I’m planning to do a research project which needs reading an analog sensor using Raspberry Pi Pico W and feed it to a TinyML model, store the results and provide a RESTFUL api for data transfer. To have a faster development process, I have decided to give MicroPython a try for this project.
There would be several examples out there about implementing a tiny webserver on rasberypi pico. But, unfortunately, I couldn’t find a framework like FastApi, or Django to make web api development easily possible on micropython.
Accordingly, I decided to start writing a Web API Framework with Micropython on Raspberry Pi Pico W from scratch, and in the meanwhile, tackle a bit more with advanced python software development topics.
This work is inspired by FastAPI framework which by now is the most popular python framework for RESTFUL api development. So, let’s briefly take a look at a very simple FastAPI example provided in its website:
from typing import Union
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return {"Hello": "World"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
return {"item_id": item_id, "q": q}Ok, what’s going on here?
FastAPI module is imported, an object from FastAPI class instantiated, two functions are defined to return a dictionary, and the route to those functions are defined by @app.get() decorator. Bravo! Such a neat design!
What is a Decorator in python?
The key element that let us design this way, is the decorators in python. In simple words, a decorator is a function that receives a function or a class and change its behavior without modifying the function itself!
Let’s see what does that mean in action. We have a function that prints the current time:
from datetime import datetime
def get_time():
print(datetime.now().strftime('%H:%M:%S'))
get_time()17:59:25Now consider having another function which receives a function as an argument and prints the phrase “current time is:” before calling it.
def decorate_time(func):
print("current time is:")
func()
decorate_time(get_time)current time is:
17:59:25It does the job we have expected, but the problem is if we call the get_time() function again it will only show time. What we want this behavior attached to the original function. To work around this issue, we can define a funtion inside the decorate_time() function and return the new function.
from datetime import datetime
def get_time():
print(datetime.now().strftime('%H:%M:%S'))
def decorate_time(func):
def add_phrase():
print("current time is:")
func()
return add_phrase
get_time = decorate_time(get_time)
get_time()current time is:
18:15:02Infact, we called the original function in another function’s definition and replaced it with the old definition. What we have done here, is the essence of python decorators. To make the use of decorators more pleasant, in python we can use @ with the decorators name on top the original function definition.
@decorate_time
def get_time():
print(datetime.now().strftime('%H:%M:%S'))
get_time()current time is:
18:43:44Python decorators with arguments and returning values
So far so good. But we still have more to do to reach the same design. In the fastapi example, we have seen that the original function could have input parameters. Let’s define the function get_time() to receive an integer value which is then subtracted from the current hour and printed.
from datetime import datetime, timedelta
def get_time(hours:int):
new_time = datetime.now() - timedelta(hours=hours)
print(new_time.strftime('%H:%M:%S'))
get_time(1)18:38:38Now if we add the decorator we have defined before, we are gonna encouter with an error:
TypeError: decorate_time.<locals>.add_phrase() takes 0 positional arguments but 1 was givenYes, since the function definition inside the decorator does not receive any arguments in its definition, it led to this error. To solve the issue, we have to define the inner function such that it can receive any number of parameters with any type. It’s done by adding *args, **kwargs to the inner function definition and then pass them to the original function call.
As a reminder I should remind that * operator is used for packing and unpacking lists in python. In a function definition it means pack the arguments, and in function calls it unpacking the list and passing to the function. In a same way, ** operator is used for packing/unpacking dictionaries in function definition and calling.
