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@ -0,0 +1,375 @@
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---
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title: 'Creating a BirdNetPi Dashboard in HomeAssistant - Part 1'
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date: 2023-09-30T11:21:55-04:00
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tags: ["homeassistant", "python", "diy"]
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author: "Me"
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showToc: true
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TocOpen: false
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draft: false
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hidemeta: false
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description: 'Learn how to take BirdNET-Pi Detections to create and display entities in HomeAssistant.'
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disableHLJS: true # to disable highlightjs
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disableShare: false
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disableHLJS: false
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hideSummary: false
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searchHidden: true
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ShowReadingTime: true
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ShowBreadCrumbs: true
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ShowPostNavLinks: true
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ShowWordCount: true
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ShowRssButtonInSectionTermList: true
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UseHugoToc: true
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cover:
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image: "birdnet-homeassistant.png"
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alt: "BirdNET-Pi and HomeAssistant: Happier together!"
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caption: "Bring together your BirdNET-Pi setup and display a dashboard in HomeAssistant"
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relative: false # when using page bundles set this to true
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hidden: true # only hide on current single page
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---
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This is Part One of a Two Part Series. You can find Part Two, [here.]({{<ref "birdnet_homeassistant_part2.md" >}})
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**Update: 10/11/2023. A huge thanks to Mastodon User [e_mobile2014](https://mastodon.social/@e_mobil2014) who found a broken link in this guide and pointed out that I never explained how to get the mqtt sensors into HomeAssistant!**
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## What you will need
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* [BirdNET-Pi](https://github.com/mcguirepr89/BirdNET-Pi)
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* [HomeAssistant](https://www.home-assistant.io/)
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* [AppDaemon](https://appdaemon.readthedocs.io/en/latest/)
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* MQTT Broker (I use [Mosquitto](https://mosquitto.org/))
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## Background
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In early 2023, at the height of the [Raspberry Pi
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shortage](https://www.tomshardware.com/news/raspberry-pi-availability-analysis) I felt like a king with an extra Rpi laying
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around, not being used. I'm a big fan of any sort of passive intake of information and had been looking around for various
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citizen science-style projects that can capture information from the world around me. Since I'm already running an ADS-B
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antenna with [Flight Aware](https://www.flightaware.com/), I figured this next project would deal with radio
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waves/transmissions. Instead, to my amazement, I discovered [BirdNET-Pi](https://github.com/mcguirepr89/BirdNET-Pi)!
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## What is BirdNET-Pi?
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In case you didn't click the links above, [BirdNET-Pi](https://github.com/mcguirepr89/BirdNET-Pi) is an app built specifically
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made for Rapsberry Pi devices, that builds off the [BirdNET Framework](https://github.com/kahst/BirdNET-Analyzer). BirdNET is
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one of the most advanced acoustic monitoring tools available for passively monitoring bird diversity populations.
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Where BirdNET-Pi takes it to the next level is the ability to setup an SBC - hopefully enclosed in a waterproof space! - and
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monitor birds in your local environment over time.
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I think this project is beyond neat. It runs a bit slow on a Raspberry Pi 3, but overall it runs smoothly. I was even able to
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[contribute a PR to the project](https://github.com/mcguirepr89/BirdNET-Pi/pull/821) in April when I noticed a bug in the
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platform after a hard reset of my Pi.
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## BirdNET-PI Notification Setup - MQTT
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Once you have BirdNET-Pi up and running, you'll need to head over to the Settings and setup the correct MQTT payloads. Here
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are the possible variables you can pass in an MQTT payload:
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* `$sciname`: Scientific Name
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* `$comname`: Common Name
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* `$confidence`: Confidence Score
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* `$confidencepct`: Confidence Score as a percentage (eg. 0.91 => 91)
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* `$listenurl`: A link to the detection
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* `$date`: Date
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* `$time`: Time
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* `$week`: Week
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* `$latitude`: Latitude
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* `$longitude`: Longitude
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* `$cutoff`: Minimum Confidence set in "Advanced Settings"
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* `$sens`: Sigmoid Sensitivity set in "Advanced Settings"
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* `$overlap`: Overlap set in "Advanced Settings"
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* `$flickrimage`: A preview image of the detected species from Flickr. Set your API key below.
