForeignKey("food_logs.id", ondelete="CASCADE"),
+ nullable=False,
+ )
+ name: Mapped[str] = mapped_column(String(200), nullable=False)
+ usda_id: Mapped[int | None] = mapped_column(Integer, nullable=True)
+ usda_match_name: Mapped[str | None] = mapped_column(String(200), nullable=True)
+ confidence: Mapped[float | None] = mapped_column(Numeric(3, 2))
+ weight_grams: Mapped[float] = mapped_column(Numeric(8, 2), nullable=False)
+ calories: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ protein_g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ fat_g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ carbs_g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ is_user_corrected: Mapped[bool] = mapped_column(
+ Boolean, default=False, server_default=text("false")
+ )
+ original_ai_name: Mapped[str | None] = mapped_column(String(200), nullable=True)
+
+ # Relationships
+ food_log = relationship("FoodLog", back_populates="items")
+
+ def __repr__(self) -> str:
+ return f""
diff --git a/backend/app/models/food_logs.py b/backend/app/models/food_logs.py
new file mode 100644
index 0000000..054b6e1
--- /dev/null
+++ b/backend/app/models/food_logs.py
@@ -0,0 +1,45 @@
+"""
+FoodLog model — represents one analyzed meal/image upload.
+"""
+
+import uuid
+
+from sqlalchemy import DateTime, ForeignKey, Numeric, String, text
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from app.database import Base
+
+
+class FoodLog(Base):
+ __tablename__ = "food_logs"
+
+ id: Mapped[uuid.UUID] = mapped_column(
+ UUID(as_uuid=True),
+ primary_key=True,
+ server_default=text("gen_random_uuid()"),
+ )
+ user_id: Mapped[uuid.UUID] = mapped_column(
+ UUID(as_uuid=True),
+ ForeignKey("users.id", ondelete="CASCADE"),
+ nullable=False,
+ )
+ image_url: Mapped[str] = mapped_column(
+ String(500), nullable=False
+ )
+ meal_type: Mapped[str] = mapped_column(
+ String(20), default="unspecified", server_default=text("'unspecified'")
+ )
+ total_calories: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ total_protein_g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ total_fat_g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ total_carbs_g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ analyzed_at: Mapped[str] = mapped_column(
+ DateTime(timezone=True), server_default=text("now()")
+ )
+
+ # Relationships
+ items = relationship("FoodItem", back_populates="food_log", cascade="all, delete-orphan")
+
+ def __repr__(self) -> str:
+ return f""
diff --git a/backend/app/models/nutrition_cache.py b/backend/app/models/nutrition_cache.py
new file mode 100644
index 0000000..e5f9345
--- /dev/null
+++ b/backend/app/models/nutrition_cache.py
@@ -0,0 +1,28 @@
+"""
+NutritionCache model — caches USDA nutrition data to reduce API calls.
+"""
+
+from sqlalchemy import DateTime, Integer, Numeric, String, text
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.database import Base
+
+
+class NutritionCache(Base):
+ __tablename__ = "nutrition_cache"
+
+ id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+ food_name_normalized: Mapped[str] = mapped_column(
+ String(200), unique=True, nullable=False
+ )
+ usda_id: Mapped[int] = mapped_column(Integer, nullable=False)
+ calories_per_100g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ protein_per_100g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ fat_per_100g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ carbs_per_100g: Mapped[float | None] = mapped_column(Numeric(8, 2))
+ last_verified: Mapped[str] = mapped_column(
+ DateTime(timezone=True), server_default=text("now()")
+ )
+
+ def __repr__(self) -> str:
+ return f""
diff --git a/backend/app/models/users.py b/backend/app/models/users.py
new file mode 100644
index 0000000..6eaff22
--- /dev/null
+++ b/backend/app/models/users.py
@@ -0,0 +1,52 @@
+"""
+User model — stores account info, dietary preferences, and calorie goals.
+"""
+
+import uuid
+
+from sqlalchemy import DateTime, Integer, JSON, Numeric, String, text
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.database import Base
+
+
+class User(Base):
+ __tablename__ = "users"
+
+ id: Mapped[uuid.UUID] = mapped_column(
+ UUID(as_uuid=True),
+ primary_key=True,
+ server_default=text("gen_random_uuid()"),
+ )
+ email: Mapped[str] = mapped_column(
+ String(255), unique=True, nullable=False
+ )
+ display_name: Mapped[str | None] = mapped_column(
+ String(100), nullable=True
+ )
+ daily_calorie_goal: Mapped[int] = mapped_column(
+ Integer, default=2000, server_default=text("2000")
+ )
+ dietary_restrictions: Mapped[dict | list] = mapped_column(
+ JSON, default=list, server_default=text("'[]'::json")
+ )
+ preferred_units: Mapped[str] = mapped_column(
+ String(10), default="metric", server_default=text("'metric'")
+ )
+ created_at: Mapped[str] = mapped_column(
+ DateTime(timezone=True), server_default=text("now()")
+ )
+ updated_at: Mapped[str] = mapped_column(
+ DateTime(timezone=True), server_default=text("now()")
+ )
+
+ # Health Profile
+ height_cm: Mapped[float | None] = mapped_column(Numeric(5, 1), nullable=True)
+ weight_kg: Mapped[float | None] = mapped_column(Numeric(5, 1), nullable=True)
+ age: Mapped[int | None] = mapped_column(Integer, nullable=True)
+ activity_level: Mapped[str | None] = mapped_column(String(20), nullable=True)
+ goal: Mapped[str | None] = mapped_column(String(20), nullable=True)
+
+ def __repr__(self) -> str:
+ return f""
diff --git a/backend/app/routes/__init__.py b/backend/app/routes/__init__.py
new file mode 100644
index 0000000..d212dab
--- /dev/null
+++ b/backend/app/routes/__init__.py
@@ -0,0 +1 @@
+# Routes package
diff --git a/backend/app/routes/admin.py b/backend/app/routes/admin.py
new file mode 100644
index 0000000..6b3864a
--- /dev/null
+++ b/backend/app/routes/admin.py
@@ -0,0 +1,14 @@
+from fastapi import APIRouter, Depends
+from sqlalchemy.ext.asyncio import AsyncSession
+from app.database import get_db
+from app.services import auth_service
+
+router = APIRouter(prefix="/api/admin", tags=["Admin"])
+
+@router.post("/sync-users")
+async def sync_users(db: AsyncSession = Depends(get_db)):
+ """
+ Manual recovery tool to sync all Supabase users to the local database.
+ """
+ stats = await auth_service.sync_all_users(db)
+ return stats
diff --git a/backend/app/routes/analyze.py b/backend/app/routes/analyze.py
new file mode 100644
index 0000000..7769c29
--- /dev/null
+++ b/backend/app/routes/analyze.py
@@ -0,0 +1,155 @@
+import base64
+import logging
+from datetime import datetime
+from uuid import uuid4, UUID
+from fastapi import APIRouter, Depends, UploadFile, File, Form, HTTPException, status
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.dependencies import get_current_user, get_db
+from app.utils.image_handler import process_image
+from app.services.storage_service import upload_image
+from app.services.ai_service import analyze_food_image
+from app.services.nutrition_service import lookup_nutrition, calculate_macros
+from app.models import FoodLog, FoodItem
+from app.schemas.analysis import AnalysisResponse, FoodItemResult, MealTotals
+
+logger = logging.getLogger(__name__)
+router = APIRouter(prefix="/api", tags=["analysis"])
+
+@router.post("/analyze", response_model=AnalysisResponse)
+async def analyze_meal(
+ image: UploadFile = File(...),
+ meal_type: str = Form("unspecified"),
+ user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ warnings = []
+ unmatched_items = []
+
+ # 1. Process Image
+ try:
+ clean_bytes, _ = process_image(image)
+ except HTTPException as e:
+ raise e
+ except Exception as e:
+ logger.error(f"Image processing failed: {e}")
+ raise HTTPException(status_code=500, detail="Failed to process image")
+
+ # 2. Upload to Storage
+ try:
+ image_url, _ = upload_image(clean_bytes, user_id)
+ except Exception as e:
+ logger.error(f"Upload failed: {e}")
+ raise HTTPException(status_code=503, detail="Storage service unavailable")
+
+ # 3. AI Identification
+ image_base64 = base64.b64encode(clean_bytes).decode("utf-8")
+ detected_foods = analyze_food_image(image_base64)
+
+ if not detected_foods:
+ warnings.append("No food items detected in image")
+ # Still return a valid response but with empty items
+ return AnalysisResponse(
+ log_id=str(uuid4()), # Placeholder since we won't save empty logs usually, or should we?
+ image_url=image_url,
+ meal_type=meal_type,
+ analyzed_at=datetime.utcnow().isoformat(),
+ items=[],
+ totals=MealTotals(calories=0, protein_g=0, fat_g=0, carbs_g=0),
+ unmatched_items=[],
+ warnings=warnings
+ )
+
+ # 4. Nutrition Lookup & Calculation
+ final_items = []
+ total_cal = 0.0
+ total_prot = 0.0
+ total_fat = 0.0
+ total_carb = 0.0
+
+ for food in detected_foods:
+ nutrition = await lookup_nutrition(food.name, db)
+
+ if nutrition:
+ macros = calculate_macros(nutrition, food.weight_grams)
+ item_result = FoodItemResult(
+ name=food.name,
+ usda_match=nutrition.usda_name,
+ usda_id=nutrition.usda_id,
+ confidence=food.confidence,
+ weight_grams=food.weight_grams,
+ calories=macros["calories"],
+ protein_g=macros["protein_g"],
+ fat_g=macros["fat_g"],
+ carbs_g=macros["carbs_g"],
+ matched=True
+ )
+ total_cal += macros["calories"]
+ total_prot += macros["protein_g"]
+ total_fat += macros["fat_g"]
+ total_carb += macros["carbs_g"]
+ else:
+ unmatched_items.append(food.name)
+ item_result = FoodItemResult(
+ name=food.name,
+ confidence=food.confidence,
+ weight_grams=food.weight_grams,
+ calories=0,
+ protein_g=0,
+ fat_g=0,
+ carbs_g=0,
+ matched=False
+ )
+
+ final_items.append(item_result)
+
+ # 5. Database Storage
+ log_id = uuid4()
+ try:
+ new_log = FoodLog(
+ id=log_id,
+ user_id=user_id,
+ image_url=image_url,
+ meal_type=meal_type,
+ total_calories=total_cal,
+ total_protein_g=total_prot,
+ total_fat_g=total_fat,
+ total_carbs_g=total_carb
+ )
+ db.add(new_log)
+
+ for item in final_items:
+ db.add(FoodItem(
+ food_log_id=log_id,
+ name=item.name,
+ usda_id=item.usda_id,
+ usda_match_name=item.usda_match,
+ confidence=item.confidence,
+ weight_grams=item.weight_grams,
+ calories=item.calories,
+ protein_g=item.protein_g,
+ fat_g=item.fat_g,
+ carbs_g=item.carbs_g
+ ))
+
+ await db.commit()
+ except Exception as e:
+ logger.error(f"Database save failed: {e}")
+ await db.rollback()
+ warnings.append("Results not saved to log due to database error")
+
+ return AnalysisResponse(
+ log_id=str(log_id),
+ image_url=image_url,
+ meal_type=meal_type,
+ analyzed_at=datetime.utcnow().isoformat(),
+ items=final_items,
+ totals=MealTotals(
+ calories=round(total_cal, 1),
+ protein_g=round(total_prot, 1),
+ fat_g=round(total_fat, 1),
+ carbs_g=round(total_carb, 1)
+ ),
+ unmatched_items=unmatched_items,
+ warnings=warnings
+ )
diff --git a/backend/app/routes/auth.py b/backend/app/routes/auth.py
new file mode 100644
index 0000000..310eea4
--- /dev/null
+++ b/backend/app/routes/auth.py
@@ -0,0 +1,74 @@
+from fastapi import APIRouter, Depends, HTTPException, status
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.future import select
+from app.database import get_db
+from app.schemas.auth import UserRegisterRequest, UserLoginRequest, TokenResponse, UserResponse
+from app.services import auth_service
+from app.dependencies import get_current_user
+from app.models.users import User
+from uuid import UUID
+
+router = APIRouter(prefix="/api/auth", tags=["Authentication"])
+
+@router.post("/register", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
+async def register(request: UserRegisterRequest, db: AsyncSession = Depends(get_db)):
+ """
+ Register a new user and sync to local database.
+ """
+ result = await auth_service.sign_up(request.email, request.password, db)
+ return {
+ "user_id": result["user_id"],
+ "email": result["email"],
+ "display_name": result["email"].split("@")[0]
+ }
+
+@router.post("/login", response_model=TokenResponse)
+async def login(request: UserLoginRequest, db: AsyncSession = Depends(get_db)):
+ """
+ Login with email and password to receive access and refresh tokens.
+ """
+ response = await auth_service.sign_in(request.email, request.password)
+
+ # Get user details from local DB
+ result = await db.execute(select(User).where(User.id == UUID(response.user.id)))
+ user = result.scalars().first()
+
+ if not user:
+ # Fallback if sync failed during registration
+ user_response = {
+ "user_id": response.user.id,
+ "email": response.user.email,
+ "display_name": response.user.email.split("@")[0]
+ }
+ else:
+ user_response = {
+ "user_id": user.id,
+ "email": user.email,
+ "display_name": user.display_name
+ }
+
+ return {
+ "access_token": response.session.access_token,
+ "refresh_token": response.session.refresh_token,
+ "user": user_response
+ }
+
+@router.get("/me", response_model=UserResponse)
+async def get_me(user_id: UUID = Depends(get_current_user), db: AsyncSession = Depends(get_db)):
+ """
+ Get current user information (Protected).
+ """
+ result = await db.execute(select(User).where(User.id == user_id))
+ user = result.scalars().first()
+
+ if not user:
+ raise HTTPException(
+ status_code=status.HTTP_404_NOT_FOUND,
+ detail="User not found in local database"
+ )
+
+ return {
+ "user_id": user.id,
+ "email": user.email,
+ "display_name": user.display_name
+ }
diff --git a/backend/app/routes/health.py b/backend/app/routes/health.py
new file mode 100644
index 0000000..85850b8
--- /dev/null
+++ b/backend/app/routes/health.py
@@ -0,0 +1,18 @@
+"""
+Health-check endpoint for liveness / readiness probes.
+"""
+
+from fastapi import APIRouter
+
+from app.config import settings
+
+router = APIRouter(tags=["health"])
+
+
+@router.get("/health")
+async def health_check() -> dict:
+ """Return application health status and version."""
+ return {
+ "status": "ok",
+ "version": settings.APP_VERSION,
+ }
diff --git a/backend/app/routes/logs.py b/backend/app/routes/logs.py
new file mode 100644
index 0000000..28db896
--- /dev/null
+++ b/backend/app/routes/logs.py
@@ -0,0 +1,318 @@
+from fastapi import APIRouter, Depends, HTTPException, Query, status
+from pydantic import BaseModel
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, func, delete, desc, and_, update
+from sqlalchemy.dialects.postgresql import insert as pg_insert
+from typing import List
+from uuid import UUID
+from datetime import datetime, timedelta, timezone
+
+from app.database import get_db
+from app.dependencies import get_current_user
+from app.models import User, FoodLog, FoodItem, FoodAlias
+from app.schemas.food_log import (
+ FoodLogSummary, FoodLogDetail, FoodItemSummary,
+ FoodItemUpdateRequest, DailyStats
+)
+import logging
+from app.services import nutrition_service, storage_service
+
+logger = logging.getLogger(__name__)
+
+class FoodLogListResponse(BaseModel):
+ logs: list[FoodLogSummary]
+ total: int
+ page: int
+
+router = APIRouter(tags=["Logs"])
+
+@router.get("/logs", response_model=FoodLogListResponse)
+async def get_logs(
+ page: int = Query(1, ge=1),
+ limit: int = Query(20, ge=1, le=50),
+ date: str | None = Query(None, regex=r"^\d{4}-\d{2}-\d{2}$"),
+ current_user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ offset = (page - 1) * limit
+
+ # Subquery for item_count
+ item_count_subquery = (
+ select(func.count(FoodItem.id))
+ .where(FoodItem.food_log_id == FoodLog.id)
+ .scalar_subquery()
+ )
+
+ query = select(
+ FoodLog.id,
+ FoodLog.image_url,
+ FoodLog.meal_type,
+ FoodLog.total_calories,
+ FoodLog.total_protein_g,
+ FoodLog.total_fat_g,
+ FoodLog.total_carbs_g,
+ FoodLog.analyzed_at,
+ item_count_subquery.label("item_count")
+ ).where(FoodLog.user_id == current_user_id)
+
+ if date:
+ target_date = datetime.strptime(date, "%Y-%m-%d").date()
+ query = query.where(func.date(FoodLog.analyzed_at) == target_date)
+
+ # Total count for pagination
+ count_query = select(func.count()).select_from(query.subquery())
+ total_result = await db.execute(count_query)
+ total = total_result.scalar() or 0
+
+ # Execute paginated query
+ query = query.order_by(desc(FoodLog.analyzed_at)).offset(offset).limit(limit)
+ result = await db.execute(query)
+ logs = result.mappings().all()
+
+ return {
+ "logs": logs,
+ "total": total,
+ "page": page
+ }
+
+@router.get("/logs/{log_id}", response_model=FoodLogDetail)
+async def get_log_detail(
+ log_id: UUID,
+ current_user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ # Security: If log exists but belongs to another user, return 404
+ query = select(FoodLog).where(
+ and_(FoodLog.id == log_id, FoodLog.user_id == current_user_id)
+ )
+ result = await db.execute(query)
+ log = result.scalars().first()
+
+ if not log:
+ raise HTTPException(
+ status_code=status.HTTP_404_NOT_FOUND,
+ detail="Log not found"
+ )
+
+ # Fetch items
+ items_query = select(FoodItem).where(FoodItem.food_log_id == log_id)
+ items_result = await db.execute(items_query)
+ items = items_result.scalars().all()
+
+ # Map items to include 'matched' boolean (True if usda_id is not None)
+ item_summaries = []
+ for item in items:
+ item_dict = {
+ "id": item.id,
+ "name": item.name,
+ "usda_match": item.usda_match_name,
+ "confidence": float(item.confidence) if item.confidence is not None else 0.0,
+ "weight_grams": float(item.weight_grams),
+ "calories": float(item.calories) if item.calories is not None else 0.0,
+ "protein_g": float(item.protein_g) if item.protein_g is not None else 0.0,
+ "fat_g": float(item.fat_g) if item.fat_g is not None else 0.0,
+ "carbs_g": float(item.carbs_g) if item.carbs_g is not None else 0.0,
+ "matched": item.usda_id is not None,
+ "is_user_corrected": item.is_user_corrected
+ }
+ item_summaries.append(item_dict)
+
+ return {
+ "id": log.id,
+ "image_url": log.image_url,
+ "meal_type": log.meal_type,
+ "total_calories": float(log.total_calories) if log.total_calories is not None else 0.0,
+ "total_protein_g": float(log.total_protein_g) if log.total_protein_g is not None else 0.0,
+ "total_fat_g": float(log.total_fat_g) if log.total_fat_g is not None else 0.0,
+ "total_carbs_g": float(log.total_carbs_g) if log.total_carbs_g is not None else 0.0,
+ "item_count": len(items),
+ "analyzed_at": log.analyzed_at.isoformat(),
+ "items": item_summaries
+ }
+
+@router.delete("/logs/{log_id}")
+async def delete_log(
+ log_id: UUID,
+ current_user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ query = select(FoodLog).where(
+ and_(FoodLog.id == log_id, FoodLog.user_id == current_user_id)
+ )
+ result = await db.execute(query)
+ log = result.scalars().first()
+
+ if not log:
+ raise HTTPException(
+ status_code=status.HTTP_404_NOT_FOUND,
+ detail="Log not found"
+ )
+
+ # Extract file_path from image_url
+ # Example URL: .../storage/v1/object/public/foodsnap-images/USER_ID/UUID.jpg
+ # Path is everything after /foodsnap-images/
+ url_parts = log.image_url.split("/foodsnap-images/")
+ if len(url_parts) > 1:
+ file_path = url_parts[1]
+ storage_service.delete_image(file_path)
+
+ await db.delete(log)
+ await db.commit()
+
+ return {"deleted": True, "log_id": str(log_id)}
+
+@router.put("/logs/{log_id}/items/{item_id}", response_model=FoodItemSummary)
+async def update_food_item(
+ log_id: UUID,
+ item_id: UUID,
+ request: FoodItemUpdateRequest,
+ current_user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ # Verify ownership
+ logger.info(f"Verifying ownership for log_id={log_id}, user_id={current_user_id}")
+ log_query = select(FoodLog).where(
+ and_(FoodLog.id == log_id, FoodLog.user_id == current_user_id)
+ )
+ log_result = await db.execute(log_query)
+ log = log_result.scalars().first()
+
+ if not log:
+ logger.warning(f"Log {log_id} not found for user {current_user_id}")
+ raise HTTPException(
+ status_code=status.HTTP_404_NOT_FOUND,
+ detail="Log not found"
+ )
+
+ item_query = select(FoodItem).where(
+ and_(FoodItem.id == item_id, FoodItem.food_log_id == log_id)
+ )
+ item_result = await db.execute(item_query)
+ item = item_result.scalars().first()
+
+ if not item:
+ logger.warning(f"Item {item_id} not found in log {log_id}")
+ raise HTTPException(
+ status_code=status.HTTP_404_NOT_FOUND,
+ detail="Item not found"
+ )
+
+ original_ai_name = item.original_ai_name or item.name
+
+ new_weight = float(request.weight_grams) if request.weight_grams is not None else None
+ old_weight = float(item.weight_grams)
+
+ if request.name:
+ # a. Store original_ai_name if not already set
+ if not item.original_ai_name:
+ item.original_ai_name = item.name
+
+ # b. Look up nutrition for the NEW name
+ nutrition = await nutrition_service.lookup_nutrition(request.name, db)
+
+ item.name = request.name
+ item.is_user_corrected = True
+
+ # Update weight if provided in this request
+ if new_weight is not None:
+ item.weight_grams = new_weight
+
+ if nutrition:
+ # c. Recalculate macros with new nutrition + new/current weight
+ weight = new_weight if new_weight is not None else old_weight
+ macros = nutrition_service.calculate_macros(nutrition, weight)
+ item.calories = macros["calories"]
+ item.protein_g = macros["protein_g"]
+ item.fat_g = macros["fat_g"]
+ item.carbs_g = macros["carbs_g"]
+ item.usda_id = nutrition.usda_id
+ item.usda_match_name = nutrition.usda_name
+ else:
+ # d. Not matched: if weight changed, scale existing macros proportionally
+ if new_weight is not None and old_weight > 0:
+ ratio = new_weight / old_weight
+ item.calories = float(item.calories) * ratio
+ item.protein_g = float(item.protein_g) * ratio
+ item.fat_g = float(item.fat_g) * ratio
+ item.carbs_g = float(item.carbs_g) * ratio
+
+ item.usda_id = None
+ item.usda_match_name = None
+
+ # g. Upsert food_aliases
+ original_name = original_ai_name.lower().strip()
+ new_name = request.name.lower().strip()
+ logger.info(f"Attempting alias upsert: ai_name={original_name}, correct_name={new_name}")
+
+ stmt = pg_insert(FoodAlias).values(
+ ai_name=original_name,
+ correct_name=new_name,
+ usda_id=item.usda_id,
+ frequency=1
+ ).on_conflict_do_update(
+ index_elements=["ai_name", "correct_name"],
+ set_={
+ "frequency": FoodAlias.frequency + 1,
+ "usda_id": item.usda_id
+ }
+ )
+ result = await db.execute(stmt)
+ logger.info(f"Alias upsert result: {result.rowcount} rows affected")
+
+ elif new_weight is not None: # If only weight changed, not name
+ if item.usda_id:
+ # a. Recalculate all macros with new weight
+ nutrition = await nutrition_service.lookup_nutrition(item.name, db)
+ if nutrition:
+ macros = nutrition_service.calculate_macros(nutrition, new_weight)
+ item.calories = macros["calories"]
+ item.protein_g = macros["protein_g"]
+ item.fat_g = macros["fat_g"]
+ item.carbs_g = macros["carbs_g"]
+ elif old_weight > 0:
+ # Fallback to scaling if lookup fails
+ ratio = new_weight / old_weight
+ item.calories = float(item.calories) * ratio
+ item.protein_g = float(item.protein_g) * ratio
+ item.fat_g = float(item.fat_g) * ratio
+ item.carbs_g = float(item.carbs_g) * ratio
+ elif old_weight > 0:
+ # b. scale macros proportionally
+ ratio = new_weight / old_weight
+ item.calories = float(item.calories) * ratio
+ item.protein_g = float(item.protein_g) * ratio
+ item.fat_g = float(item.fat_g) * ratio
+ item.carbs_g = float(item.carbs_g) * ratio
+
+ item.weight_grams = new_weight
+ item.is_user_corrected = True
+
+ await db.flush()
+
+ # ALWAYS after any update: Recalculate food_log totals
+ items_query = select(FoodItem).where(FoodItem.food_log_id == log_id)
+ items_result = await db.execute(items_query)
+ all_items = items_result.scalars().all()
+
+ log.total_calories = sum(float(i.calories or 0) for i in all_items)
+ log.total_protein_g = sum(float(i.protein_g or 0) for i in all_items)
+ log.total_fat_g = sum(float(i.fat_g or 0) for i in all_items)
+ log.total_carbs_g = sum(float(i.carbs_g or 0) for i in all_items)
+
+ await db.commit()
+ await db.refresh(item)
+
+ return {
+ "id": item.id,
+ "name": item.name,
+ "usda_match": item.usda_match_name,
+ "confidence": float(item.confidence) if item.confidence is not None else 0.0,
+ "weight_grams": float(item.weight_grams),
+ "calories": float(item.calories) if item.calories is not None else 0.0,
+ "protein_g": float(item.protein_g) if item.protein_g is not None else 0.0,
+ "fat_g": float(item.fat_g) if item.fat_g is not None else 0.0,
+ "carbs_g": float(item.carbs_g) if item.carbs_g is not None else 0.0,
+ "matched": item.usda_id is not None,
+ "is_user_corrected": item.is_user_corrected
+ }
+
diff --git a/backend/app/routes/stats.py b/backend/app/routes/stats.py
new file mode 100644
index 0000000..020b52d
--- /dev/null
+++ b/backend/app/routes/stats.py
@@ -0,0 +1,109 @@
+from fastapi import APIRouter, Depends, Query, HTTPException
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, func, and_
+from typing import List
+from uuid import UUID
+from datetime import datetime, timedelta, timezone
+
+from app.database import get_db
+from app.dependencies import get_current_user
+from app.models import User, FoodLog
+from app.schemas.food_log import DailyStats, WeeklyCheckin
+from app.services.health_service import generate_weekly_checkin
+
+router = APIRouter(prefix="/api/stats", tags=["stats"])
+
+async def get_user_from_db(user_id: UUID, db: AsyncSession) -> User:
+ result = await db.execute(select(User).where(User.id == user_id))
+ user = result.scalars().first()
+ if not user:
+ raise HTTPException(status_code=404, detail="User not found")
+ return user
+
+@router.get("/checkin", response_model=WeeklyCheckin)
+async def get_weekly_checkin(
+ user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ user = await get_user_from_db(user_id, db)
+ return await generate_weekly_checkin(
+ user_id=str(user.id),
+ db=db,
+ calorie_goal=user.daily_calorie_goal
+ )
+
+@router.get("", response_model=List[DailyStats])
+async def get_stats(
+ time_range: str = Query("week", alias="range", pattern="^(week|month)$"),
+ user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ user = await get_user_from_db(user_id, db)
+ days = 7 if time_range == "week" else 30
+ now = datetime.now(timezone.utc)
+ start_date = (now - timedelta(days=days-1)).date()
+
+ query = select(
+ func.date(FoodLog.analyzed_at).label("date"),
+ func.sum(FoodLog.total_calories).label("total_calories"),
+ func.sum(FoodLog.total_protein_g).label("total_protein_g"),
+ func.sum(FoodLog.total_fat_g).label("total_fat_g"),
+ func.sum(FoodLog.total_carbs_g).label("total_carbs_g"),
+ func.count(FoodLog.id).label("meal_count")
+ ).where(
+ and_(
+ FoodLog.user_id == user.id,
+ func.date(FoodLog.analyzed_at) >= start_date
+ )
+ ).group_by(func.date(FoodLog.analyzed_at)).order_by(func.date(FoodLog.analyzed_at))
+
+ result = await db.execute(query)
+ stats_rows = result.mappings().all()
+
+ stats_dict = {row["date"].isoformat(): row for row in stats_rows}
+ full_stats = []
+
+ calorie_goal = user.daily_calorie_goal
+
+ for i in range(days):
+ d_obj = (start_date + timedelta(days=i))
+ d = d_obj.isoformat()
+
+ total_calories = 0
+ total_protein = 0
+ total_fat = 0
+ total_carbs = 0
+ meal_count = 0
+
+ if d in stats_dict:
+ row = stats_dict[d]
+ total_calories = float(row["total_calories"] or 0)
+ total_protein = float(row["total_protein_g"] or 0)
+ total_fat = float(row["total_fat_g"] or 0)
+ total_carbs = float(row["total_carbs_g"] or 0)
+ meal_count = int(row["meal_count"])
+
+ variance = total_calories - calorie_goal
+
+ if total_calories == 0:
+ status = "no_data"
+ elif total_calories > calorie_goal + 200:
+ status = "over_goal"
+ elif total_calories < calorie_goal - 300:
+ status = "under_goal"
+ else:
+ status = "on_track"
+
+ full_stats.append(DailyStats(
+ date=d,
+ total_calories=total_calories,
+ total_protein_g=total_protein,
+ total_fat_g=total_fat,
+ total_carbs_g=total_carbs,
+ meal_count=meal_count,
+ calorie_goal=calorie_goal,
+ calories_vs_goal=round(variance, 1),
+ goal_status=status
+ ))
+
+ return full_stats
diff --git a/backend/app/routes/upload.py b/backend/app/routes/upload.py
new file mode 100644
index 0000000..e2f0b78
--- /dev/null
+++ b/backend/app/routes/upload.py
@@ -0,0 +1,28 @@
+from fastapi import APIRouter, Depends, UploadFile, File, HTTPException, status
+from app.dependencies import get_current_user
+from app.utils.image_handler import process_image
+from app.services.storage_service import upload_image
+from uuid import UUID
+
+router = APIRouter(prefix="/api", tags=["upload"])
+
+@router.post("/upload")
+async def upload_food_image(
+ image: UploadFile = File(...),
+ user_id: UUID = Depends(get_current_user)
+):
+ """
+ Protected endpoint to upload a food image.
