📖 - See the text version of this tutorial: anvil.works/learn/tutorials/g...
⏱️ - New to Anvil? Check out our 5 minute tutorial: anvil.works/learn/tutorials/f...
🧠 - Deploy an ML model with Data Files: anvil.works/learn/tutorials/d...
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🔗 Video Resources
- Colab notebook iris classifier - starter (Step 3): colab.research.google.com/dri...
- Towards Data Science iris classifier explanation: towardsdatascience.com/knn-us...
- Tutorial on hosting the model yourself: anvil.works/learn/tutorials/g...
- List of requirements (Step 5):
-- joblib
-- numpy
-- scikit-learn
-- scipy
-- sklearn
-- threadpoolctl
(The packages can be the latest version of each or you can use Anvil's 'Machine Learning' image: anvil.works/docs/server/custo...)
Finished Colab notebook iris classifier: colab.research.google.com/dri...
Finished Anvil app: anvil.works/build#clone:PA47J...
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⛓️ Other Resources
- Open-source app server: github.com/anvil-works/anvil-...
- Find out more: anvil.works/
- Community Forum: anvil.works/forum/
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📜 Video Description
Let’s say you’re a data scientist, and you’ve been asked to classify iris flowers based on their measurements (using the famous iris dataset: en.wikipedia.org/wiki/Iris_fl.... You’ve written some code in a Colab notebook that solves the problem; however, what you really want is to build an interactive tool, so people can classify the flowers themselves!
In this short tutorial, we are going to build an interactive app using Anvil for people to classify iris flowers by connecting a web app to a Colab notebook. The web app will collect the iris measurements from the user, send the data to our Colab notebook, where it will be classified, and then send the classification back to our web app to display to the user.
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1 фев 2023
