Nutrilio

An AI-powered nutrition management platform designed to help users maintain balanced, healthy diets through intelligent meal tracking, dietary analysis, and personalized recommendations.

📖 Overview

Nutrilio is a fully functional, AI-driven nutrition management system that combines cutting-edge machine learning with comprehensive dietary analysis to help users maintain balanced, healthy diets. The system features real-time food recognition, automated nutrition analysis, and intuitive mobile interfaces, making healthy eating accessible and trackable for everyone.

🎯 Current Status: LIVE & FUNCTIONAL - All core features are implemented and working!

🎯 Key Features

🍽️ Multi-Modal Meal Logging

• Image Recognition: Upload food photos for automatic identification using Vision Transformers
• Text Input: Log meals via text descriptions with NLP-powered parsing
• Voice Logging: Speak your meals for hands-free tracking

📊 Intelligent Nutrition Analysis

• Real-time Nutrient Breakdown: Automatic calculation of macros, micros, and calories
• Deficiency Detection: AI-powered analysis identifies nutritional gaps
• Historical Tracking: Long-term dietary pattern analysis and trend visualization

🤖 AI-Powered Features

• RAG Chatbot: Ask questions about your nutrition using natural language
• Habit Analysis: Detects eating patterns, timing, and portion irregularities
• Smart Recommendations: Personalized meal suggestions based on WHO/ICMR standards

📱 Cross-Platform Access

• Native mobile apps (iOS & Android) built with React Native
• Responsive web interface
• Real-time synchronization across devices

🏗️ System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Mobile App    │    │   Web Client    │    │   Voice Input   │
│  (React Native) │    │    (React)      │    │                 │
└─────────┬───────┘    └─────────┬───────┘    └─────────┬───────┘
          │                      │                      │
          └──────────────────────┼──────────────────────┘
                                 │
                    ┌─────────────▼───────────────┐
                    │      FastAPI Server         │
                    │    (Authentication &        │
                    │     Request Routing)        │
                    └─────────────┬───────────────┘
                                 │
        ┌────────────────────────┼────────────────────────┐
        │                        │                        │
┌───────▼────────┐    ┌─────────▼─────────┐    ┌─────────▼─────────┐
│ Food Recognition│    │  Habit Analysis   │    │ Recommendation    │
│    Module       │    │     Module        │    │     Engine        │
│ (Vision Trans.) │    │  (Pattern Det.)   │    │ (Collaborative +  │
└────────┬───────┘    └─────────┬─────────┘    │   Rule-based)     │
         │                      │              └─────────┬─────────┘
         │              ┌───────▼───────┐                │
         │              │  Query Bot    │                │
         │              │ (RAG + LLM)   │                │
         │              └───────────────┘                │
         │                                               │
    ┌────▼─────────────────────────────────────────────────▼────┐
    │                Database Layer                            │
    │  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐   │
    │  │   Firebase  │  │  Supabase   │  │ Nutrition DBs   │   │
    │  │ (Real-time) │  │   (SQL)     │  │ (USDA, WHO/FAO) │   │
    │  └─────────────┘  └─────────────┘  └─────────────────┘   │
    └──────────────────────────────────────────────────────────┘

🚀 Getting Started

Prerequisites

• Python 3.8+
• Node.js 16+ and npm
• Firebase Account (Create one here)
• Gemini API Key (Get it here)
• Expo CLI (installed via npm)
• Mobile Device or Emulator for testing

Project Structure

Nutrilio/
├── App/                    # React Native mobile application
│   ├── app/               # Expo Router pages
│   ├── Components/         # Reusable UI components
│   ├── assets/            # Images and static assets
│   └── utils/             # Utility functions and contexts
│       ├── Config.js      # Server configuration
│       └── firebaseConfig.js  # Firebase configuration
├── Backend/               # FastAPI server
│   ├── Engines/           # Core AI/ML engines
│   ├── routes/            # API endpoints
│   ├── main.py            # Server entry point
│   ├── req.txt            # Python dependencies
│   ├── .env               # Environment variables
│   └── firebaseSecret.json  # Firebase admin credentials
└── README.md             # Project documentation

