Support for external taggers

[LameMonster82]

Describe the feature you'd like to request

Given how limiting the built-in AI models can be it would be nice if it was possible to add external taggers that could be used instead of the built-in ones.

Describe the solution you'd like

A possible solution is using web requests to communicate with external web servers where the image can be sent over and the server can respond with tags that match the image. This will allow to use different models that aren't implemented in the recognize app itself and also stronger models that may not work in docker setups or the host hardware.

For example the https://github.com/AUTOMATIC1111/stable-diffusion-webui project hosts a web server that can generate images locally. Using the extension https://github.com/picobyte/stable-diffusion-webui-wd14-tagger and --api option, a user can send a POST request with any image encoded in base64 and get a json response of all tags in it including weights on how much the image matches set tags.

One the external setup is complete the process is simple:

1. Convert the image to base64 data
2. Craft it into a javascript object. (eg. {"image": "data:image/<type>;base64,<image data>", "model": "<model name>", "threshold": 0.35})
3. Create a POST request to a target link (eg. "http://127.0.0.1:7860/tagger/v1/interrogate")
4. Get a response like:

{
    "caption": {
        "tag": {
            "tag1": 0.6852931380271912,
            "tag2": 0.6729011535644531,
            "tag3": 0.46537110209465027,
            "tag4": 0.5044106245040894
        }
    }
}

I don't know any other image tagging software that can be interacted using web requests but I think it's a nice start. Even if its only for object tagging.
I wish I could provide a pull request with this option but unfortunately I'm not skilled enough to write clean PHP code with the nextcloud API.

Describe alternatives you've considered

Implementing new models into the app itself is time consuming and slow. And making them work with the limited dependencies of the nextcloud environment and docker instances is even harder.

[github-actions]

Hello 👋

Thank you for taking the time to open this issue with recognize. I know it's frustrating when software
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I look forward to working with you on this issue
Cheers 💙

[PhilLab]

For me, it would also be helpful to have an "incomming" API, that is setting the app's face and cluster information via a Rest API.
I would use that to run my own models on my local desktop machine, running on the files which the Nextcloud sync tool has downloaded, and uploading only the face recognition result to the nextcloud.

This way, I can use my GPU to speed up the process, I can run whichever recognition stack I need and I can work with a nextcloud instance which is hosted by a thirdparty provider (who blocked the face recognition calls on their system to not impact the other tenants).

WDYT, @marcelklehr?