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
• Features
• Demo
• Technologies Used
• Getting Started
• Prerequisites
• Installation
• Project Structure
• Usage
• API Reference
• Contributing

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.