nltk | NLTK ported to Javascript

I am trying to run my chatbot that I created with python, but I keep getting this error that I don't have numpy installed, but I do have it installed and whenever I try to install it it tells me that it is already installed. The error reads "ModuleNotFoundError: No module named 'numpy'"

I don't understand what the problem is, why is it always throwing this error? even for nltk and tensorflow even though I have them all installed.

How can I resolve this issue?

Here is a screen shot when i install numpy:

Here is a screen shot of the error:

I am using NLTK lib in python to break down each word into tagged elements (i.e. ('London', ''NNP)). However, I cannot figure out how to take this list, and capitalise locations if they are lower case. This is important because london is no longer an 'NNP' and some other locations even become verbs. If anyone knows how to do this efficiently, that would be amazing!

Here is my code:

I am working with a computer that can only access to a private network and it cannot send instrunctions from command line. So, whenever I have to install Python packages, I must do it manually (I can't even use Pypi). Luckily, the NLTK allows my to manually download corpora (from here) and to "install" them by putting them in the proper folder (as explained here).

Now, I need to do exactly what is said in this answer:

I want to extract information from different sentences so i'm using nltk to divide each sentence to words, I'm using this code:

There are a lot of Q&A about part-of-speech conversion, and they pretty much all point to WordNet derivationally_related_forms() (For example, Convert words between verb/noun/adjective forms)

However, I'm finding that the WordNet data on this has important gaps. For example, I can find no relation at all between 'succeed', 'success', 'successful' which seem like they should be V/N/A variants on the same concept. Likewise none of the lemmatizers I've tried seem to see these as related, although I can get snowball stemmer to turn 'failure' into 'failur' which isn't really much help.

So my questions are:

1. Are there any other (programmatic, ideally python) tools out there that do this POS-conversion, which I should check out? (The WordNet hits are masking every attempt I've made to google alternatives.)
2. Failing that, are there ways to submit additions to WordNet despite the "due to lack of funding" situation they're presently in? (Or, can we set up a crowdfunding campaign?)
3. Failing that, are there straightforward ways to distribute supplementary corpus to users of nltk that augments the WordNet data where needed?

There is a code chunk I found useful in my project, but I can't get it to build a data frame in the same given/desired format as it prints (2 columns).

The code chunk and desired output:

I would like to host a model on Sagemaker using the new Serverless Inference.

I wrote my own container for inference and handler following several guides. These are the requirements:

I've a Python list with several sub lists having tokens as tokens. I want to stem the tokens in it so that the output will be as stemmed_expected.

I'm trying to import the NLTK language modeling module (nltk.lm) in a Google colaboratory notebook without success. I've tried by installing everything from nltk, still without success.

What mistake or omission could I be making?

Thanks in advance.

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