🔧 Installation & Setup

Step 1: Clone the Repository

git clone https://github.com/Priyansh6747/nutrilio.git
cd nutrilio

Step 2: Backend Setup

2.1 Create Python Virtual Environment

cd Backend

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate

# On Windows:
venv\Scripts\activate

2.2 Install Python Dependencies

pip install -r req.txt

2.3 Configure Firebase Admin SDK

1. Go to Firebase Console
2. Select your project (or create a new one)
3. Navigate to Project Settings → Service Accounts
4. Click Generate New Private Key
5. Save the downloaded JSON file as firebaseSecret.json
6. Place firebaseSecret.json in the Backend root directory

2.4 Set Up Environment Variables

Create a .env file in the Backend directory:

# Your local IPv4 address (find using ipconfig/ifconfig)
HOST=192.168.1.100

# Server port
PORT=8000

# Get your Gemini API key from https://makersuite.google.com/app/apikey
GEMINI_API_KEY=your_gemini_api_key_here

To find your IPv4 address:

• Windows: Open CMD and run ipconfig, look for "IPv4 Address"
• macOS/Linux: Open Terminal and run ifconfig or ip addr, look for inet address

2.5 Start the Backend Server

python3 main.py

Expected output:

INFO:     Started server process
INFO:     Uvicorn running on http://192.168.1.100:8000

📝 Important: Copy the server URL (e.g., http://192.168.1.100:8000) - you'll need it for frontend configuration.

---

Step 3: Frontend (Mobile App) Setup

3.1 Navigate to App Directory

cd ../App

3.2 Install Node Dependencies

npm install

3.3 Configure Server URL

Open App/utils/Config.js and update the BaseURL with your backend server URL:

const ServerConfig = {
    BaseURL: 'http://192.168.1.100:8000', // Replace with YOUR server URL
}

export default ServerConfig;

3.4 Configure Firebase for Mobile App

1. Go to Firebase Console
2. Select your project
3. Add a new app (iOS or Android)
4. Copy the Firebase configuration object

Open App/utils/firebaseConfig.js and update with your Firebase credentials:

import { initializeApp } from "firebase/app";
import { initializeAuth, getReactNativePersistence } from 'firebase/auth';
import ReactNativeAsyncStorage from '@react-native-async-storage/async-storage';

const firebaseConfig = {
    apiKey: "YOUR_API_KEY",
    authDomain: "your-project.firebaseapp.com",
    projectId: "your-project-id",
    storageBucket: "your-project.appspot.com",
    messagingSenderId: "123456789",
    appId: "1:123456789:web:abcdef123456",
    measurementId: "G-XXXXXXXXXX"
};

export const app = initializeApp(firebaseConfig);
export const auth = initializeAuth(app, {
    persistence: getReactNativePersistence(ReactNativeAsyncStorage)
});

3.5 Start the Development Server

Option A: Using Android/iOS Emulator

# Start Expo development server
npx expo start

# In the Expo Dev Tools (terminal menu):
# Press 'a' for Android emulator
# Press 'i' for iOS simulator
# Press 'w' for web browser

Option B: Using Expo Go App (Physical Device)

1. Install Expo Go from App Store (iOS) or Play Store (Android)
2. Run npx expo start in the terminal
3. Scan the QR code displayed in the terminal with:
   • iOS: Camera app
   • Android: Expo Go app

⚠️ Important Note about Expo Go: If Expo Go prompts you to upgrade the project or use a different SDK version:

• Recommended: Use Expo Go SDK 53 (the app will provide a download link)
• This ensures compatibility with the current project configuration

---

🎯 Quick Start Guide

1. Start the Backend

cd Backend
source venv/bin/activate  # or venv\Scripts\activate on Windows
python3 main.py

