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Holiday Vacation Recommendation 🌎🌴

An interactive web application that recommends holiday destinations based on user-selected locations and personal descriptions. It uses a machine learning model to generate destination suggestions and displays results with descriptions and images.

Table of Contents

Overview

The "Holiday Vacation Recommendation" web app helps users discover travel destinations by selecting a location and entering personal preferences. The app communicates with a FastAPI backend to generate recommendations based on the input, delivering suggestions with images and relevant details.

Features

  • Location Selection: Choose a continent or country from a comprehensive list.
  • Custom Description Input: Describe your ideal vacation experience to receive more tailored suggestions.
  • Recommendation Display: Receive multiple destination suggestions, each with a description and accompanying image.
  • Dynamic Fetching: Uses REST API to fetch recommendations based on user inputs.

Demo

Technologies Used

  • Frontend: HTML, CSS, JavaScript
  • Backend: FastAPI (Python)
  • Machine learning: Hugging Face's sentence-transformers for text embeddings
  • Database/Storage: JSON and XLSX for storing holiday destination data

Getting Started

Follow these steps to set up and run the project locally.

Prerequisites

  • Python 3.11.4
  • Node.js (for frontend setup if applicable)
  • Git for cloning the repository
  • Virtual environment (recommended for backend dependencies)

Installation

1. Clone the repository:

git clone https://github.com/TBao-THUer/Hackathon---Holiday-Vacation-Recommendation.git
cd Hackathon---Holiday-Vacation-Recommendation

2. Backend Setup:

  • Create a virtual environment and install dependencies: 
    python -m venv env  
source env/bin/activate  # On Windows, use `env\Scripts\activate`  
pip install -r requirements.txt

ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory (if your device raises this error, the code below can help you)  
New-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1 -PropertyType DWORD -Force
  • Run the FastAPI server: 
    cd backend
uvicorn inference:app --reload

3. Frontend Setup:

  • Open web.html in your browser to launch the interface.
  • Alternatively, if using nmp or a live server. 
    npm install  
npm start # or use live server extension in VSCode

Project Structure

```graphql
.
├── backend                     # FastAPI backend for recommendations
│   ├── inference.py            # Main API endpoint
│   └── load_model.py           # Build and save the architecture and fine-tune weights model
├── files
|   ├── best_model.weights.h5   # Save the weights model after fine-tuning
|   ├── encoded_file.npy        # File saves all encoded destination embeddings
|   ├── hackathons.xlsx         # File saves all destinations and their details we collected 
|   ├── my_model.keras          # Files saves the architecture of model
├── frontend                    # HTML, CSS, and JS files for the UI
│   ├── web.html                # Main HTML file for frontend
│   └── style.css               # CSS file for frontend layout
└── README.md                   # Project documentation
└── requirements.txt            # Backend dependencies

Usage

  1. Start the backend server: 
    uvicorn inference:app --reload
  2. Open web.html in your browser.
  3. Select a location and enter your vacation description. Click "Search" or press "Enter" to receive recommendations.

API Reference

Endpoint /predict

  • Method: POST
  • Description: Accepts user preferences and returns a list of 5 recommended destinations.
  • Request Body: 
    {  
"location": "Asia",  
"description": "I enjoy beaches and historical landmarks."  
}
  • Response 
    [[ "Sukhothai Historical Park (Thailand): A site that combines natural beauty with historical ruins in a serene setting.", img_path_0], [ "Nusa Penida (Indonesia): Known for its stunning cliffs, beautiful beaches, and unique rock formations, a must-visit for nature lovers.", img_path_1], [ "Quy Nhon (Vietnam): A coastal city, known for its stunning beaches, ancient temples, and vibrant markets.", img_path_2 ], [ "Penghu Islands (Taiwan): A stunning archipelago known for its beautiful beaches, unique rock formations, and rich cultural history.", img_path_3 ], [ "Sentosa Island (Singapore): A resort destination featuring a world-renowned tourist attraction, beaches, water parks, and aquarium.", img_path_4]]

Contributing

  1. Fork the project.
  2. Create a feature branch (git checkout -b branch feature-name).
  3. Commit changes (git commit -m "Add some feature").
  4. Push to the branch (git push origin feature-name)
  5. Open a pull request.