Travel Buddy

Create beautiful itineraries with ease.

Solution

Travel Buddy is an AI-powered solution that revolutionizes trip planning by generating personalized itineraries tailored to user preferences. It simplifies the process with seamless day-wise organization and instant notifications, ensuring a hassle-free experience for travelers. Built on the foundation of open-source technologies, Travel Buddy combines the strengths of AI and a user-friendly interface to meet the demands of modern travelers, offering an efficient and enjoyable way to plan journeys.

Features

• Streamlined Itinerary Creation
• Personalized place suggestions
• Day-wise planning
• Itineraries sent via email
• Beautiful User Interface
• Passwordless Authentication
• Building persona-based itineraries
• Duration and budget-wise customization
• Clean architecture
• Mailing service for instant notifications

Flow Diagrams

Frontend

Backend

DB Diagram

System Architecture

Tech Stack

• MongoDB - NoSQL database for storing data
• Mongoose - ODM
• Next.js - React framework for production
• Tailwind CSS - Utility-first CSS framework
• TypeScript - Superset of JavaScript for type safety
• Node.js - JavaScript runtime for server-side development
• Express - Web framework for Node.js
• Puppeteer - For generating PDFs
• Nodemailer - For sending emails
• Render - Deployment platform for backend services
• Vercel - Deployment platform for frontend
• Flask - Python framework for creating APIs
• Pydantic - Data validation in Python
• Mistral AI Model - For generating customized itineraries matching the user persona

Tools Used

• Geoapify - For fetching places, routes, and place information
• Picsum - For generating images for the banner
• Together AI - For Mistral AI model API
• React-Leaflet - For displaying maps with OpenStreetMaps

Deployment Platform

• Render - Backend deployment
• Vercel - Frontend deployment

Deployed Links

Travel Buddy

Solution Future Aspects

• Booking of hotels, flights from the website
• Fine-tuning the model to get more accurate results for creating the itineraries
• AI-driven recommendations that would help users create a more fine-tuned itinerary
• Social network where users would be able to share their itineraries with others and collaborate
• Real-time collaboration for form filling when planning group trips
• Transition from monolithic backend to a microservices architecture
• Create a separate service for notifications
• Implement caching and additional fallback mechanisms to handle edge cases

Alternate Solutions

• A microservices architecture could improve scalability and maintainability.
• A dedicated notification service could enhance the robustness of the alert system.
• Consider using a serverless architecture for handling different services independently
• Explore other ML models or services for itinerary generation

Reproducing API Keys

To reproduce the API keys and secrets, ensure that sensitive information is managed securely using environment variables. Include example values in .env.example files and avoid committing real keys to version control.

Pros and Cons of Proposed Solution

Pros

• Streamlined and user-friendly interface
• Persona-based, budget, and duration-wise itinerary customization
• Clean architecture with a fallback mechanism
• Immediate email notifications

Cons

• Monolithic backend structure; could benefit from microservices
• Notification system could be more robust as a separate service
• Potential for improvements with caching and additional fallback mechanisms

Getting Started

Installation

1. Clone the repository: FIXME: Add GitHub URL here

   git clone https://github.com/TechyMT/aivelnirary.git
   

2. Navigate to the project directory:

   cd aivelnirary
   

Running Frontend

1. Navigate to the project directory:

   cd frontend
   

2. Install dependencies:

   npm install
   

3. Create a .env.local file in the root directory and add your environment variables:

   NEXTAUTH_SECRET=your_auth_secret_here
   AUTH_GOOGLE_ID=your_google_id
   AUTH_GOOGLE_SECRET=your_google_secret
   NEXT_PUBLIC_BASE_URL=your_public_base_url
   

4. Run the frontend:

   npm run dev
   

5. Open http://localhost:3000 in your browser to see the application.

Running Backend Server

1. Create a .env file in the root directory and add your environment variables:

   MONGODB_URL = "YOUR MONGODB URL"
   NODE_ENV = "development"
   GEOAPIFY_API_KEY = "YOUR GEOAPIFY API KEY"
   PORT = 10000
   ML_API_URL = "YOUR ML API URL"
   MAIL_USERNAME = "YOUR MAIL USERNAME"
   MAIL_PASSWORD = "YOUR MAIL PASSWORD"
   SELF_URL = "YOUR BACKEND SELF URL"
   
   

2. Navigate to the backend directory and start the server:

   cd backend
   npm install
   npm run dev
   

Running ML Server

1. Export your environment variables:

   export TOGETHERAI_API_KEY=your_together_ai_key
   

2. Navigate to the ML server directory and start the server using Gunicorn:

   cd ml
   pip install -r requirements.txt
   gunicorn app:app
   

Acknowledgments

• Thanks to all the open-source projects that made this possible.
• Inspired by travelers worldwide seeking easier trip planning solutions.

Contact

For any inquiries or support, please email us at [email protected] or open an issue in this repository.

Happy travels with Travel Buddy! 🌍✈️🏨