==================
Integration
==================

A flow can be easily integrated into your application. To do this, you should install the flow using
``malevich install <flow_reverse_id>`` command. The command will generate a stub for the flow in the
``malevich.flows`` module. The stub can be imported and used as a regular function.

Let's assume you developed a flow for inferencing a vision transformer model. The flow is defined as follows:

.. code-block:: python

    from malevich import collection, flow, table
    from malevich.vit import prepare_images, run, last_hidden_states

    @flow
    def vision_transformer():
        data = collection(
            'images',
            df=table(...),
            alias='images'
        )

        # Converts image links to tensors
        images = prepare_images(data)
        # Run the inference obtaining the results
        hidden_states = run(images)
        # Extracts a tensor of the last hidden states
        return last_hidden_states(hidden_states)


To install the flow, you should upload it to the platform:

.. code-block:: python

    from malevich import Space

    Space(vision_transformer).upload()


And then install it:

.. code-block:: bash

    malevich install vision_transformer


After the flow is installed, you can use it in your application:

.. code-block:: 

    from malevich.flows import vision_transformer

    results = vision_transformer(images=table(...))


If no data is supplied, the flow will run with the default data defined in the flow. You may
run the flow asynchronously:

.. code-block:: python

    run_id = vision_transformer(images=table(...), wait_for_results=False)

You can also specify the version and branch of the flow to run:

.. code-block:: python

    results = vision_transformer(images=table(...), version='1.0.0', branch='dev')
    
Also, you may run the particular deployment:

.. code-block:: python

    results = vision_transformer(images=table(...), deployment_id='...', wait_for_results=True)

    # or asynchronously
    run_id = vision_transformer(images=table(...), deployment_id='...', wait_for_results=False)


If you want to have more control over the task, you can specify ``get_task=True`` and obtain 
:class:`SpaceTask <malevich.models.task.interpreted.space.SpaceTask>` instance:

.. code-block:: python

    task = vision_transformer(images=table(...), get_task=True)
    task.run(...) # Run the task with all available options
    results = task.results() # Wait for the results