DataStructures | A collection of Data Structures | Natural Language Processing library

Thanks to @S.M. @anastaciu and @jkb for their guidance.

The original code posted here was:

upload folder is defined as app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER, but does seems like it exist, so the line of code f.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) is getting failed. you can try

1. create the folder "uploads", and it will be at the same level as templates.
2. the os.remove will look like as below(remove the first /)

In your return statement, you are using "{{}}", just remove those and it will work fine. It should be like this

ProjectTypes is a List and ProjectType is a ProjectType.

The types should match so either change the type of the source collection:

I think the part you're missing is how to parameterize the name key. I'll break it down:

While the pointers themselves are indeed local variables, they point to a specific location in memory. When you dereference the pointer by using the -> symbol, you're basically accessing the memory where that exact variable to which the pointer is pointing to is stored. This is why your changes are reflected outside the function as well.

You basically told a local variable where your tree is stored, it helped with the insertion, and then it went out of scope. The tree itself is not a local variable so the changes are reflected on it.

I suggest reading up on how pointers work.

(str(x) for x in values) is not a list comprehension, it's a generator expression.

[str(x) for x in values], on the other hand, is a list comprehension.

The first expression makes a one-time generator that gets exhausted the moment you iterate through it. The second expression places the result of that iteration into a list, so you can access the elements again.

You have a few misunderstandings here.

Firstly, as from code, you can see that model is trained on 7 columns as inputs [ip, app, device, os, channel, hour, day]. And the model is trained to predict values from is_attributed column. So feed a model list with 7 values -> receive 1 value as output. And this value seems to be 0 or 1 depends on input 7 values.

Secondly, we can proceed now to the Flask part. Basically, what you do here is that you load dataframe and select one column (attribute = df['is_attributed']). IF you have dataframe with 50000 rows and you select one column it means that you select 50000 values! And then you tried to send this to model, which wants exactly 7 values as inputs. As from my perspective, it looks like that you want to run model on each row on test dataframe.

To do that you need:

1. load test dataframe;
2. check that you have only 7 columns ([ip, app, device, os, channel, hour, day]) in dataframe. If you have more columns, remove all other;
3. go through each row in dataframe (each from total of 50000);
4. run model using 7 values from row as inputs;
5. model's output append to a python list;
6. after 50000 runs you will have python list with 50000 values;
7. return this list.