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For our purposes, we will only be using `$comname, $sciname, $date, $time, $week,` and `$confidence`. However, this entire
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process is extremely customizable, which you'll learn more about in the AppDaemon section. Please expand on it and include
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information that is pertinent to your own uses.
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Here is how I've setup my MQTT payload from BirdNET-Pi Settings:
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Here it is in text form:
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```none
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Notification Title: $comname,
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Notification Body: $sciname, $date, $time, $week, $confidence
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[ ] Notify each new infrequent species detection (< 5 visits per week)
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[ ] Notify each new species first detection of the day
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[X] Notify each new detection
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[X] Send weekly report
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Minimum time between notifications of the same species (sec): 5
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```
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To test my MQTT notifications, I use the iOS client "MQTTool". After signing up, head to "Subscribe" and type `birdnet` as
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the topic and then click Subscribe. If everything is setup correctly and there are birds being recorded by the BirdNET-Pi's
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microphone, you should start seeing those detections in the MQTTool app. If so, fantastic news! Let's move onto AppDaemon.
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## AppDaemon Script
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Now that we have the Pi communicating via MQTT, it's time to get that information into HomeAssistant. I've shared [the full
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script]({{<ref "birdnet_homeassistant.md#birdnet-appdaemon-script">}}) at the bottom of this page, but let's jump into each
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section. This is not a full tutorial of how to use AppDaemon, but it may help fill in any knowledge gaps with the system.
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### Imports
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First, we're going to import `time` and `requests`. We're going to use time as a backup to the `$time` component in the
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payload. This can be helpful to see if there delays, or if BirdNET-Pi stopped detecting. We're then going to use requests to
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pull from Wikipedia's API and grab a description for our HomeAssistant Dashboard.
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### Class Definition
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To start any AppDaemon app, you need to include a Class that is defined in the `apps.yaml` file. This is also where we
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initialize and define the various items that will be used in the remainder of the script.
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```python
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class birdnet(adbase.ADBase):
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def initialize(self):
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self.hassapi = self.get_plugin_api("HASS")
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self.adapi = self.get_ad_api()
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self.mqttapi = self.get_plugin_api("MQTT")
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self.birdnet_mqtt = "birdnet"
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self.mqttapi.listen_event(
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self.birdnet_message, "MQTT_MESSAGE", topic=self.birdnet_mqtt
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)
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```
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For this script, we need to use a lot of the AppDaemon APIs across more than just HomeAssistant, so we're going to be using
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`ADBase`. By using that, we can initialize the various APIs, which we do in the next 3 lines. In these 3 lines we need to get
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access to HomeAssistant's APIs, AppDaemon's APIs, and MQTT APIs - the first and third items are plugins of AppDaemon, and
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AppDaemon is... well... AppDaemon! Here are a few reference docs:
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* [MQTT AppDaemon API](https://appdaemon.readthedocs.io/en/latest/MQTT_API_REFERENCE.html)
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* [HomeAssistant AppDaemon API](https://appdaemon.readthedocs.io/en/latest/HASS_API_REFERENCE.html)
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* [AppDaemon API](https://appdaemon.readthedocs.io/en/latest/AD_API_REFERENCE.html)
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These will indispensable to you as you leverage AppDaemon and expand this little script.
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Once we have access to that, we need to setup the main topic for MQTT from BirdNET-Pi and finally, what event we are
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listening for that will trigger the functions in the rest of the script. `self.birdnet_mqtt = "birdnet"` is the definition
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for the MQTT topic. Let's breakdown the last line of the class.
