CocktailBerry is a Python and Qt (or React for v2) based app for a cocktail machine on the Raspberry Pi. It enables you to build your own, fully customized machine, while still be able to use the identical software on each machine. Detailed information, installation steps and SetUp can be found at the Official Documentation.

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This app is used to control a cocktail machine and easily prepare cocktails over a nice-looking user interface. It also offers the option to create and manage your recipes and ingredients over the interface and calculates the possible cocktails to prepare over given ingredients. Track and display cocktail data for different teams to even further increase the fun. Let's get started!

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TL;DR

Features

CocktailBerry currently comes in two versions, v1 and v2. The v1 is the stable version which ships the QT app as a single application. v2 is the new version with a separate API and UI, which offers more flexibility, but might have some issues on build in touchscreens. Both versions have the full feature set listed below.

CocktailBerry can do:

• Prepare cocktails of a given volume and adjusted concentration of alcoholic ingredients
• Let customers pay over NFC chips/cards
• Add new ingredients and recipes with needed information over the UI
• Specify additional ingredients for later hand add within a recipe (like sticky syrup)
• Define connected ingredients to the machine and existing additional ingredients over the UI
• Auto calculate and display possible recipes dependent on given information
• Option to serve cocktails without alcohol
• Execute a cleaning program to get rid of remaining fluids
• Visualize the cocktail data and get insights
• Run headless, so you can access it over another device
• Send cocktail production data to a given endpoint, for example a webhook
• Keep track of cocktail count and volume from different teams for some fun competition
• Select different themes to fit your liking
• Switch between user interface languages
• Support WS281x LEDs on your machine
• Support for RFID/NFC reader
• Implement your own addon to extend the base functionality

In addition, there is the possibility to use and set up a second device as a dashboard:

• Provide the teams API to post and get cocktail data
• Display different modes of data for a by team comparison
• Optional: Use the dashboard as WiFi hot-spot

Quickstart

Here are some simple steps to get CocktailBerry running. You need to have Python 3.11 or newer and git installed. If you are using a Raspberry Pi and a current OS, you should be good to go.

On the RPi, you can try the new all in one installer script. One command should install everything:

wget -O - https://raw.githubusercontent.com/AndreWohnsland/CocktailBerry/master/scripts/all_in_one.sh | bash

Otherwise, you can manually install it using uv:

cd ~
git clone https://github.com/AndreWohnsland/CocktailBerry.git
cd CocktailBerry
uv sync --extra v1 --extra nfc # <- use the nfc extra only if you want nfc support
uv run runme.py

This will start the CocktailBerry program. You may want to run the provided installer script for the RPi instead of pip. See Installation for more information.

sh scripts/setup.sh

Now you can Set Up your CocktailBerry and tweak the settings to your liking.

Interface

The interface was programmed with PyQt6 (v1) for the users to easily interact with CocktailBerry and enter new ingredients/recipes. There are different views for the tasks.

The Maker GUI:

The Ingredient GUI:

The Recipe GUI:

The Bottle GUI:

Pull Requests and Issues

If you want to support this project, feel free to fork it and create your own pull request. To get started, have a quick look into the Guidelines for contributing. Encounter a problem? Open a ticket or issue to let us know.
Have an idea for an important feature? Submit a feature request—it may be implemented in the future.

Contributing Possibilities

Here is a general list of features or refactoring things, I may do in the future. With your help, these things come even faster! If your idea is not on the list, feel free to open a feature request! Generally, it is recommended to first open an issue or an discussion to talk about your idea before directly implementing it.

• easy: Translate all dialogs / UI to your native language
• easy: Submit a feature request or issue
• easy-hard: Implement a cool addon and make it verified

Development

This project uses uv, node.js and yarn to manage all its python dependencies. To get started, you need to install the tools and then install the dependencies. See also at the dev notes section for a complete run down as well as extra information.

uv sync --all-extras

We also use pre-commits to check the code style and run some tests before every commit. You can install them with:

uv run pre-commit install --install-hooks

This will install all dependencies and you can start developing. Then just run:

uv run runme.py

If you want to develop the api, you can also run it with

uv run fastapi dev ./src/api/api.py

and run the web ui over:

cd web_app/
yarn install
yarn dev

Mocking RFID/NFC Reader and Services

You can set the MOCK_RFID and MOCK_PAYMENT_SERVICE environment variables to use the mock implementations of the RFID reader and payment service respectively. This is useful if you want to develop this features without having the actual hardware or service available.

The mocked NFC reader will cycle through two predefined IDs every minute, it will return each 5 seconds a read card id for testing purposes. If you want to test specific behavior, it is recommended not to mock but use the real hardware.

The mocked payment service will simulate successful payments without actually connecting to any external service. It will create not existing ids with a default balance of 20. The adult flag will circle between true and false for each new id.

Building Documentation

You can build the documentation locally using:

uv run mkdocs serve