2. Start the Mobile App

cd App
npx expo start

3. Test the App

• Register: Create a new account
• Scan Food: Use camera to identify food items
• Log Meals: Track your daily nutrition
• Track Water: Monitor hydration
• View Analytics: Check your nutrition trends

---

🛠️ Technology Stack

AI/ML Framework

• PyTorch 2.8.0: Deep learning model development and training
• Transformers 4.56.2: Hugging Face transformers for NLP and vision models
• Hugging Face Hub: Model hosting and deployment
• NumPy 2.3.3: Numerical computing and data processing
• Pillow 11.3.0: Image processing and manipulation

Backend

• FastAPI 0.116.2: High-performance Python web framework
• Uvicorn 0.35.0: ASGI server for concurrent request handling
• Firebase Admin 7.1.0: Real-time database and authentication
• Google Cloud Firestore 2.21.0: NoSQL database for structured data
• Pydantic 2.11.9: Data validation and serialization

Mobile Frontend

• React Native 0.79.5: Cross-platform mobile development
• Expo SDK 53.0.20: Development platform and tools
• Expo Router 5.1.4: File-based routing system
• React Native Chart Kit 6.12.0: Data visualization components
• React Native SVG 15.11.2: SVG rendering for charts
• Expo Camera 17.0.8: Camera functionality for food scanning
• Expo Barcode Scanner 13.0.1: Barcode scanning capabilities

Key Dependencies

• Firebase 12.0.0: Real-time database and authentication
• React 19.0.0: Core React library
• TypeScript 5.8.3: Type safety and development experience
• AsyncStorage 2.1.2: Local data persistence
• Expo Image Picker 16.1.4: Image selection and capture

Data Sources

• USDA FoodData Central: Comprehensive nutrition database
• Food-101: Training dataset for food recognition
• IndianFood-101: Region-specific food dataset
• WHO/FAO Guidelines: Evidence-based nutrition standards

🎯 Core Modules

1. Food Identification Module

# Vision-based food recognition with ML model
def predict_food(image_bytes):
    # Uses trained model for food classification
    prediction = predict_food(image_bytes)
    return {
        "result": prediction["result"],
        "confidence": prediction["confidence"]
    }

Features Implemented:

• Image-based food recognition using trained ML model
• Confidence scoring for predictions
• Integration with nutrition analysis pipeline
• Support for multiple food items in single image

2. Nutrition Analysis Engine

# Comprehensive nutrient breakdown
def nutrient_analysis(food_name, description, amount):
    # Analyzes nutritional content and provides detailed breakdown
    return nutrient_breakdown

Features Implemented:

• Real-time nutrient calculation
• Macro and micronutrient analysis
• Portion size adjustments
• Database integration for accurate nutrition data

3. Barcode Scanning Module

# Product identification via barcode
def read_barcode(code):
    # Scans barcode and retrieves product information
    return product_data

Features Implemented:

• Barcode scanning for packaged foods
• Product database integration
• Automatic nutrition data retrieval

4. Meal Logging System

• Text Input: Manual meal entry with description
• Image Upload: Photo-based food logging
• Barcode Scan: Product-based logging
• Background Processing: Asynchronous nutrition analysis

5. Water Tracking Module

• Daily water intake logging
• Hydration goal tracking
• Historical water consumption analysis
• Visual progress indicators

6. User Authentication & Profile

• Firebase-based authentication
• User profile management
• Secure data storage
• Cross-device synchronization

📊 Expected Model Performance

Note: These are target performance metrics based on literature review and similar systems.

Food Recognition Goals

• Food-101 Dataset: Target 95%+ classification accuracy
• Portion Estimation: Target 80-90% accuracy
• Multi-food Detection: Support for complex meal images
• Real-time Inference: Target <3 seconds on mobile devices

Nutrition Analysis Targets

• Macro Accuracy: Target 90%+ for common foods
• Micro Estimation: Target 80%+ for tracked vitamins/minerals
• Calorie Prediction: Target ±15% margin for portion-controlled foods

Actual performance metrics will be updated as development progresses.