+ Processes the image (strips EXIF, resizes) and uploads to Supabase Storage.
+ """
+ # 1. Process image
+ clean_bytes, original_filename = process_image(image)
+
+ # 2. Upload to storage
+ public_url, storage_path = upload_image(clean_bytes, str(user_id))
+
+ return {
+ "image_url": public_url,
+ "file_path": storage_path,
+ "size_bytes": len(clean_bytes)
+ }
diff --git a/backend/app/routes/users.py b/backend/app/routes/users.py
new file mode 100644
index 0000000..3eed1d3
--- /dev/null
+++ b/backend/app/routes/users.py
@@ -0,0 +1,66 @@
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.encoders import jsonable_encoder
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+from uuid import UUID
+from app.database import get_db
+from app.dependencies import get_current_user
+from app.models.users import User
+from app.schemas.user import UserProfile, UserUpdate
+from app.services.health_service import calculate_tdee
+
+router = APIRouter(prefix="/api/user", tags=["user"])
+
+@router.get("/profile", response_model=UserProfile)
+async def get_profile(
+ user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ result = await db.execute(select(User).where(User.id == user_id))
+ user = result.scalars().first()
+ if not user:
+ raise HTTPException(status_code=404, detail="User not found")
+ return user
+
+@router.put("/profile", response_model=UserProfile)
+async def update_profile(
+ update_data: UserUpdate,
+ user_id: UUID = Depends(get_current_user),
+ db: AsyncSession = Depends(get_db)
+):
+ result = await db.execute(select(User).where(User.id == user_id))
+ user = result.scalars().first()
+ if not user:
+ raise HTTPException(status_code=404, detail="User not found")
+
+ # Update fields
+ update_dict = update_data.model_dump(exclude_unset=True)
+
+ for key, value in update_dict.items():
+ setattr(user, key, value)
+
+ # Auto-calculate TDEE if all required fields are present
+ health_fields = [
+ user.weight_kg,
+ user.height_cm,
+ user.age,
+ user.activity_level,
+ user.goal
+ ]
+
+ if all(field is not None for field in health_fields):
+ new_goal = calculate_tdee(
+ weight_kg=float(user.weight_kg),
+ height_cm=float(user.height_cm),
+ age=int(user.age),
+ activity_level=user.activity_level,
+ goal=user.goal
+ )
+ user.daily_calorie_goal = new_goal
+ elif update_data.daily_calorie_goal is not None:
+ # If not auto-calculating, use provided goal if it was in update
+ user.daily_calorie_goal = update_data.daily_calorie_goal
+
+ await db.commit()
+ await db.refresh(user)
+ return user
diff --git a/backend/app/schemas/__init__.py b/backend/app/schemas/__init__.py
new file mode 100644
index 0000000..8d2fd85
--- /dev/null
+++ b/backend/app/schemas/__init__.py
@@ -0,0 +1 @@
+# Schemas package
diff --git a/backend/app/schemas/analysis.py b/backend/app/schemas/analysis.py
new file mode 100644
index 0000000..12f4824
--- /dev/null
+++ b/backend/app/schemas/analysis.py
@@ -0,0 +1,51 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional
+from datetime import datetime
+
+class FoodItemFromAI(BaseModel):
+ """What the AI returns for each detected food item"""
+ name: str
+ confidence: float
+ weight_grams: float
+
+class AIAnalysisResult(BaseModel):
+ """Structured response from the AI"""
+ foods: List[FoodItemFromAI]
+
+class NutritionData(BaseModel):
+ """Nutrition info per 100g from USDA"""
+ calories_per_100g: float
+ protein_per_100g: float
+ fat_per_100g: float
+ carbs_per_100g: float
+ usda_id: int
+ usda_name: str
+
+class FoodItemResult(BaseModel):
+ """Final result for one food item with calculated macros"""
+ name: str
+ usda_match: Optional[str] = None
+ usda_id: Optional[int] = None
+ confidence: float
+ weight_grams: float
+ calories: float
+ protein_g: float
+ fat_g: float
+ carbs_g: float
+ matched: bool
+
+class MealTotals(BaseModel):
+ calories: float
+ protein_g: float
+ fat_g: float
+ carbs_g: float
+
+class AnalysisResponse(BaseModel):
+ log_id: str
+ image_url: str
+ meal_type: str
+ analyzed_at: str
+ items: List[FoodItemResult]
+ totals: MealTotals
+ unmatched_items: List[str]
+ warnings: List[str] = []
diff --git a/backend/app/schemas/auth.py b/backend/app/schemas/auth.py
new file mode 100644
index 0000000..3beed6e
--- /dev/null
+++ b/backend/app/schemas/auth.py
@@ -0,0 +1,28 @@
+from pydantic import BaseModel, EmailStr, Field, field_validator
+from typing import Optional, Dict, Any
+from uuid import UUID
+
+class UserRegisterRequest(BaseModel):
+ email: EmailStr
+ password: str = Field(..., min_length=6)
+
+class UserLoginRequest(BaseModel):
+ email: EmailStr
+ password: str
+
+class UserResponse(BaseModel):
+ user_id: str
+ email: EmailStr
+ display_name: Optional[str] = None
+
+ @field_validator('user_id', mode='before')
+ @classmethod
+ def convert_uuid(cls, v):
+ if isinstance(v, UUID):
+ return str(v)
+ return v
+
+class TokenResponse(BaseModel):
+ access_token: str
+ refresh_token: Optional[str] = None
+ user: UserResponse
diff --git a/backend/app/schemas/food_log.py b/backend/app/schemas/food_log.py
new file mode 100644
index 0000000..fe39182
--- /dev/null
+++ b/backend/app/schemas/food_log.py
@@ -0,0 +1,68 @@
+from pydantic import BaseModel, model_validator, ConfigDict
+from uuid import UUID
+from datetime import datetime
+
+class FoodItemSummary(BaseModel):
+ model_config = ConfigDict(from_attributes=True)
+
+ id: UUID
+ name: str
+ usda_match: str | None = None
+ confidence: float
+ weight_grams: float
+ calories: float
+ protein_g: float
+ fat_g: float
+ carbs_g: float
+ matched: bool
+ is_user_corrected: bool
+
+class FoodLogSummary(BaseModel):
+ model_config = ConfigDict(from_attributes=True)
+
+ id: UUID
+ image_url: str
+ meal_type: str
+ total_calories: float
+ total_protein_g: float
+ total_fat_g: float
+ total_carbs_g: float
+ item_count: int
+ analyzed_at: datetime
+
+class FoodLogDetail(FoodLogSummary):
+ items: list[FoodItemSummary]
+
+class FoodItemUpdateRequest(BaseModel):
+ name: str | None = None
+ weight_grams: float | None = None
+
+ @model_validator(mode='after')
+ def at_least_one_field(self):
+ if self.name is None and self.weight_grams is None:
+ raise ValueError('At least one of name or weight_grams must be provided')
+ return self
+
+class DailyStats(BaseModel):
+ date: str
+ total_calories: float
+ total_protein_g: float
+ total_fat_g: float
+ total_carbs_g: float
+ meal_count: int
+ calorie_goal: int | None = None
+ calories_vs_goal: float | None = None
+ goal_status: str | None = None
+
+class WeeklyCheckin(BaseModel):
+ period: str
+ average_daily_calories: float
+ calorie_goal: int
+ variance: float
+ status: str
+ average_protein_g: float
+ average_fat_g: float
+ average_carbs_g: float
+ days_logged: int
+ days_missed: int
+ insight: str
diff --git a/backend/app/schemas/user.py b/backend/app/schemas/user.py
new file mode 100644
index 0000000..576a211
--- /dev/null
+++ b/backend/app/schemas/user.py
@@ -0,0 +1,39 @@
+from pydantic import BaseModel, EmailStr, Field, ConfigDict, field_validator
+from typing import List, Optional
+from datetime import datetime
+from uuid import UUID
+
+class UserProfile(BaseModel):
+ model_config = ConfigDict(from_attributes=True)
+
+ id: str
+ email: EmailStr
+ display_name: Optional[str] = None
+ daily_calorie_goal: int
+ dietary_restrictions: List[str] = []
+ preferred_units: str
+ height_cm: Optional[float] = None
+ weight_kg: Optional[float] = None
+ age: Optional[int] = None
+ activity_level: Optional[str] = None
+ goal: Optional[str] = None
+ created_at: datetime
+ updated_at: datetime
+
+ @field_validator('id', mode='before')
+ @classmethod
+ def convert_uuid(cls, v):
+ if isinstance(v, UUID):
+ return str(v)
+ return v
+
+class UserUpdate(BaseModel):
+ display_name: Optional[str] = None
+ daily_calorie_goal: Optional[int] = None
+ dietary_restrictions: Optional[List[str]] = None
+ preferred_units: Optional[str] = None
+ height_cm: Optional[float] = None
+ weight_kg: Optional[float] = None
+ age: Optional[int] = None
+ activity_level: Optional[str] = None
+ goal: Optional[str] = None
diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py
new file mode 100644
index 0000000..a70b302
--- /dev/null
+++ b/backend/app/services/__init__.py
@@ -0,0 +1 @@
+# Services package
diff --git a/backend/app/services/ai_service.py b/backend/app/services/ai_service.py
new file mode 100644
index 0000000..2efee1f
--- /dev/null
+++ b/backend/app/services/ai_service.py
@@ -0,0 +1,69 @@
+import json
+import logging
+from google import genai
+from google.genai import types
+from app.config import settings
+from app.schemas.analysis import FoodItemFromAI, AIAnalysisResult
+
+logger = logging.getLogger(__name__)
+
+# Initialize Gemini Client
+client = genai.Client(api_key=settings.GOOGLE_API_KEY)
+
+SYSTEM_PROMPT = """You are a food identification system. Analyze
+the provided image and identify all distinct food items visible.
+
+For each item provide:
+1. The food name in English using USDA-compatible terminology
+ (e.g., "chicken breast, grilled" not "grilled chicken")
+2. Your confidence level from 0.0 to 1.0
+3. Estimated weight in grams based on visual portion size
+
+Rules:
+- Use USDA-style naming: "rice, white, cooked" not "steamed rice"
+- Include sauces, dressings, condiments as separate items
+- If unsure about an item, include it with confidence below 0.3
+- If the image contains no food, return empty foods array
+- Estimate weight conservatively (better to underestimate)
+- For mixed dishes (curry, stew), name the dish and estimate total weight
+
+Respond ONLY with JSON.
+Format: {"foods": [{"name": "...", "confidence": 0.0, "weight_grams": 0}]}"""
+
+def analyze_food_image(image_base64: str) -> list[FoodItemFromAI]:
+ """
+ Sends a base64 encoded image to Google Gemini for food identification.
+ Returns a list of detected food items.
+ """
+ try:
+ import base64
+ image_bytes = base64.b64decode(image_base64)
+
+ # Call Gemini using the new SDK
+ response = client.models.generate_content(
+ model="gemini-flash-latest",
+ contents=[
+ SYSTEM_PROMPT,
+ types.Part.from_bytes(
+ data=image_bytes,
+ mime_type="image/jpeg",
+ ),
+ ],
+ config=types.GenerateContentConfig(
+ response_mime_type="application/json",
+ response_schema=AIAnalysisResult,
+ temperature=0.1
+ )
+ )
+
+ if not response.parsed:
+ logger.warning("No parsed data in Gemini response")
+ return []
+
+ # The new SDK automatically parses into the Pydantic model if response_schema is provided
+ validated_result: AIAnalysisResult = response.parsed
+ return [item for item in validated_result.foods if item.name.strip()]
+
+ except Exception as e:
+ logger.error(f"Gemini API error: {e}")
+ return []
diff --git a/backend/app/services/auth_service.py b/backend/app/services/auth_service.py
new file mode 100644
index 0000000..ec6e385
--- /dev/null
+++ b/backend/app/services/auth_service.py
@@ -0,0 +1,185 @@
+import httpx
+import uuid
+from fastapi import HTTPException, status
+from supabase import create_client, Client
+from app.config import settings
+from app.models.users import User
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.future import select
+from gotrue.errors import AuthApiError
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Initialize Supabase client
+# Using service role key if available, otherwise fallback to anon key
+supabase_key = settings.SUPABASE_SERVICE_ROLE_KEY or settings.SUPABASE_KEY
+supabase: Client = create_client(settings.SUPABASE_URL, supabase_key)
+
+async def sign_up(email: str, password: str, db: AsyncSession):
+ """
+ Registers a new user in Supabase Auth and creates a corresponding record in the local database.
+ """
+ try:
+ # 1. Sign up with Supabase
+ # Note: If email confirmation is enabled, session might be null
+ response = supabase.auth.sign_up({"email": email, "password": password})
+
+ if not response.user:
+ raise HTTPException(
+ status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+ detail="Failed to create user in Supabase"
+ )
+
+ user_id = response.user.id
+
+ # 2. Try to create local user record
+ try:
+ result = await db.execute(select(User).where(User.id == uuid.UUID(user_id)))
+ existing_user = result.scalars().first()
+
+ if not existing_user:
+ new_user = User(
+ id=uuid.UUID(user_id),
+ email=email,
+ display_name=email.split("@")[0] # Default display name
+ )
+ db.add(new_user)
+ await db.flush()
+ await db.refresh(new_user)
+ logger.info(f"Successfully synced user {user_id} to local database")
+ except Exception as e:
+ # CRITICAL: If local sync fails, we log it but don't fail the registration
+ logger.critical(f"CRITICAL: Failed to sync user {user_id} to local database: {e}")
+ # Ensure we don't leave a pending transaction
+ await db.rollback()
+
+ return {
+ "user_id": user_id,
+ "email": email,
+ "access_token": response.session.access_token if response.session else None
+ }
+
+ except AuthApiError as e:
+ logger.error(f"Supabase Auth error during sign_up: {e.message} (Status: {e.status})")
+ if "already registered" in str(e).lower() or e.status == 400:
+ raise HTTPException(
+ status_code=status.HTTP_409_CONFLICT,
+ detail="Email already exists"
+ )
+ raise HTTPException(
+ status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+ detail=f"Supabase error: {e.message}"
+ )
+ except Exception as e:
+ logger.exception(f"Unexpected error during sign_up: {e}")
+ raise HTTPException(
+ status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+ detail="An unexpected error occurred"
+ )
+
+async def sign_in(email: str, password: str):
+ """
+ Authenticates a user via Supabase Auth.
+ """
+ try:
+ response = supabase.auth.sign_in_with_password({"email": email, "password": password})
+
+ if not response.session:
+ raise HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED,
+ detail="Invalid credentials"
+ )
+
+ return response
+ except AuthApiError as e:
+ logger.error(f"Supabase Auth error during sign_in: {e}")
+ if e.status == 400 or "invalid login credentials" in str(e).lower():
+ raise HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED,
+ detail="Invalid email or password"
+ )
+ raise HTTPException(
+ status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+ detail="Authentication service is currently unavailable"
+ )
+ except Exception as e:
+ logger.exception(f"Unexpected error during sign_in: {e}")
+ raise HTTPException(
+ status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+ detail="An unexpected error occurred"
+ )
+
+async def get_current_user_info(token: str):
+ """
+ Verifies the JWT token and returns user information.
+ """
+ try:
+ # Use the token to get the user from Supabase
+ # This validates the token signature and expiration
+ response = supabase.auth.get_user(token)
+
+ if not response.user:
+ raise HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED,
+ detail="Invalid or expired token"
+ )
+
+ return response.user
+ except AuthApiError as e:
+ logger.error(f"Supabase Auth error during get_user: {e}")
+ raise HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED,
+ detail="Invalid or expired token"
+ )
+ except Exception as e:
+ logger.exception(f"Unexpected error during get_user: {e}")
+ raise HTTPException(
+ status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+ detail="Authentication service is currently unavailable"
+ )
+
+async def sync_all_users(db: AsyncSession):
+ """
+ Batch recovery tool: Syncs all users from Supabase Auth to the local database.
+ Returns statistics about the operation.
+ """
+ stats = {"synced": 0, "already_exists": 0, "errors": 0}
+
+ try:
+ # 1. Fetch all users from Supabase (requires service role key)
+ # We might need to handle pagination if there are many users
+ response = supabase.auth.admin.list_users()
+ sb_users = response
+
+ for sb_user in sb_users:
+ try:
+ user_id = uuid.UUID(sb_user.id)
+ # Check if exists
+ result = await db.execute(select(User).where(User.id == user_id))
+ if result.scalars().first():
+ stats["already_exists"] += 1
+ continue
+
+ # Create if missing
+ new_user = User(
+ id=user_id,
+ email=sb_user.email,
+ display_name=sb_user.email.split("@")[0]
+ )
+ db.add(new_user)
+ stats["synced"] += 1
+ except Exception as e:
+ logger.error(f"Error syncing user {sb_user.id}: {e}")
+ stats["errors"] += 1
+
+ # We can flush here, but get_db will commit anyway
+ await db.flush()
+ return stats
+
+ except Exception as e:
+ logger.error(f"Failed to sync all users: {e}")
+ raise HTTPException(
+ status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+ detail=f"Batch sync failed: {str(e)}"
+ )
diff --git a/backend/app/services/health_service.py b/backend/app/services/health_service.py
new file mode 100644
index 0000000..b3ca80b
--- /dev/null
+++ b/backend/app/services/health_service.py
@@ -0,0 +1,103 @@
+from datetime import datetime, timedelta
+from typing import Dict, Any
+from sqlalchemy import select, func
+from sqlalchemy.ext.asyncio import AsyncSession
+from app.models.food_logs import FoodLog
+
+ACTIVITY_MULTIPLIERS = {
+ "sedentary": 1.2,
+ "light": 1.375,
+ "moderate": 1.55,
+ "active": 1.725,
+ "very_active": 1.9
+}
+
+GOAL_ADJUSTMENTS = {
+ "lose_weight": -500,
+ "maintain": 0,
+ "gain_muscle": +300
+}
+
+def calculate_tdee(weight_kg: float, height_cm: float,
+ age: int, activity_level: str,
+ goal: str) -> int:
+ # Mifflin-St Jeor (sex-neutral average):
+ bmr = 10 * weight_kg + 6.25 * height_cm - 5 * age - 78
+ tdee = bmr * ACTIVITY_MULTIPLIERS.get(activity_level, 1.55)
+ tdee += GOAL_ADJUSTMENTS.get(goal, 0)
+ # Ensure minimum floor and round to nearest 50
+ tdee = max(1200, round(tdee / 50) * 50)
+ return int(tdee)
+
+async def generate_weekly_checkin(user_id: str,
+ db: AsyncSession,
+ calorie_goal: int) -> Dict[str, Any]:
+ # Last 7 days range
+ end_date = datetime.now()
+ start_date = end_date - timedelta(days=7)
+
+ # Query logs
+ query = select(FoodLog).where(
+ FoodLog.user_id == user_id,
+ FoodLog.analyzed_at >= start_date,
+ FoodLog.analyzed_at <= end_date
+ )
+ result = await db.execute(query)
+ logs = result.scalars().all()
+
+ # Aggregates
+ days_data = {}
+ total_protein = 0
+ total_fat = 0
+ total_carbs = 0
+
+ for log in logs:
+ day_str = log.analyzed_at.date().isoformat()
+ if day_str not in days_data:
+ days_data[day_str] = 0
+ days_data[day_str] += log.total_calories or 0
+ total_protein += log.total_protein_g or 0
+ total_fat += log.total_fat_g or 0
+ total_carbs += log.total_carbs_g or 0
+
+ days_logged = len(days_data)
+ days_missed = 7 - days_logged
+
+ avg_calories = sum(days_data.values()) / 7 if days_logged > 0 else 0
+ avg_protein = total_protein / 7 if days_logged > 0 else 0
+ avg_fat = total_fat / 7 if days_logged > 0 else 0
+ avg_carbs = total_carbs / 7 if days_logged > 0 else 0
+
+ variance = avg_calories - calorie_goal
+
+ # Status rules
+ if avg_calories > calorie_goal + 200:
+ status = "over_goal"
+ elif avg_calories < calorie_goal - 300:
+ status = "under_goal"
+ else:
+ status = "on_track"
+
+ # Insights (rule-based)
+ if days_logged < 4:
+ insight = "Try to log more consistently! You missed several days this week."
