It happens to me that when reading/reviewing the code, I becomes easier if I can see the 'look' of the variable a function is processing.
For that, I'd like to display a 'static' version of an instance of that variable (as a visual aid).
That variable may not be there on another run of the notebook, that's why it has to be text, not output.
This is also useful when creating documentation within the notebook.
With this little function
#----------------------------------
def vdisplay(var):
"""Converts the var to a pretty string and inserts
it on a new cell just below the present one.
Then you have to change that 'next cell' type to Markdown and execute it.
"""
# To print the var nicely.
from pprint import pformat as pf
string_to_insert=f"""
This is how it looks like:
```
{pf(var)}
```
"""
# Create a code cell and insert a string in it
get_ipython().set_next_input(string_to_insert)
return
#----------------------------------
You can do this
# This is the data we want to show
x={i:str(i)*i for i in range(10)}
# Show it!
vdisplay(x)
Visually:
I use the mouse intentionally so you can see the steps. Using keyboard shortcuts is much quicker.
Story: I explored several venues. The last one was a combination of
%store var f.txt and %load f.txt but that involved some manual
work. The evolution of that method is the one above.
Maybe I am completely off track here (and above my paygrade for sure), but what I want to do is to give users of my app (That I am writing in Python since that's the language I know) a python interpreter to control some objects within my app. Something similar like many 3D and VFX softwares have (Maya, Blender, Nuke). This is the code I got so far:
#main.py
import code
import networkx as nx
class Env():
def __init__(self):
self.graph = nx.graph.DiGraph()
# load library with functions that will be availabel for user inside the app.
import mylib
functions = {f: getattr(mylib, f) for f in dir(mylib) if not f.startswith('__')}
self._interpreter = code.InteractiveInterpreter(locals=functions)
def execute_node(self, node=None):
# In IRL the main object to be pass1ed to users' interpreter will be the self.graph object
# but I made it more clear for this question.
self._interpreter.locals['var'] = 42
node = "print(var)\nprint(some_function())\nvar = 67" # Let's pretend node object contains this code.
self._interpreter.runcode(node)
if __name__ == '__main__':
e = Env()
# some code, node creation and so on...
e.execute_code()
print(e.locals['var'])
#mylib.py
var = None # I have to put this here because if there is no variable function fails at import
def some_function():
print(var)
Output:
42 # This prints as expected
None # The print within the function prints the value that was there when module was initialized
67 # The last print returns expected value
So, it is clear that python interprets the functions on first import and "bakes" the global variables that it had at the import time. Now the question is can I somehow easily make it use the globals passed from the code.InteractiveInterpreter() or I should look for a completely different solution (and which one) :)? Of course the idea is that the two python programs should communicate, the user should use a special library to operate the software and the backend code should not be exposed to them. Do I make any sense? Thanks :)
This is the one-ish instance where you do want to use the exec() function, but please remember that the user may be able to run any Python code, including stuff that could run forever, mess up your main program, write (or delete) files, etc.
def run_code(code, add_locals={}):
code_locals = {}
code_locals.update(add_locals) # Copy in the additional locals so that dict could be reused
exec(
code,
{}, # no globals (you may wish to replace this),
code_locals,
)
return code_locals # return updated locals
class Beeper: # define a toy object
def beep(self, times):
print("Beep! " * times)
beeper = Beeper() # instantiate the object to play with
# Some user code...
user_code = """
x = 5
beeper.beep(x)
x += 3
"""
new_locals = run_code(user_code, {"beeper": beeper})
print(new_locals)
This outputs
Beep! Beep! Beep! Beep! Beep!
{'beeper': <__main__.Beeper>, 'x': 8}
So you can see we can use the locals the user has modified if need be.
I am working on a django based web app that takes python file as input which contains some function, then in backend i have some lists that are passed as parameters through the user's function,which will generate a single value output.The result generated will be used for some further computation.
Here is how the function inside the user's file look like :
def somefunctionname(list):
''' some computation performed on list'''
return float value
At present the approach that i am using is taking user's file as normal file input. Then in my views.py i am executing the file as module and passing the parameters with eval function. Snippet is given below.
Here modulename is the python file name that i had taken from user and importing as module
exec("import "+modulename)
result = eval(f"{modulename}.{somefunctionname}(arguments)")
Which is working absolutely fine. But i know this is not the secured approach.
My question , Is there any other way through which i can run users file securely as the method that i am using is not secure ? I know the proposed solutions can't be full proof but what are the other ways in which i can run this (like if it can be solved with dockerization then what will be the approach or some external tools that i can use with API )?
Or if possible can somebody tell me how can i simply sandbox this or any tutorial that can help me..?
Any reference or resource will be helpful.
It is an important question. In python sandboxing is not trivial.
It is one of the few cases where the question which version of python interpreter you are using. For example, Jyton generates Java bytecode, and JVM has its own mechanism to run code securely.
For CPython, the default interpreter, originally there were some attempts to make a restricted execution mode, that were abandoned long time ago.
Currently, there is that unofficial project, RestrictedPython that might give you what you need. It is not a full sandbox, i.e. will not give you restricted filesystem access or something, but for you needs it may be just enough.
