The purpose of SvelteML is to offer simple Components that can make ML more accessible. It leverages TensorflowJS to offer Svelte apps ML features out-of-the-box. It relies heavily on Svelte's reactivity feature and event hooks can be used to extend out the ML flow. e.g. on:poses in the Pose Estimator will give you the raw poses directfrom TensorflowJS.

npm install svelteml --save

• Image Classification
• Body Segmentation
• Basic Multi-Pose Estimation
• Object Detection

• Sentence Encoding
• Text Toxicity
• Question and Answers

• Blur Body Parts
• Bokeh Effect
• Face Mesh
• Hand Pose Detection

• Switching to Lerna for multiple repos so the lib can expand in the different areas. Also helpful for tfjs3 when it will have code-splitting 😃
  • @svelteml/ui
  • @svelteml/classification
  • @svelteml/segmentation
  • @svelteml/automl
  • @svelteml/text
  • @svelteml/audio
• Unlock slots with Facial recognition, maybe use faceapi.js
• Demo site for more details
• Audio and speech recognition features
• Additional models using the lower level tfjs apis
• .... and a few other top secret ideas 🤭

All Components try to be reactive so although it feels very declarative, it is also reacting to your input. Add an issue in Github if you need a specific behaviour or if there is a bug or would like to recommend something. You know the drill.