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You're reading from  Learn Python by Building Data Science Applications

Product typeBook
Published inAug 2019
Reading LevelIntermediate
PublisherPackt
ISBN-139781789535365
Edition1st Edition
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Authors (2):
Philipp Kats
Philipp Kats
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Philipp Kats

Philipp Kats is a researcher at the Urban Complexity Lab, NYU CUSP, a research fellow at Kazan Federal University, and a data scientist at StreetEasy, with many years of experience in software development. His interests include data analysis, urban studies, data journalism, and visualization. Having a bachelor's degree in architectural design and a having followed the rocky path (at first) of being a self-taught developer, Philipp knows the pain points of learning programming and is eager to share his experience.
Read more about Philipp Kats

David Katz
David Katz
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David Katz

David Katz is a researcher and holds a Ph.D. in mathematics. As a mathematician at heart, he sees code as a tool to express his questions. David believes that code literacy is essential as it applies to most disciplines and professions. David is passionate about sharing his knowledge and has 6 years of experience teaching college and high school students.
Read more about David Katz

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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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Learn Python by Building Data Science Applications
Published in: Aug 2019Publisher: PacktISBN-13: 9781789535365

Authors (2)

author image
Philipp Kats

Philipp Kats is a researcher at the Urban Complexity Lab, NYU CUSP, a research fellow at Kazan Federal University, and a data scientist at StreetEasy, with many years of experience in software development. His interests include data analysis, urban studies, data journalism, and visualization. Having a bachelor's degree in architectural design and a having followed the rocky path (at first) of being a self-taught developer, Philipp knows the pain points of learning programming and is eager to share his experience.
Read more about Philipp Kats

author image
David Katz

David Katz is a researcher and holds a Ph.D. in mathematics. As a mathematician at heart, he sees code as a tool to express his questions. David believes that code literacy is essential as it applies to most disciplines and professions. David is passionate about sharing his knowledge and has 6 years of experience teaching college and high school students.
Read more about David Katz