Basically the guys there just rewrote the python compilation in a more restricted way.
What it allows to do is to compile a piece of code and then execute, all in a restricted mode. For example:
from RestrictedPython import safe_builtins, compile_restricted
source_code = """
print('Hello world, but secure')
"""
byte_code = compile_restricted(
source_code,
filename='<string>',
mode='exec'
)
exec(byte_code, {__builtins__ = safe_builtins})
>>> Hello world, but secure
Running with builtins = safe_builtins disables the dangerous functions like open file, import or whatever. There are also other variations of builtins and other options, take some time to read the docs, they are pretty good.
EDIT:
Here is an example for you use case
from RestrictedPython import safe_builtins, compile_restricted
from RestrictedPython.Eval import default_guarded_getitem
def execute_user_code(user_code, user_func, *args, **kwargs):
""" Executed user code in restricted env
Args:
user_code(str) - String containing the unsafe code
user_func(str) - Function inside user_code to execute and return value
*args, **kwargs - arguments passed to the user function
Return:
Return value of the user_func
"""
def _apply(f, *a, **kw):
return f(*a, **kw)
try:
# This is the variables we allow user code to see. #result will contain return value.
restricted_locals = {
"result": None,
"args": args,
"kwargs": kwargs,
}
# If you want the user to be able to use some of your functions inside his code,
# you should add this function to this dictionary.
# By default many standard actions are disabled. Here I add _apply_ to be able to access
# args and kwargs and _getitem_ to be able to use arrays. Just think before you add
# something else. I am not saying you shouldn't do it. You should understand what you
# are doing thats all.
restricted_globals = {
"__builtins__": safe_builtins,
"_getitem_": default_guarded_getitem,
"_apply_": _apply,
}
# Add another line to user code that executes #user_func
user_code += "\nresult = {0}(*args, **kwargs)".format(user_func)
# Compile the user code
byte_code = compile_restricted(user_code, filename="<user_code>", mode="exec")
# Run it
exec(byte_code, restricted_globals, restricted_locals)
# User code has modified result inside restricted_locals. Return it.
return restricted_locals["result"]
except SyntaxError as e:
# Do whaever you want if the user has code that does not compile
raise
except Exception as e:
# The code did something that is not allowed. Add some nasty punishment to the user here.
raise
Now you have a function execute_user_code, that receives some unsafe code as a string, a name of a function from this code, arguments, and returns the return value of the function with the given arguments.
Here is a very stupid example of some user code:
example = """
def test(x, name="Johny"):
return name + " likes " + str(x*x)
"""
# Lets see how this works
print(execute_user_code(example, "test", 5))
# Result: Johny likes 25
But here is what happens when the user code tries to do something unsafe:
malicious_example = """
import sys
print("Now I have the access to your system, muhahahaha")
"""
# Lets see how this works
print(execute_user_code(malicious_example, "test", 5))
# Result - evil plan failed:
# Traceback (most recent call last):
# File "restr.py", line 69, in <module>
# print(execute_user_code(malitious_example, "test", 5))
# File "restr.py", line 45, in execute_user_code
# exec(byte_code, restricted_globals, restricted_locals)
# File "<user_code>", line 2, in <module>
#ImportError: __import__ not found
Possible extension:
Pay attention that the user code is compiled on each call to the function. However, it is possible that you would like to compile the user code once, then execute it with different parameters. So all you have to do is to save the byte_code somewhere, then to call exec with a different set of restricted_locals each time.
EDIT2:
If you want to use import, you can write your own import function that allows to use only modules that you consider safe. Example:
def _import(name, globals=None, locals=None, fromlist=(), level=0):
safe_modules = ["math"]
if name in safe_modules:
globals[name] = __import__(name, globals, locals, fromlist, level)
else:
raise Exception("Don't you even think about it {0}".format(name))
safe_builtins['__import__'] = _import # Must be a part of builtins
restricted_globals = {
"__builtins__": safe_builtins,
"_getitem_": default_guarded_getitem,
"_apply_": _apply,
}
....
i_example = """
import math
def myceil(x):
return math.ceil(x)
"""
print(execute_user_code(i_example, "myceil", 1.5))
Note that this sample import function is VERY primitive, it will not work with stuff like from x import y. You can look here for a more complex implementation.
EDIT3
Note, that lots of python built in functionality is not available out of the box in RestrictedPython, it does not mean it is not available at all. You may need to implement some function for it to become available.
Even some obvious things like sum or += operator are not obvious in the restricted environment.
