Tasks

User Tasks

Creating a User Task

When you click on a user task in the BPMN modeler, the Properties Panel includes a form tab. Use this tab to build your questions.

The following example shows how a form might be set up in Camumda.

User Task configuration

Manual Tasks

Creating a Manual Task

We can use the BPMN element Documentation field to display more information about the context of the item.

Spiff is set up in a way that you could use any templating library you want, but we have used Jinja.

In this example, we’ll present an order summary to our customer.

Element Documentation

Running The Model

If you have set up our example repository, this model can be run with the following command:

./camunda-bpmn-runner.py -p order_product -d bpmn/tutorial/product_prices.dmn -b bpmn/camunda/task_types.bpmn

Example Application Code

Handling the User Task

dct = {}
for field in task.task_spec.form.fields:
    if isinstance(field, EnumFormField):
        option_map = dict([ (opt.name, opt.id) for opt in field.options ])
        options = "(" + ', '.join(option_map) + ")"
        prompt = f"{field.label} {options} "
        option = input(prompt)
        while option not in option_map:
            print(f'Invalid selection!')
            option = input(prompt)
        response = option_map[option]
    else:
        response = input(f"{field.label} ")
        if field.type == "long":
            response = int(response)
    update_data(dct, field.id, response)
DeepMerge.merge(task.data, dct)

The list of form fields for a task is stored in task.task_spec.form_fields.

For Enumerated fields, we want to get the possible options and present them to the user. The variable names of the fields were stored in field.id, but since we set labels for each of the fields, we’d like to display those instead, and map the user’s selection back to the variable name.

For other fields, we’ll just store whatever the user enters, although in the case where the data type was specified to be a long, we’ll convert it to a number.

Finally, we need to explicitly store the user-provided response in a variable with the expected name with update_data(dct, field.id, response) and merge the newly collected data into our task data with DeepMerge.merge(task.data, dct).

Our update_data function handles “dot notation” in field names, which creates nested dictionaries based on the path components.

def update_data(dct, name, value):
    path = name.split('.')
    current = dct
    for component in path[:-1]:
        if component not in current:
            current[component] = {}
        current = current[component]
    current[path[-1]] = value