xiaomi_mi_scale/README.md

# Xiaomi Mi Scale

Code to read weight measurements from [Mi Body Composition Scale](https://www.mi.com/global/mi-body-composition-scale/) (aka Xiaomi Mi Scale V2)

![Mi Scale](Screenshots/Mi_Scale.png)

Note: Framework is present to also read from Xiaomi Scale V1, although I do not own one to test so code has not been maintained

## Getting the Mac Address of your Scale:

1. Retrieve the scale's MAC Address (you can identify your scale by looking for `MIBCS` entries) using this command:
```
$ sudo hcitool lescan
LE Scan ...
F8:04:33:AF:AB:A2 [TV] UE48JU6580
C4:D3:8C:12:4C:57 MIBCS
[...]
```
1. Note down your `MIBCS` mac address - we will need to use this as part of your configuration...

## Setup & Configuration:
### Running script with Docker:

1. Supported platforms: linux/amd64, linux/arm64, linux/arm/v6, linux/arm/v7 - so should cover all Raspberry Pis (^_^)
1. Open `docker-compose.yml` (see below) and edit the environment to suit your configuration... 
1. Stand up the container - `docker-compose up -d`

### docker-compose:
```
version: '3'
services:

  mi-scale:
    image: lolouk44/xiaomi-mi-scale:latest
    container_name: mi-scale
    restart: always

    network_mode: host
    privileged: true

    environment:
      MISCALE_MAC: 00:00:00:00:00:00 # Mac address of your scale
      MQTT_HOST: 127.0.0.1  # MQTT Server (defaults to 127.0.0.1)
      MQTT_PREFIX: miScale
      # MQTT_USERNAME:        # Username for MQTT server (comment out if not required)
      # MQTT_PASSWORD:        # Password for MQTT (comment out if not required)
      # MQTT_PORT:            # Defaults to 1883
      # MQTT_TIMEOUT: 30      # Defaults to 60

      # Auto-gender selection/config -- This is used to create the calculations such as BMI, Water/Bone Mass etc...
      # Multi user possible as long as weitghs do not overlap!

      USER1_GT: 70            # If the weight is greater than this number, we'll assume that we're weighing User #1
      USER1_SEX: male
      USER1_NAME: Jo          # Name of the user
      USER1_HEIGHT: 175       # Height (in cm) of the user
      USER1_DOB: "1990-01-01" # DOB (in yyyy-mm-dd format)

      USER2_LT: 35            # If the weight is less than this number, we'll assume that we're weighing User #2
      USER2_SEX: female
      USER2_NAME: Serena      # Name of the user
      USER2_HEIGHT: 95        # Height (in cm) of the user
      USER2_DOB: "1990-01-01" # DOB (in yyyy-mm-dd format)

      USER3_SEX: female
      USER3_NAME: Missy       # Name of the user
      USER3_HEIGHT: 150       # Height (in cm) of the user
      USER3_DOB: "1990-01-01" # DOB (in yyyy-mm-dd format)
```

### Running script directly on your host system:

1. Install python requirements (pip3 install -r requirements.txt)
1. Open `wrapper.sh` and configure your environment variables to suit your setup.
1. Add a cron-tab entry to wrapper like so:

```sh
*/5 * * * * bash /path/to/wrapper.sh
```

**NOTE**: It's best to schedule via crontab at most, every 5 min (so as not to drain the battery on your scale):
```
*/5 * * * * python3 /path-to-script/Xiaomi_Scale.py
```

## Home-Assistant Setup:
Under the `sensor` block, enter as many blocks as users configured in your environment variables:

```yaml
  - platform: mqtt
    name: "Example Name Weight"
    state_topic: "miScale/USERS_NAME/weight"
    value_template: "{{ value_json['Weight'] }}"
    unit_of_measurement: "kg"
    json_attributes_topic: "miScale/USERS_NAME/weight"
    icon: mdi:scale-bathroom

  - platform: mqtt
    name: "Example Name BMI"
    state_topic: "miScale/USERS_NAME/weight"
    value_template: "{{ value_json['BMI'] }}"
    icon: mdi:human-pregnant

```

![Mi Scale](Screenshots/HA_Lovelace_Card.png)

![Mi Scale](Screenshots/HA_Lovelace_Card_Details.png)

## Acknowledgements: 
Thanks to @syssi (https://gist.github.com/syssi/4108a54877406dc231d95514e538bde9) and @prototux (https://github.com/wiecosystem/Bluetooth) for their initial code
Special thanks to @ned-kelly (https://github.com/ned-kelly) for his help turning a "simple" python script into a fully fledge docker container