GooeyBrain

GUI app for generating data, such as images, using Tensorflow

Current Beta release focuses on image generation.

Installation

To run on your comuter:
Clone the repository and cd to the dir

git clone https://github.com/artBoffin/GooeyBrain.git
cd GooeyBrain

Create a Python virtual enviroment and activate it

pip install virtualenv  
virtualenv venv  
source venv/bin/activate  

Install all Python requirments:

pip install -r requirements.txt  

Install required node modules:
You need to have node.js installed on your computer

npm install

Run the app:

npm start

Running the app after installation

In the app source dir type:

source venv/bin/activate 
npm start

Development

For development puerposes, you also want to run:

webpack --watch

In another shell window.To keep compiling React.js files

Architecture

Frontend

main.js is the entry point that start an Electron application.
All other frontend files are located in app/
React.js files that are responsible for rendering all the components are in app/src Webpack bundles everyuthing to app/static/bundle.js.
For development, run webpack --watch in another shell window.

Calling npm start is like calling electron ., which in turn starts the elcetron application.

Backend

The Electron application spawns a Python process starting main.py, which starts a Flask app. models_manager.py is a help file for taking care of all different Deep Learning models, making it simple to add a new models (currently, only dcgan model exsists)
run_tf.py is called as a subprocess from main.py when "Train" ot "Generate" buttons are clicked. It is givena parameters filepath in it's arguments and a boolean if this is a Train or not.

To Add a New Model:

Add a folder with the model name in the "models" directory Make sure to create an __init__.py file and define get_model and get_parameters Create a class that extendes Base_model (include train and generate functions).

##TODO:

• use stepper to encapsualte the process:
  • First the user selects if she wants to train a new model or load exsisting one
    • if trainin new, select what model from a list of exsisting models, only then parameters list is shown
    • else, uplaod a parameters file and a path to a Tensorflow chekcoint directory
• "Generate" button
• "Load Parameters" button
• Add uplaod example image option, and use javascript to detemine input size
• Fix the pullover jump when clicking the question marks