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https://github.com/pydevcasts/travelai

TravelAI is a dedicated team focused on enhancing travel experiences with personalized AI-driven recommendations. We aim to simplify trip planning and make every journey memorable for our users.
https://github.com/pydevcasts/travelai

django-rest-framework docker graphana gunicorn machine-learning nginx pinia postgressql tailwind typescript vuejs

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TravelAI is a dedicated team focused on enhancing travel experiences with personalized AI-driven recommendations. We aim to simplify trip planning and make every journey memorable for our users.

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README 

        
# TravelAI



## Description

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.



## Features

- **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.



## Machine Learning Integration

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.



## Technologies Used

- **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



## Architecture

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.



## Getting Started

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



1. Clone the repository:

   ```bash

   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