🥗 AI Nutrition Tracker

A full-stack web application for tracking daily nutrition intake with AI-powered recommendations. Built with vanilla JavaScript frontend and Flask backend, powered by Firebase for authentication and Firestore for data storage.

✨ Features

🔐 Authentication

• Secure Sign Up & Login - Firebase-based authentication with email verification
• User Session Management - Persistent login with JWT tokens
• Data Isolation - Each user's data is completely isolated and secure

📊 Dashboard

• Real-time Macro Tracking - Track calories, protein, carbs, and fats
• Daily Summary - View all meals logged for the day
• Progress Bars - Visual representation of macro consumption vs. goals
• Quick Add Meal - Easily add meals from a form or food database

🍽️ Meal Management

• Add Meals - Log meals manually or from the 223-food database
• Food Database - Search through 223+ pre-loaded foods with nutritional data
• Delete Meals - Remove meals if logged incorrectly
• Meal Types - Categorize meals as breakfast, lunch, dinner, or snack

🎯 Goals Management

• Set Personal Goals - Define daily calorie and macro targets
• Edit Goals - Modify goals at any time
• Reset to Defaults - Quickly reset to recommended values
• Goal Tracking - Monitor progress against your personal goals

💡 AI-Powered Recommendations

• Smart Alerts - Get notified when exceeding or falling short on macros
• Personalized Suggestions - AI recommends foods to help meet your goals
• Weekly Insights - View trends and patterns in your nutrition over time
• Eating Patterns - AI detects your eating habits and patterns

📈 Analytics & Insights

• Weekly Trends - Average daily consumption across macros
• Daily History - View detailed breakdown for each day of the week
• Pattern Detection - Identify your nutrition habits and tendencies
• Responsive Design - Works perfectly on desktop, tablet, and mobile

🛠️ Tech Stack

Frontend

• HTML5, CSS3, Vanilla JavaScript - No heavy frameworks, fast and lightweight
• Firebase SDK - Real-time authentication
• CSV Parser - Efficient food database loading
• Responsive Design - Mobile-first approach

Backend

• Flask 2.3.3 - Lightweight Python web framework
• Firebase Admin SDK - Server-side Firebase operations
• Python 3 - Backend logic and AI recommendations
• CORS Enabled - Seamless frontend-backend communication

Database & Auth

• Firebase Authentication - Secure user authentication
• Firestore - Real-time NoSQL database
• JWT Tokens - Bearer token authentication for API calls

📋 Project Structure

AI-Nutrition-tracker/
├── frontend/
│   ├── index.html                 # Main HTML file
│   ├── assets/
│   │   ├── css/
│   │   │   └── styles.css        # Responsive styling (1,189 lines)
│   │   ├── js/
│   │   │   ├── app.js            # Main app logic (1,097 lines)
│   │   │   └── firebase-config.js # Firebase configuration
│   │   └── data/
│   │       └── foods.csv         # 223 foods database
│   └── README.md
├── backend/
│   ├── run.py                    # Flask entry point
│   ├── requirements.txt          # Python dependencies
│   ├── Procfile                  # Deployment configuration
│   ├── serviceAccountKey.json    # Firebase credentials (in .gitignore)
│   └── app/
│       ├── __init__.py           # Flask app factory
│       ├── models/
│       │   ├── meal.py           # Meal data model
│       │   └── user_goals.py     # User goals model
│       ├── routes/
│       │   ├── auth.py           # Authentication endpoints
│       │   ├── goals.py          # Goals management endpoints
│       │   ├── meals.py          # Meal management endpoints
│       │   ├── recommendations.py # AI recommendations
│       │   └── summary.py        # Daily summary endpoints
│       ├── services/
│       │   └── ai_recommendations.py # AI logic
│       └── utils/
│           ├── firebase_config.py    # Firebase setup
│           └── decorators.py         # Auth decorators
└── README.md

🚀 Getting Started

Prerequisites

• Python 3.8+
• Node.js (optional, for frontend development)
• Firebase account & project
• Google Cloud credentials

1️⃣ Clone the Repository

git clone https://github.com/vaibhav8a/AI-Nutrition-tracker.git
cd AI-Nutrition-tracker

2️⃣ Backend Setup

1. Create Python Virtual Environment

   cd backend
   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install Dependencies

   pip install -r requirements.txt

3. Configure Firebase

   • Go to Firebase Console
   • Create a new project or use existing one
   • Generate a service account key (Project Settings → Service Accounts)
   • Save as backend/serviceAccountKey.json

4. Create .env File (in backend folder)

   FLASK_PORT=5001
   FLASK_DEBUG=True
   FIREBASE_DATABASE_URL=https://your-project.firebaseio.com

