TravelAI is an innovative travel application that utilizes advanced artificial intelligence to deliver personalized travel experiences and house price predictions. By analyzing user preferences and past behaviors, TravelAI provides tailored recommendations for unique destinations, engaging activities, and comfortable accommodations. Additionally, the app leverages machine learning algorithms to predict house prices, helping users make informed decisions about their travel and real estate investments. This intelligent approach simplifies the travel planning process while enriching the overall journey, ensuring that every trip aligns perfectly with individual tastes and desires.

• Personalized Travel Suggestions: Analyze user data to offer customized destination and activity recommendations.
• Smart Trip Planning: Create tailored itineraries based on user preferences and travel history.
• Virtual Travel Guide: Access real-time information and assistance during your travels.
• Group Travel Options: Facilitate shared experiences and planning for group trips.
• Exclusive Discounts: Receive special offers and discounts based on user history and preferences.

TravelAI incorporates machine learning to enhance user experience through:

• Recommendation Systems: Analyzing user interactions to suggest personalized travel options.
• Predictive Analytics: Forecasting user preferences based on historical data to improve suggestion accuracy.
• Natural Language Processing: Understanding user queries to provide relevant information and assistance.

• Frontend: TypeScript, Vue.js, Pinia, Tailwind CSS
• Backend: Python with Django
• Microservices: Implemented using Django REST Framework and Flask
• Database: PostgreSQL or MongoDB
• Caching: Redis for improved performance and data caching
• Containerization: Docker for easy deployment and management of services
• API: REST API for efficient communication between frontend and backend services

TravelAI is built on a microservices architecture, enhancing scalability and maintainability. Each microservice is dedicated to specific functionalities, allowing for independent development, testing, and deployment. This architecture not only increases system resilience but also enables rapid iteration and feature enhancements.

To run the project locally using Docker, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/TravelAI.git
   cd TravelAI
   
   cd Back
   # for linux
   source ./venv/bin/activate 
   # for win
   source ./venv/Scripts/activate 
   python manage.py makemigrations
   python manage.py migrate
   python manage.py createsuperuser
   python manage.py runserver
   
   cd Front
   npm install
   npm run dev