Another thing to be concered of is how the original function name as you can see in the error message is changed now to decorate_time.add_phrase(). To preseve the original function name and docstring, we have to use a built-in python decorator @functools.wraps() available in functools module.
from datetime import datetime, timedelta
import functools
def decorate_time(func):
@functools.wraps(func)
def add_phrase(*args, **kwargs):
print("current time is:")
func(*args, **kwargs)
return add_phrase
@decorate_time
def get_time(hours:int):
new_time = datetime.now() - timedelta(hours=hours)
print(new_time.strftime('%H:%M:%S'))
get_time(1)18:47:08Great! Now we can pass parameters to the decorated functions. What if we need to return a value instead of printing it? With the same logic, the decorator inner function should also return the value returned by the original function.
from datetime import datetime, timedelta
import functools
def decorate_time(func)
@functools.wraps(func):
def add_phrase(*args, **kwargs):
print("current time is:")
ret_val = func(*args, **kwargs)
return ret_val
return add_phrase
@decorate_time
def get_time(hours:int):
return datetime.now() - timedelta(hours=hours)
newtime = get_time(1)
print(newtime.strftime('%H:%M:%S')) 19:04:39Awesome! What if we need to pass an argument to the decorator itself? I mean like the way we can pass the route to the decorator in fastapi.
@app.get("/items/{item_id}")Let’s give it a try.
@decorate_time("/")
def get_time(hours:int):
return datetime.now() - timedelta(hours=hours)
newtime = get_time(1)
print(get_time) TypeError: 'str' object is not callableWhy the code ends up calling a string object? Infact, the decorator @ syntactic sugar will translate into something like this:
get_time = decorate_time(get_time)thus, if we pass an argument to the decorator python will translate it to:
get_time = decorate_time("/")(get_time)That’s why we got an error. Instead of receiving a function object we passed a string to the decorator and it ended up calling a string object! To work aroud this issue, we wrap the entire decorator in another decorator!
from datetime import datetime, timedelta
import functools
def outer_decorator(route):
print(f"The route is: {route}")
def decorate_time(func):
@functools.wraps(func)
def add_phrase(*args, **kwargs):
print("current time is:")
ret_val = func(*args, **kwargs)
return ret_val
return add_phrase
return decorate_time
@outer_decorator("/")
def get_time(hours:int):
return datetime.now() - timedelta(hours=hours)
newtime = get_time(1)
print(get_time) Now, as before, the decorator actually translate to:
get_time = outer_decorator("/")(get_time)But this time, outer_decorator returns another function which is the previous decorator, and then the it will get called.
The route is: /
current time is:
<function get_time at 0x000002366A209AB0>Works like a charm! Note that @functools.wraps is still successfully preserve the original function name and docstring.
Python decorators and classes
Now we decorated a function and it’s exactly doing the job it was supposed to do. There is still something missing. In the fastapi example, functions are decorated using the @ with the object name followed by a . and the name of the deocrator.
We can have different scenarios with decorators and classes. The decorators can easily be used on a class method definition just like we did on global functions. Let’s assume we have class named PyTime as below:
from datetime import datetime, timedelta
class PyTime:
def __init__(self):
pass
def get_time(self, hours:int):
return datetime.now() - timedelta(hours=hours)
pytime = PyTime()
newtime = pytime.get_time(1)
print(newtime)2023-01-04 23:35:40.229966We can simply decorate it using the decorator we have defined before.
from datetime import datetime, timedelta
import functools
def outer_decorator(route):
print(f"The route is: {route}")
def decorate_time(func):
@functools.wraps(func)
def add_phrase(*args, **kwargs):
print("current time is:")
ret_val = func(*args, **kwargs)
return ret_val
return add_phrase
return decorate_time
class PyTime:
def __init__(self):
pass
@outer_decorator("/")
def get_time(self, hours:int):
return datetime.now() - timedelta(hours=hours)
pytime = PyTime()
newtime = pytime.get_time(1)
print(newtime)The route is: /
current time is:
2023-01-04 23:38:06.261602Note that python also provides several built-in decorators like @property, @classmethod, and @staticmethod to be used in decorating class methods.
