Boilerplate for bootstrapping scalable multi-page Dash applications
Dash is a Python framework for building analytical web applications. Slapdash provides a sensible project layout for quickly building out a multi-page Dash application with room for growth. It also includes:
- A skeleton Dash app with multi-pages built using Dash Pages.
- Pre-built layouts built with Dash Bootstrap Components), which can be extended or swapped out for layouts constructed using your own Dash/CSS components.
- Scripts for conveniently launching your app in both dev and prod environments
This project is intended for bootstrapping initial Dash applications, rather than being a dependency for your application. You shouldn't assume that Slapdash's internal structure and interfaces will be stable, as they will change.
app.pyEntry point into the app. Creates both the Flask and Dash instances used for the app and then imports the rest of the app through the
settings.pyConfigurable settings for the application.
exceptions.pyExceptions used by your app can be defined here.
components.pyConvenient Python pseudo-Dash components are defined here.
wsgi.pyContains the Flask
applicationattribute for pointing WSGI servers
pages/Place Python modules corresponding to your pages in here.
assets/Location for static assets that will be exposed to the web server.
Note: Slapdash requires Python 3.6+
Slapdash is a Cookiecutter project. This means you first need to generate your own project from the Slapdash project template.
Install the latest Cookiecutter if you haven't installed it yet:
pip install -U cookiecutter
Generate your project by running this command and following the prompts:
The resulting project is a Python package, which you then need to install like so:
$ pip install PATH_TO_PROJECT
During development you will likely want to perform an editable install so that changes to the source code take immediate effect on the installed package.
$ pip install -e PATH_TO_PROJECT
app.py, select the main layout you want from
- Create the pages of your app in different files within the
pagesdirectory, by defining within each a top-level
layoutvariable or function and callbacks registered using the
assets/app.cssor add additional stylesheets in
- Modify config in
Installing this package into your virtualenv will result into the development
executable being installed into your path when the virtualenv is activated. This
command invokes your Dash app's
run_server method, which in turn uses the
Flask development server to run your app. The command is invoked as follows,
proj_slug being replaced by the value provided for this cookiecutter
The script takes a couple of arguments optional parameters, which you can
discover with the
--help flag. You may need to set the port using the
parameter. If you need to expose your app outside your local machine, you will
want to set
While convenient, the development webserver should not be used in
production. Installing this package will also result in a production executable
being installed in your virtualenv. This is a wrapper around the
mod_wsgi-express command, which streamlines use of the mod_wsgi Apache
module to run your your app. In addition to
mod_wsgi Python package, you will need to have installed
Apache. See installation instructions in the mod_wsgi
documentation. This script also takes a
range of command line arguments, which can be discovered with the
This script will also apply settings found in the module
project_slug.prod_settings (or a custom Python file supplied with the
--settings flag) and which takes precedence over the same settings found in
A notable advantage of using
mod_wsgi over other WSGI servers is that we do
not need to configure and run a web server separate to the WSGI server. When
using other WSGI servers (such as Gunicorn or uWSGI), you do not want to expose
them directly to web requests from the outside world for two reasons: 1)
incoming requests will not be buffered, exposing you to potential denial of
service attacks, and 2) you will be serving your static assets via Dash's Flask
instance, which is slow. The production script uses
mod_wsgi-express to spin
up an Apache process (separate to any process already running and listening on
port 80) that will buffer requests, passing them off to the worker processes
running your app, and will also set up the Apache instance to serve your static
assets much faster than would be the case through the Python worker processes.
Note: You will need to reinstall this package in order for changes to the
prod script to take effect even if you used an editable install
pip install -e).
You can easily run your app using a WSGI server of your choice (such as Gunicorn
for example) with the
project_slug.wsgi entry point
wsgi.py) like so:
$ gunicorn <app>.wsgi
Note: if you want to enable Dash's debug mode while running with a WSGI server,
you'll need to export the
DASH_DEBUG environment variable to
true. See the
Dev Tools section of the Dash Docs for more
Besides Dash itself, Slapdash builds on a few libraries for getting fully functional applications off the ground faster. These include:
- Dash Bootstrap Components: A suite of Dash components that wrap Bootstrap classes, allowing for cleaner integration of Bootstrap with Dash layouts.
- Font Awesome - Local copy of Font Awesome files for offline access. Because everyone wants pretty icons.
Plotly Python client figure reference Documents the contents of
plotly.graph_objs, which contains the different types of charts available, as well the
Layoutclass, for customising the appearance of charts.
PRs are welcome! If you have broader changes in mind, then creating an issue first for discussion would be best.
After changing directory to the top level Slapdash directory:
- Install Slapdash into your virtualenv:
$ pip install -e .
- Install the development requirements:
$ pip install -r requirements-dev.txt
- Install the pre-commit hook (for the Black code formatter)
$ pre-commit install