🐾 VetTrack AI - AI-Powered Pet Health Management System

A Flask-based AI web application that helps pet owners manage health more effectively with AI-driven analysis, photo diagnosis, smart tracking, and Murf-powered voice guidance.

💡 Inspiration:

This project is deeply personal to me. I once had a pet who suffered from parvovirus.
We didn’t have access to a professional vet in our city at the time, so he only received basic treatment without identifying the real issue.
By the time we took him to a bigger hospital, it was too late to save him. Losing him was one of the hardest experiences, and it inspired me to build VetTrack AI — so that other pet owners can have faster access to health insights and never feel helpless in such moments.

✨ Features

🤖 AI-Powered Analysis

• AI Symptom Checker: Advanced symptom analysis using Gemini AI for accurate preliminary diagnoses
• Photo Analysis: Upload pet photos for AI-powered visual health assessment
• Smart Diagnosis: Machine learning-based condition likelihood and urgency assessment
• Voice Integration: Murf TTS API for audio playback of analysis results

🏥 Health Management

• Pet Profiles: Comprehensive pet information management (species, breed, age, medical notes)
• Health History: Detailed tracking of symptoms, diagnoses, and treatments
• Urgency Assessment: AI-powered urgency level classification (Low/Medium/High/Emergency)
• Medical Records: Export consultation summaries and health reports

📱 User Experience

• Personalized Dashboard: User-specific pet overview and health statistics
• Voice Greetings: Personalized TTS welcome messages upon login
• Responsive Design: Mobile-friendly interface with Bootstrap 5
• Real-time Updates: Live health history and reminder tracking

🔔 Smart Notifications

• Health Reminders: Vaccination, medication, and checkup scheduling
• Urgency Alerts: Immediate notifications for high-priority health concerns
• Progress Tracking: Completion status for health tasks and appointments

💬 Consultation System

• Virtual Consultations: Jitsi Meet integration for video calls with veterinarians
• Health Summaries: AI-generated consultation notes and recommendations
• Professional Integration: Share health history with veterinary professionals

🚀 Technology Stack

Backend

• Flask 3.1.2: Modern Python web framework
• SQLAlchemy 2.0.43: Database ORM and management
• SQLite: Lightweight database (configurable for production)
• Python 3.11+: Core programming language

AI & External APIs

• Google Gemini AI: Advanced symptom analysis and diagnosis
• Murf TTS API: High-quality text-to-speech for accessibility
• Jitsi Meet: Video consultation platform integration

Frontend

• Bootstrap 5.3.0: Responsive CSS framework
• Font Awesome 6.0.0: Icon library
• Vanilla JavaScript: Modern ES6+ features
• HTML5: Semantic markup

Security & Authentication

• Flask-Login: User session management
• Password Hashing: Secure credential storage
• Session Management: Secure user authentication

📋 Prerequisites

• Python 3.11 or higher
• pip package manager
• Modern web browser (Chrome, Firefox, Safari, Edge)
• Internet connection for AI APIs

🛠️ Installation

1. Clone the Repository

git clone https://github.com/yourusername/vettrack-ai.git
cd vettrack-ai

2. Create Virtual Environment

python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Environment Configuration

Create a .env file in the project root:

# Required API Keys
GOOGLE_API_KEY=your_gemini_api_key_here
MURF_API_KEY=your_murf_tts_api_key_here

# Optional: Database Configuration
DATABASE_URL=sqlite:///instance/pet_health.db

# Optional: Session Security
SESSION_SECRET=your_secure_session_secret_here

5. Initialize Database

python main.py

The database will be automatically created on first run.

🚀 Running the Application

Development Mode

python main.py

The application will be available at http://127.0.0.1:5000

Production Mode

# Set environment variables
export FLASK_ENV=production
export FLASK_APP=main.py

# Run with production server
gunicorn -w 4 -b 0.0.0.0:5000 main:app

📱 Usage Guide

Getting Started

1. Sign Up: Create a new account with your email and password
2. Add Pets: Register your pets with species, breed, age, and medical notes
3. Access Dashboard: View your pets' health overview and quick actions

AI Symptom Checker

1. Select a pet from your profile
2. Describe symptoms in detail (or use quick-select checkboxes)
3. Receive AI-powered analysis with:
   • Preliminary diagnosis
   • Urgency level assessment
   • Treatment recommendations
   • Possible causes
   • Audio playback of results

Photo Analysis

1. Upload a clear photo of the health concern
2. Add optional description for context
3. Get AI visual analysis including:
   • Condition likelihood
   • Severity assessment
   • Urgency classification
   • Professional recommendations

