John

Hey Kin, 

Great questions!

Seeq-SysID Add-on Packaging:

You're right. The Seeq-SysID Add-on in the Add-on Gallery predates the Add-on Manager and uses a different installation process. It gets  installed from the Data Lab terminal through a series of commands. This is acceptable for specific cases, but the newer, packaging approach offers more flexibility for future development.

The Add-on Tool Packaging Option:

For a more robust and maintainable Add-on, consider the packaging approach that you referenced (Add-on Development).  This method allows for flexibility in the types of Add-ons you are able to develop (AddOnTools, Frontend Plugins, and Data Lab Functions).  The .addon file is a zip file with your notebook and an addon.json file that describes the structure of Add-on.  Once your addon.json file is defined there is an helper build script to create the .addon file (Packaging)  However for simpler Add-on Tools, packaging is not necessarily required. 

Add-on Tool Development (Simple Approach):

If you prefer a simpler approach, Add-on Tools (Jupyter Notebooks run in Add-on Mode) are a good option and they don't require formal packaging (.addon file). Here's a helpful guide: How To Develop a Simple Interactive Add-on Tool

Publishing and Access Management:

Once your Add-on Tool functions as desired, you can publish it for wider use (assuming admin privileges). Here's the process:

1. Development: Complete your code and ensure it meets expectations.
2. Publishing (Admins): Use the User Tool Creator for publishing: Add-on Tool Administration and Development

• The Add-on Manager automatically adopts the tool within an hour.
• You can then manage user access through the Add-on Manager.

3. Publishing (Non-Admins): Contact an admin to use the User Tool Creator for you.

Overall, the packaging approach is recommended for long-term maintainability and future expansion of your Add-on. However, the Add-on Tool option provides a simpler starting point if needed.

@Nitish, Great question!  What kind of Add-on are you developing?  Is it an Add-on Tool or a frontend Add-on that uses a Data Lab Function as a backend? 

If you are developing an Add-on uses a Data Lab Function as a backend then you can directly call the reserved `LOG` variable to write to the Data Lab logger.  

Lets look at a really simple example of a function in the cell of a Data Lab Functions notebook.  

def add_ten(number=0):
  # This function adds 10 to a number
  
  LOG.info("Starting the process")
  try:
  	new_number = number + 10
    return new_number
  except Exception as e
  	# Lets log this failure
    LOG.error(f"Failed to add ten with error - {e}"

Alternatively, you can use the `getLogger` method from the logging module to get the Data Lab Functions Logger:

import os
import logging

my_log = logging.getLogger(os.environ['SEEQ_DATALAB_FUNCTIONS_LOGGER'])
my_log.info("Hello World!")

The logs for the Data Lab Function can be viewed from : 

`https://your.seeq.server/data-lab/<DATALAB_PROJECT_ID>/functions/logs`

Check out https://support.seeq.com/kb/latest/cloud/reserved-variables#id-(R64)ReservedVariables-TheLOGObject for more information. 

Welcome to the Seeq Developer Club Forum!

This forum is your go-to destination for all things related to developing Add-ons, API integrations, and leveraging Seeq's capabilities to enhance your projects. Whether you're a seasoned developer or just starting out, this is the place to discuss, share insights, and collaborate with fellow Seeq users.

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3. Resource Utilization: Before posting questions, explore our extensive documentation, tutorials, and the Seeq Support site. You may find answers to your queries or helpful resources that address your needs. Seeq Support - Developing Seeq Add-ons and Connectors
4. Specificity is Key: When posting questions, provide as much detail as possible about your environment, the Seeq version you're using, and any error messages encountered. Screenshots or code snippets can greatly aid in understanding and resolving issues.
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We're thrilled to have you as part of our developer community! Let's collaborate, innovate, and empower each other to unlock the full potential of Seeq. Thank you for your participation!
 

In more recent versions of Seeq (+R58),  we have changed our Add-on rendering engine to Voila and renamed the functionality to "Add-on Mode" from "AppMode."  The rendered Voila interface is designed for interactivity and is based on widgets. As you've noticed this means that code in a cell may not function the same way in both Jupyter and Voila. This is particularly true around interactions (i.e. inputs).  A way to get an input is to use a widget from ipywidgets and then use the value from the widget in your code. Here is a sample code snippet that uses an input widget and renders the value when a button is clicked. 

import ipywidgets as widgets

#Define the input
user_input = widgets.Text(
    value='Hello World',
    placeholder='Type something',
    description='String:',
    disabled=False
)

#define a button
button = widgets.Button(
    description="Execute",
    button_style='success'
)

#define an Output - someplace to render the button click
output = widgets.Output()

#Do something when button is clicked
def get_input_value(b):
    with output:
        print(user_input.value)

button.on_click(get_input_value)

#display input, button, and output Vertically
widgets.VBox([user_input,button,output])

Tip: 
In the new Advanced Mode in Seeq Data Lab you can render your notebook without having to navigate to a separate tab.  You can access the "Add-on Mode Preview" from the  View section in the top menu.  For information about the new Advanced Mode Seeq Data Lab check please check out Advanced Mode for Data Lab.  

Additional Information:
ipywidgets

Seeq Version: R21.0.42+

Question: 

How can i create a signal that forcasts a value out into the future based on some rolling period of historical data? For this example say i want to predict the Area A Temperature  3 days in the future based on the previous week (7 days) of data.