Go Guardian is an AI-powered safety companion application specifically designed to help women navigate cities safely. Using advanced AI capabilities through Google's Gemini API, the app provides real-time safety analysis, intelligent route planning, and emergency assistance with immediate response protocols in threatening situations.

• 1 in 3 women worldwide experience physical or sexual violence
• Limited access to real-time safety information
• Lack of community-based safety networks
• Insufficient emergency response systems

Women and Girl's Safety

• Real-time AI-powered safety analysis
• Community-driven safety networks
• Emergency response optimization
• Behavioral pattern recognition
• Safe route planning

• Reduced response time to safety incidents
• Increased community safety awareness
• Enhanced emergency service coordination
• Improved access to safe spaces
• Data-driven safety improvements

• Real-time safety analysis of routes and areas
• Visual analysis of surroundings using Gemini Pro Vision
• Natural language processing for emergency response
• Contextual safety recommendations
• Behavior pattern analysis
• Environmental threat detection

Key Features:

• Multi-modal analysis (text, image, location data)
• Real-time safety scoring
• Contextual recommendations
• Emergency response generation

• Google Maps Platform
  • Routes API for safe path planning
  • Places API for safe location identification
  • Geocoding API for location services
  • Distance Matrix API for route optimization
• Google Cloud Platform
  • App Engine for deployment
  • Cloud Storage for data
  • Cloud Logging for monitoring

• AI-powered route recommendations based on:
  • Street lighting infrastructure analysis (working vs total lights)
  • Time-of-day safety scoring
  • Historical incident data
  • Real-time crowd density
  • Public transit accessibility
• Natural language processing for intuitive destination input
• Dynamic rerouting based on real-time safety conditions
• Safety score visualization for each route

• Motion-triggered SOS activation
• Intelligent emergency contact management with relationship categorization
• One-tap emergency services access with:
  • Automatic location sharing
  • Audio/video recording
  • Custom emergency scripts
• Real-time situation analysis with AI guidance
• Fallback emergency protocols when offline

• Real-time environment analysis using:
  • Street lighting conditions monitoring
  • Infrastructure status assessment
  • Time-based risk evaluation
  • Pattern recognition for threat assessment
  • Voice command recognition for hands-free operation
• Contextual safety recommendations based on:
  • Current location
  • Time of day
  • Historical data
  • Environmental factors
• Safety score calculation with multiple parameters:
  • Area coverage
  • Working infrastructure
  • Historical incidents
  • Emergency resource proximity

Features referenced from typescript:frontend/src/app/community/page.tsx:

• Neighborhood safety groups
• Real-time safety alerts
• Verified incident reporting
• Community-driven safety updates
• Location-based group suggestions
• Active member monitoring

Features referenced from typescript:frontend/src/components/SafePlacesSearch.tsx:

• Categorized safe locations:
  • Police stations
  • Hospitals
  • Safe businesses
  • Community safe spaces
• Real-time availability status
• Distance and routing information
• Emergency contact information
• Operating hours tracking

Features referenced from typescript:frontend/src/components/SafetyDataVisualization.tsx:

• Infrastructure status monitoring
• Working lights percentage
• Area safety trends
• Risk level assessment
• Emergency resource mapping

Referenced from python:backend/app/services/gemini_service.py:

• Real-time route safety analysis
• Time-aware risk assessment
• Multi-factor safety scoring
• Contextual recommendation generation

Referenced from python:backend/app/services/monitoring_service.py:

• Real-time location tracking
• Route deviation detection
• Dynamic safety assessment
• Automated alert triggering

Referenced from python:backend/app/services/emergency_service.py:

• Intelligent response routing
• Safe place identification
• Emergency guidance generation
• Fallback protocol management

• Frontend: Next.js 14
  • App Router for improved routing
  • Server Components for better performance
  • Tailwind CSS for styling
  • shadcn/ui for beautiful, accessible components
  • Server Actions for form handling
  • Edge Runtime support
• Backend: Flask/Python
  • Fast API responses
  • Efficient data processing
  • Scalable architecture
• AI/ML: Google Gemini API
  • Image processing
  • Natural language understanding
  • Contextual analysis
• Maps: Google Maps API
• Database: TBD

• Street lighting data
• Crime statistics
• Public transit schedules
• Business operating hours
• Emergency service locations
• Community-reported incidents

