============
Introduction
============

Apps are the main building blocks of the system. They contain 
the actual logic of your product. You either can intergrate with 
existing apps or build your custom apps.

Apps can be understood as an environment consisting of the following objects:

* **Processors**: logical units of computation that transform data or do useful work.
* **Inputs**: filter units that pre-process inputs before handing them off to the processors.
* **Outputs**: post-processors that perform actions on the outputs of the processors.
* **Inits**: initializers that prepare the environment for the execution of the processors.

By interconnecting these objects, you can create a pipeline that solves particular problems. 

Currently, apps resides within Docker images. To ensure seamless integration with Malevich, 
an image app must meet the following criteria:

1. The image must be based on one of the following `base images <https://hub.docker.com/r/malevichai/app/tags>`_:

    * ``malevichai/app:python_v0.1`` - Optimized for Python 3.9 projects.
    * ``malevichai/app:python11_v0.1`` - Geared towards Python 3.11 projects.
    * ``malevichai/app:python-torch_v0.1`` - Customized for Python 3.11 projects requiring PyTorch and CUDA.
    * ``malevichai/app:python-ubuntu_v0.1`` - Perfect for projects based on Ubuntu 20.04. Requires to install Python manually.
    * ``malevichai/app:dask_v0.1`` - Python environment with `Dask <https://www.dask.org/>`_ dataframe backend (instead of Pandas).

2. The app's **codebase must** reside within the `./apps` directory **inside the Docker image**. The inner structure of the `./apps` directory does not matter, all Python files will be parsed and all processor functions will be registered automatically regardless of their location.

Here is a Dockerfile template illustrating how to assemble the `Utility <https://github.com/MalevichAI/malevich-library/tree/main/lib/src/utility>`_ app, a component of our library:

.. code-block:: docker

    # Starting from a Python 3.9 base image
    FROM malevichai/app:python_v0.1

    # Transfer the requirements.txt file
    COPY requirements.txt requirements.txt

    # Process the installation of dependencies
    RUN if test -e requirements.txt; then pip install --no-cache-dir -r requirements.txt; fi

    # Populate the image with our application's code
    COPY ./apps ./apps


Library
=======

The Malevich development team curates a comprehensive library of apps tailored for use in your endeavors. Our repository on `GitHub <https://github.com/MalevichAI/malevich-library>`_ houses this collection.

To add an app from the library to your project, execute:

.. code-block:: bash

    malevich install <APP_NAME>

This will integrate the app and its relevant dependencies into your project, making it ready for use within your pipeline. As an illustration, let's install the `utility <https://github.com/MalevichAI/malevich-library/tree/main/lib/src/utility>`_ app:

.. code-block:: bash

    malevich install utility

Following installation, the app is primed for pipeline deployment:

.. code-block:: python

    from malevich.utility import add_column
    from malevich import flow, collection

    @flow()
    def foo():
        data = collection("data", df=pd.DataFrame({"a": [1, 2, 3]}))
        enriched_data = add_column(data, config={"column": "b", "value": 1})
        return enriched_data

Remember, the library is actively expanding with new apps designed to further streamline and enhance your project workflows.