============ DashAI

.. image:: https://img.shields.io/pypi/v/dashai.svg :target: https://pypi.python.org/pypi/dashai

.. image:: https://readthedocs.org/projects/dashai/badge/?version=latest :target: https://dashai.readthedocs.io/en/latest/?version=latest :alt: Documentation Status

A graphical toolbox for training, evaluating and deploying state-of-the-art AI models

.. image:: ./images/DashAI_banner.png :alt: DashAI Logo

Quick installation (Pypi)

DashAI needs Python 3.8 or greater to be installed. Once that requirement is satisfied, you can install DashAI via pip:

.. code:: bash

$ pip install dashai

Then, to initialize the server and the graphical interface, run:

.. code:: bash

$ dashai

Finally, go to http://localhost:3000/ <http://localhost:3000/>_ in your browser to access to the DashAI graphical interface.

Test datasets

Some datasets you can use to try DashAI are available here <https://github.com/DashAISoftware/DashAI_Datasets>_.

Development

To download and run the development version of DashAI, first, download the repository and switch to the developing branch: :

.. code:: bash

$ git clone https://github.com/DashAISoftware/DashAI.git
$ git checkout develop

Frontend

.. warning::

All commands executed in this section must be run
from `DashAI/front`. To move there, run:

.. code::

    $ cd DashAI/front

Prepare the environment

1. `Install the LTS node version <https://nodejs.org/en>`_.

2. Install `Yarn` package manager following the instructions located on the
   `yarn getting started <https://yarnpkg.com/getting-started>`_ page.

3. Move to `DashAI/front` and Install the project packages
   using yarn:

.. code:: bash

    $ cd DashAI/front
    $ yarn install


Running the frontend
~~~~~~~~~~~~~~~~~~~~~~

Move to DashAI/front if you are not on that route:

.. code:: bash

    $ cd DashAI/front

Then, launch the front-end development server by running the following command:

.. code:: bash

    $ yarn start

If you want to launch the front-end test server (without launching the backend) with dummy data, run:

.. code:: bash

    $ yarn json-server

Linting and formatting
~~~~~~~~~~~~~~~~~~~~~~

The project uses as default linter `eslint <https://eslint.org/>`_ with
the `react/recommended`, `standard-with-typescript`` and `prettier`` styles.

To manually run the linter, move to `DashAI/front` and run:

.. code:: bash

    $ yarn eslint src


The project uses `prettier <https://prettier.io/>`_ as default formatter.

To format the code manually, move to `DashAI/front` and execute:

.. code:: bash

    $ yarn prettier --write src


Build the frontend
~~~~~~~~~~~~~~~~~~

Execute from `DashAI/front`:

.. code:: bash

    $ yarn build

Backend
-------


Prepare the environment

First, set the python enviroment using conda <https://docs.conda.io/en/latest/miniconda.html>_:

.. code: bash

$ conda create -n dashai python=3.10
$ conda activate dashai

Then, move to DashAI/back

.. code:: bash

$ cd DashAI/back

Later, install the requirements:

.. code:: bash

$ pip install -r requirements.txt
$ pip install -r requirements-dev.txt

Running the Backend

There are three ways to run DashAI:

1. By executing DashAI as a module:

.. code:: bash

    $ python -m DashAI

2. Or,  installing the default build:

.. code:: bash

    $ pip install .
    $ dashai

If you chose the second way, remember to install it each time you make changes.

**Setting the local execution path**

With the `--local-path` option you can determine where DashAI will save its local
files, such as datasets, experiments, runs and others.
The following example shows how to set the folder in the local `.DashAI` directory:

.. code:: bash

    $ python -m DashAI --local-path "~/.DashAI"


**Setting the logging level**

Through the --logging_level parameter, you can set which logging level the DashAI
backend server will have.

.. code:: bash

    $ python -m DashAI --logging-level INFO

The possible levels available are: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`.

Note that the --logging-level not only affects the DashAI loggers, but also
the datasets (which is set to the same level as DashAI) and the
SQLAlchemy (which is only activated when logging level is DEBUG).


**Checking Available Options**

You can check all available options through the command:

.. code:: bash

    $ python -m DashAI --help

Execute tests
~~~~~~~~~~~~~

DashAI uses `pytest <https://docs.pytest.org/>`_ to perform the backend
tests.
To execute the backend tests

1. Move to `DashAI/back`

.. code:: bash

    $ cd DashAI/back

2. Run:

.. code:: bash

    $ pytest tests/

.. note::

    The database session is parametrized in every endpoint as
    ``db: Session = Depends(get_db)`` so we can test endpoints on a test database
    without making changes to the main database.


Linting and formatting

The project uses as default backend linter ruff <https://github.com/charliermarsh/ruff>_:

To manually run the linter, move to DashAI/back and execute:

.. code:: bash

$ ruff .

The project uses black <https://black.readthedocs.io/en/stable/>_ as default formatter.

To manually format the code, move to DashAI/back and execute:

.. code:: bash

$ black .

Acknowledgments

This project is sponsored by the National Center for Artificial Intelligence - CENIA <https://cenia.cl/en/>_ (FB210017), and the Millennium Institute for Foundational Data Research - IMFD <https://imfd.cl/en/>_ (ICN17_002).

The core of the development is carried out by students from the Computer Science Department of the University of Chile and the Federico Santa Maria Technical University.

To see the full list of contributors, visit in Contributors <https://github.com/DashAISoftware/DashAI/graphs/contributors>_ the DashAI repository on Github.

.. image:: ./images/logos.png :alt: Collaboration Logos