1. Clone the repository

git clone https://github.com/yourusername/go-guardian.git
cd go-guardian

2. Install frontend dependencies

# Navigate to frontend directory
cd frontend
npm install

3. Install shadcn/ui components

# Install individual components as needed
npx shadcn-ui@latest add scroll-area
npx shadcn-ui@latest add button
# Add other components similarly

4. Create and configure environment variables

# Frontend
cp .env.example .env.local

# Backend
cd ../backend
cp .env.example .env

5. Add your API keys to the respective .env files:

# Frontend (@frontend/.env.local)
NEXT_PUBLIC_GOOGLE_MAPS_API_KEY=your_google_maps_api_key
NEXT_PUBLIC_API_URL=http://localhost:8000/api
GEMINI_API_KEY=your_gemini_api_key
WEATHER_API_KEY=your_weather_api_key

# Backend (@backend/.env)
FLASK_DEBUG=1
PORT=8000
FLASK_LOG_LEVEL=INFO

Required API Keys:

• Google Maps API key (for maps and location services)
• Gemini API key (for AI-powered safety analysis)
• Weather API key (for weather alerts and conditions)

You can obtain these API keys from:

• Google Maps API: Google Cloud Console
• Gemini API: Google AI Studio
• Weather API: OpenWeatherMap

Note: Make sure to never commit your actual API keys to version control.

6. Set up Python virtual environment and install backend dependencies
```bash
# From the backend directory
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

7. Run the tests

python3 -m pytest tests/

• Node.js 18.17 or later
• Python 3.8+
• Google Cloud account with Gemini API access
• Google Maps API key

1. Start the frontend development server:

# From the frontend directory
npm run dev

2. Start the backend server:

# From the backend directory
source venv/bin/activate  # If not already activated
python3 run.py

go-guardian/
├── frontend/
│   ├── app/
│   │   ├── community/
│   │   │   └�� page.tsx
│   │   ├── components/
│   │   │   ├── SafePlacesSearch.tsx
│   │   │   └── SafetyDataVisualization.tsx
│   │   ├── lib/
│   │   └── public/
├── backend/
│   ├── app/
│   │   ├── services/
│   │   │   ├── gemini_service.py
│   │   │   ├── monitoring_service.py
│   │   │   └── emergency_service.py
│   │   └── routes/
│   └── tests/
└── README.md

Run the automated test suite:

# Frontend tests
cd frontend
npm run test

# Backend tests
cd backend
python -m pytest tests/

• Empowering women's safe mobility in urban environments
• Reducing barriers to women's freedom of movement
• Creating safer communities through technology
• Optimizing emergency response for women's safety
• Building a supportive safety network for women
• Contributing to violence prevention through early warning systems

• Beta testing with women
• Application scaling initiatives

• Implementation of "Hi Shield!" wake word detection
• Voice-activated emergency protocols
• Hands-free safety commands:
  • "Hi Shield! Call emergency contacts"
  • "Hi Shield! Share my location"
• Multi-language voice command support
• Noise-resistant voice recognition
• Custom wake word training options

• Advanced visual threat detection using Gemini Pro Vision
  • Real-time crowd behavior analysis
  • Suspicious activity recognition
  • Environmental hazard detection
• Multi-modal safety assessment
  • Combined audio-visual analysis
  • Contextual environment understanding
  • Behavioral pattern recognition
• Predictive safety analytics
  • Route risk forecasting
  • Incident probability modeling
  • Dynamic safety score adjustments
• Natural language understanding improvements
  • Context-aware emergency response
  • Emotional state analysis
  • Situation-specific guidance

• Integration with San Francisco city databases:
  • SFPD station locations and response times
  • SF hospitals and emergency care centers
  • Registered safe businesses and organizations
  • Street lighting infrastructure data
  • Historical crime statistics by neighborhood
• Real-time data pipelines:
  • Public transit status updates
  • Business operating hours
  • Community-reported incidents
• Data partnerships:
  • SF Safe (neighborhood safety organization)
  • SF Women Against Rape (SFWAR)
    • Local business associations
    • Neighborhood watch groups
• Historical data analysis:
  • Crime pattern recognition
  • Safety

1. Fork the repository
2. Create a 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

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

For support, email [email protected].

• San Francisco Open Data Portal
• Women's safety organizations

Made with ❤️ for women's safety worldwide