.. _example_application:

Deploy an Example App
=====================

.. note::
   This tutorial will show you the pieces required to deploy an example bot to the 
   Rasa Platform. 

Goal
----

We will deploy one of the example bots from the Rasa Core repo to your running instance of the
platform.
The easiest way to follow along is if you carry out these steps while in an ``ssh`` session on
the server where you deployed the platform. In the final section we discuss how to build and 
deploy to a remote server. 


There are two main steps to deploying a bot to the Rasa Platform:

1. Creating a docker container where all your actions will be executed
2. Making models available to the platform's Rasa Core and Rasa NLU containers.


Creating an Application and a Docker Container
----------------------------------------------

If you haven't done so already, ``ssh`` into your server. 

Clone the demo app:

.. code-block:: bash

   git clone https://github.com/RasaHQ/platform-demo.git
   cd platform-demo/moodbot


Have a look at the file ``main.py``.

This example app uses the ``RasaChatInput``. you can find more information
on that here: :ref:`channel_rasa`.

Running your bot on the platform makes use of a class ``RemoteAgent``.

.. literalinclude:: ../_static/code/moodbot.py

.. note::

   To run this application locally or to build it on a CI server, you need an
   account on ``https://pypi.rasa.ai`` as this will allow you to fetch the
   enterprise versions of Rasa (the ``rasa_extensions`` package).
   More information can be found in :ref:`installation_package`.


Building Your Docker Image
--------------------------

To build a docker image which will execute the bot's actions, run:

.. code-block:: bash

   docker build -t $IMAGE_TAG  --build-arg RASA_PYPI_USER=user --build-arg RASA_PYPI_PASSWORD=pw .


This will use the Dockerfile in the moodbot directory.
For now, you can use something like ``IMAGE_TAG=demobot:v1``. 
If you are deploying to a remote server, you will want to push the image to a registry first.
An image tag like ``IMAGE_TAG=username/demobot:v1`` will allow you to push this tag to a docker registry like GCR or docker Hub. 
You can read more about docker tags here: https://docs.docker.com/engine/reference/commandline/tag/


Start the application
-------------------------

Once your docker image is built, you can start the container using a command like the below.
This will copy your trained dialogue model to the directory where Rasa Core can find it,
and start up your container so that it can talk to the other containers of the platform.
You will have to set the ``$RASA_CORE_TOKEN`` environment variable, which you can find
in the settings at http://rasa.examples.com/settings . 


.. literalinclude:: ../_static/code/start_app.sh


Add Some NLU Data
-----------------

Unlike the Rasa Core model, the Rasa NLU model is trained directly on the server.
The training data in the platform should therefore be considered the master copy.
Navigate to the NLU tab in the platform at http://rasa.example.com/inbox, click on ``Trained Examples``,
and click ``Upload and Replace``. Upload the training data at ``data/training_data.json`` 
and hit the ``train`` button.


Try out your bot!
-----------------

Go to http://rasa.example.com/chat - you should see a chat interface similar to the one below.

For more details read :ref:`channel_rasa`.

.. image:: ../_static/images/chat.png

Deploying to a remote server
----------------------------

For a CI/CD set up you probably don't want to build your docker image on the server that's running your bot.
In the ``remote`` directory there are two scripts, ``deploy_model.sh`` and ``deploy_app.sh`` that show how you
can deploy the application to a remote server. However you will need to set up a docker registry to hold your images.
The easiest option is to create an account at https://hub.docker.com and push your images there.