May 2020
Intermediate to advanced
404 pages
10h 52m
English
Content preview from Hands-On Python Deep Learning for the Web
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Creating a Flask API to work with server-side Python
We have completed our deep learning model and stored its structure in the model.json file and the weights for the model in the weights.h5 file. We are now ready to wrap the model data in an API so that we can expose the model to web-based calls via the GET or POST methods. Here, we will be discussing the POST method. Let's begin with the required setup on the server.
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