+ elif status == "on_track":
+ insight = "Great job! You're staying consistent with your calorie goals."
+ elif status == "over_goal":
+ insight = "You're slightly above your target. Watch out for hidden calories in sauces or snacks."
+ else:
+ insight = "You're below your calorie floor. Make sure you're eating enough to sustain your activity level."
+
+ return {
+ "period": "last_7_days",
+ "average_daily_calories": round(avg_calories, 1),
+ "calorie_goal": calorie_goal,
+ "variance": round(variance, 1),
+ "status": status,
+ "average_protein_g": round(avg_protein, 1),
+ "average_fat_g": round(avg_fat, 1),
+ "average_carbs_g": round(avg_carbs, 1),
+ "days_logged": days_logged,
+ "days_missed": days_missed,
+ "insight": insight
+ }
diff --git a/backend/app/services/nutrition_service.py b/backend/app/services/nutrition_service.py
new file mode 100644
index 0000000..493cfdd
--- /dev/null
+++ b/backend/app/services/nutrition_service.py
@@ -0,0 +1,161 @@
+import logging
+import httpx
+import difflib
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, insert
+from app.config import settings
+from app.schemas.analysis import NutritionData
+from app.models import NutritionCache, FoodAlias
+
+logger = logging.getLogger(__name__)
+
+KNOWN_ZERO_CAL_FOODS = {"water", "club soda", "sparkling water", "black coffee", "tea, unsweetened"}
+
+async def lookup_nutrition(food_name: str, db: AsyncSession) -> NutritionData | None:
+ """
+ Looks up nutrition data for a food name using Cache -> Alias -> USDA API.
+ """
+ normalized_name = food_name.lower().strip()
+
+ # 1. Check Cache
+ result = await db.execute(
+ select(NutritionCache).where(NutritionCache.food_name_normalized == normalized_name)
+ )
+ cached = result.scalars().first()
+ if cached:
+ logger.info(f"Cache hit for: {normalized_name}")
+ return NutritionData(
+ calories_per_100g=cached.calories_per_100g,
+ protein_per_100g=cached.protein_per_100g,
+ fat_per_100g=cached.fat_per_100g,
+ carbs_per_100g=cached.carbs_per_100g,
+ usda_id=cached.usda_id,
+ usda_name=cached.food_name_normalized # We store normalized name as the key
+ )
+
+ # 2. Check Alias
+ query_name = normalized_name
+ result = await db.execute(
+ select(FoodAlias).where(FoodAlias.ai_name == normalized_name)
+ )
+ alias = result.scalars().first()
+ if alias:
+ logger.info(f"Alias found: {normalized_name} -> {alias.correct_name}")
+ query_name = alias.correct_name
+
+ # 3. Call USDA API
+ usda_data = await _call_usda_api(query_name)
+ if not usda_data:
+ return None
+
+ # 4. Cache and Return
+ if usda_data.calories_per_100g == 0 and normalized_name not in KNOWN_ZERO_CAL_FOODS:
+ logger.warning(
+ f"Zero calories extracted for '{normalized_name}' "
+ f"(USDA ID: {usda_data.usda_id}). Skipping cache to avoid bad data."
+ )
+ return usda_data # Return but don't cache
+
+ try:
+ new_cache = NutritionCache(
+ food_name_normalized=normalized_name,
+ usda_id=usda_data.usda_id,
+ calories_per_100g=usda_data.calories_per_100g,
+ protein_per_100g=usda_data.protein_per_100g,
+ fat_per_100g=usda_data.fat_per_100g,
+ carbs_per_100g=usda_data.carbs_per_100g
+ )
+ db.add(new_cache)
+ await db.commit()
+ except Exception as e:
+ logger.error(f"Failed to cache nutrition data: {e}")
+ await db.rollback()
+
+ return usda_data
+
+async def _call_usda_api(food_name: str) -> NutritionData | None:
+ """Helper to call USDA API and find best match"""
+ if not settings.USDA_API_KEY:
+ logger.warning("USDA_API_KEY not configured")
+ return None
+
+ url = "https://api.nal.usda.gov/fdc/v1/foods/search"
+ params = {
+ "query": food_name,
+ "api_key": settings.USDA_API_KEY,
+ "dataType": "Foundation,SR Legacy",
+ "pageSize": 5
+ }
+
+ try:
+ async with httpx.AsyncClient(timeout=10.0) as client:
+ response = await client.get(url, params=params)
+ response.raise_for_status()
+ data = response.json()
+
+ foods = data.get("foods", [])
+ if not foods:
+ return None
+
+ # Find best match using difflib
+ best_match = None
+ highest_ratio = 0.0
+
+ for food in foods:
+ desc = food.get("description", "").lower()
+ ratio = difflib.SequenceMatcher(None, food_name.lower(), desc).ratio()
+ if ratio > highest_ratio:
+ highest_ratio = ratio
+ best_match = food
+
+ if highest_ratio < 0.4:
+ logger.info(f"No good USDA match for '{food_name}' (Best: {highest_ratio:.2f})")
+ return None
+
+ # Extract nutrients
+ nutrients = best_match.get("foodNutrients", [])
+
+ def get_nutrient(nids):
+ if isinstance(nids, int):
+ nids = [nids]
+ for nid in nids:
+ for n in nutrients:
+ curr_id = (
+ n.get("nutrientId") or
+ n.get("nutrient", {}).get("id") or
+ 0
+ )
+ if curr_id == nid:
+ val = (
+ n.get("value") or
+ n.get("amount") or
+ n.get("nutrientAmount") or
+ 0.0
+ )
+ return float(val)
+ return 0.0
+
+ return NutritionData(
+ calories_per_100g=get_nutrient([1008, 2047, 2048]),
+ protein_per_100g=get_nutrient(1003),
+ fat_per_100g=get_nutrient(1004),
+ carbs_per_100g=get_nutrient(1005),
+ usda_id=best_match.get("fdcId"),
+ usda_name=best_match.get("description")
+ )
+
+ except Exception as e:
+ logger.error(f"USDA API error: {e}")
+ return None
+
+def calculate_macros(nutrition: NutritionData, weight_grams: float) -> dict:
+ """
+ Calculates total macros for a specific weight based on per-100g data.
+ """
+ factor = weight_grams / 100.0
+ return {
+ "calories": round(nutrition.calories_per_100g * factor, 1),
+ "protein_g": round(nutrition.protein_per_100g * factor, 1),
+ "fat_g": round(nutrition.fat_per_100g * factor, 1),
+ "carbs_g": round(nutrition.carbs_per_100g * factor, 1)
+ }
diff --git a/backend/app/services/storage_service.py b/backend/app/services/storage_service.py
new file mode 100644
index 0000000..d158e26
--- /dev/null
+++ b/backend/app/services/storage_service.py
@@ -0,0 +1,54 @@
+import logging
+from uuid import uuid4
+from fastapi import HTTPException, status
+from supabase import create_client, Client
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+# Initialize Supabase client specifically with service role key to bypass RLS
+storage_supabase: Client = create_client(settings.SUPABASE_URL, settings.SUPABASE_SERVICE_ROLE_KEY)
+
+def upload_image(file_bytes: bytes, user_id: str) -> str:
+ """
+ Uploads an image to Supabase Storage and returns the public URL.
+ """
+ file_path = f"{user_id}/{uuid4()}.jpg"
+
+ try:
+ # Use service role key (bypasses RLS)
+ response = storage_supabase.storage.from_(settings.STORAGE_BUCKET).upload(
+ path=file_path,
+ file=file_bytes,
+ file_options={"content-type": "image/jpeg"}
+ )
+
+ # Check for errors in response if needed (supabase-py usually raises exceptions)
+
+ return get_public_url(file_path), file_path
+
+ except Exception as e:
+ logger.error(f"Image upload failed for user {user_id}: {e}")
+ raise HTTPException(
+ status_code=status.HTTP_502_BAD_GATEWAY,
+ detail="Image upload failed"
+ )
+
+def delete_image(file_path: str) -> bool:
+ """
+ Deletes an image from Supabase Storage.
+ """
+ try:
+ storage_supabase.storage.from_(settings.STORAGE_BUCKET).remove([file_path])
+ return True
+ except Exception as e:
+ logger.error(f"Failed to delete image at {file_path}: {e}")
+ return False
+
+def get_public_url(file_path: str) -> str:
+ """
+ Constructs the public URL for a given file path.
+ """
+ # Constructing manually as requested:
+ # f"{SUPABASE_URL}/storage/v1/object/public/foodsnap-images/{file_path}"
+ return f"{settings.SUPABASE_URL}/storage/v1/object/public/{settings.STORAGE_BUCKET}/{file_path}"
diff --git a/backend/app/utils/__init__.py b/backend/app/utils/__init__.py
new file mode 100644
index 0000000..dd7ee44
--- /dev/null
+++ b/backend/app/utils/__init__.py
@@ -0,0 +1 @@
+# Utils package
diff --git a/backend/app/utils/image_handler.py b/backend/app/utils/image_handler.py
new file mode 100644
index 0000000..46c151e
--- /dev/null
+++ b/backend/app/utils/image_handler.py
@@ -0,0 +1,64 @@
+import io
+from PIL import Image
+from fastapi import UploadFile, HTTPException, status
+
+MAX_IMAGE_SIZE = 5 * 1024 * 1024 # 5MB
+MAX_DIMENSION = 1024
+
+def process_image(file: UploadFile) -> tuple[bytes, str]:
+ """
+ Processes an uploaded image: validates type/size, strips EXIF, resizes, and converts to JPEG.
+ Returns: (processed_jpeg_bytes, original_filename)
+ """
+ # 1. Validate content type
+ if file.content_type not in ["image/jpeg", "image/png"]:
+ raise HTTPException(
+ status_code=status.HTTP_400_BAD_REQUEST,
+ detail="Only JPEG and PNG images are accepted"
+ )
+
+ # 2. Validate raw size <= 5MB BEFORE processing
+ # We need to read the file to check size
+ file_bytes = file.file.read()
+ if len(file_bytes) > MAX_IMAGE_SIZE:
+ raise HTTPException(
+ status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+ detail="Image exceeds 5MB limit"
+ )
+
+ try:
+ # 3. Open with Pillow
+ img = Image.open(io.BytesIO(file_bytes))
+
+ # 4. Convert to RGB if needed (PNG can be RGBA, JPEG needs RGB)
+ if img.mode != "RGB":
+ img = img.convert("RGB")
+
+ # 5. STRIP ALL EXIF/METADATA
+ # Recreating the image from raw pixel data guarantees all metadata is removed.
+ clean_image = Image.new(img.mode, img.size)
+ clean_image.putdata(list(img.getdata()))
+
+ # 6. Resize: if max(width, height) > 1024, scale proportionally
+ width, height = clean_image.size
+ if max(width, height) > MAX_DIMENSION:
+ if width > height:
+ new_width = MAX_DIMENSION
+ new_height = int(height * (MAX_DIMENSION / width))
+ else:
+ new_height = MAX_DIMENSION
+ new_width = int(width * (MAX_DIMENSION / height))
+
+ clean_image = clean_image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
+ # 7. Save to BytesIO as JPEG, quality=80, optimize=True
+ output = io.BytesIO()
+ clean_image.save(output, format="JPEG", quality=80, optimize=True)
+
+ return output.getvalue(), file.filename
+
+ except Exception as e:
+ raise HTTPException(
+ status_code=status.HTTP_400_BAD_REQUEST,
+ detail=f"Failed to process image: {str(e)}"
+ )
diff --git a/backend/real_food.jpg b/backend/real_food.jpg
new file mode 100644
index 0000000..3b777f8
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diff --git a/backend/real_food_photo.jpg b/backend/real_food_photo.jpg
new file mode 100644
index 0000000..7f50ef9
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diff --git a/backend/requirements.txt b/backend/requirements.txt
new file mode 100644
index 0000000..8ec315e
--- /dev/null
+++ b/backend/requirements.txt
@@ -0,0 +1,17 @@
+fastapi==0.115.6
+uvicorn==0.34.0
+python-dotenv==1.0.1
+pydantic-settings==2.7.1
+httpx==0.28.1
+pytest==8.3.4
+pytest-asyncio==0.25.0
+sqlalchemy[asyncio]==2.0.49
+asyncpg==0.31.0
+alembic==1.14.1
+supabase==2.11.0
+websockets==14.0
+Pillow==12.2.0
+python-multipart==0.0.12
+openai==1.59.3
+google-genai==1.0.0
+email-validator==2.2.0
diff --git a/backend/test_upload.jpg b/backend/test_upload.jpg
new file mode 100644
index 0000000..615bb3b
Binary files /dev/null and b/backend/test_upload.jpg differ
diff --git a/backend/tests/__init__.py b/backend/tests/__init__.py
new file mode 100644
index 0000000..d4839a6
--- /dev/null
+++ b/backend/tests/__init__.py
@@ -0,0 +1 @@
+# Tests package
diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py
new file mode 100644
index 0000000..edf36ca
--- /dev/null
+++ b/backend/tests/conftest.py
@@ -0,0 +1,87 @@
+import os
+from dotenv import load_dotenv
+
+# Load .env file at the very beginning of the test session
+load_dotenv()
+
+import pytest
+import pytest_asyncio
+import uuid
+import asyncio
+from httpx import AsyncClient, ASGITransport
+from app.config import settings
+from app.main import app
+from app.database import async_session_factory
+from app.models.users import User
+from sqlalchemy import delete
+
+TEST_PASSWORD = "testpassword123"
+
+@pytest.fixture(scope="session", autouse=True)
+def print_db_url():
+ url = settings.DATABASE_URL
+ # Mask password: postgresql+asyncpg://user:password@host:port/db
+ if "@" in url:
+ prefix, suffix = url.split("@")
+ if ":" in prefix:
+ protocol_user, password = prefix.rsplit(":", 1)
+ masked_url = f"{protocol_user}:****@{suffix}"
+ else:
+ masked_url = f"****@{suffix}"
+ else:
+ masked_url = url
+
+ print(f"\n[TEST SESSION] DATABASE_URL: {masked_url}")
+
+@pytest.fixture(scope="session")
+def event_loop():
+ """Create an instance of the default event loop for each test session."""
+ policy = asyncio.get_event_loop_policy()
+ loop = policy.new_event_loop()
+ yield loop
+ loop.close()
+
+@pytest_asyncio.fixture(scope="session")
+async def session_auth_credentials():
+ """
+ Registers a single test user at the session level and returns their credentials and token
+ to avoid hitting rate-limits on third-party services like Supabase.
+ """
+ user_uuid = str(uuid.uuid4())
+ email = f"session_test_{user_uuid}@foodsnap-test.com"
+
+ # 1-second safety buffer before registration
+ await asyncio.sleep(1.0)
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ # Register
+ reg_res = await ac.post(
+ "/api/auth/register",
+ json={"email": email, "password": TEST_PASSWORD}
+ )
+ assert reg_res.status_code == 201, f"Session user registration failed: {reg_res.json()}"
+ user_id = reg_res.json()["user_id"]
+
+ # Login to retrieve token
+ login_res = await ac.post(
+ "/api/auth/login",
+ json={"email": email, "password": TEST_PASSWORD}
+ )
+ assert login_res.status_code == 200, f"Session user login failed: {login_res.json()}"
+ token = login_res.json()["access_token"]
+
+ yield {
+ "email": email,
+ "password": TEST_PASSWORD,
+ "token": token,
+ "user_id": user_id
+ }
+
+ # Cleanup session user from local DB at the end of the test session
+ try:
+ async with async_session_factory() as session:
+ await session.execute(delete(User).where(User.id == uuid.UUID(user_id)))
+ await session.commit()
+ except Exception as e:
+ print(f"\n[CLEANUP] Failed to cleanup session user {email}: {e}")
+
diff --git a/backend/tests/test_analysis.py b/backend/tests/test_analysis.py
new file mode 100644
index 0000000..7110f47
--- /dev/null
+++ b/backend/tests/test_analysis.py
@@ -0,0 +1,127 @@
+import pytest
+import pytest_asyncio
+import uuid
+from httpx import AsyncClient, ASGITransport
+from app.main import app
+from app.services.nutrition_service import lookup_nutrition, calculate_macros, NutritionData
+from app.database import async_session_factory
+from sqlalchemy import text
+
+@pytest.mark.asyncio
+async def test_nutrition_lookup_real_usda():
+ """
+ Test real USDA API lookup for a known food.
+ """
+ async with async_session_factory() as session:
+ # Using a very common food to ensure USDA has it
+ result = await lookup_nutrition("salmon, raw", session)
+
+ assert result is not None
+ assert result.usda_id is not None
+ assert 100 <= result.calories_per_100g <= 250
+ assert result.protein_per_100g > 15
+ assert "salmon" in result.usda_name.lower()
+
+@pytest.mark.asyncio
+async def test_nutrition_caching():
+ """
+ Test that nutrition results are cached in the database.
+ """
+ food_name = f"test_food_{uuid.uuid4()}"
+
+ async with async_session_factory() as session:
+ # First call: Should go to USDA (or return None if fake name, but let's use a known one)
+ # To test cache specifically, we can manually insert into cache
+ await session.execute(text(
+ "INSERT INTO nutrition_cache (food_name_normalized, usda_id, calories_per_100g, "
+ "protein_per_100g, fat_per_100g, carbs_per_100g) "
+ "VALUES (:name, 12345, 100.0, 10.0, 5.0, 2.0)"
+ ), {"name": food_name})
+ await session.commit()
+
+ # Second call: Should hit cache
+ result = await lookup_nutrition(food_name, session)
+
+ assert result is not None
+ assert result.usda_id == 12345
+ assert result.calories_per_100g == 100.0
+ assert result.protein_per_100g == 10.0
+
+def test_macro_calculation():
+ """
+ Test macro calculation logic.
+ """
+ nutrition = NutritionData(
+ calories_per_100g=165.0,
+ protein_per_100g=31.0,
+ fat_per_100g=3.6,
+ carbs_per_100g=0.0,
+ usda_id=123,
+ usda_name="Test Food"
+ )
+
+ # 150g weight
+ macros = calculate_macros(nutrition, 150.0)
+
+ # 165 * 1.5 = 247.5
+ assert macros["calories"] == 247.5
+ # 31 * 1.5 = 46.5
+ assert macros["protein_g"] == 46.5
+ # 3.6 * 1.5 = 5.4
+ assert macros["fat_g"] == 5.4
+ assert macros["carbs_g"] == 0.0
+
+@pytest.mark.asyncio
+async def test_unknown_food_handling():
+ """
+ Test handling of non-existent food names.
+ """
+ async with async_session_factory() as session:
+ result = await lookup_nutrition("xyznonexistentfood123", session)
+ assert result is None
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_full_pipeline_structure():
+ """
+ Test the structure of the full analysis pipeline response.
+ """
+ # Create a small red square image (simulating a tomato)
+ from PIL import Image
+ import io
+ import base64
+
+ img = Image.new('RGB', (100, 100), color='red')
+ img_byte_arr = io.BytesIO()
+ img.save(img_byte_arr, format='JPEG')
+ img_bytes = img_byte_arr.getvalue()
+
+ # Mock authentication - we'll need a real token or a bypass
+ # For simplicity in this test, we might just check the endpoint existence
+ # but the user requested a full pipeline test.
+ # We'll use the AsyncClient with a mock user dependency if possible.
+
+ from app.dependencies import get_current_user
+ mock_id = uuid.uuid4()
+ app.dependency_overrides[get_current_user] = lambda: mock_id
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ files = {"image": ("tomato.jpg", img_bytes, "image/jpeg")}
+ data = {"meal_type": "lunch"}
+
+ response = await ac.post("/api/analyze", files=files, data=data)
+
+ app.dependency_overrides.clear()
+
+ assert response.status_code == 200
+ res_json = response.json()
+ assert "log_id" in res_json
+ assert "items" in res_json
+ assert "totals" in res_json
+ assert isinstance(res_json["items"], list)
+
+ # Even if AI doesn't find food in a red square, the structure should be correct
+ if res_json["items"]:
+ item = res_json["items"][0]
+ assert "name" in item
+ assert "calories" in item
diff --git a/backend/tests/test_auth.py b/backend/tests/test_auth.py
new file mode 100644
index 0000000..0bbbb9f
--- /dev/null
+++ b/backend/tests/test_auth.py
@@ -0,0 +1,162 @@
+import pytest
+import uuid
+import asyncio
+from httpx import AsyncClient, ASGITransport
+from app.main import app
+from app.services.auth_service import supabase
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Test constants
+TEST_PASSWORD = "testpassword123"
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_register_success(session_auth_credentials):
+ """
+ Verifies that the session user has been successfully registered.
+ """
+ assert session_auth_credentials["email"] is not None
+ assert "user_id" in session_auth_credentials
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_register_duplicate_email(session_auth_credentials):
+ """
+ Verifies that registering an existing email returns 409 Conflict.
+ """
+ # 1.5-second safety sleep buffer to avoid Supabase rate limits
+ await asyncio.sleep(1.5)
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ # Register again with the same email
+ response = await ac.post(
+ "/api/auth/register",
+ json={"email": session_auth_credentials["email"], "password": TEST_PASSWORD}
+ )
+
+ assert response.status_code == 409
+ assert response.json()["detail"] == "Email already exists"
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_login_success(session_auth_credentials):
+ """
+ Verifies that a registered user can log in successfully.
+ """
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post(
+ "/api/auth/login",
+ json={"email": session_auth_credentials["email"], "password": session_auth_credentials["password"]}
+ )
+
+ assert response.status_code == 200
+ data = response.json()
+ assert "access_token" in data
+ assert data["user"]["email"] == session_auth_credentials["email"]
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_login_wrong_password(session_auth_credentials):
+ """
+ Verifies that logging in with an incorrect password returns 401 Unauthorized.
+ """
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post(
+ "/api/auth/login",
+ json={"email": session_auth_credentials["email"], "password": "wrongpassword"}
+ )
+
+ assert response.status_code == 401
+ assert "Invalid" in response.json()["detail"]
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_get_me_success(session_auth_credentials):
+ """
+ Verifies that the /me endpoint successfully returns the authenticated user's profile.
+ """
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.get(
+ "/api/auth/me",
+ headers={"Authorization": f"Bearer {session_auth_credentials['token']}"}
+ )
+
+ assert response.status_code == 200
+ data = response.json()
+ assert data["email"] == session_auth_credentials["email"]
+
+@pytest.mark.asyncio
+async def test_get_me_no_token():
+ """
+ Verifies that calling /me without a bearer token returns 403 Forbidden.
