Calista Engine

A Deep Learning powered engine to measure your Website's Aesthetics

Paper: "Calista: A deep learning-based system for understanding and evaluating website aesthetics"

Cite as:

@article{DELITZAS2023,
	title = {Calista: A deep learning-based system for understanding and evaluating website aesthetics},
	journal = {International Journal of Human-Computer Studies},
	volume = {175},
	pages = {103019},
	year = {2023},
	issn = {1071-5819},
	doi = {https://doi.org/10.1016/j.ijhcs.2023.103019},
	url = {https://www.sciencedirect.com/science/article/pii/S1071581923000253},
	author = {Alexandros Delitzas and Kyriakos C. Chatzidimitriou and Andreas L. Symeonidis}
}

How to use

• Step 1: Insert the URL of the webpage that you want to evaluate its aesthetics

• Step 2: Wait a few seconds for the assessment process to complete

• Step 3: The aesthetics score is ready!

Prerequisites

• Docker
• Docker-compose

Deployment

Step 1 - Download the pretrained model

Download the model in the folder CNN/src/cnn_model/ from here.

Step 2 - Environment variables (optional)

Add a .env file in the root folder of the project and set the following variables:

Environment variable	Description
BASEURL	Base URL that is used for the requests

Step 3 - Run

Start:

docker-compose -f docker-compose.yml up --build

Stop:

Ctrl-C

For detached mode:

Start:

docker-compose -f docker-compose.yml up -d --build

Stop:

docker-compose down