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Here's a breakdown of each of the items in that last line. You can find the official documentation [here](https://appdaemon.readthedocs.io/en/latest/MQTT_API_REFERENCE.html#appdaemon.plugins.mqtt.mqttapi.Mqtt.listen_event).
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* `self.mqttapi.listen_event` - this is what we use in AppDaemon to listen for an MQTT event in order to trigger a function.
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* `self.birdnet_message` - the name of the function you'd like to trigger
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* `"MQTT_MESSAGE"` - The default event in AppDaemon's MQTT API plugin. This is used because MQTT doesn't keep a state in this
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plugin.
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* `topic=self.birdnet_mqtt` - The topic that will be received to trigger the function. Defined on the previous line.
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In other words, what we are telling AppDaemon is the following: "When AppDaemon's MQTT API plugin receives a message with the
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topic of 'birdnet', run the function `birdnet_message`."
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### birdnet_message Function
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#### Part 1: Variables Management
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Now we get into our first function of the script. The first portion of this script is splitting up the payload that we
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defined from the BirdNET-Pi UI into individual variables that we can better manage later on. If you test this script out by
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adding `print()` statements at various points, you'll notice that the payload is received with the following json formatting:
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```json
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{
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"payload": {
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"data": "data"
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}
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}
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```
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As such, we need to look _inside_ the payload to begin grabbing the data. The `pre_split` variable is now just looking at the
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data inside the payload and the rest of the variables take all the date into the payload, split it by the comma, and then
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grab the string by their index. If you remember what [we did above]({{<ref "birdnet_homeassistant.md#birdnet-pi-notification-setup-mqtt" >}}) above, you'll see that we have the various BirdNET information at each of the indexes in the AppDaemon script - 0 through 5.
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#### Part 2: Re-Publishing MQTT Payloads
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This next section is shooting all the variables we just defined back via MQTT. The reason why we do it this way is because we
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need HomeAssistant to grab each of these variables as individual sensors. BirdNET doesn't give us that capability - it's a
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single message with all the information in one. [Here is the documentation from AppDaemon](https://appdaemon.readthedocs.io/en/latest/MQTT_API_REFERENCE.html) on `mqtt_publish`. Later on, I'll show you how to ensure that HomeAssistant takes those topic payloads and adds them as
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entities in your HA setup.
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#### Part 3: Wikipedia Sensor
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The next eight lines are a fairly straightforward [API call to Wikipedia](https://en.wikipedia.org/api/rest_v1/). We start
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out by passing the `science_name` into the URL. The rest of the flags that we are passing into the URL comes from Wikipedia's
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Docs.
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`url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro&explaintext&redirects=1&titles={science_name}"`
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Once that's done we call it with `response.get(url)` and format it with `response.json()`. Wikipedia returns the json payload
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with the top level of `query` (which was our action in the url ;) ), and we're looking for the value within that query.
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All that's left is to take that query value and push it to HomeAssistant! We can do that with the `self.hassapi.set_state`
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function. Within the parenthesis we define the name of the sensor (`sensor.birdnet_wiki`), what it's state should be (`on`),
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and any attributes associated with the entity. Since we can't assign a long description to the basic status of the entity,
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we're adding an attribute with the key of `description` and the value will be the wikipedia description garnered from the API
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call.
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```python
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url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro&explaintext&redirects=1&titles={science_name}"
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response = requests.get(url)
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response = response.json()
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for value in response['query']['pages']:
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wiki_desc = response['query']['pages'][value]['extract']
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self.hassapi.set_state("sensor.birdnet_wiki", state='on',
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attributes = {"description": wiki_desc})
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```
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#### Part 4: Generate Picture for Detection (Optional)
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This part is optional but I noticed that BirdNET-Pi was already grabbing a Flickr Picture for it's front end, so I took the
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code from the BirdNET code base and adjusted it a bit for my needs. This will work very similarly to the Wikipedia API call,
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the main difference here being that you need an API key for Flickr. You can find more [information here](https://www.flickr.com/services/api/misc.api_keys.html).
