open-sourced-olaf / code-kindle Goto Github PK

I seems like virtual envs are redundant given that we will be containerizing our application in docker. So we could get rid of it and have requirements.txt file instead? or something like that. @codingbug671 What do you think?

• currently, only the frontend part is deployed in dfinity.

• There is a lot of scope for making the code modular and refactor it wherever possible.

• The node API file upload was not working properly.

• Many pages were not integrated into the final app as per the design given below.
• https://www.figma.com/proto/6kQe9wCuWzWjSRYmV85rV1/pseudoGenerator?node-id=1%3A49&scaling=scale-down

• We can use Pylint since we have many python related codes.
• We can have a CI/CD, for deploying it to DFINITY easily.

• Making a cpp pseudocode generator.

• Add many unit tests for each function.
• Add e2e(end to end)/integration tests as well.

Code Kindle misses its logo. If possible, anyone can design a logo.

What about your views @DebugAgrawal @anjalisoni3655

Features:

• A text editor where the user can put the code that needs to be translated.
• Upload button to upload the file to translate.
• Output console to show the translated file.
• Download button to download the translated text file.

• Can we have inter-conversion of codes.
• Example, the user can provide will provide the code in one language and want the source code for other languages.

• Right now, we can convert source code to pseudocode, can we do vice versa?

If anyone can add a contributors bot, it will be helpful

Is your feature request related to a problem? Please describe.
Heroku Deployments takes more time to load for user, because of servers.

Describe the solution you'd like
Deployment on Vercel/Netlify/ etc. because they provide too quick response time.

• we can use the fileReader to see the content the file as a file preview

Implement pseudocode generator for python

• Currently, we have code conversion for python, C, C++.

• Documentation can be improved both for Readme as well as Wiki,
• In Wiki, we can provide the logic of each of the ML Models that we have for the conversions.
• We should also add more comments to each of the functions