🔧 API Documentation

✅ APIs are fully implemented and functional!

Base URL

http://localhost:8000

Authentication Endpoints

# User Registration
POST /api/v1/user/register
Content-Type: application/json

{
  "email": "user@example.com",
  "password": "password123",
  "displayName": "John Doe"
}

# User Login
POST /api/v1/user/login
Content-Type: application/json

{
  "email": "user@example.com",
  "password": "password123"
}

Food Recognition & Analysis

# Image-based Food Prediction
POST /api/v1/log/predict
Content-Type: multipart/form-data

{
  "name": "Apple",
  "image": <file>,
  "description": "Red apple" (optional)
}

# Response:
{
  "result": {
    "name": "Apple",
    "confidence": 0.95,
    "nutrition": {...}
  },
  "suggested_food": "Apple",
  "confidence": 0.95,
  "original_ml_confidence": 0.92,
  "timestamp": "2024-01-15T10:30:00Z"
}

Meal Logging

# Log Meal with Analysis
POST /api/v1/log/analyse
Content-Type: application/json

{
  "username": "user123",
  "name": "Chicken Breast",
  "description": "Grilled chicken breast",
  "amnt": 200
}

# Response:
{
  "status": "started",
  "doc_id": "meal_12345"
}

Water Tracking

# Log Water Intake
POST /api/v1/water/log
Content-Type: application/json

{
  "username": "user123",
  "amount": 250,
  "timestamp": "2024-01-15T10:30:00Z"
}

# Get Water History
GET /api/v1/water/history/{username}

Barcode Scanning

# Get Product by Barcode
GET /api/v1/log/barcode/read/{barcode}

# Response:
{
  "name": "Product Name",
  "brand": "Brand Name",
  "nutrition": {...},
  "barcode": "1234567890"
}

Available Endpoints Summary

• POST /api/v1/user/register - User registration
• POST /api/v1/user/login - User authentication
• POST /api/v1/log/predict - Food image recognition
• POST /api/v1/log/analyse - Meal nutrition analysis
• GET /api/v1/log/barcode/read/{code} - Barcode product lookup
• POST /api/v1/water/log - Water intake logging
• GET /api/v1/water/history/{username} - Water consumption history

📱 Mobile App Features

Implemented UI Components

• Onboarding Flow: Step-by-step user introduction
• Authentication Screens: Login, registration, and email verification
• Tab Navigation: Home, Journal, Log, and Profile tabs
• Food Logging: Camera scan, barcode scanner, and manual entry
• Water Tracking: Hydration dashboard with progress visualization
• Charts & Analytics: Comprehensive data visualization
• Profile Management: User settings and preferences

Key Mobile Features

• Camera Integration: Direct food photo capture and analysis
• Barcode Scanner: Instant product identification
• Real-time Sync: Firebase-powered data synchronization
• Offline Support: Local data storage with AsyncStorage
• Responsive Design: Optimized for various screen sizes
• Gesture Support: Intuitive touch interactions

🐛 Troubleshooting

Backend Issues

Problem: ModuleNotFoundError when running the server

# Solution: Ensure virtual environment is activated and dependencies installed
source venv/bin/activate  # macOS/Linux
pip install -r req.txt

Problem: Firebase authentication error

# Solution: Verify firebaseSecret.json is in Backend root directory
# Check that the file has valid JSON credentials

Problem: Server not accessible from mobile device

# Solution: Ensure HOST in .env is your local network IP (not 127.0.0.1)
# Check that your device is on the same WiFi network
# Disable firewall temporarily to test connectivity

Frontend Issues

Problem: Metro bundler error or dependency issues

# Solution: Clear cache and reinstall
rm -rf node_modules
npm install
npx expo start --clear

Problem: Expo Go SDK version mismatch

# Solution: Download Expo Go SDK 53 from the link provided in the app
# Or upgrade project: npx expo install expo@latest

Problem: Camera/Barcode scanner not working

# Solution: Ensure permissions are granted in device settings
# For iOS: Settings → Nutrilio → Allow Camera
# For Android: Settings → Apps → Nutrilio → Permissions

🤝 Contributing

This project is actively maintained and welcomes contributions!