The other case is using a class itself as a decorator. Basically, when we instantiate a class, we are calling its constructor fucntion. Now, what if we pass the function we intend to decorate to the class constructor function __init__ ? We can then store the function in the class. Then by implementing __call__ function in the class, it’s possible to make the object callable. That’s all we need to to make a class act as a decorator. The only difference is to preserve the function name and docstring, instead of using the @functools.wraps decorator, we have explicitly call functools.update_wrapper(self, func).
from datetime import datetime, timedelta
import functools
class TimeDecorator:
def __init__(self, func):
functools.update_wrapper(self, func)
self.func = func
def __call__(self, *args, **kwargs):
print("current time is:")
return self.func(*args, **kwargs)
@TimeDecorator # equivalant to obj = TimeDecorator(get_time)
def get_time(hours:int=0):
return datetime.now() - timedelta(hours=hours)
newtime = get_time(1)
print(newtime)current time is:
2023-01-09 14:46:53.283555Using a class method as a decorator
Yet we couldn’t find the proper design choice for our purpose. FastApi is creating an intance and use a method of that instance to decorate api routines. So, let’s give it a try by writing a class called MicroFastApi as follows.
class MicroFastApi:
def __init__(self, ip, mac,):
self.routes = {}
self.address = ip
self.mac = mac
def __str__(self):
server_info = f"""
device ip = {self.address}
device mac = {self.mac}
open the URL provided below in your browser \n http://{self.address}/"""
return server_infothen for the http GET method, we add a new function called get. This function should receive a string to the corresponding route it supposed to get called from.
It’s quite important to comprehend the underlying mechanics. The final goal is to decorate a function called for example index() or sensors(userId:int=None) like this:
@app.get('/')
def index():
return "root"
@app.get('/sensors/')
def sensors(userId:int=None):
return f"sensor data for userId={userId}"Since we need to feed the top-level decorator with an string input, we have to implement a nested decorator as discussed before in this post. The decorations methods will translate to:
index = app.get('/')(index)
sensors = app.get('/sensors/')(sensors)Note that the functions could have parameters and return values which means that we have pack/unpack parameters in the inner decorator function, and also return the values of the original fucntions. The decorator function is gonna be something like this:
def get(self,route:str):
def decorate_get_api(func):
@functools.wraps(func)
def decorated_get_func(*args, **kwargs):
# pre-processing
ret_val = func(*args, **kwargs)
# post-processing
return ret_val
self.routes[route] = decorated_get_func
return decorated_get_func
return decorate_get_apiNeed to mention that to have @functools in MicroPython you should install micropython-functools using pip:
pip install micropython-functoolsIf you use Thonny for development on MicroPython, you can use the manage packages option from tools menu to easily install any python package.
The final class is defined a below:
import functools #pip install micropython-functools
class MicroFastApi:
def __init__(self, ip, mac,):
self.routes = {}
self.address = ip
self.mac = mac
def __str__(self):
server_info = f"""
device ip = {self.address}
device mac = {self.mac}
open the URL provided below in your browser \n http://{self.address}/"""
return server_info
def get(self,route:str):
def decorate_get_api(func):
@functools.wraps(func)
def decorated_get_func(*args, **kwargs):
# pre-processing
ret_val = func(*args, **kwargs)
# post-processing
return ret_val
self.routes[route] = decorated_get_func
return decorated_get_func
return decorate_get_api
def run():
passTo verify if everything is working as expected:
from microfastapi import MicroFastApi
from wifi import Wifi
from secrets import *
wifihandle = Wifi(ssid = ssid, password = password)
if not wifihandle.connect():
raise Exception("Connection failed!")
app = MicroFastApi(ip= wifihandle.ip, mac= wifihandle.mac)
@app.get('/')
def index():
return "root"
@app.get('/sensors/')
def sensors(userId:int=None):
return f"sensor data for userId={userId}"
print(app)
for route in app.routes:
print(f"route {route} is mapped to {app.routes[route].__name__}. result:"+ app.routes[route]())Connecting to: 1122 apt 1224
Waiting for WiFi connection...
Connected: True
device ip = 10.0.0.65
device mac = xx:xx:xx:xx:xx:xx
open the URL provided below in your browser
http://10.0.0.65/
route / is mapped to decorated_get_func. result:root
route /sensors/ is mapped to decorated_get_func. result:sensor data for userId=NoneGreat! Now, we have the basic design of the web server on our tiny shiny Rasbpery PI Pico W!
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