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Given Flickr's fairly robust API, by passing in the detected bird's common name, we get amazing results from the community of
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various pictures of the same species of bird. Ever since I've set this up, I've not seen a mislabeled picture in my
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dashboard!
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The most confusion portion of this section is the `image_url` as you'll notice a bunch of `data["value"]` strings at various
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portions of the URL. The short answer to this is in the previous line with the `data` variable. A successful query has Flickr
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returning a large payload of information. We're specifically using [this](https://www.flickr.com/services/api/flickr.photos.search.html)
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Flickr API endpoint. While you can pass a lot of variables for your needs, if you scroll down, you can see that the example
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response contains multiple photos in a single response. We're passing `per_page=5` to limit some of those response items.
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Left out of that response, though, is a one-stop-shop for a URL to that photo. Thankfully, Flickr can help us put together a
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URL from the data in the response.
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{{< box info >}}
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_Note: Full Transparency that I only learned about this after reading through BirdNET-Pi's code base. Full credit goes to
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[mcguirepr89](https://github.com/mcguirepr89). For additional reference, here is Flickr's [official page on construction
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photo image URLS](https://www.flickr.com/services/api/misc.urls.html)_
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{{< /box >}}
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With this response, we now have the variables we need to construct the URL to actually render the image. Those variables are:
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Farm ID, Server ID, ID and Secret. I haven't yet looked into why we need "farm" when the official documentation doesn't state
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anything about it.
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Almost there! We now do the same as we did with the Wikipedia API response. We create a sensor in HomeAssistant! We're
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calling this sensor `sensor.birdpic`, ensuring the `state=on`, and giving it the attributes of the `image_url` as garnered
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from Flickr.
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```python
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headers = {'User-Agent': 'Python_Flickr/1.0'}
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flickr_api = "enter_your_api_key"
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flickr_url = f"https://www.flickr.com/services/rest/?method=flickr.photos.search&api_key={flickr_api}&text={common_name} bird&sort=relevance&per_page=5&media=photos&format=json&nojsoncallback=1"
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flickr_resp = requests.get(url=flickr_url, headers=headers)
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data = flickr_resp.json()["photos"]["photo"][0]
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image_url = 'https://farm'+str(data["farm"])+'.static.flickr.com/'+str(data["server"])+'/'+str(data["id"])+'_'+str(data["secret"])+'_n.jpg'
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self.hassapi.set_state("sensor.birdpic", state='on',
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attributes={"image": image_url})
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```
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## Importing MQTT Sensors into HomeAssistant
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Now that we have all the sensors defined and communicating via MQTT, we have one more step to import them into HomeAssistant.
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[This MQTT documentation](https://www.home-assistant.io/integrations/mqtt/) by HomeAssistant is good to read about if you
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need a broker setup. I will not be going over the broker in this tutorial, but may add one in the future. I tend to like the
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yaml configuration for HomeAssistant, so for the sake of this guide, I'll be referencing the [manual configuration of MQTT
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items and sensors](https://www.home-assistant.io/integrations/mqtt/#manual-configured-mqtt-items).
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To add the sensors from above, open up your `configuration.yaml` file in your favorite editor. You'll then want to add the
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mqtt platform and domain:
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```yaml
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mqtt:
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- { domain }:
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```
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For the BirdNet sensors, we will be using a single domain: `sensor`. Feel free to copy and paste my config from below, but
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make sure the names of each entity align with your needs, syntax, and nomenclature/system.
