Voygr

This web application generates personalized travel itineraries based on user preferences such as budget, interests, and trip duration. By providing a user-friendly interface and dynamic recommendations, it delivers detailed itineraries tailored to the individual user's travel style.

Table of Contents

• Project Overview
• Features
• Tech Stack
• Installation
• Usage
• API Endpoints
• Project Architecture
• Database Schema
• Future Improvements

Project Overview

The Personalized Travel Itinerary Generator enables users to create travel plans by selecting preferences for budget, interests, activities, and dates. The system recommends the best places to visit based on these preferences and also provides a downloadable PDF of the itinerary.

Key Features:

• Selectable budget, interests, and activities.
• Dynamic itinerary generation based on user input.
• Supports specific date range selection (start date and end date).
• Personalized recommendations ranked by relevance to preferences.
• Option to download the generated itinerary as a PDF.
• Deployed frontend on GitHub Pages and backend on Render.

Features

• Interactive Form: Allows users to input preferences (budget, interests, activities) and select travel dates.
• Itinerary Recommendations: Backend logic to suggest top places based on user preferences, budget, and travel dates.
• PDF Export: Users can download their customized itinerary in PDF format.
• Dark Theme: The user interface incorporates a sleek dark theme for improved aesthetics.

Tech Stack

Frontend:

• React.js for a responsive and interactive UI.
• Bootstrap for styling (dark theme).

Backend:

• Node.js with Express.js for API handling.
• MongoDB for storing places, interests, activities, and hotels.
• pdfkit for generating downloadable PDFs.

Deployment:

• Frontend: Hosted on GitHub Pages.
• Backend: Hosted on Render.

Installation

Prerequisites

• Node.js (v14 or above)
• MongoDB (local or Atlas)

Clone the Repository

git clone https://github.com/prernamittal/travel-itinerary-generator.git
cd travel-itinerary-generator

Install Dependencies

# Install frontend dependencies
cd frontend
npm install

# Install backend dependencies
cd ../backend
npm install

Configure Environment Variables

In the backend folder, create a .env file with the following contents:

MONGO_URI=<connection_string>

Run the Application

Start both frontend and backend servers:

# Start the backend
cd backend
npm start

# Start the frontend
cd ../frontend
npm start

The frontend will be running at http://localhost:3000 and the backend at http://localhost:5000.

Usage

1. Open the application in your browser (http://localhost:3000).
2. Fill in your preferences: budget, interests, activities, and travel dates.
3. Submit the form to generate your personalized itinerary.
4. Download the generated itinerary as a PDF by clicking on the "Export to PDF" button.

API Endpoints

• GET /api/places/interests-activities: Fetches the available interests and activities from the database.
• POST /api/itinerary/generate: Generates personalized travel itineraries based on user input.

Example request body for /api/itinerary/generate:

{
  "budget": 2000,
  "interests": ["Adventure", "Culture"],
  "activities": ["Hiking", "Museum"],
  "startDate": "2024-09-01",
  "endDate": "2024-09-10"
}

Project Architecture

Client-Server Architecture

• Frontend (React.js): Handles the user interface, form inputs, and communicates with the backend to display results.
• Backend (Node.js/Express.js): Processes user inputs, interacts with the MongoDB database, and returns recommendations.
• Database (MongoDB): Stores place data, including interests, activities, and hotels.

High-Level Diagram

Database Schema

Future Improvements

• User Authentication: Adding user login functionality would allow users to save and return to their previous itineraries.
• Real-time Pricing: Integrating APIs for flight and hotel pricing could make the itinerary more precise and actionable.
• Enhanced Recommendation Algorithm: Currently, the cost function focuses on a weighted combination of preferences. Future iterations could include user ratings or reviews to further personalize suggestions.