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Learn Python by Building Data Science Applications

You're reading from   Learn Python by Building Data Science Applications A fun, project-based guide to learning Python 3 while building real-world apps

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789535365
Length 482 pages
Edition 1st Edition
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Authors (2):
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Philipp Kats Philipp Kats
Author Profile Icon Philipp Kats
Philipp Kats
David Katz David Katz
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David Katz
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Table of Contents (26) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Preparing the Workspace FREE CHAPTER 3. First Steps in Coding - Variables and Data Types 4. Functions 5. Data Structures 6. Loops and Other Compound Statements 7. First Script – Geocoding with Web APIs 8. Scraping Data from the Web with Beautiful Soup 4 9. Simulation with Classes and Inheritance 10. Shell, Git, Conda, and More – at Your Command 11. Section 2: Hands-On with Data
12. Python for Data Applications 13. Data Cleaning and Manipulation 14. Data Exploration and Visualization 15. Training a Machine Learning Model 16. Improving Your Model – Pipelines and Experiments 17. Section 3: Moving to Production
18. Packaging and Testing with Poetry and PyTest 19. Data Pipelines with Luigi 20. Let's Build a Dashboard 21. Serving Models with a RESTful API 22. Serverless API Using Chalice 23. Best Practices and Python Performance 24. Assessments 25. Other Books You May Enjoy

Setting up a simple model

Similar to how we built a REST API in Chapter 18, Serving Models with a RESTful API, let's start by serving median values from a JSON file. This will help us to set the model for working with Chalice:

  1. First of all, we need to load the JSON object:
import json

with open('./model.json', 'r') as f:
model = json.load(f)
  1. Now we will rename the route and define the last resource to map to the complaint type (in the same way we would for FastAPI, again!). We will also have to import a Response object:
from chalice import Response

@app.route('/predict/{complaint_type}', methods=['GET'])
def predict(complaint_type:str) -> Response:

  1. Finally, finalize the function by adding simple lookup logic; here, we decided to be nice and let our user know if they pass a wrong complaint type:
@app.route('/predict...
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