Put your functions to REST
Project description
Tranquilizer
Deploy a REST API with one line by decorating your functions.
Install
The package is available for all Mac, Linux, and Windows on my conda channel. Python 2 is not supported.
> conda install -c conda-forge tranquilizer
Quick start
In a script file called cheese_shop.py
the decorated function
will be served as an end point called cheese
with the GET method. The
function must return a JSON serializable object. Dictionaries are preferable.
See the complete description of @tranquilize()
below.
from tranquilizer import tranquilize
@tranquilize()
def order(cheese):
'''I'd like to buy some cheese!'''
return {'response':"I'm afraid we're fresh out of {}, Sir.".format(cheese)}
The REST API is served by Flask and Flask-RESTPlus
using the tranquilizer
command.
> tranquilizer cheese_shop.py
* Serving Flask app "tranquilizer.application" (lazy loading)
* Environment: production
WARNING: Do not use the development server in a production environment.
Use a production WSGI server instead.
* Debug mode: off
* Running on http://0.0.0.0:8086/ (Press CTRL+C to quit)
Let's see if there is any Red Leicester.
> curl -G http://localhost:8086/order --data-urlencode "cheese=Red Leicester"
{"response":"I'm afraid we're fresh out of Red Leicester, Sir."}
How about in Python?
In [1]: import requests
In [2]: response = requests.get('http://localhost:8086/order', params={'cheese':'Red Leicester'})
In [3]: response.json()
Out[3]: {'response': "I'm afraid we're fresh out of Red Leicester, Sir."}
The tranquilized API is documented with Swagger and is accessible in your web browser at http://localhost:8086.
Tranquilize Decorator
The @tranqulize
decorator will assign the GET method by default. POST is also supported with method='post'
.
Other methods are under consideration.
By default a tranquilized function will receive all inputs as strings. This behavior can be modified by using type hints. When data is received by the Flask server it will use the provided type function to transform the string to the requested data type. This avoids having to perform the conversion in your tranquilized function.
Supported source formats
Tranquilizer can serve functions written in Python source (.py
) files or Jupyter Notebooks (.ipynb
).
When working interactively in Jupyter Notebooks the decorated functions will continue to operate as normal.
Note that all calls to Jupyter Magic
and Shell (!
) commands will be ignored when the REST API is served.
Only those lines will be ignored, the rest of the cell will continue to run.
Data Types
In addition to builtin types Tranquilizer provides specialized support for Lists, date/datetime, and files.
Type | Description |
---|---|
datetime.date or datetime.datetime |
Converts string with dateutil.parser.parse and returns specified type. |
list |
Converts repeated arguments to a list of strings. |
typing.List[<type>] |
Converts repeated arguments to a list; each value is converted to <type> . |
List
arguments are constructed using the action='append'
argument described in
the Flask RESTPlus documentation.
Any valid type can be used in List[]
.
The following file-like types are handled by werkzeug FileStorage
.
FileStorage
is a file-like object that supports methods like .read()
and .readlines()
.
These types support sending files with cURL using -F
.
Type | Description |
---|---|
typing.BinaryIO |
File-like object to read binary data. |
typing.TextIO |
Converts FileStorage type to io.StringIO() . |
Further, specific support for Image and NumPy files are provided. The binary contents of the file are automatically converted.
Type | Description |
---|---|
PIL.Image.Image |
Converts FileStorage type to PIL Image. |
numpy.ndarray |
Converts FileStorage type to NumPy array using np.load() . |
Custom types
Custom type classes can be built...
Type hints example
The example below uses int
, datetime.datetime
, and typing.List
. datetime.datetime
support
has been built with datetutil
and will convert any compatible datetime string to a datetime.datetime
object. typing.List
supports specialization with []
and will transform all repeated arguments passed to the REST API into a list and convert
the type of each element.
Finally, tranquilizer supports default arguments.
from tranquilizer import tranquilize
from datetime import date
from typing import List
@tranquilize(method='post')
def convert(string: str, date: date, items: List[float], factor: int = 10):
'''Let's convert strings to something useful'''
new_items = [i * factor for i in items]
response = {
'string': string.upper(),
'date' : date.strftime('%c'),
'items' : new_items
}
return response
Let's see what happens when I POST to this REST API.
In [1]: data = {'string':'hello, world!', 'date':'4th July 1776', 'items':range(5)}
In [2]: import requests
In [3]: response = requests.post('http://localhost:8086/convert', data=data)
In [4]: response.json()
Out[4]:
{'date': 'Thu Jul 4 00:00:00 1776',
'items': [0.0, 10.0, 20.0, 30.0, 40.0],
'string': 'HELLO, WORLD!'}
In [5]:
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