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Generating UI layout code from Zeplin screens and components using a seq2seq network

License

Introduction

The aim of the project is to layout UI code from Zeplin screens and components in React Native

High Level Requirements

  • Output of the generated code shall match the design specs given by screen and component data structures.
  • Generated code shall be usable by a developer building on top of the design given by data structures. So, a typical workflow of a developer could be; see the design -> generate code of the design -> implement business logic over that generated UI code.
  • Generated code shall contain not only presentation of the design but semantics of the design elements. (A paragraph shall be <p>)

Challenges

  • The data structures contain just enough information for a design to be represented. So, it does not contain any layout implementation details. We need to understand and extract those details.
  • After extracting layout details; we need to generate code that also contains styling of the components. (Getting style information from the data is trivial since that representation maps to implementation)
  • The non-trivial part of generating code after extracting layout details is understanding the semantic context of the component. Is this a Heading or is this component a List component that includes other components…
  • After understanding semantic context of the components, we can generate the code.

Current state

Currently, we've only dealt with extracting layout details and generating a code containing them using a seq2seq deep learning model.

Usage instructions

You can follow the same steps to redo the experiments.

Scraper

This module downloads website rectangles and markup from top 1 million Alexa websites database and converts them to dataset structure with rectangles as x and output markup as y.

npm install
npm run download-html # Downloads html files into "scraper/out" directory
npm run generate-dataset # Converts downloaded html to ".json" files with dataset file structure.

Compiler

This module converts "scraper/dataset" file with html markup to data points with DSL markup. Also, exports a function for converting DSL code to RN code.

To convert dataset with html markup to data points with DSL markup; we run

node compiler/index.js --decompile # You can change the datasetDir in "compiler/index.js" to point another folder

Training

After the upper steps are completed, we have a dataset that our model understands.

You need to install tensorflow, keras and numpy for model.

To point out to the newly database we've created; change dir_path of dataset generator in "model.py" Also, there are some parameters that in the code fine tuned for my environment; you can freely remove or adjust them.

Then you run python model.py. This saves our model improvements into weights/ for each epoch.

After the training complete and you have saved models. You can convert them using tensorflowjs_converter. To do that, you need tensorflowjs dependency as well.

pip install tensorflowjs

Then follow the instructions in here: https://github.com/tensorflow/tfjs-converter

Zeplin Extension

Currently, in zpl-extension folder; there is an extension with pretrained and converted models. You can open index.js and change tf.loadLayersModel function's argument to try with different models.

You can follow running a Zeplin Extension and using it in Zeplin instructions from here: https://github.com/zeplin/zem

Similar studies

These studies worked on generating UI code from drawing images. So, they tried to solve two problems: firstly, they tried to convert drawing images into meaningful data structures; secondly, they tried to generate code from generated data structures. Since we have meaningful data structures representing the UI we only are interested in the second part of the problem.