Health Management

• Track History: Monitor symptoms, treatments, and outcomes
• Set Reminders: Schedule vaccinations, medications, and checkups
• Export Reports: Generate consultation summaries for veterinarians

🔧 Configuration

API Keys Setup

Google Gemini AI

1. Visit Google AI Studio
2. Create a new API key
3. Add to your .env file as GOOGLE_API_KEY

Murf TTS API

1. Sign up at Murf AI
2. Generate your API key
3. Add to your .env file as MURF_API_KEY

Database Configuration

• Default: SQLite database in instance/pet_health.db
• Production: Set DATABASE_URL in .env for PostgreSQL/MySQL
• Backup: Database files are automatically created and managed

📊 Project Structure

vettrack-ai/
├── app.py                 # Main Flask application
├── main.py               # Application entry point
├── models.py             # Database models and schemas
├── gemini.py             # AI integration module
├── requirements.txt      # Python dependencies
├── .env                  # Environment configuration
├── static/               # Static assets
│   ├── css/             # Stylesheets
│   ├── js/              # JavaScript files
│   └── uploads/         # User-uploaded images
├── templates/            # HTML templates
│   ├── dashboard.html   # Main dashboard
│   ├── symptom.html     # Symptom checker
│   ├── image.html       # Photo analysis
│   └── ...              # Other pages
└── instance/            # Database and uploads

🔒 Security Features

• Password Hashing: Secure bcrypt-based password storage
• Session Management: Secure user authentication
• Input Validation: Comprehensive form validation and sanitization
• File Upload Security: Secure image handling with size limits
• SQL Injection Protection: Parameterized database queries

🌐 API Endpoints

Authentication

• POST /signup - User registration
• POST /login - User authentication
• GET /logout - User logout

Pet Management

• GET /api/get_pets - Retrieve user's pets
• POST /api/add_pet - Add new pet profile
• GET /api/pet/<id>/recent-history - Pet health history

AI Analysis

• POST /api/check_symptoms - Symptom analysis
• POST /api/upload_image - Photo analysis
• POST /api/get_diagnosis_explanation - Detailed diagnosis info

TTS Integration

• POST /api/tts_generate - Generate speech from text
• GET /api/tts_status - Check TTS service status

Health Management

• GET /api/get_health_history - User's health records
• GET /api/get_reminders - User's health reminders
• POST /api/add_reminder - Create new health reminder

🧪 Testing

Manual Testing

1. User Registration: Test signup and login flows
2. Pet Management: Add, edit, and view pet profiles
3. AI Features: Test symptom checker and photo analysis
4. TTS Integration: Verify audio playback functionality

API Testing

# Test TTS endpoint
curl -X POST http://localhost:5000/api/tts_generate \
  -H "Content-Type: application/json" \
  -d '{"text":"Test message"}'

# Test symptom checker
curl -X POST http://localhost:5000/api/check_symptoms \
  -H "Content-Type: application/json" \
  -d '{"pet_id":1,"symptoms":"lethargy, loss of appetite"}'

🚀 Deployment

Local Development

python main.py

Production Deployment

1. Set Environment Variables:

   export FLASK_ENV=production
   export DATABASE_URL=postgresql://user:pass@localhost/vettrack

2. Use Production Server:

   pip install gunicorn
   gunicorn -w 4 -b 0.0.0.0:5000 main:app

3. Reverse Proxy (Nginx):

   server {
       listen 80;
       server_name yourdomain.com;
       
       location / {
           proxy_pass http://127.0.0.1:5000;
           proxy_set_header Host $host;
           proxy_set_header X-Real-IP $remote_addr;
       }
   }

🤝 Contributing

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

Development Guidelines

• Follow PEP 8 Python style guidelines
• Add comprehensive docstrings for new functions
• Include error handling for all API endpoints
• Test new features thoroughly before submitting

📝 License

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

🙏 Acknowledgments

• Google Gemini AI for advanced natural language processing
• Murf AI for high-quality text-to-speech capabilities
• Bootstrap for responsive UI components
• Flask community for the excellent web framework

📞 Support

• Issues: Report bugs and feature requests via GitHub Issues
• Documentation: Check this README and inline code comments
• Community: Join discussions in GitHub Discussions

🔮 Future Enhancements

• Mobile App: Native iOS/Android applications
• Machine Learning: Enhanced diagnosis accuracy with custom models
• Telemedicine: Direct integration with veterinary services
• Health Monitoring: IoT device integration for continuous monitoring
• Multi-language Support: Internationalization for global users
• Advanced Analytics: Health trend analysis and predictive insights

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VetTrack AI - Empowering pet owners with AI-driven health insights 🐾✨

Built with ❤️ for the pet care community