For Contributors:

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

Development Guidelines:

• Follow existing code style and patterns
• Add tests for new features
• Update documentation for API changes
• Ensure mobile app compatibility
• Test on both iOS and Android platforms

Areas for Contribution:

• UI/UX Improvements: Enhanced user interface design
• Performance Optimization: Faster API responses and app loading
• New Features: Additional nutrition tracking capabilities
• Testing: Comprehensive test coverage
• Documentation: Improved API and user documentation

📋 Project Status

🎉 This project is actively developed and functional!

✅ Completed Features

• Backend API Implementation: Complete FastAPI server with all endpoints
• Food Recognition: ML-powered image classification system
• Nutrition Analysis: Comprehensive nutrient breakdown engine
• Barcode Scanning: Product identification and nutrition lookup
• Meal Logging: Multi-modal food logging (text, image, barcode)
• Water Tracking: Hydration monitoring and goal tracking
• User Authentication: Firebase-based user management
• Mobile App: React Native app with Expo Router
• Database Integration: Firebase Firestore for data persistence
• UI Components: Comprehensive component library
• Charts & Visualization: Data visualization with React Native Chart Kit

🚧 Currently In Progress

• Model Optimization: Improving food recognition accuracy
• Performance Tuning: Optimizing API response times
• UI/UX Enhancements: Improving user experience
• Testing: Comprehensive test coverage

📝 Upcoming Features

• Habit Analysis: Pattern detection and insights
• Recommendation Engine: Personalized meal suggestions
• RAG Chatbot: AI-powered nutrition assistant
• Social Features: Community and sharing capabilities
• Advanced Analytics: Detailed nutrition insights
• Export Features: Data export and reporting

🎯 Current Status

Project Status: FUNCTIONAL - Core features are implemented and working Development Phase: Active development and feature enhancement Deployment: Ready for testing and user feedback

🏆 Project Achievements

Technical Milestones Reached

• ✅ Complete Backend API: Full FastAPI implementation with all endpoints
• ✅ ML Model Integration: Working food recognition with confidence scoring
• ✅ Mobile App Development: Cross-platform React Native application
• ✅ Database Integration: Firebase Firestore for real-time data sync
• ✅ Authentication System: Secure user management with Firebase Auth
• ✅ Multi-modal Input: Image, barcode, and text-based food logging
• ✅ Nutrition Analysis: Comprehensive nutrient breakdown engine
• ✅ Data Visualization: Interactive charts and progress tracking

Key Technical Features

• Real-time Food Recognition: Instant food identification from photos
• Barcode Product Lookup: Automatic nutrition data from product codes
• Background Processing: Asynchronous nutrition analysis
• Cross-platform Compatibility: iOS, Android, and Web support
• Offline Capability: Local data storage with sync
• Scalable Architecture: Modular design for easy feature additions

Development Statistics

• Backend: 7 API endpoints, 3 core engines, Firebase integration
• Mobile App: 15+ screens, 20+ components, full navigation
• AI/ML: Trained model with 25,500+ training steps
• Database: Real-time sync with Firebase Firestore
• Dependencies: 90+ packages across backend and frontend

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• WHO/FAO for nutrition guidelines and standards
• USDA for comprehensive food nutrition database
• Food-101 and IndianFood-101 dataset contributors
• Open source community for amazing tools and frameworks

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📞 Support

For project-related queries during development phase, please contact the team members directly.

Public support channels will be established upon project completion and deployment.

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Happy Exploring! 🎉

Advancing AI-driven nutrition management for better health outcomes