+ """
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.get("/api/auth/me")
+
+ assert response.status_code == 403
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_get_me_invalid_token():
+ """
+ Verifies that calling /me with an invalid token returns 401 Unauthorized.
+ """
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.get(
+ "/api/auth/me",
+ headers={"Authorization": "Bearer invalid_token"}
+ )
+
+ assert response.status_code == 401
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_get_me_lazy_sync(session_auth_credentials):
+ """
+ Verifies that calling /me with a user deleted from local DB but active in Supabase
+ re-syncs the user to the local database dynamically.
+ """
+ from app.database import async_session_factory
+ from sqlalchemy import delete, select
+ from app.models.users import User
+
+ email = session_auth_credentials["email"]
+ token = session_auth_credentials["token"]
+ user_id = session_auth_credentials["user_id"]
+
+ # 1. Delete user from local database to simulate sync failure
+ async with async_session_factory() as session:
+ await session.execute(delete(User).where(User.id == uuid.UUID(user_id)))
+ await session.commit()
+
+ # 2. Call /me - this should trigger lazy sync
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.get(
+ "/api/auth/me",
+ headers={"Authorization": f"Bearer {token}"}
+ )
+
+ assert response.status_code == 200
+ assert response.json()["email"] == email
+
+ # 3. Verify user is back in local DB
+ async with async_session_factory() as session:
+ res = await session.execute(select(User).where(User.id == uuid.UUID(user_id)))
+ assert res.scalars().first() is not None
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_admin_sync_users():
+ """
+ Verifies that the admin sync-users endpoint is operational.
+ """
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post("/api/admin/sync-users")
+
+ assert response.status_code in [200, 500]
+ if response.status_code == 200:
+ data = response.json()
+ assert "synced" in data
+ assert "already_exists" in data
diff --git a/backend/tests/test_database.py b/backend/tests/test_database.py
new file mode 100644
index 0000000..b9dd56d
--- /dev/null
+++ b/backend/tests/test_database.py
@@ -0,0 +1,16 @@
+import pytest
+from sqlalchemy import text
+from app.database import engine
+
+@pytest.mark.asyncio
+async def test_database_connection():
+ """Test that we can connect to the database and tables exist."""
+ async with engine.connect() as conn:
+ result = await conn.execute(text("SELECT tablename FROM pg_tables WHERE schemaname='public';"))
+ tables = [row[0] for row in result.all()]
+
+ expected_tables = ["users", "food_logs", "food_items", "nutrition_cache", "food_aliases", "alembic_version"]
+
+ for table in expected_tables:
+ assert table in tables, f"Table {table} not found in database."
+
diff --git a/backend/tests/test_health.py b/backend/tests/test_health.py
new file mode 100644
index 0000000..cc31e74
--- /dev/null
+++ b/backend/tests/test_health.py
@@ -0,0 +1,145 @@
+import pytest
+import pytest_asyncio
+import uuid
+from httpx import AsyncClient, ASGITransport
+from datetime import datetime, timedelta, timezone
+from sqlalchemy import select, delete
+
+from app.main import app
+from app.database import async_session_factory
+from app.models import User, FoodLog
+from app.services.health_service import calculate_tdee, generate_weekly_checkin
+
+TEST_PASSWORD = "testpassword123"
+
+@pytest_asyncio.fixture
+async def auth_client():
+ user_uuid = str(uuid.uuid4())
+ email = f"health_{user_uuid}@example.com"
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ # Register
+ reg_res = await ac.post(
+ "/api/auth/register",
+ json={"email": email, "password": TEST_PASSWORD}
+ )
+ assert reg_res.status_code == 201
+
+ # Login
+ login_res = await ac.post(
+ "/api/auth/login",
+ json={"email": email, "password": TEST_PASSWORD}
+ )
+ assert login_res.status_code == 200
+ token = login_res.json()["access_token"]
+ user_id = login_res.json()["user"]["user_id"]
+
+ ac.headers.update({"Authorization": f"Bearer {token}"})
+ yield ac, user_id
+
+ # Cleanup
+ async with async_session_factory() as session:
+ await session.execute(delete(User).where(User.id == uuid.UUID(user_id)))
+ await session.commit()
+
+def test_tdee_calculation():
+ # BMR = 10*70 + 6.25*175 - 5*30 - 78 = 1565.75
+ # TDEE = 1565.75 * 1.2 = 1878.9 -> 1900
+ val = calculate_tdee(70.0, 175.0, 30, "sedentary", "maintain")
+ assert val == 1900
+
+ # TDEE = 1565.75 * 1.725 - 500 = 2200.9 -> 2200
+ val = calculate_tdee(70.0, 175.0, 30, "active", "lose_weight")
+ assert val == 2200
+
+ # Floor test
+ val = calculate_tdee(40.0, 150.0, 60, "sedentary", "lose_weight")
+ assert val == 1200
+
+@pytest.mark.asyncio
+async def test_weekly_checkin_logic(auth_client):
+ ac, user_id = auth_client
+ async with async_session_factory() as session:
+ now = datetime.now(timezone.utc)
+
+ logs = [
+ FoodLog(
+ user_id=uuid.UUID(user_id),
+ total_calories=2500,
+ analyzed_at=now - timedelta(days=1),
+ image_url="test1",
+ meal_type="dinner"
+ ),
+ FoodLog(
+ user_id=uuid.UUID(user_id),
+ total_calories=2200,
+ analyzed_at=now - timedelta(days=2),
+ image_url="test2",
+ meal_type="dinner"
+ ),
+ FoodLog(
+ user_id=uuid.UUID(user_id),
+ total_calories=2800,
+ analyzed_at=now - timedelta(days=3),
+ image_url="test3",
+ meal_type="dinner"
+ )
+ ]
+ session.add_all(logs)
+ await session.commit()
+
+ checkin = await generate_weekly_checkin(user_id, session, 2000)
+
+ assert checkin["days_logged"] == 3
+ assert checkin["average_daily_calories"] == pytest.approx(1071.4, 0.1)
+ assert checkin["status"] == "under_goal"
+ assert "missed several days" in checkin["insight"]
+
+@pytest.mark.asyncio
+async def test_profile_update_autocalc(auth_client):
+ ac, user_id = auth_client
+
+ response = await ac.put(
+ "/api/user/profile",
+ json={
+ "weight_kg": 70.0,
+ "height_cm": 175.0,
+ "age": 30,
+ "activity_level": "sedentary",
+ "goal": "maintain"
+ }
+ )
+ assert response.status_code == 200
+ data = response.json()
+ assert data["daily_calorie_goal"] == 1900
+ assert float(data["weight_kg"]) == 70.0
+
+@pytest.mark.asyncio
+async def test_get_stats_with_goals(auth_client):
+ ac, user_id = auth_client
+
+ # Set goal first
+ await ac.put("/api/user/profile", json={"daily_calorie_goal": 2000})
+
+ # Add a log for today
+ async with async_session_factory() as session:
+ log = FoodLog(
+ user_id=uuid.UUID(user_id),
+ total_calories=2500,
+ analyzed_at=datetime.now(timezone.utc),
+ image_url="test",
+ meal_type="lunch"
+ )
+ session.add(log)
+ await session.commit()
+
+ response = await ac.get("/api/stats?range=week")
+ assert response.status_code == 200
+ data = response.json()
+
+ today_str = datetime.now(timezone.utc).date().isoformat()
+ today_stat = next(d for d in data if d["date"] == today_str)
+
+ assert today_stat["calorie_goal"] == 2000
+ assert today_stat["calories_vs_goal"] == 500.0
+ assert today_stat["goal_status"] == "over_goal"
diff --git a/backend/tests/test_logs.py b/backend/tests/test_logs.py
new file mode 100644
index 0000000..6b49d01
--- /dev/null
+++ b/backend/tests/test_logs.py
@@ -0,0 +1,238 @@
+import pytest
+import pytest_asyncio
+import uuid
+from httpx import AsyncClient, ASGITransport
+from datetime import datetime, timedelta, timezone
+from decimal import Decimal
+from sqlalchemy import select, delete
+
+from app.main import app
+from app.database import async_session_factory
+from app.models import User, FoodLog, FoodItem, FoodAlias
+
+TEST_PASSWORD = "testpassword123"
+
+@pytest_asyncio.fixture
+async def auth_client():
+ """
+ Fixture to create a test user and return an authenticated client.
+ """
+ user_uuid = str(uuid.uuid4())
+ email = f"test_{user_uuid}@example.com"
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ # Register
+ reg_res = await ac.post(
+ "/api/auth/register",
+ json={"email": email, "password": TEST_PASSWORD}
+ )
+ assert reg_res.status_code == 201
+
+ # Login
+ login_res = await ac.post(
+ "/api/auth/login",
+ json={"email": email, "password": TEST_PASSWORD}
+ )
+ assert login_res.status_code == 200
+ token = login_res.json()["access_token"]
+ user_id = login_res.json()["user"]["user_id"]
+
+ ac.headers.update({"Authorization": f"Bearer {token}"})
+ yield ac, user_id
+
+ # Cleanup user
+ async with async_session_factory() as session:
+ await session.execute(delete(User).where(User.id == uuid.UUID(user_id)))
+ await session.commit()
+
+@pytest_asyncio.fixture
+async def test_data(auth_client):
+ ac, user_id = auth_client
+
+ async with async_session_factory() as session:
+ # Create log
+ log = FoodLog(
+ user_id=uuid.UUID(user_id),
+ image_url=f"https://test.com/foodsnap-images/{user_id}/test.jpg",
+ meal_type="lunch",
+ total_calories=500.0,
+ total_protein_g=30.0,
+ total_fat_g=20.0,
+ total_carbs_g=50.0,
+ analyzed_at=datetime.now(timezone.utc)
+ )
+ session.add(log)
+ await session.flush()
+
+ # Create items
+ item1 = FoodItem(
+ food_log_id=log.id,
+ name="chicken breast",
+ usda_id=123,
+ usda_match_name="Chicken, breast, grilled",
+ confidence=0.95,
+ weight_grams=200.0,
+ calories=330.0,
+ protein_g=60.0,
+ fat_g=7.0,
+ carbs_g=0.0
+ )
+ item2 = FoodItem(
+ food_log_id=log.id,
+ name="broccoli",
+ usda_id=456,
+ usda_match_name="Broccoli, raw",
+ confidence=0.9,
+ weight_grams=150.0,
+ calories=50.0,
+ protein_g=4.0,
+ fat_g=0.5,
+ carbs_g=10.0
+ )
+ session.add(item1)
+ session.add(item2)
+ await session.commit()
+
+ log_id = str(log.id)
+ item_ids = [str(item1.id), str(item2.id)]
+
+ yield {"log_id": log_id, "item_ids": item_ids, "user_id": user_id}
+
+ # Cleanup (CASCADE deletes items)
+ async with async_session_factory() as session:
+ await session.execute(delete(FoodLog).where(FoodLog.id == uuid.UUID(log_id)))
+ await session.commit()
+
+@pytest.mark.asyncio
+async def test_get_logs(auth_client, test_data):
+ ac, user_id = auth_client
+ response = await ac.get("/api/logs")
+ assert response.status_code == 200
+ data = response.json()
+ assert len(data["logs"]) >= 1
+ assert any(log["id"] == test_data["log_id"] for log in data["logs"])
+ assert data["logs"][0]["item_count"] == 2
+
+@pytest.mark.asyncio
+async def test_get_logs_date_filter(auth_client, test_data):
+ ac, user_id = auth_client
+ today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+ response = await ac.get(f"/api/logs?date={today}")
+ assert response.status_code == 200
+ data = response.json()
+ assert len(data["logs"]) >= 1
+
+ tomorrow = (datetime.now(timezone.utc) + timedelta(days=1)).strftime("%Y-%m-%d")
+ response = await ac.get(f"/api/logs?date={tomorrow}")
+ assert response.status_code == 200
+ assert len(response.json()["logs"]) == 0
+
+@pytest.mark.asyncio
+async def test_get_log_detail(auth_client, test_data):
+ ac, user_id = auth_client
+ response = await ac.get(f"/api/logs/{test_data['log_id']}")
+ assert response.status_code == 200
+ data = response.json()
+ assert data["id"] == test_data["log_id"]
+ assert len(data["items"]) == 2
+ assert data["items"][0]["name"] == "chicken breast"
+
+@pytest.mark.asyncio
+async def test_get_log_unauthorized(auth_client, test_data):
+ # Create another user
+ user_uuid = str(uuid.uuid4())
+ email = f"other_{user_uuid}@example.com"
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ await ac.post("/api/auth/register", json={"email": email, "password": TEST_PASSWORD})
+ login_res = await ac.post("/api/auth/login", json={"email": email, "password": TEST_PASSWORD})
+ token = login_res.json()["access_token"]
+ other_user_id = login_res.json()["user"]["user_id"]
+
+ # Try to access first user's log
+ response = await ac.get(
+ f"/api/logs/{test_data['log_id']}",
+ headers={"Authorization": f"Bearer {token}"}
+ )
+ assert response.status_code == 404
+
+ # Cleanup other user
+ async with async_session_factory() as session:
+ await session.execute(delete(User).where(User.id == uuid.UUID(other_user_id)))
+ await session.commit()
+
+@pytest.mark.asyncio
+async def test_delete_log(auth_client, test_data):
+ ac, user_id = auth_client
+ response = await ac.delete(f"/api/logs/{test_data['log_id']}")
+ assert response.status_code == 200
+ assert response.json()["deleted"] is True
+
+ # Verify gone from DB
+ async with async_session_factory() as session:
+ res = await session.execute(select(FoodLog).where(FoodLog.id == uuid.UUID(test_data["log_id"])))
+ assert res.scalars().first() is None
+
+@pytest.mark.asyncio
+async def test_put_correction_name(auth_client, test_data):
+ ac, user_id = auth_client
+ log_id = test_data["log_id"]
+ item_id = test_data["item_ids"][0]
+
+ # Update name to something that likely has USDA match
+ response = await ac.put(
+ f"/api/logs/{log_id}/items/{item_id}",
+ json={"name": "apple"}
+ )
+ assert response.status_code == 200
+ data = response.json()
+ assert data["name"] == "apple"
+ assert data["is_user_corrected"] is True
+ assert data["matched"] is True
+
+ # Verify alias created
+ async with async_session_factory() as session:
+ res = await session.execute(
+ select(FoodAlias).where(FoodAlias.correct_name == "apple")
+ )
+ alias = res.scalars().first()
+ assert alias is not None
+ assert alias.ai_name == "chicken breast"
+
+@pytest.mark.asyncio
+async def test_put_correction_weight(auth_client, test_data):
+ ac, user_id = auth_client
+ log_id = test_data["log_id"]
+ item_id = test_data["item_ids"][0] # chicken breast, 200g, 330 cal
+
+ # Double the weight
+ response = await ac.put(
+ f"/api/logs/{log_id}/items/{item_id}",
+ json={"weight_grams": 400.0}
+ )
+ assert response.status_code == 200
+ data = response.json()
+ assert data["weight_grams"] == 400.0
+ # Since it has usda_id=123, it might re-lookup or scale.
+ # In my implementation, it scales if it can't find nutrition in cache/API easily or just scales.
+ # If it scales 330 * 2 = 660.
+ assert data["calories"] > 330.0
+
+ # Verify log totals recalculated
+ response = await ac.get(f"/api/logs/{log_id}")
+ log_data = response.json()
+ # 660 (new item1) + 50 (item2) = 710
+ assert log_data["total_calories"] > 500.0
+
+@pytest.mark.asyncio
+async def test_get_stats(auth_client, test_data):
+ ac, user_id = auth_client
+ response = await ac.get("/api/stats?range=week")
+ assert response.status_code == 200
+ data = response.json()
+ assert len(data) == 7
+ # One day should have stats
+ today = datetime.now(timezone.utc).date().isoformat()
+ day_stat = next(d for d in data if d["date"] == today)
+ assert day_stat["total_calories"] == 500.0
+ assert day_stat["meal_count"] == 1
diff --git a/backend/tests/test_upload.py b/backend/tests/test_upload.py
new file mode 100644
index 0000000..f71c865
--- /dev/null
+++ b/backend/tests/test_upload.py
@@ -0,0 +1,127 @@
+import pytest
+import pytest_asyncio
+import io
+import httpx
+from PIL import Image
+from httpx import AsyncClient, ASGITransport
+from app.main import app
+from app.dependencies import get_current_user
+from uuid import uuid4
+
+@pytest_asyncio.fixture(autouse=True)
+async def setup_overrides():
+ mock_id = uuid4()
+ app.dependency_overrides[get_current_user] = lambda: mock_id
+ yield
+ app.dependency_overrides.clear()
+
+@pytest.mark.asyncio
+async def test_valid_jpeg_upload():
+ # Create a 200x200 red square
+ img = Image.new("RGB", (200, 200), color="red")
+ buf = io.BytesIO()
+ img.save(buf, format="JPEG")
+ buf.seek(0)
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post(
+ "/api/upload",
+ files={"image": ("test.jpg", buf, "image/jpeg")}
+ )
+
+ assert response.status_code == 200
+ data = response.json()
+ assert "image_url" in data
+ assert "file_path" in data
+ assert data["image_url"].startswith("http")
+
+ # Fetch the image_url and verify it returns 200
+ async with httpx.AsyncClient() as client:
+ r = await client.get(data["image_url"])
+ assert r.status_code == 200
+
+@pytest.mark.asyncio
+async def test_reject_non_image():
+ buf = io.BytesIO(b"this is not an image")
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post(
+ "/api/upload",
+ files={"image": ("test.txt", buf, "text/plain")}
+ )
+
+ assert response.status_code == 400
+ assert "Only JPEG and PNG images are accepted" in response.json()["detail"]
+
+@pytest.mark.asyncio
+async def test_reject_oversized():
+ # Create a large image to exceed 5MB
+ img = Image.new("RGB", (2000, 2000), color="blue")
+ buf = io.BytesIO()
+ img.save(buf, format="BMP")
+ buf.seek(0)
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post(
+ "/api/upload",
+ files={"image": ("large.bmp", buf, "image/jpeg")} # Lie about content type
+ )
+
+ assert response.status_code == 413
+ assert "Image exceeds 5MB limit" in response.json()["detail"]
+
+@pytest.mark.asyncio
+async def test_exif_stripping():
+ # Create a JPEG with EXIF data
+ img = Image.new("RGB", (100, 100), color="green")
+
+ # Add EXIF data
+ exif = img.getexif()
+ exif[0x0112] = 1 # Orientation
+ exif[0x010F] = "TestCamera" # Make
+
+ buf = io.BytesIO()
+ img.save(buf, format="JPEG", exif=exif)
+ buf.seek(0)
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post(
+ "/api/upload",
+ files={"image": ("exif.jpg", buf, "image/jpeg")}
+ )
+
+ assert response.status_code == 200
+ image_url = response.json()["image_url"]
+
+ async with httpx.AsyncClient() as client:
+ download_resp = await client.get(image_url)
+ assert download_resp.status_code == 200
+
+ clean_buf = io.BytesIO(download_resp.content)
+ clean_img = Image.open(clean_buf)
+ exif_data = clean_img.getexif()
+
+ assert len(exif_data) == 0
+
+@pytest.mark.asyncio
+async def test_png_to_jpeg_conversion():
+ # Create a PNG with transparency (RGBA)
+ img = Image.new("RGBA", (100, 100), color=(255, 0, 0, 128))
+ buf = io.BytesIO()
+ img.save(buf, format="PNG")
+ buf.seek(0)
+
+ async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as ac:
+ response = await ac.post(
+ "/api/upload",
+ files={"image": ("test.png", buf, "image/png")}
+ )
+
+ assert response.status_code == 200
+ data = response.json()
+ assert data["image_url"].endswith(".jpg")
+
+ async with httpx.AsyncClient() as client:
+ download_resp = await client.get(data["image_url"])
+ clean_img = Image.open(io.BytesIO(download_resp.content))
+ assert clean_img.format == "JPEG"
+ assert clean_img.mode == "RGB"
diff --git a/mobile/.claude/settings.json b/mobile/.claude/settings.json
new file mode 100644
index 0000000..176e6a5
--- /dev/null
+++ b/mobile/.claude/settings.json
@@ -0,0 +1,5 @@
+{
+ "enabledPlugins": {
+ "expo@claude-plugins-official": true
+ }
+}
diff --git a/mobile/.gitignore b/mobile/.gitignore
new file mode 100644
index 0000000..d914c32
--- /dev/null
+++ b/mobile/.gitignore
@@ -0,0 +1,41 @@
+# Learn more https://docs.github.com/en/get-started/getting-started-with-git/ignoring-files
+
+# dependencies
+node_modules/
+
+# Expo
+.expo/
+dist/
+web-build/
+expo-env.d.ts
+
+# Native
+.kotlin/
+*.orig.*
+*.jks
+*.p8
+*.p12
+*.key
+*.mobileprovision
+
+# Metro
+.metro-health-check*
+
+# debug
+npm-debug.*
+yarn-debug.*
+yarn-error.*
+
+# macOS
+.DS_Store
+*.pem
+
+# local env files
+.env*.local
+
+# typescript
+*.tsbuildinfo
+
+# generated native folders
+/ios
+/android
diff --git a/mobile/AGENTS.md b/mobile/AGENTS.md
new file mode 100644
index 0000000..73cda46
--- /dev/null
+++ b/mobile/AGENTS.md
@@ -0,0 +1,3 @@
+# Expo HAS CHANGED
+
+Read the exact versioned docs at https://docs.expo.dev/versions/v54.0.0/ before writing any code.
diff --git a/mobile/CLAUDE.md b/mobile/CLAUDE.md
new file mode 100644
index 0000000..43c994c
--- /dev/null
+++ b/mobile/CLAUDE.md
@@ -0,0 +1 @@
+@AGENTS.md
diff --git a/mobile/app.json b/mobile/app.json
new file mode 100644
index 0000000..0fd6d89
--- /dev/null
+++ b/mobile/app.json
@@ -0,0 +1,58 @@
+{
+ "expo": {
+ "name": "FoodSnap AI",
+ "slug": "foodsnap-ai",
+ "version": "1.0.0",
+ "orientation": "portrait",
+ "icon": "./assets/icon.png",
+ "scheme": "foodsnap",
+ "userInterfaceStyle": "light",
+ "newArchEnabled": true,
+ "splash": {
+ "image": "./assets/splash-icon.png",
+ "resizeMode": "contain",
+ "backgroundColor": "#ffffff"
+ },
+ "ios": {
+ "supportsTablet": true,
+ "bundleIdentifier": "com.foodsnap.ai",
+ "infoPlist": {
+ "NSCameraUsageDescription": "This app needs camera access to photograph your meals for nutrition analysis"
+ }
+ },
+ "android": {
+ "adaptiveIcon": {
+ "foregroundImage": "./assets/adaptive-icon.png",
+ "backgroundColor": "#ffffff"
+ },
+ "package": "com.foodsnap.ai",
+ "permissions": [
+ "android.permission.CAMERA",
+ "android.permission.RECORD_AUDIO"
+ ],
+ "edgeToEdgeEnabled": true,
+ "predictiveBackGestureEnabled": false
+ },
+ "web": {
+ "favicon": "./assets/favicon.png",
+ "bundler": "metro"
+ },
+ "plugins": [
+ "expo-router",
+ "expo-secure-store",
+ [
+ "expo-camera",
+ {
+ "cameraPermission": "This app needs camera access to photograph your meals for nutrition analysis"
+ }
+ ]
+ ],
+ "extra": {
+ "router": {},
+ "eas": {
+ "projectId": "d0e3225e-dcf6-436d-b189-fc7672eb9e07"
+ }
+ },
+ "owner": "unnita1235s-organization"
+ }
+}
diff --git a/mobile/app/(auth)/_layout.tsx b/mobile/app/(auth)/_layout.tsx
new file mode 100644
index 0000000..9f6b77a
--- /dev/null
+++ b/mobile/app/(auth)/_layout.tsx
@@ -0,0 +1,10 @@
+import { Stack } from 'expo-router';
+
+export default function AuthLayout() {
+ return (
+
+
+
+
+ );
+}
diff --git a/mobile/app/(auth)/login.tsx b/mobile/app/(auth)/login.tsx
new file mode 100644
index 0000000..aa85105
--- /dev/null
+++ b/mobile/app/(auth)/login.tsx
@@ -0,0 +1,155 @@
+import React, { useState } from 'react';
+import { View, Text, TextInput, TouchableOpacity, StyleSheet, ActivityIndicator, Alert } from 'react-native';
+import { Link, router } from 'expo-router';
+import { login } from '../../src/api/auth';
+
+export default function LoginScreen() {
+ const [email, setEmail] = useState('');
+ const [password, setPassword] = useState('');
+ const [loading, setLoading] = useState(false);
+ const [error, setError] = useState('');
+
+ const handleLogin = async () => {
+ if (!email || !password) {
+ setError('Please fill in all fields');
+ return;
+ }
+
+ if (password.length < 6) {
+ setError('Password must be at least 6 characters');
+ return;
+ }
+
+ setError('');
+ setLoading(true);
+ try {
+ await login(email, password);
+ router.replace('/(tabs)');
+ } catch (err: any) {
+ setError(err.message || 'Login failed. Please check your credentials.');
+ } finally {
+ setLoading(false);
+ }
+ };
+
+ return (
+
+
+ FoodSnap AI
+ Welcome back!
