MOVED -> this project has been merged into fletched, any further development will happen there.
This library provides tools that make it a bit easier to follow architecture patterns in your flet app that leverage on immutable models and unidirectional control flow. Those are mostly based on the Model-View-Presenter/MVP pattern, hence the name of the library. At this stage, it can be used to ease working with any model-based architecture pattern though.
flowchart TB
View-- "intent methods call handlers"-->Presenter
Presenter-- pass new model to render-->View
Presenter-- handle intent concretely -->DataSource
DataSource-- notify of new model -->Presenter
DataSource-- create from intended/resulting changes -->Model
DataSource<-- query/modify according to intent -->DB/API/etc
One of the main goals of this library is to reduce the amount of boilerplate code that is needed in order to implement an MVP-based architecture for flet apps. This should however not come at the cost of typechecking and autocomplete. That is why for each class you will interact with, the general way of doing things stays the same:
class MyClass(LibraryClass):
variable_needed_by_library_class_and_this_class: MyOtherClass
def some_method(self):
...This approach solves the following problem:
In order to do their helpful work behind the scenes,
the library classes need to access objects
that the concrete subclasses receive or create,
e.g. the DataSource in MvpPresenter implementations.
MvpPresenter can only know that the DataSource is an instance of MvpDataSource,
so a subclass accessing a self.data_source variable set in the parent class
(how it is set is more or less irrelevant) would not know anymore than that
and thus your IDE can't properly autocomplete for you anymore.
There is a bit of magic
(namely abstract class properties and a bit of dataclass wizardry)
going on behind the scenes that makes this work,
but it should save you from ever having to write an __init__() method
while still getting helpful autocomplete in MyClass for
variable_needed_by_library_class_and_this_class,
which itself will be autocompleted for you when defining MyClass.
It also makes the approach more declarative rather than imperative,
which some developers might prefer (or so I've heard).
If you feel you need this library to properly design your flet app, it is probably complex enough to need routing and maybe even an app state solution as well. Lucky for you, flet-routed-app and flet-mvp-utils are designed to compliment each other while none of both strictly requires usage of the other in order to work.
Say you have a form and want to validate the TextFields in it when a submit button is clicked.
Your view uses refs.
The actual UI code may be located somewhere else
and simply receive the refs and/or callbacks
and return a component that is connected to the ref.
When creating the view class, you inherit from MvpView
and create a class variable named ref_map,
containing a dictionary that maps the attribute names
of your model to the respective ref
of the control that should be tied to it.
Any variable intended for the flet.View constructor will be accepted
and passed on by the default __init__() method,
so you don't need to define your own in most cases.
import flet as ft
from flet_mvp_utils import MvpView
class FormView(MvpView):
ref_map = {
"last_name": ft.Ref[ft.TextField](),
"first_name": ft.Ref[ft.TextField](),
"age": ft.Ref[ft.TextField](),
}
def some_intent_method(self, e) -> None:
...MvpView has a render(model) method that takes a model
and updates any refs' current value to the model value if they aren't the same.
This method is supposed to be called in the callback
you register with the DataSource,
so that a changed model is immediately reflected in the view.
As you will learn in the next section,
this doesn't have to concern you as it can be done automatically.
Any class that inherits from MvpPresenter updates the view automatically
once it is notified of a model update.
MvpPresenter is a dataclass
and so should its subclasses be.
This helps to reduce the amount of boilerplate code
(specifically __init__() methods) you have to write
and keeps the general API of this library consistent.
Since both the DataSource and the View are known to it
(because the subclass fields override the fields of the same name in the superclass),
MvpPresenter will automatically register a method as a callback with the DataSource
that renders the new model in the given view in its __post_init__() hook.
from dataclasses import dataclass
from flet_mvp_utils import MvpPresenter
from my_package.views.form import FormDataSource, FormViewProtocol
@dataclass
class FormPresenter(MvpPresenter):
data_source: FormDataSource
view: FormViewProtocol
def some_intent_handling_method(self) -> None:
...MvpPresenter also provides a generic build() method
that simply calls the build(presenter) method of the view
with itself as the sole argument.
If you need a custom build method for your presenter,
just override it with your own.
The DataSource class, inheriting from MvpDataSource,
is where the business logic of your component/virtual page will live.
Since the latter inherits from Observable,
brokers of any kind (presenter classes in MVP-based architectures)
can register callback functions with your DataSource class
that will be executed when you call self.notify_observers() in it.
As mentioned above, subclasses of MvpPresenter do this for you automatically
after you initialized them properly.
These callbacks are meant to be used to inform a presenter that a new,
updated model has been created.
Since creating new models to replace the current one is a rather repetitive
and uniform task,
MvpDataSource will do it for you.
All you have to do is pass your model class to its constructor
and call self.update_model_partial(changes: dict)
or self.update_model_complete(new_model: dict) depending on your use case.
from flet_mvp_utils import MvpDataSource
class FormDataSource(MvpDataSource):
current_model = FormModel()
def some_method(self) -> None:
...The model is supposed to act as the state of your view. It should contain everything the view needs to know in order to render/update itself. This can be data from a database, an API, a config file or even just another component.
Your model inherits from MvpModel,
which is an immutable pydantic BaseModel.
This means you can write custom validators for each attribute
and validate all your data whenever a new instance of the model is created.
The model is immutable to force good habits upon the ones using it. Good habits in this context means not to modify your current model anywhere but in your DataSource class as that should be the single source of truth. Of course immutability is never enforced 100% in python, but this should suffice.
from flet_mvp_utils import MvpModel
class FormModel(MvpModel):
last_name: str = ""
first_name: str = ""
age: int = 0Notice that age is an int,
even though the ref we assigned to it earlier points to a TextField?
That's no problem at all,
you'll have to do no manual conversion.
Pydantic will parse the text input into an int
and raise an error if that fails.
We probably want to inform our user though that they have input invalid data.
To do this, we'll simply typehint age differently.
from flet_mvp_utils import ErrorMessage, MvpModel
class FormModel(MvpModel):
last_name: str = ""
first_name: str = ""
age: ErrorMessage | int = 0It's important to specify the narrower type (ErrorMessage) first, otherwise every error message would just say that the field is not an instance of ErrorMessage.
This is where the magic of the update_model methods of MvpDataSource comes to light.
If the creation of a new model fails,
e.g. because a user put "old" into the age TextField instead of a number,
our DataSource will now catch this error,
wrap its message in an ErrorMessage object
and assign it to the age field
of a new model that contains all changes,
both the valid inputs and the error mesages.
Multiple errors at once are no problem at all,
each ErrorMessage will be assigned to the field that caused it.
Since we probably don't want to make any calls to a database, API etc. in that case, the update_model methods will return a bool to let you know if there was an error.
The subscribed observers will be notified either way
and the model will thus be rendered.
MvpView.render() will try to assign fields that are instances of ErrorMessage
to the error_text property of the control that the associated ref points to.
This means that you should only use this technique for model fields
that are associated with controls that actually have that property,
like TextField or Dropdown.