• Overview
• Features
• Tech Stack
• Architecture
• Installation
• Configuration
• Deployment
• Contributing
• Team
• License

Patra is a modern dating application built with Flutter and powered by AI-driven matching algorithms. The app combines intuitive user experience with intelligent recommendation systems to help users find meaningful connections.

• 🤖 AI-Powered Matching: Smart recommendations using machine learning
• 🔥 Real-time Chat: Instant messaging with Firebase integration
• 📱 Cross-Platform: Works on iOS, Android, and Web
• ☁️ Cloud Integration: Seamless image uploads with Cloudinary
• 🔒 Secure Authentication: Firebase Auth with multiple providers

• ✅ User Authentication (College Mail Id)
• ✅ Profile Management with photo uploads
• ✅ Smart Matching Algorithm using ML recommendations
• ✅ Swipe Interface (Like, Pass, Super Like)
• ✅ Real-time Messaging
• ✅ Discovery Page with advanced filters
• ✅ Location-based Matching
• ✅ Push Notifications

• 🔄 ML Data Refresh for improved recommendations
• 📊 Analytics Integration with Firebase Analytics
• 🎨 Modern UI/UX with smooth animations
• 🌓 Theme Support (Light/Dark mode)
• 🔐 Privacy Controls and data encryption
• 📱 Responsive Design for all screen sizes

• Framework: Flutter 3.0+
• Language: Dart
• State Management: Provider / Riverpod
• UI Components: Material Design 3
• Navigation: Go Router

• Authentication: Firebase Auth
• Database: Cloud Firestore
• Storage: Firebase Storage + Cloudinary
• Analytics: Firebase Analytics
• Notifications: Firebase Cloud Messaging

• Language: Python 3.8+
• Framework: Flask
• ML Libraries: scikit-learn, pandas, numpy
• Database: Firebase Admin SDK

• Web: Vercel
• Mobile: Firebase App Distribution
• ML Service: Flask + Docker

┌─────────────────────────────────────────────────────────┐
│                    Client Layer                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐      │
│  │     iOS     │  │   Android   │  │     Web     │      │
│  └─────────────┘  └─────────────┘  └─────────────┘      │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                 Flutter Application                     │
│  ┌─────────────────────────────────────────────────┐    │
│  │              ConfigService                      │    │
│  │         (Environment Management)                │    │
│  └─────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                   Service Layer                         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │
│  │   Firebase   │  │  Cloudinary  │  │  ML Service  │   │
│  │   Services   │  │   (Images)   │  │ (Python API) │   │
│  └──────────────┘  └──────────────┘  └──────────────┘   │
└─────────────────────────────────────────────────────────┘

• Flutter SDK (3.0+)
• Dart SDK (2.17+)
• Firebase CLI
• Python 3.8+ (for ML service)
• Git

git clone https://github.com/5umitpandey/Patra.git
cd Patra1/patra_initial

flutter pub get

Create environment files:

# Copy example files
cp .env.example .env
cp api_keys.env.example api_keys.env

# Edit with your actual keys
nano .env
nano api_keys.env

# Install Firebase CLI
npm install -g firebase-tools

# Login and configure
firebase login
flutterfire configure

# Development mode
flutter run

# Web development
flutter run -d chrome

# Release build
flutter build apk --release  # Android
flutter build ios --release  # iOS
flutter build web --release  # Web

# Firebase Configuration
FIREBASE_PROJECT_ID=your-project-id
FIREBASE_WEB_API_KEY=your-api-key
FIREBASE_WEB_APP_ID=your-app-id
FIREBASE_AUTH_DOMAIN=your-domain.firebaseapp.com

# Cloudinary Configuration
CLOUDINARY_CLOUD_NAME=your-cloud-name
CLOUDINARY_API_KEY=your-api-key
CLOUDINARY_API_SECRET=your-api-secret
CLOUDINARY_UPLOAD_PRESET=your-preset

# Environment
ENVIRONMENT=development # development, staging, production
DEBUG_MODE=true
ML_MATCHING_ENABLED=true

# Feature Flags
PREMIUM_FEATURES_ENABLED=false
VIDEO_CALL_ENABLED=false
PUSH_NOTIFICATIONS_ENABLED=true

# Limits
MAX_PHOTOS_PER_USER=6
MAX_SWIPES_PER_DAY=100
MAX_MATCHES_PER_REQUEST=20

# External Services
GOOGLE_ANALYTICS_ID=your-ga-id
SENTRY_DSN=your-sentry-dsn

1. Create a new Firebase project
2. Enable Authentication (Email, Google, Facebook)
3. Create Firestore database
4. Configure storage rules
5. Add your app (iOS/Android/Web)

1. Create Cloudinary account
2. Get cloud name and API credentials
3. Create upload preset for image processing

The AI-powered recommendation system runs as a separate Python service.

• User behavior analysis
• Compatibility scoring
• Preference learning
• Real-time recommendations

cd ml_backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

# Setup Firebase credentials
export GOOGLE_APPLICATION_CREDENTIALS="path/to/firebase-key.json"

# Run service
python app.py

GET  /health                    # Service status
POST /recommendations/{user_id} # Get recommendations
POST /refresh-data              # Refresh ML model

# Build image
docker build -t patra-ml-service .

# Run container
docker run -p 5000:5000 -e GOOGLE_APPLICATION_CREDENTIALS=/app/firebase-key.json patra-ml-service

# Install Vercel CLI
npm install -g vercel

# Deploy
flutter build web --release
vercel --prod

# Android
flutter build appbundle --release

# iOS (requires Mac)
flutter build ipa --release

# Firebase App Distribution
firebase appdistribution:distribute build/app/outputs/bundle/release/app-release.aab \
  --app 1:123456789:android:abcdef \
  --groups "testers"

# Staging
flutter build web --dart-define=ENVIRONMENT=staging

# Production
flutter build web --dart-define=ENVIRONMENT=production \
  --dart-define=DEBUG_MODE=false

lib/
├── main.dart                    # App entry point
├── auth/                        # Authentication logic
├── pages/                       # UI screens
│   ├── homePage.dart           # Main app interface
│   ├── loginPage.dart          # User login
│   ├── signUpPage.dart         # User registration
│   └── discoveryPage.dart      # Match discovery
├── services/                    # Business logic
│   ├── config_service.dart     # Environment management
│   ├── auth_service.dart       # Authentication service
│   ├── firestore_service.dart  # Database operations
│   ├── ml_service.dart         # ML API integration
│   └── cloudinary_service.dart # Image uploads
├── models/                      # Data models
├── widgets/                     # Reusable components
└── utils/                       # Helper functions

• Linting: flutter analyze
• Formatting: dart format .
• Testing: flutter test

# Feature branch
git checkout -b feature/new-feature
git commit -m "feat: add new feature"
git push origin feature/new-feature

# Create pull request

# Debug mode
flutter run --debug

# Profile mode
flutter run --profile

# Release mode
flutter run --release

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

• Follow Flutter/Dart style guide
• Write unit tests for new features
• Update documentation
• Ensure CI/CD passes

Development Team

Utkarsh Shukla Full Stack Engineer 💻

Sumit Pandey AI/ML Engineer 🤖

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

• Flutter team for the amazing framework
• Firebase for backend services
• Cloudinary for image management
• Open source community for inspiration