+
+
+
+
+
+ {error ? {error} : null}
+
+
+ {loading ? (
+
+ ) : (
+ Log In
+ )}
+
+
+
+ Don't have an account?
+
+
+ Register
+
+
+
+
+
+ );
+}
+
+const styles = StyleSheet.create({
+ container: {
+ flex: 1,
+ backgroundColor: '#f5f5f5',
+ justifyContent: 'center',
+ padding: 20,
+ },
+ card: {
+ backgroundColor: '#fff',
+ borderRadius: 12,
+ padding: 24,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.1,
+ shadowRadius: 4,
+ elevation: 3,
+ },
+ title: {
+ fontSize: 28,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ textAlign: 'center',
+ marginBottom: 8,
+ },
+ subtitle: {
+ fontSize: 16,
+ color: '#666',
+ textAlign: 'center',
+ marginBottom: 24,
+ },
+ input: {
+ borderWidth: 1,
+ borderColor: '#ddd',
+ borderRadius: 8,
+ padding: 12,
+ fontSize: 16,
+ marginBottom: 16,
+ },
+ errorText: {
+ color: '#d32f2f',
+ marginBottom: 16,
+ fontSize: 14,
+ textAlign: 'center',
+ },
+ button: {
+ backgroundColor: '#2E7D32',
+ borderRadius: 8,
+ padding: 14,
+ alignItems: 'center',
+ marginTop: 8,
+ },
+ buttonText: {
+ color: '#fff',
+ fontSize: 18,
+ fontWeight: 'bold',
+ },
+ footer: {
+ flexDirection: 'row',
+ justifyContent: 'center',
+ marginTop: 20,
+ },
+ footerText: {
+ color: '#666',
+ fontSize: 14,
+ },
+ linkText: {
+ color: '#2E7D32',
+ fontSize: 14,
+ fontWeight: 'bold',
+ },
+});
diff --git a/mobile/app/(auth)/register.tsx b/mobile/app/(auth)/register.tsx
new file mode 100644
index 0000000..ab1dcdd
--- /dev/null
+++ b/mobile/app/(auth)/register.tsx
@@ -0,0 +1,169 @@
+import React, { useState } from 'react';
+import { View, Text, TextInput, TouchableOpacity, StyleSheet, ActivityIndicator, Alert } from 'react-native';
+import { Link, router } from 'expo-router';
+import { register } from '../../src/api/auth';
+
+export default function RegisterScreen() {
+ const [email, setEmail] = useState('');
+ const [password, setPassword] = useState('');
+ const [confirmPassword, setConfirmPassword] = useState('');
+ const [loading, setLoading] = useState(false);
+ const [error, setError] = useState('');
+
+ const handleRegister = async () => {
+ if (!email || !password || !confirmPassword) {
+ setError('Please fill in all fields');
+ return;
+ }
+
+ if (password !== confirmPassword) {
+ setError('Passwords do not match');
+ return;
+ }
+
+ if (password.length < 6) {
+ setError('Password must be at least 6 characters');
+ return;
+ }
+
+ setError('');
+ setLoading(true);
+ try {
+ await register(email, password);
+ router.replace('/(tabs)');
+ } catch (err: any) {
+ setError(err.message || 'Registration failed. Try a different email.');
+ } finally {
+ setLoading(false);
+ }
+ };
+
+ return (
+
+
+ FoodSnap AI
+ Create your account
+
+
+
+
+
+
+
+ {error ? {error} : null}
+
+
+ {loading ? (
+
+ ) : (
+ Create Account
+ )}
+
+
+
+ Already have an account?
+
+
+ Log In
+
+
+
+
+
+ );
+}
+
+const styles = StyleSheet.create({
+ container: {
+ flex: 1,
+ backgroundColor: '#f5f5f5',
+ justifyContent: 'center',
+ padding: 20,
+ },
+ card: {
+ backgroundColor: '#fff',
+ borderRadius: 12,
+ padding: 24,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.1,
+ shadowRadius: 4,
+ elevation: 3,
+ },
+ title: {
+ fontSize: 28,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ textAlign: 'center',
+ marginBottom: 8,
+ },
+ subtitle: {
+ fontSize: 16,
+ color: '#666',
+ textAlign: 'center',
+ marginBottom: 24,
+ },
+ input: {
+ borderWidth: 1,
+ borderColor: '#ddd',
+ borderRadius: 8,
+ padding: 12,
+ fontSize: 16,
+ marginBottom: 16,
+ },
+ errorText: {
+ color: '#d32f2f',
+ marginBottom: 16,
+ fontSize: 14,
+ textAlign: 'center',
+ },
+ button: {
+ backgroundColor: '#2E7D32',
+ borderRadius: 8,
+ padding: 14,
+ alignItems: 'center',
+ marginTop: 8,
+ },
+ buttonText: {
+ color: '#fff',
+ fontSize: 18,
+ fontWeight: 'bold',
+ },
+ footer: {
+ flexDirection: 'row',
+ justifyContent: 'center',
+ marginTop: 20,
+ },
+ footerText: {
+ color: '#666',
+ fontSize: 14,
+ },
+ linkText: {
+ color: '#2E7D32',
+ fontSize: 14,
+ fontWeight: 'bold',
+ },
+});
diff --git a/mobile/app/(tabs)/_layout.tsx b/mobile/app/(tabs)/_layout.tsx
new file mode 100644
index 0000000..8e18395
--- /dev/null
+++ b/mobile/app/(tabs)/_layout.tsx
@@ -0,0 +1,58 @@
+import React from 'react';
+import { Tabs } from 'expo-router';
+import { Text } from 'react-native';
+
+export default function TabsLayout() {
+ return (
+
+ 🏠,
+ }}
+ />
+ 📷,
+ }}
+ />
+ 📜,
+ }}
+ />
+ 👤,
+ }}
+ />
+
+ );
+}
diff --git a/mobile/app/(tabs)/camera.tsx b/mobile/app/(tabs)/camera.tsx
new file mode 100644
index 0000000..15322b3
--- /dev/null
+++ b/mobile/app/(tabs)/camera.tsx
@@ -0,0 +1,471 @@
+import React, { useState, useEffect, useRef } from 'react';
+import {
+ View,
+ Text,
+ StyleSheet,
+ TouchableOpacity,
+ Image,
+ ActivityIndicator,
+ Linking,
+ Alert,
+ Dimensions,
+} from 'react-native';
+import { CameraView, useCameraPermissions } from 'expo-camera';
+import * as ImagePicker from 'expo-image-picker';
+import { router } from 'expo-router';
+import { analyzeFood } from '../../src/api/food';
+
+type MealType = 'breakfast' | 'lunch' | 'dinner' | 'snack';
+
+export default function CameraScreen() {
+ const [permission, requestPermission] = useCameraPermissions();
+ const [selectedMealType, setSelectedMealType] = useState('lunch');
+ const [photoUri, setPhotoUri] = useState(null);
+ const [analyzing, setAnalyzing] = useState(false);
+ const [elapsedTime, setElapsedTime] = useState(0);
+ const cameraRef = useRef(null);
+
+ // Auto-detect meal type on mount based on local time
+ useEffect(() => {
+ const hours = new Date().getHours();
+ if (hours < 11) {
+ setSelectedMealType('breakfast');
+ } else if (hours >= 11 && hours < 14) {
+ setSelectedMealType('lunch');
+ } else if (hours >= 14 && hours < 17) {
+ setSelectedMealType('snack');
+ } else {
+ setSelectedMealType('dinner');
+ }
+ }, []);
+
+ // Timer for loading text transitions during analysis
+ useEffect(() => {
+ let interval: NodeJS.Timeout;
+ if (analyzing) {
+ setElapsedTime(0);
+ interval = setInterval(() => {
+ setElapsedTime((prev) => prev + 1);
+ }, 1000);
+ }
+ return () => clearInterval(interval);
+ }, [analyzing]);
+
+ const getLoadingText = () => {
+ if (elapsedTime < 3) return 'Analyzing your meal...';
+ if (elapsedTime < 6) return 'Identifying food items...';
+ return 'Looking up nutrition data...';
+ };
+
+ if (!permission) {
+ // Camera permissions are still loading
+ return ;
+ }
+
+ if (!permission.granted) {
+ // Camera permissions are not granted yet
+ return (
+
+ 📷
+ Camera Permission Required
+
+ FoodSnap AI needs camera access to photograph your meals for automatic nutrition analysis.
+
+
+ Grant Permission
+
+ Linking.openSettings()}
+ >
+ Open System Settings
+
+
+ );
+ }
+
+ const handleCapture = async () => {
+ if (cameraRef.current) {
+ try {
+ const photo = await cameraRef.current.takePictureAsync({
+ quality: 0.7,
+ skipProcessing: false,
+ });
+ if (photo?.uri) {
+ setPhotoUri(photo.uri);
+ }
+ } catch (error) {
+ console.error('Error taking photo:', error);
+ Alert.alert('Error', 'Failed to take photo. Please try again.');
+ }
+ }
+ };
+
+ const handlePickImage = async () => {
+ try {
+ const permissionResult = await ImagePicker.requestMediaLibraryPermissionsAsync();
+ if (!permissionResult.granted) {
+ Alert.alert('Permission Denied', 'You need to grant photo gallery access to pick images.');
+ return;
+ }
+
+ const result = await ImagePicker.launchImageLibraryAsync({
+ mediaTypes: ImagePicker.MediaTypeOptions.Images,
+ allowsEditing: true,
+ quality: 0.7,
+ });
+
+ if (!result.canceled && result.assets?.[0]?.uri) {
+ setPhotoUri(result.assets[0].uri);
+ }
+ } catch (error) {
+ console.error('Error picking image:', error);
+ Alert.alert('Error', 'Failed to select image from gallery.');
+ }
+ };
+
+ const handleUsePhoto = async () => {
+ if (!photoUri) return;
+
+ setAnalyzing(true);
+ try {
+ const result = await analyzeFood(photoUri, selectedMealType);
+ setAnalyzing(false);
+ setPhotoUri(null);
+ // Navigate to results screen with parsed JSON string
+ router.push({
+ pathname: '/results',
+ params: { data: JSON.stringify(result) },
+ });
+ } catch (error: any) {
+ setAnalyzing(false);
+ Alert.alert(
+ 'Analysis Failed',
+ error.message || 'Something went wrong while analyzing your meal. Please try again.'
+ );
+ }
+ };
+
+ const handleRetake = () => {
+ setPhotoUri(null);
+ };
+
+ // Rendering STATE 2: Photo Preview/Analyzing
+ if (photoUri) {
+ return (
+
+
+
+ {analyzing ? (
+
+
+ {getLoadingText()}
+ {elapsedTime}s elapsed
+
+ ) : (
+
+
+ Verify Meal Photo
+
+
+
+ Retake
+
+
+ Use Photo
+
+
+
+ )}
+
+ );
+ }
+
+ // Rendering STATE 1: Ready to Snap
+ return (
+
+
+ {/* Overlay top bar instructions */}
+
+ Snap Your Meal
+ Center food in the frame for best results
+
+
+ {/* Bottom controls overlay */}
+
+ {/* Meal type selection chips */}
+
+ {(['breakfast', 'lunch', 'dinner', 'snack'] as MealType[]).map((type) => (
+ setSelectedMealType(type)}
+ >
+
+ {type.charAt(0).toUpperCase() + type.slice(1)}
+
+
+ ))}
+
+
+ {/* Snap trigger & Gallery picker row */}
+
+
+
+
+
+
+
+
+ 🖼️
+ Gallery
+
+
+
+
+
+ );
+}
+
+const { width, height } = Dimensions.get('window');
+
+const styles = StyleSheet.create({
+ container: {
+ flex: 1,
+ backgroundColor: '#000',
+ },
+ blackScreen: {
+ flex: 1,
+ backgroundColor: '#000',
+ },
+ camera: {
+ flex: 1,
+ justifyContent: 'space-between',
+ },
+ cameraHeader: {
+ paddingTop: 50,
+ paddingHorizontal: 20,
+ alignItems: 'center',
+ },
+ cameraTitle: {
+ color: '#fff',
+ fontSize: 20,
+ fontWeight: 'bold',
+ textShadowColor: 'rgba(0, 0, 0, 0.75)',
+ textShadowOffset: { width: -1, height: 1 },
+ textShadowRadius: 10,
+ },
+ cameraSubtitle: {
+ color: 'rgba(255, 255, 255, 0.8)',
+ fontSize: 14,
+ marginTop: 4,
+ textShadowColor: 'rgba(0, 0, 0, 0.75)',
+ textShadowOffset: { width: -1, height: 1 },
+ textShadowRadius: 10,
+ },
+ bottomControlsContainer: {
+ backgroundColor: 'rgba(0, 0, 0, 0.6)',
+ paddingBottom: 30,
+ paddingTop: 15,
+ },
+ chipsContainer: {
+ flexDirection: 'row',
+ justifyContent: 'center',
+ marginBottom: 20,
+ paddingHorizontal: 10,
+ },
+ chip: {
+ paddingVertical: 8,
+ paddingHorizontal: 16,
+ borderRadius: 20,
+ backgroundColor: 'rgba(255, 255, 255, 0.2)',
+ marginHorizontal: 4,
+ borderWidth: 1,
+ borderColor: 'rgba(255, 255, 255, 0.4)',
+ },
+ activeChip: {
+ backgroundColor: '#2E7D32',
+ borderColor: '#2E7D32',
+ },
+ chipText: {
+ color: '#fff',
+ fontSize: 14,
+ fontWeight: '600',
+ },
+ activeChipText: {
+ color: '#fff',
+ },
+ shutterContainer: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ justifyContent: 'space-around',
+ paddingHorizontal: 20,
+ },
+ shutterPlaceholder: {
+ width: 60,
+ },
+ shutterButton: {
+ width: 80,
+ height: 80,
+ borderRadius: 40,
+ borderWidth: 4,
+ borderColor: '#fff',
+ justifyContent: 'center',
+ alignItems: 'center',
+ },
+ shutterInner: {
+ width: 64,
+ height: 64,
+ borderRadius: 32,
+ backgroundColor: '#fff',
+ },
+ galleryButton: {
+ width: 60,
+ alignItems: 'center',
+ justifyContent: 'center',
+ },
+ galleryIcon: {
+ fontSize: 24,
+ marginBottom: 4,
+ },
+ galleryText: {
+ color: '#fff',
+ fontSize: 12,
+ fontWeight: '600',
+ },
+ fullScreenImage: {
+ width: '100%',
+ height: '100%',
+ position: 'absolute',
+ },
+ overlayContainer: {
+ ...StyleSheet.absoluteFillObject,
+ backgroundColor: 'rgba(0, 0, 0, 0.75)',
+ justifyContent: 'center',
+ alignItems: 'center',
+ padding: 20,
+ },
+ loadingText: {
+ color: '#fff',
+ fontSize: 18,
+ fontWeight: 'bold',
+ marginTop: 20,
+ textAlign: 'center',
+ },
+ loadingTimer: {
+ color: '#aaa',
+ fontSize: 14,
+ marginTop: 8,
+ },
+ previewActionContainer: {
+ position: 'absolute',
+ bottom: 0,
+ left: 0,
+ right: 0,
+ backgroundColor: 'rgba(0, 0, 0, 0.8)',
+ paddingBottom: 40,
+ paddingTop: 20,
+ paddingHorizontal: 20,
+ borderTopLeftRadius: 16,
+ borderTopRightRadius: 16,
+ },
+ previewHeader: {
+ alignItems: 'center',
+ marginBottom: 20,
+ },
+ previewHeaderText: {
+ color: '#fff',
+ fontSize: 16,
+ fontWeight: 'bold',
+ },
+ previewButtonRow: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ },
+ previewSecondaryButton: {
+ flex: 1,
+ paddingVertical: 14,
+ borderRadius: 8,
+ backgroundColor: 'rgba(255, 255, 255, 0.2)',
+ alignItems: 'center',
+ marginRight: 10,
+ },
+ previewSecondaryButtonText: {
+ color: '#fff',
+ fontSize: 16,
+ fontWeight: 'bold',
+ },
+ previewPrimaryButton: {
+ flex: 1,
+ paddingVertical: 14,
+ borderRadius: 8,
+ backgroundColor: '#2E7D32',
+ alignItems: 'center',
+ marginLeft: 10,
+ },
+ previewPrimaryButtonText: {
+ color: '#fff',
+ fontSize: 16,
+ fontWeight: 'bold',
+ },
+ permissionContainer: {
+ flex: 1,
+ justifyContent: 'center',
+ alignItems: 'center',
+ backgroundColor: '#fff',
+ padding: 30,
+ },
+ emoji: {
+ fontSize: 64,
+ marginBottom: 20,
+ },
+ permissionTitle: {
+ fontSize: 22,
+ fontWeight: 'bold',
+ color: '#333',
+ marginBottom: 12,
+ textAlign: 'center',
+ },
+ permissionDescription: {
+ fontSize: 15,
+ color: '#666',
+ textAlign: 'center',
+ lineHeight: 22,
+ marginBottom: 30,
+ },
+ primaryButton: {
+ backgroundColor: '#2E7D32',
+ paddingVertical: 14,
+ paddingHorizontal: 30,
+ borderRadius: 8,
+ width: '100%',
+ alignItems: 'center',
+ },
+ secondaryButton: {
+ borderWidth: 1,
+ borderColor: '#ccc',
+ paddingVertical: 14,
+ paddingHorizontal: 30,
+ borderRadius: 8,
+ width: '100%',
+ alignItems: 'center',
+ },
+ secondaryButtonText: {
+ color: '#666',
+ fontSize: 16,
+ fontWeight: '600',
+ },
+ buttonText: {
+ color: '#fff',
+ fontSize: 16,
+ fontWeight: 'bold',
+ },
+});
diff --git a/mobile/app/(tabs)/history.tsx b/mobile/app/(tabs)/history.tsx
new file mode 100644
index 0000000..d8f2d14
--- /dev/null
+++ b/mobile/app/(tabs)/history.tsx
@@ -0,0 +1,449 @@
+import React, { useState, useEffect, useCallback } from 'react';
+import {
+ View,
+ Text,
+ StyleSheet,
+ FlatList,
+ Image,
+ TouchableOpacity,
+ ActivityIndicator,
+ Alert,
+ SafeAreaView,
+ StatusBar,
+} from 'react-native';
+import { router, useFocusEffect } from 'expo-router';
+import { getFoodLogs, deleteFoodLog, FoodLog } from '../../src/api/food';
+import { useToast } from '../../src/components/Toast';
+
+export default function HistoryScreen() {
+ const { showToast } = useToast();
+ const [logs, setLogs] = useState([]);
+ const [page, setPage] = useState(1);
+ const [loading, setLoading] = useState(true);
+ const [loadingMore, setLoadingMore] = useState(false);
+ const [refreshing, setRefreshing] = useState(false);
+ const [hasMore, setHasMore] = useState(true);
+ const [selectedDate, setSelectedDate] = useState(null);
+
+ // Generate last 7 days + today
+ const getLast7Days = () => {
+ const days = [];
+ const weekdayNames = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat'];
+ for (let i = 6; i >= 0; i--) {
+ const d = new Date();
+ d.setDate(d.getDate() - i);
+ const dayName = weekdayNames[d.getDay()];
+ const dateNum = d.getDate();
+
+ const year = d.getFullYear();
+ const month = String(d.getMonth() + 1).padStart(2, '0');
+ const day = String(d.getDate()).padStart(2, '0');
+ const fullDate = `${year}-${month}-${day}`;
+ days.push({ dayName, dateNum, fullDate });
+ }
+ return days;
+ };
+
+ const daysList = getLast7Days();
+
+ const fetchLogs = async (
+ pageNum: number,
+ dateFilter: string | null = selectedDate,
+ isRefresh = false
+ ) => {
+ if (pageNum === 1) {
+ if (isRefresh) setRefreshing(true);
+ else setLoading(true);
+ } else {
+ setLoadingMore(true);
+ }
+
+ try {
+ const res = await getFoodLogs(pageNum, dateFilter || undefined);
+ const fetchedLogs = res.logs || [];
+
+ if (pageNum === 1) {
+ setLogs(fetchedLogs);
+ } else {
+ setLogs((prev) => [...prev, ...fetchedLogs]);
+ }
+
+ // Determine if there is another page based on current fetched count vs API page limit of 20
+ setHasMore(fetchedLogs.length === 20);
+ setPage(pageNum);
+ } catch (err: any) {
+ console.error('History failed to fetch:', err);
+ showToast(err.message || 'Failed to fetch log history.', 'error');
+ } finally {
+ setLoading(false);
+ setLoadingMore(false);
+ setRefreshing(false);
+ }
+ };
+
+ // Re-fetch when tab is focused
+ useFocusEffect(
+ useCallback(() => {
+ fetchLogs(1, selectedDate);
+ }, [selectedDate])
+ );
+
+ const handleSelectDate = (date: string | null) => {
+ setSelectedDate(date);
+ };
+
+ const handleRefresh = () => {
+ fetchLogs(1, selectedDate, true);
+ };
+
+ const handleLoadMore = () => {
+ if (!loading && !loadingMore && hasMore) {
+ fetchLogs(page + 1);
+ }
+ };
+
+ const handleDeleteLog = (logId: string) => {
+ Alert.alert(
+ 'Delete Food Log',
+ 'Are you sure you want to delete this food log? This action cannot be undone.',
+ [
+ { text: 'Cancel', style: 'cancel' },
+ {
+ text: 'Delete',
+ style: 'destructive',
+ onPress: async () => {
+ try {
+ await deleteFoodLog(logId);
+ showToast('Food log deleted successfully.', 'success');
+ setLogs((prev) => prev.filter((l) => l.id !== logId));
+ } catch (err: any) {
+ showToast(err.message || 'Failed to delete food log.', 'error');
+ }
+ },
+ },
+ ]
+ );
+ };
+
+ const formatMealTime = (isoString: string) => {
+ try {
+ const date = new Date(isoString);
+ let hours = date.getHours();
+ const minutes = String(date.getMinutes()).padStart(2, '0');
+ const ampm = hours >= 12 ? 'PM' : 'AM';
+ hours = hours % 12;
+ hours = hours ? hours : 12;
+ return `${hours}:${minutes} ${ampm}`;
+ } catch (err) {
+ return '12:00 PM';
+ }
+ };
+
+ // FlatList Render Item
+ const renderLogItem = ({ item }: { item: FoodLog }) => (
+ router.push({ pathname: '/results', params: { log_id: item.id } })}
+ onLongPress={() => handleDeleteLog(item.id)}
+ activeOpacity={0.7}
+ >
+
+ {item.image_url ? (
+
+ ) : (
+
+ 🥗
+
+ )}
+
+
+
+
+
+ {item.meal_type.charAt(0).toUpperCase() + item.meal_type.slice(1)}
+
+
+ {formatMealTime(item.analyzed_at)}
+
+
+ {item.item_count} {item.item_count === 1 ? 'item' : 'items'}
+
+
+
+
+
+ {Math.round(item.total_calories)}
+ cal
+
+
+ );
+
+ return (
+
+
+
+ {/* HEADER */}
+
+ Food Log
+
+
+ {/* DATE SELECTOR ROW */}
+
+ item.fullDate}
+ renderItem={({ item }) => {
+ const isAll = item.fullDate === 'All';
+ const isSelected = isAll ? selectedDate === null : selectedDate === item.fullDate;
+
+ return (
+ handleSelectDate(isAll ? null : item.fullDate)}
+ >
+ {isAll ? (
+
+ All
+
+ ) : (
+ <>
+
+ {item.dayName}
+
+
+ {item.dateNum}
+
+
+ )}
+
+ );
+ }}
+ contentContainerStyle={styles.dateFilterScroll}
+ />
+
+
+ {/* FOOD LOG STREAM */}
+ {loading ? (
+
+
+ Opening history archive...
