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license: apache-2.0
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<p align="center">
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  <img width=600 src="https://cdn-uploads.huggingface.co/production/uploads/6286462340423ef48fb6c45e/ElX3V79ViRx0BUcCPVJQG.png">
  <a href="https://github.com/zama-ai/concrete-ml"> 📁 Github</a> | <a href="https://docs.zama.ai/concrete-ml"> 📒 Documentation</a> | <a href="https://zama.ai/community"> 💛 Community support</a> | <a href="https://github.com/zama-ai/awesome-zama"> 📚 FHE resources by Zama</a>
</p>
<hr>

# Iris classification with a QNN with Concrete ML

In this repository, we allow Iris classification, without seing the inputs! Indeed, inputs are sent encrypted to the HF endpoints, and are classified (with a built-in small neural network) without the server seeing them in the clear, thanks to fully homomorphic encryption (FHE). This is done thanks to Zama's Concrete ML.

Concrete ML is Zama's open-source privacy-preserving ML package, FHE. We refer the reader to fhe.org or Zama's websites for more information on FHE.

## Deploying a compiled model on HF inference endpoint

If you would like to deploy, it is very easy.
- click on 'Deploy' button in HF interface
- chose "Inference endpoints"
- chose the right model repository
- (the rest of the options are classical to HF end points; we refer you to their documentation for more information)
and then click on 'Create endpoint'

And now, your model should be deployed, after few secunds of installation.

## Using HF entry points on privacy-preserving models

Now, this is the final step: using the entry point. You should:
- if your inference endpoint is private, set an environment variable HF_TOKEN with your HF token
- edit `play_with_endpoint.py`
- replace `API_URL` by your entry point URL

Finally, you'll be able to launch your application with `python play_with_endpoint.py`.