For example, the for loop uses _getiter_ function that you must implement and provide yourself (in globals). Since you want to avoid infinite loops, you may want to put some limits on the number of iterations allowed. Here is a sample implementation that limits number of iterations to 100:
MAX_ITER_LEN = 100
class MaxCountIter:
def __init__(self, dataset, max_count):
self.i = iter(dataset)
self.left = max_count
def __iter__(self):
return self
def __next__(self):
if self.left > 0:
self.left -= 1
return next(self.i)
else:
raise StopIteration()
def _getiter(ob):
return MaxCountIter(ob, MAX_ITER_LEN)
....
restricted_globals = {
"_getiter_": _getiter,
....
for_ex = """
def sum(x):
y = 0
for i in range(x):
y = y + i
return y
"""
print(execute_user_code(for_ex, "sum", 6))
If you don't want to limit loop count, just use identity function as _getiter_:
restricted_globals = {
"_getiter_": labmda x: x,
Note that simply limiting the loop count does not guarantee security. First, loops can be nested. Second, you cannot limit the execution count of a while loop. To make it secure, you have to execute unsafe code under some timeout.
Please take a moment to read the docs.
Note that not everything is documented (although many things are). You have to learn to read the project's source code for more advanced things. Best way to learn is to try and run some code, and to see what kind function is missing, then to see the source code of the project to understand how to implement it.
EDIT4
There is still another problem - restricted code may have infinite loops. To avoid it, some kind of timeout is required on the code.
Unfortunately, since you are using django, that is multi threaded unless you explicitly specify otherwise, simple trick for timeouts using signeals will not work here, you have to use multiprocessing.
Easiest way in my opinion - use this library. Simply add a decorator to execute_user_code so it will look like this:
#timeout_decorator.timeout(5, use_signals=False)
def execute_user_code(user_code, user_func, *args, **kwargs):
And you are done. The code will never run more than 5 seconds.
Pay attention to use_signals=False, without this it may have some unexpected behavior in django.
Also note that this is relatively heavy on resources (and I don't really see a way to overcome this). I mean not really crazy heavy, but it is an extra process spawn. You should hold that in mind in your web server configuration - the api which allows to execute arbitrary user code is more vulnerable to ddos.
For sure with docker you can sandbox the execution if you are careful. You can restrict CPU cycles, max memory, close all network ports, run as a user with read only access to the file system and all).
Still,this would be extremely complex to get it right I think. For me you shall not allow a client to execute arbitrar code like that.
I would be to check if a production/solution isn't already done and use that. I was thinking that some sites allow you to submit some code (python, java, whatever) that is executed on the server.
So I'm new at multiprocessing and subprocessing and I am not sure if I am doing it right.
I have two scripts. One runs the main GUI, and has buttons that run other scrips. I want my entry boxes to be read by my other script, so that it can change the axis of a graph.For now, I simplified it so that it can print it so I can see that the values are being passed to begin with.
When I run the scrips like this:
###class_testing.py### (main script)
class Amplifier_Data_Analysis:
def saving_graph_stuff(self):
global int_startfreq,int_stopfreq,float_steps,float_add_tick
STARTFREQUENCY = self.Start_Freq.get()
int_startfreq = int(STARTFREQUENCY)
STOPFREQUENCY = self.Stop_Freq.get()
int_stopfreq = int(STOPFREQUENCY)
STEPS = self.Steps.get()
float_steps = float(STEPS)
ADD_TICK = self.Add_Tick.get()
float_add_tick = float(ADD_TICK)
print(int_startfreq,int_stopfreq,float_steps,float_add_tick)
return int_startfreq,int_stopfreq,float_steps,float_add_tick
def testreport(self):
subprocess.Popen([sys.executable,'test.py'])
###test.py###
from class_testing import *
int_startfreq,int_stopfreq,float_steps,float_add_tick = Amplifier_Data_Analysis.saving_graph_stuff()
print(startfrequency)
print(stopfrequency)
I get
int_startfreq,int_stopfreq,float_steps,float_add_tick = Amplifier_Data_Analysis.saving_graph_stuff()
TypeError: saving_graph_stuff() missing 1 required positional argument: 'self'
But when I put self, It says it is not defined which makes sense since it's a different script from the main. The GUI is generated form the PAGE app so it's very lengthy, but this is how it looks like: GUI
How do I pass or read variables between two scripts?
It's a class - you have to initialize it
int_startfreq,int_stopfreq,float_steps,float_add_tick = Amplifier_Data_Analysis().saving_graph_stuff()
I'm looking for a function that makes it easier to switch between two frames. Right now, every time I need to switch between frames, I'm doing this by the following code:
driver.switch_to.frame(driver.find_element_by_css_selector("frame[name='nav']"))
driver.switch_to.frame(driver.find_element_by_css_selector("frame[name='content']"))
My goal is to get a function that takes an argument just to change nav or content since the rest is basically the same.
What I've already tried is:
def frame_switch(content_or_nav):
x = str(frame[name=str(content_or_nav)] #"frame[name='content_or_nav']"
driver.switch_to.frame(driver.find_element_by_css_selector(x))
But it gives me an error
x = str(frame[name=str(content_or_nav)]
^
SyntaxError: invalid syntax
The way this is written, it's trying to parse CSS code as Python code. You don't want that.
This function is suitable:
def frame_switch(css_selector):
driver.switch_to.frame(driver.find_element_by_css_selector(css_selector))
If you are just trying to switch to the frame based on the name attribute, then you can use this:
def frame_switch(name):
driver.switch_to.frame(driver.find_element_by_name(name))
To switch back to the main window, you can use
driver.switch_to.default_content()