DAWG | based dictionary-like read | Dictionary library

I am trying to deploy my Python app on Heroku, but have been unsuccessful. It seems that a problem is occurring with the PyICU package, which I'm unsure how to correct. I've confirmed that this is the only issue with my deployment; when I remove PyICU from my requirements file, everything works. But of course my site can't work without it.

Can anyone please guide me in how to correctly install this package on Heroku? I've tried various methods, including downloading the .whl file and then adding that to my requirements file, but then I get another error:

ERROR: PyICU-2.7.3-cp38-cp38m-win_amd64.whl is not a supported wheel on this platform. I don't understand why - it's the correct Python and os version.

Here are the relevant excerpts from the build log:

My default speed records to Firestore as Meters Per Second and I need Miles Per Hour. I understand I can multiply by 2.23694 and this will be the equivalent conversion but I do not understand how to add this to my code. How do I change the speed from meters per second to miles per hour? Thanks for reading and any suggestions.

App.js

When updating a value in a nested Map in Elixir, we can use Kernel.put_in/3

I would like to calculate distance between two stations (behind and ahead) from the middle station. Each station has a GPS location. For example:

So my school has an SQL Server "SQL.SCHOOL.EDU\STUDENTSQLSERVER,4500" and I cannot for the life of me figure out how to connect to it using tedious. I have an express back end that is using react for the front end side of things. However I am currently only working on the express index.js to try and get some sort of feedback from the Server and eventually the data base.

Here is my index.js file in its entirity

I am working through a sample app to familiarize myself with Swift and SwiftUI and am wanting to combine the ideas of composite layouts using custom views and GeometryReader to determine the screen-size of the device.

The page I'm trying to build is a scrollable list of friends where each row item is a button that can be selected to expand the list to see more details of each friend. I've built the button and all containing views as a FriendView to use inside of my FriendListView. I have defined 2 GeometryReader views within the FriendListView but do not know how I can define the size of each row from within the FriendView to make it maintain the appropriate size.

The need for this comes from not every image that could be set by a user later on for their friend will have the same dimensions so I want to keep it consistent. I understand I could set a .frame() modifier and hardcode the width but I would like it to be relational to the screen to be consistent across all phone screens.

Example: When a row is not expanded, the image should take up half of the device width. When it is expanded I would want it to take up one quarter of the device screen width.

Screenshot

Application View

Sample Code

I am trying to implement a little game with the following rules: Given a set of random letters (e.g. 10), I want to find all possible words one can form out of these letters. I am using a standard dictionary for this.

Letters are allowed to be used multiple times, and not all letters have to be used, as long it results in words with 4 characters or more. I think this is similar to solving anagrams, except that letters are allowed to be used multiple times.

E.g. Letters given: q r b d t e s Possible words: bedders, dessert, etc.

In search of a data structure supporting O(1) for checking if a proposed word is in the dictionary, I found this paper and subsequently also found a working Java DAWG implementation here.

Here's where I am stuck: When trying to generate a list of possible words one can create from these letters, I am wondering if I am missing something using the DAWG implementation. I have seen other threads on here that are suggestion using a Trie and recursively go through the nodes, but I was hoping I could do something similar with the DAWG.

The implementation I currently have working is by going through all the words of the dictionary, then skip any word that contains a letter that is NOT part of the letters earlier assigned. This works, but is slow, going through all the words of the dictionary * ~20 letters worst-case.

I'm using pythonanywere.com to deploy my Django application. So I've installed a few machine learning libraries to that virtual environment.