5. Start Backend Server

   python run.py

   Backend will run on http://localhost:5001

3️⃣ Frontend Setup

1. Navigate to Frontend

   cd ../frontend

2. Configure Firebase (if needed)

   • Edit assets/js/firebase-config.js
   • Add your Firebase project credentials

3. Start Frontend Server

   python3 -m http.server 8000

   Frontend will run on http://localhost:8000

4. Open in Browser

   • Navigate to http://localhost:8000
   • Create an account or login

📚 API Endpoints

Authentication

• POST /api/auth/signup - Register new user
• POST /api/auth/login - Login user

Goals

• GET /api/goals/get - Get user's goals
• POST /api/goals/set - Set/update goals
• POST /api/goals/reset - Reset to defaults

Meals

• POST /api/meals/add - Add a meal
• GET /api/meals/daily/{date} - Get meals for a specific date
• DELETE /api/meals/delete/{date}/{mealId} - Delete a meal

Summary

• GET /api/summary/today - Get today's nutrition summary

Recommendations

• GET /api/recommendations/today - Get daily recommendations
• GET /api/recommendations/weekly-trends - Get weekly insights

🍔 Food Database

The app comes with 223 pre-loaded foods organized by category:

• Proteins - Chicken, beef, fish, eggs, tofu, etc.
• Carbs - Rice, pasta, bread, potatoes, oats, etc.
• Fats - Olive oil, nuts, seeds, avocado, etc.
• Vegetables - Broccoli, carrots, spinach, tomatoes, etc.
• Fruits - Apples, bananas, berries, oranges, etc.
• Dairy - Milk, yogurt, cheese, etc.

Each food includes:

• Calories per serving
• Protein content (g)
• Carbs content (g)
• Fats content (g)

🎯 How to Use

1. Create Account

• Go to http://localhost:8000
• Click "Sign Up"
• Enter email and password
• Account created and automatically logged in

2. Set Your Goals

• A modal will appear asking for daily nutrition goals
• Enter target calories, protein, carbs, and fats
• Click "Save Goals"

3. Add Your First Meal

• Go to "Add Meal" page
• Choose to:
  • Search Food Database - Find from 223+ foods
  • Custom Meal - Enter custom nutritional values
• Select meal type (breakfast, lunch, dinner, snack)
• Click "Add Meal"

4. View Dashboard

• See today's nutrition summary
• Track progress towards your goals
• View all meals logged today

5. Check Insights

• Go to "Insights" page
• See weekly averages
• View eating patterns detected by AI
• Review daily history

6. Manage Settings

• Go to "Settings" page
• Edit or reset your daily goals
• View your account information

🚀 Deployment

Deploy Backend to Railway

1. Push code to GitHub
2. Connect Railway to your GitHub repo
3. Set environment variables (Firebase credentials)
4. Railway auto-detects Procfile and starts the app

Deploy Frontend to Vercel

1. Push code to GitHub
2. Connect Vercel to your GitHub repo
3. Set build output to frontend directory
4. Update API_BASE_URL in app.js to your Railway backend URL

[See detailed deployment guide in project documentation]

🔒 Security

• Firebase Authentication - Industry-standard auth
• JWT Tokens - Secure API communication
• CORS Enabled - Only allow frontend requests
• Environment Variables - Sensitive data in .env files
• Git Ignore - serviceAccountKey.json never committed

📱 Responsive Design

The app is fully responsive:

• Desktop (900px+) - 3-column layout
• Tablet (768px-899px) - 2-column layout
• Mobile (320px-767px) - 1-column stacked layout

🐛 Troubleshooting

"API connection failed"

• Ensure backend is running: python run.py from /backend
• Check API_BASE_URL in app.js matches backend URL
• Verify CORS is enabled in backend

"Firebase not initialized"

• Check firebase-config.js has correct credentials
• Ensure Firebase SDK script is loaded (check browser console)
• Verify Firebase project exists and is active

"Goals modal not closing"

• Clear browser cache and reload
• Check browser console for JavaScript errors
• Ensure modal classes are correctly applied

"Food database not loading"

• Verify foods.csv exists in frontend/assets/data/
• Check browser Network tab for 404 errors
• Ensure CSV is properly formatted

📊 Food Database Format

foods.csv format:

Food Name,Calories,Protein,Carbs,Fats
Chicken Breast,165,31,0,3.6
Brown Rice,111,2.6,23,0.9
...

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is open source and available under the MIT License.

👨‍💻 Author

Vaibhav Srivastava

• GitHub: @vaibhav8a
• Project: AI-Nutrition-tracker

🙏 Acknowledgments

• Firebase for authentication and database
• Flask for backend framework
• Community feedback and contributions

📞 Support

For issues, questions, or suggestions:

• Open an issue on GitHub
• Check existing issues for solutions
• Review troubleshooting section above

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Happy tracking! 🥗💪