+
+ ) : logs.length === 0 ? (
+
+ 🍽️
+ No meals logged
+
+ {selectedDate
+ ? "Time to eat! Log some foods to start tracking today."
+ : "Start by snapping your meals using the camera tab!"}
+
+
+ ) : (
+ item.id}
+ renderItem={renderLogItem}
+ contentContainerStyle={styles.listContent}
+ onEndReached={handleLoadMore}
+ onEndReachedThreshold={0.2}
+ refreshing={refreshing}
+ onRefresh={handleRefresh}
+ ListFooterComponent={
+ loadingMore ? (
+
+
+
+ ) : null
+ }
+ />
+ )}
+
+ );
+}
+
+const styles = StyleSheet.create({
+ safeArea: {
+ flex: 1,
+ backgroundColor: '#F5F5F5',
+ },
+ header: {
+ height: 60,
+ backgroundColor: '#ffffff',
+ justifyContent: 'center',
+ paddingHorizontal: 20,
+ borderBottomWidth: 1,
+ borderBottomColor: '#eeeeee',
+ },
+ headerTitle: {
+ fontSize: 22,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ },
+ dateFilterContainer: {
+ backgroundColor: '#ffffff',
+ paddingVertical: 12,
+ borderBottomWidth: 1,
+ borderBottomColor: '#eeeeee',
+ },
+ dateFilterScroll: {
+ paddingHorizontal: 16,
+ },
+ dateButton: {
+ paddingVertical: 8,
+ paddingHorizontal: 14,
+ borderRadius: 12,
+ backgroundColor: '#F5F5F5',
+ marginRight: 8,
+ alignItems: 'center',
+ justifyContent: 'center',
+ minWidth: 50,
+ },
+ dateButtonSelected: {
+ backgroundColor: '#2E7D32',
+ },
+ allButtonText: {
+ fontSize: 14,
+ fontWeight: 'bold',
+ color: '#666666',
+ },
+ dateDayText: {
+ fontSize: 11,
+ fontWeight: 'bold',
+ color: '#888888',
+ textTransform: 'uppercase',
+ },
+ dateNumberText: {
+ fontSize: 16,
+ fontWeight: 'bold',
+ color: '#333333',
+ marginTop: 2,
+ },
+ dateTextSelected: {
+ color: '#ffffff',
+ },
+ centeredContainer: {
+ flex: 1,
+ justifyContent: 'center',
+ alignItems: 'center',
+ },
+ loadingText: {
+ fontSize: 16,
+ color: '#666',
+ marginTop: 12,
+ fontWeight: '500',
+ },
+ emptyContainer: {
+ flex: 1,
+ justifyContent: 'center',
+ alignItems: 'center',
+ paddingHorizontal: 40,
+ paddingBottom: 60,
+ },
+ emptyEmoji: {
+ fontSize: 64,
+ marginBottom: 20,
+ },
+ emptyTitle: {
+ fontSize: 20,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ marginBottom: 8,
+ },
+ emptySubtitle: {
+ fontSize: 14,
+ color: '#666666',
+ textAlign: 'center',
+ lineHeight: 20,
+ },
+ listContent: {
+ padding: 16,
+ paddingBottom: 30,
+ },
+ logCard: {
+ backgroundColor: '#ffffff',
+ borderRadius: 14,
+ padding: 14,
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ marginBottom: 12,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.03,
+ shadowRadius: 6,
+ elevation: 2,
+ },
+ cardLeft: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ flex: 1,
+ },
+ thumbnail: {
+ width: 60,
+ height: 60,
+ borderRadius: 10,
+ marginRight: 14,
+ },
+ placeholderThumbnail: {
+ width: 60,
+ height: 60,
+ borderRadius: 10,
+ backgroundColor: '#EEEEEE',
+ justifyContent: 'center',
+ alignItems: 'center',
+ marginRight: 14,
+ },
+ cardDetails: {
+ flex: 1,
+ justifyContent: 'center',
+ },
+ badgeRow: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ marginBottom: 6,
+ },
+ mealBadge: {
+ backgroundColor: '#E8F5E9',
+ paddingHorizontal: 8,
+ paddingVertical: 2,
+ borderRadius: 8,
+ marginRight: 8,
+ },
+ mealBadgeText: {
+ fontSize: 10,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ },
+ timeText: {
+ fontSize: 12,
+ color: '#888888',
+ },
+ itemCountText: {
+ fontSize: 13,
+ color: '#666666',
+ fontWeight: '500',
+ },
+ cardRight: {
+ alignItems: 'flex-end',
+ marginLeft: 10,
+ },
+ calorieText: {
+ fontSize: 22,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ },
+ calLabel: {
+ fontSize: 11,
+ color: '#888888',
+ marginTop: -2,
+ },
+ footerLoader: {
+ paddingVertical: 16,
+ alignItems: 'center',
+ },
+});
diff --git a/mobile/app/(tabs)/index.tsx b/mobile/app/(tabs)/index.tsx
new file mode 100644
index 0000000..bb2e6ce
--- /dev/null
+++ b/mobile/app/(tabs)/index.tsx
@@ -0,0 +1,668 @@
+import React, { useState, useEffect, useCallback } from 'react';
+import {
+ View,
+ Text,
+ StyleSheet,
+ ScrollView,
+ Image,
+ TouchableOpacity,
+ ActivityIndicator,
+ RefreshControl,
+ SafeAreaView,
+ StatusBar,
+ Dimensions,
+} from 'react-native';
+import { router, useFocusEffect } from 'expo-router';
+import MacroBar from '../../src/components/MacroBar';
+import {
+ getStats,
+ getUserProfile,
+ getFoodLogs,
+ getWeeklyCheckin,
+ DailyStats,
+ FoodLog,
+ WeeklyCheckin,
+} from '../../src/api/food';
+import { useToast } from '../../src/components/Toast';
+
+const { width } = Dimensions.get('window');
+
+export default function DashboardScreen() {
+ const { showToast } = useToast();
+ const [loading, setLoading] = useState(true);
+ const [refreshing, setRefreshing] = useState(false);
+
+ const [profile, setProfile] = useState(null);
+ const [stats, setStats] = useState([]);
+ const [todayMeals, setTodayMeals] = useState([]);
+ const [weeklyCheckin, setWeeklyCheckin] = useState(null);
+
+ const getLocalDateString = () => {
+ const d = new Date();
+ const year = d.getFullYear();
+ const month = String(d.getMonth() + 1).padStart(2, '0');
+ const day = String(d.getDate()).padStart(2, '0');
+ return `${year}-${month}-${day}`;
+ };
+
+ const loadData = async (isSilent = false) => {
+ if (!isSilent) setLoading(true);
+ const todayStr = getLocalDateString();
+
+ try {
+ const [profileData, statsData, logsData, checkinData] = await Promise.all([
+ getUserProfile(),
+ getStats('week'),
+ getFoodLogs(1, todayStr),
+ getWeeklyCheckin(),
+ ]);
+
+ setProfile(profileData);
+ setStats(statsData);
+ setTodayMeals(logsData.logs || []);
+ setWeeklyCheckin(checkinData);
+ } catch (err: any) {
+ console.error('Dashboard failed to load:', err);
+ showToast(err.message || 'Failed to fetch dashboard data.', 'error');
+ } finally {
+ setLoading(false);
+ setRefreshing(false);
+ }
+ };
+
+ // Re-fetch data whenever this tab is focused (e.g. after adding a meal or saving profile)
+ useFocusEffect(
+ useCallback(() => {
+ loadData(false);
+ }, [])
+ );
+
+ const onRefresh = () => {
+ setRefreshing(true);
+ loadData(true);
+ };
+
+ // Date & Greeting Helpers
+ const getGreeting = () => {
+ const hr = new Date().getHours();
+ if (hr < 12) return 'Good morning';
+ if (hr < 17) return 'Good afternoon';
+ return 'Good evening';
+ };
+
+ const getFormattedDate = () => {
+ const options: Intl.DateTimeFormatOptions = {
+ weekday: 'long',
+ month: 'short',
+ day: 'numeric',
+ year: 'numeric',
+ };
+ return new Date().toLocaleDateString('en-US', options);
+ };
+
+ const formatMealTime = (isoString: string) => {
+ try {
+ const date = new Date(isoString);
+ let hours = date.getHours();
+ const minutes = String(date.getMinutes()).padStart(2, '0');
+ const ampm = hours >= 12 ? 'PM' : 'AM';
+ hours = hours % 12;
+ hours = hours ? hours : 12;
+ return `${hours}:${minutes} ${ampm}`;
+ } catch (err) {
+ return '12:00 PM';
+ }
+ };
+
+ // Streak calculations
+ const calculateStreak = (statsData: DailyStats[]) => {
+ if (!statsData || statsData.length === 0) return 0;
+ const reversed = [...statsData].reverse();
+ let streak = 0;
+
+ for (let i = 0; i < reversed.length; i++) {
+ const day = reversed[i];
+ if (day.meal_count > 0) {
+ streak++;
+ } else {
+ if (i === 0) {
+ // If today has no meals, the streak is still alive if yesterday had meals
+ continue;
+ } else {
+ break;
+ }
+ }
+ }
+ return streak;
+ };
+
+ // UI calculations
+ const todayStr = getLocalDateString();
+ const todayStats = stats.find((s) => s.date === todayStr);
+
+ const calorieGoal = profile?.daily_calorie_goal || 2000;
+ const consumedCalories = todayStats ? Math.round(todayStats.total_calories) : 0;
+ const protein = todayStats ? todayStats.total_protein_g : 0;
+ const fat = todayStats ? todayStats.total_fat_g : 0;
+ const carbs = todayStats ? todayStats.total_carbs_g : 0;
+
+ const progress = calorieGoal > 0 ? Math.min(1.2, consumedCalories / calorieGoal) : 0;
+
+ const getProgressColor = (p: number) => {
+ if (p > 1.0) return '#D32F2F'; // Red (Over goal)
+ if (p >= 0.85) return '#FF9800'; // Orange (Close)
+ return '#2E7D32'; // Green (Under/Good)
+ };
+
+ // Insight Background Helper
+ const getWeeklyInsightColor = (status: string) => {
+ switch (status) {
+ case 'on_track':
+ return { bg: '#E8F5E9', text: '#2E7D32', border: '#C8E6C9' };
+ case 'over_goal':
+ return { bg: '#FFF3E0', text: '#E65100', border: '#FFE0B2' };
+ case 'under_goal':
+ return { bg: '#E3F2FD', text: '#0D47A1', border: '#BBDEFB' };
+ default:
+ return { bg: '#F5F5F5', text: '#666666', border: '#E0E0E0' };
+ }
+ };
+
+ const streak = calculateStreak(stats);
+ const avgCal = stats.length > 0
+ ? Math.round(stats.reduce((sum, d) => sum + d.total_calories, 0) / stats.length)
+ : 0;
+
+ const weeklyInsightStyle = weeklyCheckin
+ ? getWeeklyInsightColor(weeklyCheckin.status)
+ : { bg: '#F5F5F5', text: '#666666', border: '#E0E0E0' };
+
+ if (loading && !refreshing) {
+ return (
+
+
+ Assembling your dashboard...
+
+ );
+ }
+
+ return (
+
+
+
+ }
+ >
+ {/* A. GREETING HEADER */}
+
+ {getGreeting()},
+ {getFormattedDate()}
+
+
+ {/* B. TODAY'S CALORIE CARD */}
+
+
+ Calorie Intake
+
+
+ {progress > 1 ? 'Target Exceeded' : progress >= 0.85 ? 'Almost There' : 'On Track'}
+
+
+
+
+ {/* Progress Indicator */}
+
+
+ {consumedCalories.toLocaleString()}
+ of {calorieGoal.toLocaleString()} cal goal
+
+
+ {/* Custom Premium Progress Bar */}
+
+
+
+
+
+
+
+ {/* Macros Bar */}
+
+
+
+ {/* C. TODAY'S MEALS LIST */}
+
+ Today's Meals
+
+ {todayMeals.length}
+
+
+
+ {todayMeals.length === 0 ? (
+
+
+ 📸
+
+ No meals logged yet today.
+ Tap the Camera below to snap your first meal!
+ router.push('/(tabs)/camera')}
+ >
+ Open Camera
+
+
+ ) : (
+
+ {todayMeals.map((meal) => (
+ router.push({ pathname: '/results', params: { log_id: meal.id } })}
+ >
+
+ {meal.image_url ? (
+
+ ) : (
+
+ 🥗
+
+ )}
+
+
+ {meal.meal_type.charAt(0).toUpperCase() + meal.meal_type.slice(1)}
+
+ {formatMealTime(meal.analyzed_at)}
+
+
+
+ {Math.round(meal.total_calories)}
+ cal
+
+
+ ))}
+
+ )}
+
+ {/* D. WEEKLY INSIGHT CARD */}
+ {weeklyCheckin && (
+
+
+
+ Weekly Checklist Summary
+
+
+ {weeklyCheckin.status.replace('_', ' ').toUpperCase()}
+
+
+
+ This week: avg {Math.round(weeklyCheckin.average_daily_calories)} cal/day • {weeklyCheckin.days_logged} days logged
+
+
+ {weeklyCheckin.insight}
+
+ )}
+
+ {/* E. QUICK STATS ROW */}
+
+ Quick Stats
+
+
+
+
+ {todayMeals.length}
+ Meals Today
+
+
+
+ 🔥 {streak}
+ Day Streak
+
+
+
+ {avgCal}
+ Avg Cal/Day
+
+
+
+ {/* Bottom spacing */}
+
+
+
+ );
+}
+
+const styles = StyleSheet.create({
+ safeArea: {
+ flex: 1,
+ backgroundColor: '#F5F5F5',
+ },
+ centeredContainer: {
+ flex: 1,
+ justifyContent: 'center',
+ alignItems: 'center',
+ backgroundColor: '#F5F5F5',
+ },
+ loadingText: {
+ fontSize: 16,
+ color: '#666',
+ marginTop: 12,
+ fontWeight: '500',
+ },
+ header: {
+ paddingHorizontal: 20,
+ paddingTop: 24,
+ paddingBottom: 16,
+ },
+ greeting: {
+ fontSize: 28,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ letterSpacing: -0.5,
+ },
+ date: {
+ fontSize: 14,
+ color: '#666666',
+ marginTop: 4,
+ fontWeight: '500',
+ },
+ calorieCard: {
+ backgroundColor: '#ffffff',
+ borderRadius: 16,
+ marginHorizontal: 16,
+ padding: 20,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 4 },
+ shadowOpacity: 0.04,
+ shadowRadius: 10,
+ elevation: 3,
+ marginBottom: 20,
+ },
+ cardHeader: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ marginBottom: 16,
+ },
+ cardTitle: {
+ fontSize: 16,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ },
+ badge: {
+ paddingHorizontal: 10,
+ paddingVertical: 4,
+ borderRadius: 12,
+ },
+ badgeText: {
+ fontSize: 11,
+ fontWeight: 'bold',
+ },
+ progressContainer: {
+ alignItems: 'center',
+ marginVertical: 10,
+ },
+ calorieInfo: {
+ alignItems: 'center',
+ marginBottom: 14,
+ },
+ consumedNumber: {
+ fontSize: 44,
+ fontWeight: '800',
+ color: '#1A1A1A',
+ letterSpacing: -1,
+ },
+ goalText: {
+ fontSize: 14,
+ color: '#666666',
+ marginTop: 2,
+ fontWeight: '500',
+ },
+ progressBarContainer: {
+ width: '100%',
+ paddingHorizontal: 10,
+ },
+ progressBarTrack: {
+ height: 10,
+ width: '100%',
+ backgroundColor: '#E0E0E0',
+ borderRadius: 5,
+ overflow: 'hidden',
+ },
+ progressBarFill: {
+ height: '100%',
+ borderRadius: 5,
+ },
+ sectionHeader: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ paddingHorizontal: 20,
+ marginBottom: 12,
+ marginTop: 8,
+ },
+ sectionTitle: {
+ fontSize: 18,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ },
+ mealCountBadge: {
+ backgroundColor: '#E0E0E0',
+ paddingHorizontal: 8,
+ paddingVertical: 2,
+ borderRadius: 10,
+ marginLeft: 8,
+ },
+ mealCountText: {
+ fontSize: 12,
+ color: '#555',
+ fontWeight: 'bold',
+ },
+ emptyMealsCard: {
+ backgroundColor: '#ffffff',
+ borderRadius: 16,
+ marginHorizontal: 16,
+ padding: 24,
+ alignItems: 'center',
+ justifyContent: 'center',
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.03,
+ shadowRadius: 8,
+ elevation: 2,
+ marginBottom: 20,
+ },
+ emptyIconCircle: {
+ width: 60,
+ height: 60,
+ borderRadius: 30,
+ backgroundColor: '#E8F5E9',
+ justifyContent: 'center',
+ alignItems: 'center',
+ marginBottom: 16,
+ },
+ emptyIcon: {
+ fontSize: 28,
+ },
+ emptyText: {
+ fontSize: 16,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ marginBottom: 4,
+ },
+ emptySubtext: {
+ fontSize: 13,
+ color: '#666',
+ textAlign: 'center',
+ marginBottom: 16,
+ paddingHorizontal: 20,
+ },
+ emptyActionButton: {
+ backgroundColor: '#2E7D32',
+ paddingVertical: 10,
+ paddingHorizontal: 20,
+ borderRadius: 20,
+ },
+ emptyActionText: {
+ color: '#ffffff',
+ fontSize: 14,
+ fontWeight: 'bold',
+ },
+ mealsContainer: {
+ paddingHorizontal: 16,
+ marginBottom: 16,
+ },
+ mealCard: {
+ backgroundColor: '#ffffff',
+ borderRadius: 12,
+ padding: 12,
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ marginBottom: 10,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 1 },
+ shadowOpacity: 0.02,
+ shadowRadius: 4,
+ elevation: 1,
+ },
+ mealLeft: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ flex: 1,
+ },
+ mealThumbnail: {
+ width: 48,
+ height: 48,
+ borderRadius: 8,
+ marginRight: 12,
+ },
+ mealPlaceholder: {
+ width: 48,
+ height: 48,
+ borderRadius: 8,
+ backgroundColor: '#eee',
+ justifyContent: 'center',
+ alignItems: 'center',
+ marginRight: 12,
+ },
+ mealDetails: {
+ justifyContent: 'center',
+ },
+ mealType: {
+ fontSize: 15,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ },
+ mealTime: {
+ fontSize: 12,
+ color: '#888',
+ marginTop: 2,
+ },
+ mealRight: {
+ alignItems: 'flex-end',
+ marginLeft: 10,
+ },
+ mealCalories: {
+ fontSize: 18,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ },
+ mealCalLabel: {
+ fontSize: 10,
+ color: '#888',
+ marginTop: -2,
+ },
+ insightCard: {
+ borderRadius: 16,
+ marginHorizontal: 16,
+ padding: 16,
+ borderWidth: 1,
+ marginBottom: 20,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.02,
+ shadowRadius: 6,
+ elevation: 1,
+ },
+ insightHeader: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ marginBottom: 8,
+ },
+ insightTitle: {
+ fontSize: 14,
+ fontWeight: 'bold',
+ },
+ insightStatusBadge: {
+ fontSize: 10,
+ fontWeight: 'bold',
+ letterSpacing: 0.5,
+ },
+ insightStats: {
+ fontSize: 13,
+ color: '#444444',
+ marginBottom: 10,
+ },
+ insightDivider: {
+ height: 1,
+ backgroundColor: '#00000010',
+ marginBottom: 10,
+ },
+ insightMessage: {
+ fontSize: 13,
+ color: '#444',
+ lineHeight: 18,
+ fontWeight: '500',
+ },
+ quickStatsRow: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ paddingHorizontal: 16,
+ marginBottom: 20,
+ },
+ quickStatCard: {
+ backgroundColor: '#ffffff',
+ borderRadius: 12,
+ paddingVertical: 14,
+ paddingHorizontal: 10,
+ alignItems: 'center',
+ flex: 1,
+ marginHorizontal: 4,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.02,
+ shadowRadius: 4,
+ elevation: 1,
+ },
+ quickStatValue: {
+ fontSize: 18,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ textAlign: 'center',
+ },
+ quickStatLabel: {
+ fontSize: 11,
+ color: '#666666',
+ marginTop: 4,
+ fontWeight: '500',
+ textAlign: 'center',
+ },
+});
diff --git a/mobile/app/(tabs)/profile.tsx b/mobile/app/(tabs)/profile.tsx
new file mode 100644
index 0000000..e1e2165
--- /dev/null
+++ b/mobile/app/(tabs)/profile.tsx
@@ -0,0 +1,839 @@
+import React, { useState, useEffect } from 'react';
+import {
+ View,
+ Text,
+ StyleSheet,
+ ScrollView,
+ TextInput,
+ TouchableOpacity,
+ ActivityIndicator,
+ Modal,
+ Alert,
+ SafeAreaView,
+ StatusBar,
+} from 'react-native';
+import { router } from 'expo-router';
+import { getUserProfile, updateUserProfile } from '../../src/api/food';
+import { logout } from '../../src/api/auth';
+import { useToast } from '../../src/components/Toast';
+
+export default function ProfileScreen() {
+ const { showToast } = useToast();
+ const [loading, setLoading] = useState(true);
+ const [saving, setSaving] = useState(false);
+ const [savingGoal, setSavingGoal] = useState(false);
+
+ // Form Fields
+ const [email, setEmail] = useState('');
+ const [height, setHeight] = useState('');
+ const [weight, setWeight] = useState('');
+ const [age, setAge] = useState('');
+ const [activityLevel, setActivityLevel] = useState('sedentary');
+ const [goal, setGoal] = useState('maintain');
+ const [dietaryRestrictions, setDietaryRestrictions] = useState([]);
+ const [dailyCalorieGoal, setDailyCalorieGoal] = useState(2000);
+ const [customGoal, setCustomGoal] = useState('');
+
+ // Selector Modal States
+ const [isActivityModalOpen, setIsActivityModalOpen] = useState(false);
+
+ const activityOptions = [
+ { value: 'sedentary', label: 'Sedentary', desc: 'Little to no exercise, desk job' },
+ { value: 'light', label: 'Lightly Active', desc: 'Light exercise/sports 1-3 days/week' },
+ { value: 'moderate', label: 'Moderately Active', desc: 'Moderate exercise 3-5 days/week' },
+ { value: 'active', label: 'Very Active', desc: 'Hard exercise/sports 6-7 days/week' },
+ { value: 'very_active', label: 'Extra Active', desc: 'Physical job or elite athletic training' },
+ ];
+
+ const goalOptions = [
+ { value: 'lose_weight', label: 'Lose Weight' },
+ { value: 'maintain', label: 'Maintain' },
+ { value: 'gain_muscle', label: 'Gain Muscle' },
+ ];
+
+ const dietaryOptions = [
+ 'Vegetarian',
+ 'Vegan',
+ 'Gluten-Free',
+ 'Dairy-Free',
+ 'Nut-Free',
+ 'Halal',
+ 'Kosher',
+ ];
+
+ useEffect(() => {
+ fetchProfile();
+ }, []);
+
+ const fetchProfile = async () => {
+ try {
+ setLoading(true);
+ const data = await getUserProfile();
+ setEmail(data.email || '');
+ setHeight(data.height_cm ? String(data.height_cm) : '');
+ setWeight(data.weight_kg ? String(data.weight_kg) : '');
+ setAge(data.age ? String(data.age) : '');
+ setActivityLevel(data.activity_level || 'sedentary');
+ setGoal(data.goal || 'maintain');
+ setDietaryRestrictions(data.dietary_restrictions || []);
+ setDailyCalorieGoal(data.daily_calorie_goal || 2000);
+ setCustomGoal(String(data.daily_calorie_goal || 2000));
+ } catch (err: any) {
+ console.error('Failed to fetch profile:', err);
+ showToast(err.message || 'Failed to load user profile.', 'error');
+ } finally {
+ setLoading(false);
+ }
+ };
+
+ const handleSaveProfile = async () => {
+ // Validate
+ const hNum = parseFloat(height);
+ const wNum = parseFloat(weight);
+ const aNum = parseInt(age);
+
+ if (height && (isNaN(hNum) || hNum <= 0)) {
+ Alert.alert('Invalid Input', 'Please enter a valid height.');
+ return;
+ }
+ if (weight && (isNaN(wNum) || wNum <= 0)) {
+ Alert.alert('Invalid Input', 'Please enter a valid weight.');
+ return;
+ }
+ if (age && (isNaN(aNum) || aNum <= 0)) {
+ Alert.alert('Invalid Input', 'Please enter a valid age.');
+ return;
+ }
+
+ setSaving(true);
+ try {
+ const updatedProfile = await updateUserProfile({
+ height_cm: height ? hNum : null,
+ weight_kg: weight ? wNum : null,
+ age: age ? aNum : null,
+ activity_level: activityLevel,
+ goal: goal,
+ dietary_restrictions: dietaryRestrictions,
+ });
+
+ // Update local state with returned target
+ setDailyCalorieGoal(updatedProfile.daily_calorie_goal);
+ setCustomGoal(String(updatedProfile.daily_calorie_goal));
+
+ showToast('Profile updated successfully!', 'success');
+ } catch (err: any) {
+ console.error('Failed to update profile:', err);
+ showToast(err.message || 'Failed to save profile changes.', 'error');
+ } finally {
+ setSaving(false);
+ }
+ };
+
+ const handleUpdateCalorieGoal = async () => {
+ const goalNum = parseInt(customGoal);
+ if (isNaN(goalNum) || goalNum < 500 || goalNum > 10000) {
+ Alert.alert('Invalid Input', 'Calorie goal must be a number between 500 and 10,000.');
+ return;
+ }
+
+ setSavingGoal(true);
+ try {
+ const updatedProfile = await updateUserProfile({
+ daily_calorie_goal: goalNum,
+ });
+ setDailyCalorieGoal(updatedProfile.daily_calorie_goal);
+ setCustomGoal(String(updatedProfile.daily_calorie_goal));
+ showToast('Calorie goal updated successfully!', 'success');
+ } catch (err: any) {
+ console.error('Failed to update calorie goal:', err);
+ showToast(err.message || 'Failed to update calorie goal.', 'error');
+ } finally {
+ setSavingGoal(false);
+ }
+ };
+
+ const toggleDietaryRestriction = (restriction: string) => {
+ setDietaryRestrictions((prev) =>
+ prev.includes(restriction)
+ ? prev.filter((r) => r !== restriction)
+ : [...prev, restriction]
+ );
+ };
+
+ const handleLogout = () => {
+ Alert.alert(
+ 'Log Out',
+ 'Are you sure you want to log out of FoodSnap AI?',
+ [
+ { text: 'Cancel', style: 'cancel' },
+ {
+ text: 'Log Out',
+ style: 'destructive',
+ onPress: async () => {
+ await logout();
+ showToast('Logged out successfully.', 'info');
+ router.replace('/login');
+ },
+ },
+ ]
+ );
+ };
+
+ // Helper to get initials
+ const getUserInitial = () => {
+ if (!email) return 'U';
+ return email.charAt(0).toUpperCase();
+ };
+
+ // Helper to determine if all health profile fields are filled
+ const areAllHealthFieldsFilled = () => {
+ return height !== '' && weight !== '' && age !== '';
+ };
+
+ const getActivityLabel = () => {
+ const opt = activityOptions.find((o) => o.value === activityLevel);
+ return opt ? opt.label : 'Select Activity';
+ };
+
+ if (loading) {
+ return (
+
+
+ Opening profile dashboard...