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**Full MQTT Sensors in Configuration.yml**
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```yaml
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mqtt:
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sensor:
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- name: "Bird Common Name"
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state_topic: "birdnet/sensors/common_name"
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- name: "Bird Science Name"
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state_topic: "birdnet/sensors/science_name"
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- name: "Bird Time Seen"
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state_topic: "birdnet/sensors/time_seen"
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- name: "Bird Date Seen"
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state_topic: "birdnet/sensors/date_seen"
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- name: "Bird Confidence"
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state_topic: "birdnet/sensors/confidence"
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value_template: '{{ (value|float(0) *100) | round(1) }}'
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unit_of_measurement: '%'
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```
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You might be looking at the list above and wondering where the Flickr and Wikipedia Description entities are. They were
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already created by the AppDaemon script! Specifically, `self.hassapi.set_state()` function will either update the state for
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an exisiting entity or, if the entity doesn't exist, it will create a new one.
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For the rest of the mqtt payloads, we need HomeAssistant to create them as they come in, which is why we add the above
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code block to our HomeAssistant configuration file. To be clear, you _do not_ need to add the Wikipedia and Flickr sensors to
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HA's configuration file!
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By this point, you should have successfully created 7 new sensors in HomeAssistant. In Part 2 of this article, we'll take a
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look at Home Assistant, see what these sensors look like, and create a rudimentary dashboard.
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## Birdnet AppDaemon Script
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```python
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import time
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import requests
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class birdnet(adbase.ADBase):
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def initialize(self):
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self.hassapi = self.get_plugin_api("HASS")
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self.adapi = self.get_ad_api()
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self.mqttapi = self.get_plugin_api("MQTT")
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self.birdnet_mqtt = "birdnet"
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self.mqttapi.listen_event(
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self.birdnet_message, "MQTT_MESSAGE", topic=self.birdnet_mqtt
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)
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def birdnet_message(self, event_name, data, kwargs):
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pre_split = data["payload"]
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common_name = pre_split.split(',')[0].strip()
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science_name = pre_split.split(',')[1].strip()
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date_seen = pre_split.split(',')[2].strip()
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time_seen = pre_split.split(',')[3].strip()
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week_seen = pre_split.split(',')[4].strip()
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confidence = pre_split.split(',')[5].strip()
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# print(f"A {common_name} was seen on {date_seen} at {time_seen}. Confidence is {confidence}.")
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self.mqttapi.mqtt_publish("birdnet/sensors/common_name", common_name)
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self.mqttapi.mqtt_publish("birdnet/sensors/science_name", science_name)
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self.mqttapi.mqtt_publish("birdnet/sensors/time_seen", time_seen)
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self.mqttapi.mqtt_publish("birdnet/sensors/date_seen", date_seen)
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self.mqttapi.mqtt_publish("birdnet/sensors/confidence", confidence)
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url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro&explaintext&redirects=1&titles={science_name}"
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response = requests.get(url)
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response = response.json()
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for value in response['query']['pages']:
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wiki_desc = response['query']['pages'][value]['extract']
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self.hassapi.set_state("sensor.birdnet_wiki", state='on',
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attributes = {"description": wiki_desc})
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headers = {'User-Agent': 'Python_Flickr/1.0'}
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flickr_api = "enter_your_api_key"
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flickr_url = f"https://www.flickr.com/services/rest/?method=flickr.photos.search&api_key={flickr_api}&text={common_name} bird&sort=relevance&per_page=5&media=photos&format=json&nojsoncallback=1"
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flickr_resp = requests.get(url=flickr_url, headers=headers)
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data = flickr_resp.json()["photos"]["photo"][0]
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image_url = 'https://farm'+str(data["farm"])+'.static.flickr.com/'+str(data["server"])+'/'+str(data["id"])+'_'+str(data["secret"])+'_n.jpg'
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self.hassapi.set_state("sensor.birdpic", state='on',
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attributes={"image": image_url})
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```
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<style>
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|
.box-shortcode {
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|
|
color: #e8e8e8;
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|
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border: none;
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}
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.post-content img {
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margin: auto
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}
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</style>
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Norm Rasmussen