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https://github.com/acroz/data-science-apis
An example Flask application showcasing the recipes from my talk at PyCon UK 2017.
https://github.com/acroz/data-science-apis
data-science flask machine-learning python python-rq
Last synced: 7 days ago
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An example Flask application showcasing the recipes from my talk at PyCon UK 2017.
- Host: GitHub
- URL: https://github.com/acroz/data-science-apis
- Owner: acroz
- Created: 2017-10-28T08:49:24.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2017-11-03T00:01:08.000Z (about 7 years ago)
- Last Synced: 2024-10-28T07:29:46.350Z (about 2 months ago)
- Topics: data-science, flask, machine-learning, python, python-rq
- Language: Python
- Size: 7.81 KB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Science API Example
This repository accompanies my talk at PyCon UK 2017, where I gave a tutorial
providing some tips for effective deployment of data science APIs using Flask.
Specifically, my talk demonstrated:
* Flask
* JSON-encoding error handlers
* Authentication with API keys and the `before_request` decorator
* Flask-SQLAlchemy
* Avoiding SQL injection by using SQLAlchmemy's query interpolator
* Worker queues with python-rq
* Deployment with gunicorn
All of these recommendations have been implemented together in this project. In
the near future I will write a blog post or series of posts covering the same
material as my talk - once completed it will be linked here.
## Installation
```sh
$ pip install .
```
To generate the sqlite database:
```sh
$ python gendb.py
```
You will also need to install redis and have it running for the app to function
properly.
With homebrew:
```sh
$ brew install redis
$ brew tap homebrew/services # If not done previously
$ brew services start redis
```
## Running the Server
Run the api server with:
```sh
$ gunicorn --workers 4 dsapi:app
```
In a separate terminal(s), run one or more python-rq workers:
```sh
rq worker
```
Also make sure that redis-server is running.
## Calling the API
With the server and worker running, the API can be called externally with any
HTTP client. From Python, I recommend using the requests library, available
from PyPI:
```sh
$ pip install requests
```
### Simple Endpoint and Authentication
You can then query the `/predict` endpoint using one of the valid keys in the
`api_keys` table:
```python
>>> import requests
>>> response = requests.get(
>>> 'http://localhost:8000/predict/feature_a/1.0/feature_b/1.0',
>>> headers={'X-API-Key': 'PE1tlZti1TXJ9nTIri30OnPcquDUpjVrayieAAzY'}
>>> )
>>> response.status_code
200
>>> response.json()
{'class': 1, 'class_probabilities': [0.0961415144762564, 0.9038584855237436]})
```
Note how missing or invalid API keys will return a `401 UNAUTHORIZED` response:
```python
>>> response = requests.get(
>>> 'http://localhost:8000/predict/feature_a/1.0/feature_b/1.0',
>>> headers={'X-API-Key': 'invalid-key'}
>>> )
>>> response.status_code
401
>>> response.json()
{'error': True, 'message': 'Invalid API Key'}
```
### Slow Task Endpoints
The `/predict-slow` endpoint accepts a `POST` request with the features in the
body and returns an ID field that can be redeemed later for the result:
```python
>>> response = requests.post(
>>> 'http://localhost:8000/predict-slow',
>>> json={'feature_a': 1.3, 'feature_b': -3.2},
>>> headers={'X-API-Key': 'PE1tlZti1TXJ9nTIri30OnPcquDUpjVrayieAAzY'}
>>> )
>>> response.status_code
202
>>> response.json()
{'id': 'a779fb1a-8811-48f2-b79e-00f223bb9f43'}
```
To check for the result:
```python
>>> id = response.json()['id']
>>> response = requests.get(
>>> f'http://localhost:8000/predict-slow/{id}',
>>> headers={'X-API-Key': 'PE1tlZti1TXJ9nTIri30OnPcquDUpjVrayieAAzY'}
>>> )
```
Before the result is ready this will yield a `404 NOT FOUND`:
```python
>>> response.status_code
404
```
Once ready, the result will be returned with a `200 OK` response:
```python
>>> response.status_code
200
>>> response.json()
{'class': 0}
```