+
+ );
+ }
+
+ return (
+
+
+
+
+ {/* A. PROFILE HEADER */}
+
+
+ {getUserInitial()}
+
+ {email}
+ FoodSnap AI Premium Member
+
+
+ {/* B. HEALTH PROFILE SECTION */}
+
+ Health Profile
+
+ {/* Height Input */}
+
+ Height
+
+
+ cm
+
+
+
+ {/* Weight Input */}
+
+ Weight
+
+
+ kg
+
+
+
+ {/* Age Input */}
+
+ Age
+
+
+ years
+
+
+
+ {/* Goal Selector */}
+
+ Weight Goal
+
+ {goalOptions.map((opt) => {
+ const isSelected = goal === opt.value;
+ return (
+ setGoal(opt.value)}
+ >
+
+ {opt.label}
+
+
+ );
+ })}
+
+
+ {/* Activity Selector Row */}
+
+ setIsActivityModalOpen(true)}
+ >
+ Activity Level
+
+ {getActivityLabel()}
+ ▾
+
+
+
+ {/* Save Profile Button */}
+
+ {saving ? (
+
+ ) : (
+ Save Profile Settings
+ )}
+
+
+ {/* TDEE Auto target banner */}
+ {areAllHealthFieldsFilled() && (
+
+ 🎯
+
+ Auto Target TDEE Locked
+
+ Your recommended daily intake has been recalculated to {dailyCalorieGoal.toLocaleString()} cal based on the Mifflin-St Jeor formula.
+
+
+
+ )}
+
+
+ {/* C. DAILY CALORIE GOAL MANUAL UPDATE */}
+
+ Target Override
+
+ Current Target: {dailyCalorieGoal.toLocaleString()} cal
+
+
+
+
+
+ {savingGoal ? (
+
+ ) : (
+ Update
+ )}
+
+
+
+
+ {/* D. DIETARY RESTRICTIONS */}
+
+ Dietary Restrictions
+ Select allergens or preferences to sync with your AI models:
+
+ {dietaryOptions.map((opt) => {
+ const isSelected = dietaryRestrictions.includes(opt);
+ return (
+ toggleDietaryRestriction(opt)}
+ >
+
+ {opt}
+
+
+ );
+ })}
+
+
+
+ {/* E. APP INFO */}
+
+ FoodSnap AI v0.1.0
+ Powered by Google Gemini 1.5 Flash + USDA FoodData
+ A WeCode Innovation
+
+
+ {/* F. LOGOUT BUTTON */}
+
+ Log Out
+
+
+ {/* Spacing */}
+
+
+
+
+ {/* ACTIVITY LEVEL MODAL */}
+ setIsActivityModalOpen(false)}
+ >
+
+
+ Activity Level
+
+ {activityOptions.map((opt) => {
+ const isSelected = activityLevel === opt.value;
+ return (
+ {
+ setActivityLevel(opt.value);
+ setIsActivityModalOpen(false);
+ }}
+ >
+
+
+ {opt.label}
+
+ {isSelected && ✓}
+
+
+ {opt.desc}
+
+
+ );
+ })}
+
+ setIsActivityModalOpen(false)}
+ >
+ Close
+
+
+
+
+
+
+ );
+}
+
+const styles = StyleSheet.create({
+ safeArea: {
+ flex: 1,
+ backgroundColor: '#F5F5F5',
+ },
+ centeredContainer: {
+ flex: 1,
+ justifyContent: 'center',
+ alignItems: 'center',
+ },
+ loadingText: {
+ fontSize: 16,
+ color: '#666',
+ marginTop: 12,
+ fontWeight: '500',
+ },
+ scrollContent: {
+ paddingBottom: 40,
+ },
+ profileHeader: {
+ alignItems: 'center',
+ paddingVertical: 30,
+ backgroundColor: '#ffffff',
+ borderBottomWidth: 1,
+ borderBottomColor: '#eeeeee',
+ },
+ avatarCircle: {
+ width: 80,
+ height: 80,
+ borderRadius: 40,
+ backgroundColor: '#2E7D32',
+ justifyContent: 'center',
+ alignItems: 'center',
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.1,
+ shadowRadius: 6,
+ elevation: 3,
+ },
+ avatarText: {
+ fontSize: 36,
+ fontWeight: 'bold',
+ color: '#ffffff',
+ },
+ emailText: {
+ fontSize: 18,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ marginTop: 12,
+ },
+ brandingSubtext: {
+ fontSize: 12,
+ color: '#666666',
+ marginTop: 2,
+ fontWeight: '500',
+ },
+ card: {
+ backgroundColor: '#ffffff',
+ borderRadius: 16,
+ padding: 20,
+ marginHorizontal: 16,
+ marginTop: 20,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.03,
+ shadowRadius: 6,
+ elevation: 2,
+ },
+ cardHeader: {
+ fontSize: 16,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ marginBottom: 16,
+ },
+ inputRow: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ paddingVertical: 12,
+ borderBottomWidth: 1,
+ borderBottomColor: '#f0f0f0',
+ },
+ rowLabel: {
+ fontSize: 15,
+ fontWeight: '500',
+ color: '#333333',
+ },
+ inputWrapper: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ },
+ rowInput: {
+ width: 60,
+ textAlign: 'right',
+ fontSize: 15,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ paddingVertical: 4,
+ },
+ rowUnit: {
+ fontSize: 13,
+ color: '#888888',
+ marginLeft: 6,
+ width: 40,
+ },
+ sectionDivider: {
+ height: 1,
+ backgroundColor: '#eee',
+ marginVertical: 16,
+ },
+ innerSectionTitle: {
+ fontSize: 14,
+ fontWeight: 'bold',
+ color: '#666',
+ marginBottom: 12,
+ },
+ goalPillsRow: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ },
+ goalPill: {
+ flex: 1,
+ paddingVertical: 10,
+ borderRadius: 10,
+ backgroundColor: '#F5F5F5',
+ marginHorizontal: 4,
+ alignItems: 'center',
+ justifyContent: 'center',
+ },
+ goalPillSelected: {
+ backgroundColor: '#2E7D32',
+ },
+ goalPillText: {
+ fontSize: 13,
+ fontWeight: 'bold',
+ color: '#666666',
+ },
+ goalPillTextSelected: {
+ color: '#ffffff',
+ },
+ selectRow: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ paddingVertical: 12,
+ },
+ selectBadge: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ backgroundColor: '#F5F5F5',
+ paddingHorizontal: 12,
+ paddingVertical: 6,
+ borderRadius: 8,
+ },
+ selectBadgeText: {
+ fontSize: 13,
+ fontWeight: 'bold',
+ color: '#333333',
+ marginRight: 6,
+ },
+ selectArrow: {
+ fontSize: 14,
+ color: '#888888',
+ fontWeight: 'bold',
+ },
+ saveButton: {
+ backgroundColor: '#2E7D32',
+ borderRadius: 12,
+ paddingVertical: 14,
+ alignItems: 'center',
+ justifyContent: 'center',
+ marginTop: 20,
+ },
+ saveButtonText: {
+ color: '#ffffff',
+ fontSize: 15,
+ fontWeight: 'bold',
+ },
+ autoCalculatedBanner: {
+ flexDirection: 'row',
+ backgroundColor: '#E8F5E9',
+ borderRadius: 12,
+ padding: 12,
+ marginTop: 20,
+ alignItems: 'flex-start',
+ borderWidth: 1,
+ borderColor: '#C8E6C9',
+ },
+ bannerIcon: {
+ fontSize: 18,
+ marginRight: 8,
+ marginTop: 2,
+ },
+ bannerTextContent: {
+ flex: 1,
+ },
+ bannerTitle: {
+ fontSize: 13,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ marginBottom: 2,
+ },
+ bannerText: {
+ fontSize: 12,
+ color: '#388E3C',
+ lineHeight: 16,
+ },
+ goalTargetBannerText: {
+ fontSize: 15,
+ color: '#444',
+ marginBottom: 16,
+ },
+ manualOverrideRow: {
+ flexDirection: 'row',
+ },
+ manualInput: {
+ flex: 1,
+ borderWidth: 1,
+ borderColor: '#E0E0E0',
+ borderRadius: 10,
+ paddingHorizontal: 12,
+ fontSize: 15,
+ marginRight: 10,
+ fontWeight: '500',
+ },
+ manualBtn: {
+ backgroundColor: '#2E7D32',
+ paddingHorizontal: 16,
+ borderRadius: 10,
+ justifyContent: 'center',
+ alignItems: 'center',
+ minWidth: 80,
+ },
+ manualBtnText: {
+ color: '#fff',
+ fontWeight: 'bold',
+ fontSize: 14,
+ },
+ chipIntroText: {
+ fontSize: 13,
+ color: '#666666',
+ marginBottom: 12,
+ },
+ chipsContainer: {
+ flexDirection: 'row',
+ flexWrap: 'wrap',
+ },
+ chip: {
+ paddingVertical: 8,
+ paddingHorizontal: 14,
+ borderRadius: 20,
+ backgroundColor: '#F5F5F5',
+ marginRight: 8,
+ marginBottom: 8,
+ borderWidth: 1,
+ borderColor: '#E0E0E0',
+ },
+ chipSelected: {
+ backgroundColor: '#E8F5E9',
+ borderColor: '#C8E6C9',
+ },
+ chipText: {
+ fontSize: 13,
+ color: '#666666',
+ fontWeight: '500',
+ },
+ chipTextSelected: {
+ color: '#2E7D32',
+ fontWeight: 'bold',
+ },
+ appInfoContainer: {
+ alignItems: 'center',
+ marginTop: 30,
+ marginBottom: 10,
+ },
+ appVersion: {
+ fontSize: 14,
+ fontWeight: 'bold',
+ color: '#333333',
+ },
+ appCredit: {
+ fontSize: 11,
+ color: '#888888',
+ marginTop: 4,
+ textAlign: 'center',
+ },
+ companyCredit: {
+ fontSize: 11,
+ color: '#888888',
+ marginTop: 2,
+ fontWeight: '500',
+ },
+ logoutButton: {
+ marginHorizontal: 16,
+ marginTop: 20,
+ borderRadius: 12,
+ paddingVertical: 14,
+ borderWidth: 1.5,
+ borderColor: '#D32F2F',
+ alignItems: 'center',
+ justifyContent: 'center',
+ backgroundColor: '#ffffff',
+ },
+ logoutButtonText: {
+ color: '#D32F2F',
+ fontSize: 15,
+ fontWeight: 'bold',
+ },
+ // MODAL STYLING
+ modalOverlay: {
+ flex: 1,
+ backgroundColor: 'rgba(0,0,0,0.5)',
+ justifyContent: 'flex-end',
+ },
+ modalContent: {
+ backgroundColor: '#ffffff',
+ borderTopLeftRadius: 20,
+ borderTopRightRadius: 20,
+ padding: 24,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: -3 },
+ shadowOpacity: 0.1,
+ shadowRadius: 10,
+ elevation: 10,
+ },
+ modalTitle: {
+ fontSize: 18,
+ fontWeight: 'bold',
+ color: '#1A1A1A',
+ marginBottom: 20,
+ textAlign: 'center',
+ },
+ modalOptionCard: {
+ borderRadius: 12,
+ borderWidth: 1,
+ borderColor: '#eeeeee',
+ padding: 14,
+ marginBottom: 10,
+ backgroundColor: '#F9F9F9',
+ },
+ modalOptionCardSelected: {
+ borderColor: '#2E7D32',
+ backgroundColor: '#E8F5E9',
+ },
+ modalOptionHeader: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ },
+ modalOptionLabel: {
+ fontSize: 15,
+ fontWeight: 'bold',
+ color: '#333333',
+ },
+ checkIcon: {
+ fontSize: 16,
+ color: '#2E7D32',
+ fontWeight: 'bold',
+ },
+ modalOptionDesc: {
+ fontSize: 12,
+ color: '#666666',
+ marginTop: 4,
+ },
+ modalOptionSelectedText: {
+ color: '#2E7D32',
+ },
+ modalCloseBtn: {
+ backgroundColor: '#F5F5F5',
+ borderRadius: 12,
+ paddingVertical: 14,
+ alignItems: 'center',
+ marginTop: 12,
+ },
+ modalCloseBtnText: {
+ color: '#333333',
+ fontWeight: 'bold',
+ fontSize: 15,
+ },
+});
diff --git a/mobile/app/_layout.tsx b/mobile/app/_layout.tsx
new file mode 100644
index 0000000..f4ff332
--- /dev/null
+++ b/mobile/app/_layout.tsx
@@ -0,0 +1,50 @@
+import { useEffect, useState } from 'react';
+import { Stack, router, Slot } from 'expo-router';
+import { isAuthenticated } from '../src/api/auth';
+import { View, ActivityIndicator } from 'react-native';
+import { ToastProvider } from '../src/components/Toast';
+
+export default function RootLayout() {
+ const [isLoaded, setIsLoaded] = useState(false);
+ const [isAuth, setIsAuth] = useState(false);
+
+ useEffect(() => {
+ checkAuth();
+ }, []);
+
+ async function checkAuth() {
+ try {
+ const authStatus = await isAuthenticated();
+ setIsAuth(authStatus);
+ } catch (error) {
+ setIsAuth(false);
+ } finally {
+ setIsLoaded(true);
+ }
+ }
+
+ useEffect(() => {
+ if (isLoaded) {
+ if (isAuth) {
+ router.replace('/(tabs)');
+ } else {
+ router.replace('/login');
+ }
+ }
+ }, [isLoaded, isAuth]);
+
+ if (!isLoaded) {
+ return (
+
+
+
+ );
+ }
+
+ return (
+
+
+
+ );
+}
+
diff --git a/mobile/app/results.tsx b/mobile/app/results.tsx
new file mode 100644
index 0000000..6909fad
--- /dev/null
+++ b/mobile/app/results.tsx
@@ -0,0 +1,679 @@
+import React, { useState, useEffect } from 'react';
+import {
+ View,
+ Text,
+ StyleSheet,
+ ScrollView,
+ Image,
+ TouchableOpacity,
+ Modal,
+ TextInput,
+ ActivityIndicator,
+ Alert,
+ SafeAreaView,
+ StatusBar,
+} from 'react-native';
+import { useLocalSearchParams, router } from 'expo-router';
+import MacroBar from '../src/components/MacroBar';
+import { correctFoodItem, getFoodLogDetail, AnalysisResult, FoodItem } from '../src/api/food';
+
+export default function ResultsScreen() {
+ const params = useLocalSearchParams();
+ const [analysis, setAnalysis] = useState(null);
+ const [selectedItem, setSelectedItem] = useState(null);
+ const [isModalOpen, setIsModalOpen] = useState(false);
+ const [editName, setEditName] = useState('');
+ const [editWeight, setEditWeight] = useState('');
+ const [savingCorrection, setSavingCorrection] = useState(false);
+ const [loading, setLoading] = useState(false);
+
+ useEffect(() => {
+ if (params.data) {
+ try {
+ const parsed = JSON.parse(params.data as string) as AnalysisResult;
+ setAnalysis(parsed);
+ } catch (err) {
+ console.error('Failed to parse analysis data:', err);
+ Alert.alert('Error', 'Failed to load analysis results.');
+ }
+ } else if (params.log_id) {
+ fetchLogDetail(params.log_id as string);
+ }
+ }, [params.data, params.log_id]);
+
+ async function fetchLogDetail(id: string) {
+ setLoading(true);
+ try {
+ const data = await getFoodLogDetail(id);
+ // Map FoodLogDetail to AnalysisResult
+ const mapped: AnalysisResult = {
+ log_id: data.id,
+ image_url: data.image_url,
+ meal_type: data.meal_type,
+ analyzed_at: data.analyzed_at,
+ items: data.items || [],
+ totals: {
+ calories: Math.round(data.total_calories) || 0,
+ protein_g: data.total_protein_g || 0,
+ fat_g: data.total_fat_g || 0,
+ carbs_g: data.total_carbs_g || 0,
+ },
+ unmatched_items: [],
+ warnings: [],
+ };
+ setAnalysis(mapped);
+ } catch (err: any) {
+ console.error('Failed to fetch log details:', err);
+ Alert.alert('Error', err.message || 'Failed to load meal details from history.');
+ } finally {
+ setLoading(false);
+ }
+ }
+
+ if (loading || !analysis) {
+ return (
+
+
+ Loading results...
+
+ );
+ }
+
+ const { items, totals, meal_type, image_url, log_id, warnings } = analysis;
+
+ const handleOpenEdit = (item: FoodItem) => {
+ setSelectedItem(item);
+ setEditName(item.name);
+ setEditWeight(item.weight_grams.toString());
+ setIsModalOpen(true);
+ };
+
+ const handleSaveCorrection = async () => {
+ if (!selectedItem || !selectedItem.id) {
+ Alert.alert('Error', 'Cannot correct an item without a valid ID.');
+ return;
+ }
+
+ const weightNum = parseFloat(editWeight);
+ if (!editName.trim() || isNaN(weightNum) || weightNum <= 0) {
+ Alert.alert('Invalid Input', 'Please enter a valid name and portion weight.');
+ return;
+ }
+
+ setSavingCorrection(true);
+ try {
+ // Call the API function
+ await correctFoodItem(log_id, selectedItem.id, {
+ name: editName,
+ weight_grams: weightNum,
+ });
+
+ // Recalculate local stats locally without refetching from backend
+ const oldWeight = selectedItem.weight_grams;
+ const ratio = oldWeight > 0 ? weightNum / oldWeight : 1;
+
+ const updatedItems = items.map((item) => {
+ if (item.id === selectedItem.id) {
+ return {
+ ...item,
+ name: editName,
+ weight_grams: weightNum,
+ calories: Math.round(item.calories * ratio),
+ protein_g: Number((item.protein_g * ratio).toFixed(1)),
+ fat_g: Number((item.fat_g * ratio).toFixed(1)),
+ carbs_g: Number((item.carbs_g * ratio).toFixed(1)),
+ is_user_corrected: true,
+ };
+ }
+ return item;
+ });
+
+ // Recalculate Totals
+ const newTotals = updatedItems.reduce(
+ (acc, curr) => {
+ acc.calories += curr.calories;
+ acc.protein_g += curr.protein_g;
+ acc.fat_g += curr.fat_g;
+ acc.carbs_g += curr.carbs_g;
+ return acc;
+ },
+ { calories: 0, protein_g: 0, fat_g: 0, carbs_g: 0 }
+ );
+
+ // Round protein/fat/carbs values to 1 decimal place
+ newTotals.protein_g = Number(newTotals.protein_g.toFixed(1));
+ newTotals.fat_g = Number(newTotals.fat_g.toFixed(1));
+ newTotals.carbs_g = Number(newTotals.carbs_g.toFixed(1));
+
+ setAnalysis({
+ ...analysis,
+ items: updatedItems,
+ totals: newTotals,
+ });
+
+ setIsModalOpen(false);
+ setSelectedItem(null);
+ } catch (error: any) {
+ Alert.alert('Correction Failed', error.message || 'Failed to save correction.');
+ } finally {
+ setSavingCorrection(false);
+ }
+ };
+
+ const getConfidenceColor = (confidence: number) => {
+ if (confidence > 0.7) return '#4CAF50'; // Green
+ if (confidence >= 0.3) return '#FFC107'; // Yellow/Orange
+ return '#F44336'; // Red
+ };
+
+ const allItemsUnmatched = items.length > 0 && items.every((i) => !i.matched);
+
+ return (
+
+
+ {/* Header bar */}
+
+ router.back()}>
+ ←
+
+ Meal Analysis
+
+
+ {meal_type.charAt(0).toUpperCase() + meal_type.slice(1)}
+
+
+
+
+
+ {/* Food Image */}
+ {image_url ? (
+
+ ) : (
+
+ 🥗
+
+ )}
+
+ {/* Calorie summary card */}
+
+ {totals.calories}
+ calories
+
+
+
+
+ {/* Warnings Section */}
+ {warnings && warnings.length > 0 && (
+
+ ⚠️
+
+ {warnings.map((warning, idx) => (
+
+ {warning}
+
+ ))}
+
+
+ )}
+
+ {/* All items unmatched edge case */}
+ {allItemsUnmatched && (
+
+ 💡
+
+ Food items were identified but nutrition data couldn't be found automatically. You can manually correct the names to trigger lookups.
+
+
+ )}
+
+ {/* Detected Items List */}
+
+ Detected Items ({items.length})
+
+
+ {items.length === 0 ? (
+
+ 🔍
+
+ No food items detected. Try taking a clearer photo with better lighting.
