Featherweight web API provider for serving R&D methods as web functions
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README.md
example_iris.py
example_scipy.py
example_tiny_function.py
featherweight_api.py
test.py
test_featherweight.py

README.md

Featherweight function-to-Internet-callable-function server

Expose Python functions (or class methods) as a web-enabled function for others to call

"I used your featherweight_api in order to deploy a phishing classifier as a prototype REST service. By using your API I definitely reduced the time to market a lot." - Alejandro Correa Bahnsen, Lead Data Scientist at Easy Solutions, Colombia (Feb 2016)

Goals:

  • Data scientist focused tool to publish simple APIs
  • It is a "featherweight" server which turns your R&D code into a web-enabled function
  • Solve the "but how can we quickly plumb our new-data-sci-code into the demo environment so it shows value to the bosses?" problem without writing a "proper server" (especially if you don't know how to write a Proper Server)
  • Publishes a function using Flask with just 3 lines and little web knowledge
  • Supports scikit-learn and numpy objects (without making you think about correct JSON encoding)
  • Useful error messages are provided at run-time to help diagnose issues
  • Text arguments from an HTTP call are automatically converted to float arguments by default

It does not solve these problems:

  • It is not scalable (it isn't designed for production use)
  • It has no security
  • It does not replace Flask, Django or any other Proper Web Framework

Written for:

  • Python 3.4+
  • Flask 0.10+

##Example (example_tiny_function.py):

Let's make a Python function that calculates something with a couple of input arguments (myfn) and then expose it as a web-callable function (http://localhost:5000/myfn?<args>). Once you've called it the result is passed back as a JSON block:

import featherweight_api

def myfn(x, c):
    """Example function"""
    return = x*x + c

featherweight_api.register(myfn) 
featherweight_api.run()  # run the server on localhost:5000

If you put the following into the URL bar in your browser it'll make a GET request and you'll get a successful result:

http://localhost:5000/myfn?x=2&c=10
->
{"result": 14.0,
 "success": true, 
 "error_msg": null
}

You can also make this call at the command line:

$ curl "http://localhost:5000/myfn?x=2&c=10"
{
  "result": 14.0,
  "success": true,
  "error_msg": null
}

requests makes calling this sort of API quite trivial!

In [1]: import requests
In [2]: result = requests.get("http://localhost:5000/myfn?x=2&c=10")
In [3]: result.json()
Out[3]: {'error_msg': None, 'result': 14.0, 'success': True}

We can use curl to send a POST request:

curl -H "Content-Type: application/json" -X POST --data '{"x":2,"c":10}' http://localhost:5000/myfn
->
{
  "result": 14,
  "success": true,
  "error_msg": null
}

If you call this without the right arguments then you'll get a useful error message:

http://localhost:5000/myfn
->
{
 "result": null,
 "success": false,
 "error_msg": "TypeError(\"myfn() missing 2 required 
                           positional arguments: 'x' and 'c'\",)"
}

If your code raises an exception then you'll get a useful error message, here for example we can provide a bad argument:

http://localhost:5000/myfn?x=2&c=somemistake
->
{"result": null, 
 "success": false,
 "error_msg": "TypeError(\"unsupported operand type(s) for +: 'float' and 'str'\",)", 
}

##Scikit-learn example (example_iris.py):

You can use this to wrap up more complex code including classes. Here we'll build a class that makes a scikit-learn Iris classifier and then serves up a score function which a user can call. If they call it with the 4 arguments (2 lengths and 2 widths) then a classification is made by scikit-learn and the resulting numpy objects are converted to JSON and passed back.

http://localhost:5000/score?sepal_length=5.9&sepal_width=3&petal_length=5.1&petal_width=1.8
->
{
    "result": 
    {
        "guessed_class": 2,
        "guessed_label": "virginica"
    },
    "success": true,
    "error_msg": null
}

##Scipy example (example_scipy.py):

Shows a call to optimize.fmbound (thanks Peadar!).

#Notes

By default the register function has auto_convert_arguments=True whereby each argument that's passed into the call is converted from a string into a float (if possible).

You can change the serving details by passing in a different host= or port=. For example to serve on your public IP you could use host="0.0.0.0", port=8080 and then if you use your web-facing IP address (e.g. using ifconfig to find this) then a visit to something like http://192.168.0.12:8080/myfn?x=2&c=10 can be made by other machines on your network.

Some Python modules aren't encoded by default by the JSON module including numpy objects. I've added an encoder which tries to do sensible things.

In Firefox you'll probably want to add the http://jsonview.com/ pretty-printer for a nicer output.

#Further steps

#Possible additions

  • Python 3.5's type annotations could be used to sanity check the input (without you having to declare anything web-centric)
  • Exposed docstrings as Swagger (but probably the more complex tools above offer this for free?)
  • Decorator support to avoid the registration (?)
  • The current tests are very light, these need to be extended to cover more cases and data types

#Thanks

This API was inspired by chats with Willem Ligtenberg who presented http://www.openanalytics.eu/r-service-bus at BudapestBI2015.

#Discussion