Cocktails based on your mood created by a Raspberry Pi bartender
Build instructions for a fully automated home build cocktail maker. How to use a Raspberry Pi, camera plus a few peristaltic pumps assembled into a home bartender.
Drinks are selected based on your emotion and multilingual voice prompts let you know when your drink is available. 🍸
For a complete video see here
I used 4 peristaltic pumps to provide a "food safe" way to pump the liquids from the drink bottles
The pumps are mounted on a basic wooden frame higher than the tallest bottle
A view from the rear show the placement of pumpts and liquids
These are 12 volt motors. To operate them via the Raspberry Pi I used a 4 Channel 12V Relay Module
The Raspberry Pi is mounted with the relay board
Recipes are stored as a map of ingredients with the number of millitres required for the perfect drink
pump_map = {"PUMP_A": "Vodka", "PUMP_B": "Cranberry", "PUMP_C": "Tonic", "PUMP_D": "Lime"}
recipe_vodkasoda = {"Name": "Vodka Soda", "Vodka": 20, "Tonic": 70}
recipe_vodkasodacranburry = {"Name": "Vodka Soda Cranberry", "Vodka": 20, "Tonic": 70, "Cranberry": 40}
recipe_vodkalimesoda = {"Name": "Vodka Lime Soda", "Vodka": 20, "Tonic": 70, "Lime": 30}
recipe_limesoda = {"Name": "Lime Soda", "Tonic": 70, "Lime": 30}
recipe_test = {"Name": "Test", "Vodka": 10, "Cranberry": 20, "Tonic": 30, "Lime":40}The pumps I'm using dispense 1.9mL of liquid per second. I can deligate each pump to a thread timer so it knows how long to operate for once the drink is selected. Have a look at pump.py for complete code
# Return the number of seconds to run
def lookup_time(drink_name, pump_name):
ML_per_second = 1.9
num_ML = drink_name[pump_map[pump_name]]
return num_ML / ML_per_second
# Run a pump for given number of seconds
def pump_thread_runner(gpio_pump_name, run_seconds):
GPIO.output(gpio_pump_name, GPIO.HIGH)
time.sleep(run_seconds)
GPIO.output(gpio_pump_name, GPIO.LOW)
# Pick a drink
this_drink = recipe_vodkasoda
# How long to run each pump ...
duration_a = lookup_time(this_drink, "PUMP_A")
duration_b = lookup_time(this_drink, "PUMP_B")
# these threads start in background
pump_thread_start(config.gpio_pump_a, duration_a)
pump_thread_start(config.gpio_pump_b, duration_b)I use the AWS Rekognition service to determine likely emotion and approximate age. Have a look at cloud.py for complete code
# Save camera image to file file_jpg
with picamera.PiCamera() as camera:
camera.capture(file_jpg)
# Process file_jpg using AWS rekognition to extract emotion and age
with open(file_jpg, 'rb') as f_file_jpg:
b_a_jpg = bytearray(f_file_jpg.read())
rclient = boto3.client('rekognition')
response = rclient.detect_faces(Image={'Bytes': b_a_jpg}, Attributes=['ALL'])For text to speech I used the AWS Polly service. Have a look at cloud.py for complete code
# Using "Emma" as a voice, generate text to voice
voice='Emma'
client = boto3.client('polly')
response = client.synthesize_speech(OutputFormat='mp3', Text=audiotext, VoiceId=voice)
thebytes = response['AudioStream'].read()
thefile = open(file_mp3, 'wb')
thefile.write(thebytes)
thefile.close()
# Play mp3 file via speaker
os.system('mpg123 -q {}'.format(file_mp3))A few hours and a bit of programming and you too can enjoy a 🍹 cocktail pi. Enjoy!