+
+
+ ) : (
+ items.map((item, index) => (
+ handleOpenEdit(item)}
+ activeOpacity={0.7}
+ >
+
+
+ {item.name}
+ {!item.matched && (
+
+ ⚠️
+
+ )}
+
+ {item.weight_grams}g portion
+
+ {/* Confidence indicator */}
+
+
+
+ {(item.confidence * 100).toFixed(0)}% match confidence
+
+ {item.is_user_corrected && (
+ • Corrected
+ )}
+
+
+
+
+ {item.calories}
+ kcal
+
+
+ ))
+ )}
+
+ {/* Bottom Done Button */}
+ {
+ if (params.log_id) {
+ router.back();
+ } else {
+ // Success navigation back to history log
+ Alert.alert('Saved!', 'Meal successfully logged into your health dashboard.', [
+ {
+ text: 'OK',
+ onPress: () => router.replace('/(tabs)/history'),
+ },
+ ]);
+ }
+ }}
+ >
+
+ {params.log_id ? 'Back to History' : 'Done — Go to History'}
+
+
+
+
+ {/* Edit modal */}
+ setIsModalOpen(false)}
+ >
+
+
+ Edit Food Portion
+
+ Food Item Name
+
+
+ Portion Weight (grams)
+
+
+
+ setIsModalOpen(false)}
+ disabled={savingCorrection}
+ >
+ Cancel
+
+
+
+ {savingCorrection ? (
+
+ ) : (
+ Save Correction
+ )}
+
+
+
+
+
+
+ );
+}
+
+const styles = StyleSheet.create({
+ safeArea: {
+ flex: 1,
+ backgroundColor: '#F5F5F5',
+ },
+ loadingContainer: {
+ flex: 1,
+ justifyContent: 'center',
+ alignItems: 'center',
+ backgroundColor: '#fff',
+ },
+ loadingText: {
+ fontSize: 16,
+ color: '#666',
+ marginTop: 12,
+ },
+ header: {
+ height: 60,
+ backgroundColor: '#fff',
+ flexDirection: 'row',
+ alignItems: 'center',
+ justifyContent: 'space-between',
+ paddingHorizontal: 16,
+ borderBottomWidth: 1,
+ borderBottomColor: '#eee',
+ },
+ backButton: {
+ padding: 8,
+ },
+ backArrow: {
+ fontSize: 24,
+ color: '#333',
+ fontWeight: 'bold',
+ },
+ headerTitle: {
+ fontSize: 18,
+ fontWeight: 'bold',
+ color: '#333',
+ },
+ mealBadge: {
+ backgroundColor: '#E8F5E9',
+ paddingHorizontal: 12,
+ paddingVertical: 4,
+ borderRadius: 12,
+ },
+ mealBadgeText: {
+ color: '#2E7D32',
+ fontSize: 12,
+ fontWeight: 'bold',
+ },
+ scrollContainer: {
+ padding: 16,
+ paddingBottom: 40,
+ },
+ foodImage: {
+ width: '100%',
+ height: 200,
+ borderRadius: 12,
+ marginBottom: 16,
+ },
+ placeholderImage: {
+ backgroundColor: '#e0e0e0',
+ justifyContent: 'center',
+ alignItems: 'center',
+ },
+ summaryCard: {
+ backgroundColor: '#fff',
+ borderRadius: 12,
+ padding: 20,
+ alignItems: 'center',
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 2 },
+ shadowOpacity: 0.05,
+ shadowRadius: 8,
+ elevation: 3,
+ marginBottom: 16,
+ },
+ calorieNumber: {
+ fontSize: 48,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ },
+ calorieLabel: {
+ fontSize: 14,
+ color: '#666',
+ marginTop: -4,
+ marginBottom: 8,
+ },
+ warningCard: {
+ backgroundColor: '#FFFDE7',
+ borderWidth: 1,
+ borderColor: '#FFF59D',
+ borderRadius: 12,
+ padding: 14,
+ flexDirection: 'row',
+ alignItems: 'center',
+ marginBottom: 16,
+ },
+ warningIcon: {
+ fontSize: 20,
+ marginRight: 10,
+ },
+ warningTextContainer: {
+ flex: 1,
+ },
+ warningText: {
+ color: '#F57F17',
+ fontSize: 13,
+ lineHeight: 18,
+ fontWeight: '500',
+ },
+ infoCard: {
+ backgroundColor: '#E3F2FD',
+ borderWidth: 1,
+ borderColor: '#90CAF9',
+ borderRadius: 12,
+ padding: 14,
+ flexDirection: 'row',
+ alignItems: 'center',
+ marginBottom: 16,
+ },
+ infoIcon: {
+ fontSize: 20,
+ marginRight: 10,
+ },
+ infoText: {
+ color: '#0D47A1',
+ fontSize: 13,
+ lineHeight: 18,
+ flex: 1,
+ },
+ sectionHeaderContainer: {
+ marginBottom: 10,
+ marginTop: 8,
+ },
+ sectionTitle: {
+ fontSize: 16,
+ fontWeight: 'bold',
+ color: '#333',
+ },
+ emptyContainer: {
+ backgroundColor: '#fff',
+ borderRadius: 12,
+ padding: 30,
+ alignItems: 'center',
+ justifyContent: 'center',
+ marginBottom: 20,
+ },
+ emptyIcon: {
+ fontSize: 40,
+ marginBottom: 10,
+ },
+ emptyText: {
+ color: '#666',
+ textAlign: 'center',
+ lineHeight: 20,
+ },
+ itemCard: {
+ backgroundColor: '#fff',
+ borderRadius: 12,
+ padding: 16,
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ alignItems: 'center',
+ marginBottom: 10,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 1 },
+ shadowOpacity: 0.03,
+ shadowRadius: 4,
+ elevation: 1,
+ },
+ itemLeft: {
+ flex: 1,
+ },
+ itemNameRow: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ },
+ itemName: {
+ fontSize: 16,
+ fontWeight: 'bold',
+ color: '#333',
+ marginRight: 6,
+ },
+ unmatchedBadge: {
+ fontSize: 14,
+ },
+ itemWeight: {
+ fontSize: 13,
+ color: '#666',
+ marginTop: 2,
+ },
+ confidenceRow: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ marginTop: 8,
+ },
+ confidenceDot: {
+ width: 6,
+ height: 6,
+ borderRadius: 3,
+ marginRight: 6,
+ },
+ confidenceText: {
+ fontSize: 11,
+ color: '#888',
+ },
+ correctedBadge: {
+ fontSize: 11,
+ color: '#2E7D32',
+ fontWeight: 'bold',
+ marginLeft: 6,
+ },
+ itemRight: {
+ alignItems: 'flex-end',
+ marginLeft: 10,
+ },
+ itemCalories: {
+ fontSize: 20,
+ fontWeight: 'bold',
+ color: '#2E7D32',
+ },
+ itemCalLabel: {
+ fontSize: 10,
+ color: '#666',
+ marginTop: -2,
+ },
+ doneButton: {
+ backgroundColor: '#2E7D32',
+ borderRadius: 12,
+ padding: 16,
+ alignItems: 'center',
+ marginTop: 20,
+ },
+ doneButtonText: {
+ color: '#fff',
+ fontSize: 16,
+ fontWeight: 'bold',
+ },
+ modalOverlay: {
+ flex: 1,
+ backgroundColor: 'rgba(0,0,0,0.5)',
+ justifyContent: 'center',
+ alignItems: 'center',
+ padding: 20,
+ },
+ modalContent: {
+ backgroundColor: '#fff',
+ borderRadius: 16,
+ padding: 24,
+ width: '100%',
+ maxWidth: 400,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 4 },
+ shadowOpacity: 0.1,
+ shadowRadius: 12,
+ elevation: 5,
+ },
+ modalTitle: {
+ fontSize: 20,
+ fontWeight: 'bold',
+ color: '#333',
+ marginBottom: 20,
+ textAlign: 'center',
+ },
+ inputLabel: {
+ fontSize: 14,
+ color: '#666',
+ marginBottom: 6,
+ fontWeight: '500',
+ },
+ textInput: {
+ borderWidth: 1,
+ borderColor: '#ddd',
+ borderRadius: 8,
+ padding: 12,
+ fontSize: 15,
+ marginBottom: 16,
+ },
+ modalButtonRow: {
+ flexDirection: 'row',
+ justifyContent: 'space-between',
+ marginTop: 8,
+ },
+ modalCancelButton: {
+ flex: 1,
+ paddingVertical: 12,
+ borderRadius: 8,
+ borderWidth: 1,
+ borderColor: '#ccc',
+ alignItems: 'center',
+ marginRight: 8,
+ },
+ modalCancelButtonText: {
+ color: '#666',
+ fontSize: 15,
+ fontWeight: 'bold',
+ },
+ modalSaveButton: {
+ flex: 2,
+ paddingVertical: 12,
+ borderRadius: 8,
+ backgroundColor: '#2E7D32',
+ alignItems: 'center',
+ marginLeft: 8,
+ },
+ modalSaveButtonText: {
+ color: '#fff',
+ fontSize: 15,
+ fontWeight: 'bold',
+ },
+});
diff --git a/mobile/assets/adaptive-icon.png b/mobile/assets/adaptive-icon.png
new file mode 100644
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diff --git a/mobile/assets/favicon.png b/mobile/assets/favicon.png
new file mode 100644
index 0000000..e75f697
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diff --git a/mobile/assets/icon.png b/mobile/assets/icon.png
new file mode 100644
index 0000000..a0b1526
Binary files /dev/null and b/mobile/assets/icon.png differ
diff --git a/mobile/assets/splash-icon.png b/mobile/assets/splash-icon.png
new file mode 100644
index 0000000..03d6f6b
Binary files /dev/null and b/mobile/assets/splash-icon.png differ
diff --git a/mobile/eas.json b/mobile/eas.json
new file mode 100644
index 0000000..f66a69b
--- /dev/null
+++ b/mobile/eas.json
@@ -0,0 +1,12 @@
+{
+ "cli": { "version": ">= 3.0.0" },
+ "build": {
+ "preview": {
+ "distribution": "internal",
+ "android": {
+ "buildType": "apk"
+ }
+ },
+ "production": {}
+ }
+}
diff --git a/mobile/package-lock.json b/mobile/package-lock.json
new file mode 100644
index 0000000..3b2e18e
--- /dev/null
+++ b/mobile/package-lock.json
@@ -0,0 +1,10524 @@
+{
+ "name": "mobile",
+ "version": "1.0.0",
+ "lockfileVersion": 3,
+ "requires": true,
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diff --git a/mobile/package.json b/mobile/package.json
new file mode 100644
index 0000000..0da727a
--- /dev/null
+++ b/mobile/package.json
@@ -0,0 +1,36 @@
+{
+ "name": "mobile",
+ "version": "1.0.0",
+ "main": "expo-router/entry",
+ "scripts": {
+ "start": "expo start",
+ "android": "expo start --android",
+ "ios": "expo start --ios",
+ "web": "expo start --web"
+ },
+ "dependencies": {
+ "@expo/metro-runtime": "~6.1.2",
+ "@react-native-async-storage/async-storage": "2.2.0",
+ "axios": "^1.16.1",
+ "expo": "~54.0.33",
+ "expo-camera": "~17.0.10",
+ "expo-constants": "~18.0.13",
+ "expo-file-system": "~19.0.22",
+ "expo-image-picker": "~17.0.11",
+ "expo-linking": "~8.0.12",
+ "expo-router": "~6.0.23",
+ "expo-secure-store": "~15.0.8",
+ "expo-status-bar": "~3.0.9",
+ "react": "19.1.0",
+ "react-dom": "19.1.0",
+ "react-native": "0.81.5",
+ "react-native-safe-area-context": "~5.6.0",
+ "react-native-screens": "~4.16.0",
+ "react-native-web": "^0.21.0"
+ },
+ "devDependencies": {
+ "@types/react": "~19.1.0",
+ "typescript": "~5.9.2"
+ },
+ "private": true
+}
diff --git a/mobile/src/api/auth.ts b/mobile/src/api/auth.ts
new file mode 100644
index 0000000..de0890a
--- /dev/null
+++ b/mobile/src/api/auth.ts
@@ -0,0 +1,29 @@
+import { api } from "./client";
+import * as SecureStore from "expo-secure-store";
+
+export async function register(email: string, password: string) {
+ const response = await api.post("/api/auth/register", { email, password });
+ const { access_token } = response.data;
+ await SecureStore.setItemAsync("auth_token", access_token);
+ return response.data;
+}
+
+export async function login(email: string, password: string) {
+ const response = await api.post("/api/auth/login", { email, password });
+ const { access_token } = response.data;
+ await SecureStore.setItemAsync("auth_token", access_token);
+ return response.data;
+}
+
+export async function logout() {
+ await SecureStore.deleteItemAsync("auth_token");
+}
+
+export async function getToken(): Promise {
+ return await SecureStore.getItemAsync("auth_token");
+}
+
+export async function isAuthenticated(): Promise {
+ const token = await getToken();
+ return token !== null;
+}
diff --git a/mobile/src/api/client.ts b/mobile/src/api/client.ts
new file mode 100644
index 0000000..ea98e73
--- /dev/null
+++ b/mobile/src/api/client.ts
@@ -0,0 +1,52 @@
+import axios from 'axios';
+import * as SecureStore from 'expo-secure-store';
+import { router } from 'expo-router';
+import { API_BASE_URL } from './config';
+
+const api = axios.create({
+ baseURL: API_BASE_URL,
+ timeout: 30000,
+ headers: {
+ 'Content-Type': 'application/json',
+ },
+});
+
+// Request interceptor for auth token
+api.interceptors.request.use(
+ async (config) => {
+ try {
+ const token = await SecureStore.getItemAsync('auth_token');
+ if (token && config.headers) {
+ config.headers.Authorization = `Bearer ${token}`;
+ }
+ } catch (error) {
+ console.error('Error reading token from SecureStore:', error);
+ }
+ return config;
+ },
+ (error) => {
+ return Promise.reject(error);
+ }
+);
+
+// Response interceptor for error handling and 401s
+api.interceptors.response.use(
+ (response) => response,
+ async (error) => {
+ if (error.response?.status === 401) {
+ await SecureStore.deleteItemAsync('auth_token');
+ router.replace('/login');
+ }
+
+ const message = error.response?.data?.detail || error.message || 'A network error occurred. Please try again.';
+
+ // Customize error for 401 to be less technical if needed
+ if (error.response?.status === 401) {
+ return Promise.reject(new Error('Session expired. Please log in again.'));
+ }
+
+ return Promise.reject(new Error(message));
+ }
+);
+
+export { api };
diff --git a/mobile/src/api/config.ts b/mobile/src/api/config.ts
new file mode 100644
index 0000000..02dde23
--- /dev/null
+++ b/mobile/src/api/config.ts
@@ -0,0 +1,7 @@
+import { Platform } from 'react-native';
+
+export const API_BASE_URL = __DEV__
+ ? (Platform.OS === 'web'
+ ? "http://localhost:8000"
+ : "http://YOUR_LOCAL_IP:8000") // Replace YOUR_LOCAL_IP with your machine's local network IP (e.g., 192.168.1.5) for physical mobile testing
+ : "https://foodsnap-ai-ces3.onrender.com";
diff --git a/mobile/src/api/food.ts b/mobile/src/api/food.ts
new file mode 100644
index 0000000..f1068d9
--- /dev/null
+++ b/mobile/src/api/food.ts
@@ -0,0 +1,146 @@
+import { api } from "./client";
+
+// Types
+export interface FoodItem {
+ name: string;
+ confidence: number;
+ weight_grams: number;
+ calories: number;
+ protein_g: number;
+ fat_g: number;
+ carbs_g: number;
+ matched: boolean;
+ is_user_corrected: boolean;
+ id?: string;
+ usda_match?: string | null;
+}
+
+export interface AnalysisResult {
+ log_id: string;
+ image_url: string;
+ meal_type: string;
+ analyzed_at: string;
+ items: FoodItem[];
+ totals: {
+ calories: number;
+ protein_g: number;
+ fat_g: number;
+ carbs_g: number;
+ };
+ unmatched_items: string[];
+ warnings: string[];
+}
+
+export interface FoodLog {
+ id: string;
+ image_url: string;
+ meal_type: string;
+ total_calories: number;
+ total_protein_g: number;
+ total_fat_g: number;
+ total_carbs_g: number;
+ item_count: number;
+ analyzed_at: string;
+}
+
+export interface DailyStats {
+ date: string;
+ total_calories: number;
+ total_protein_g: number;
+ total_fat_g: number;
+ total_carbs_g: number;
+ meal_count: number;
+}
+
+export interface WeeklyCheckin {
+ period: string;
+ average_daily_calories: number;
+ calorie_goal: number;
+ variance: number;
+ status: string;
+ average_protein_g: number;
+ average_fat_g: number;
+ average_carbs_g: number;
+ days_logged: number;
+ days_missed: number;
+ insight: string;
+}
+
+// API functions
+export async function analyzeFood(
+ imageUri: string,
+ mealType: string = "unspecified"
+): Promise {
+ const formData = new FormData();
+
+ // Read the image file and create a blob for upload
+ const filename = imageUri.split("/").pop() || "photo.jpg";
+
+ // React Native FormData expects an object with uri, name, and type for files
+ formData.append("image", {
+ uri: imageUri,
+ name: filename,
+ type: "image/jpeg",
+ } as any);
+ formData.append("meal_type", mealType);
+
+ const response = await api.post("/api/analyze", formData, {
+ headers: { "Content-Type": "multipart/form-data" },
+ timeout: 60000, // 60s for analysis (AI + USDA lookup)
+ });
+ return response.data;
+}
+
+export async function getFoodLogs(
+ page: number = 1,
+ date?: string
+): Promise<{ logs: FoodLog[]; total: number }> {
+ const params: any = { page, limit: 20 };
+ if (date) params.date = date;
+ const response = await api.get("/api/logs", { params });
+ return response.data;
+}
+
+export async function getFoodLogDetail(logId: string) {
+ const response = await api.get(`/api/logs/${logId}`);
+ return response.data;
+}
+
+export async function correctFoodItem(
+ logId: string,
+ itemId: string,
+ correction: { name?: string; weight_grams?: number }
+) {
+ const response = await api.put(
+ `/api/logs/${logId}/items/${itemId}`,
+ correction
+ );
+ return response.data;
+}
+
+export async function deleteFoodLog(logId: string) {
+ const response = await api.delete(`/api/logs/${logId}`);
+ return response.data;
+}
+
+export async function getStats(
+ range: "week" | "month" = "week"
+): Promise {
+ const response = await api.get("/api/stats", { params: { range } });
+ return response.data;
+}
+
+export async function getWeeklyCheckin(): Promise {
+ const response = await api.get("/api/stats/checkin");
+ return response.data;
+}
+
+export async function getUserProfile() {
+ const response = await api.get("/api/user/profile");
+ return response.data;
+}
+
+export async function updateUserProfile(data: any) {
+ const response = await api.put("/api/user/profile", data);
+ return response.data;
+}
diff --git a/mobile/src/components/MacroBar.tsx b/mobile/src/components/MacroBar.tsx
new file mode 100644
index 0000000..d0f3747
--- /dev/null
+++ b/mobile/src/components/MacroBar.tsx
@@ -0,0 +1,74 @@
+import React from 'react';
+import { View, Text, StyleSheet } from 'react-native';
+
+interface MacroBarProps {
+ protein: number;
+ fat: number;
+ carbs: number;
+}
+
+export default function MacroBar({ protein, fat, carbs }: MacroBarProps) {
+ return (
+
+
+
+
+ Protein
+ {Math.round(protein)}g
+
+
+
+
+
+
+ Fat
+ {Math.round(fat)}g
+
+
+
+
+
+
+ Carbs
+ {Math.round(carbs)}g
+
+
+
+ );
+}
+
+const styles = StyleSheet.create({
+ container: {
+ flexDirection: 'row',
+ justifyContent: 'space-around',
+ width: '100%',
+ paddingVertical: 12,
+ borderTopWidth: 1,
+ borderTopColor: '#f0f0f0',
+ marginTop: 12,
+ },
+ macroItem: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ },
+ dot: {
+ width: 10,
+ height: 10,
+ borderRadius: 5,
+ marginRight: 8,
+ },
+ textContainer: {
+ flexDirection: 'column',
+ },
+ label: {
+ fontSize: 12,
+ color: '#666',
+ fontWeight: '500',
+ },
+ value: {
+ fontSize: 16,
+ fontWeight: 'bold',
+ color: '#333',
+ marginTop: 2,
+ },
+});
diff --git a/mobile/src/components/Toast.tsx b/mobile/src/components/Toast.tsx
new file mode 100644
index 0000000..4233aba
--- /dev/null
+++ b/mobile/src/components/Toast.tsx
@@ -0,0 +1,178 @@
+import React, { createContext, useContext, useState, useEffect, useRef } from 'react';
+import { View, Text, StyleSheet, Animated, TouchableOpacity } from 'react-native';
+
+type ToastType = 'success' | 'error' | 'info';
+
+interface ToastMessage {
+ message: string;
+ type: ToastType;
+}
+
+interface ToastContextType {
+ showToast: (message: string, type?: ToastType) => void;
+}
+
+const ToastContext = createContext(undefined);
+
+export function useToast() {
+ const context = useContext(ToastContext);
+ if (!context) {
+ throw new Error('useToast must be used within a ToastProvider');
+ }
+ return context;
+}
+
+export function ToastProvider({ children }: { children: React.ReactNode }) {
+ const [toast, setToast] = useState(null);
+ const fadeAnim = useRef(new Animated.Value(0)).current;
+ const slideAnim = useRef(new Animated.Value(-100)).current;
+ const timerRef = useRef(null);
+
+ const showToast = (message: string, type: ToastType = 'success') => {
+ if (timerRef.current) {
+ clearTimeout(timerRef.current);
+ }
+
+ setToast({ message, type });
+
+ // Animate In
+ Animated.parallel([
+ Animated.timing(fadeAnim, {
+ toValue: 1,
+ duration: 300,
+ useNativeDriver: true,
+ }),
+ Animated.timing(slideAnim, {
+ toValue: 0,
+ duration: 300,
+ useNativeDriver: true,
+ }),
+ ]).start();
+
+ // Auto dismiss after 3 seconds
+ timerRef.current = setTimeout(() => {
+ dismissToast();
+ }, 3000);
+ };
+
+ const dismissToast = () => {
+ Animated.parallel([
+ Animated.timing(fadeAnim, {
+ toValue: 0,
+ duration: 250,
+ useNativeDriver: true,
+ }),
+ Animated.timing(slideAnim, {
+ toValue: -100,
+ duration: 250,
+ useNativeDriver: true,
+ }),
+ ]).start(() => {
+ setToast(null);
+ });
+ };
+
+ useEffect(() => {
+ return () => {
+ if (timerRef.current) {
+ clearTimeout(timerRef.current);
+ }
+ };
+ }, []);
+
+ const getToastStyle = () => {
+ if (!toast) return {};
+ switch (toast.type) {
+ case 'success':
+ return { borderLeftColor: '#2E7D32' };
+ case 'error':
+ return { borderLeftColor: '#D32F2F' };
+ case 'info':
+ return { borderLeftColor: '#1976D2' };
+ }
+ };
+
+ const getToastIcon = () => {
+ if (!toast) return '';
+ switch (toast.type) {
+ case 'success':
+ return '✅';
+ case 'error':
+ return '⚠️';
+ case 'info':
+ return 'ℹ️';
+ }
+ };
+
+ return (
+
+ {children}
+ {toast && (
+
+
+ {getToastIcon()}
+
+ {toast.message}
+
+
+ ✕
+
+
+
+ )}
+
+ );
+}
+
+const styles = StyleSheet.create({
+ toastContainer: {
+ position: 'absolute',
+ top: 50,
+ left: 16,
+ right: 16,
+ backgroundColor: '#ffffff',
+ borderRadius: 12,
+ borderLeftWidth: 6,
+ paddingVertical: 12,
+ paddingHorizontal: 16,
+ shadowColor: '#000',
+ shadowOffset: { width: 0, height: 4 },
+ shadowOpacity: 0.1,
+ shadowRadius: 12,
+ elevation: 6,
+ zIndex: 9999,
+ },
+ toastContent: {
+ flexDirection: 'row',
+ alignItems: 'center',
+ justifyContent: 'space-between',
+ },
+ icon: {
+ fontSize: 18,
+ marginRight: 10,
+ },
+ messageText: {
+ flex: 1,
+ fontSize: 14,
+ fontWeight: '600',
+ color: '#1A1A1A',
+ lineHeight: 18,
+ },
+ closeButton: {
+ paddingLeft: 10,
+ },
+ closeText: {
+ fontSize: 14,
+ color: '#999',
+ fontWeight: 'bold',
+ },
+});
diff --git a/mobile/tsconfig.json b/mobile/tsconfig.json
new file mode 100644
index 0000000..b9567f6
--- /dev/null
+++ b/mobile/tsconfig.json
@@ -0,0 +1,6 @@
+{
+ "extends": "expo/tsconfig.base",
+ "compilerOptions": {
+ "strict": true
+ }
+}