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yashasvini121 / predictive-calc

An interactive web application developed with Streamlit, designed for making predictions using various machine learning models. The app dynamically generates forms and pages from JSON configuration files. ⭐ If you found this helpful, consider starring the repo!
https://predictive-calc.streamlit.app/
MIT License
20 stars 48 forks source link

Implement Docker Containerization for the Project #45

Open RandomSummer opened 1 week ago

RandomSummer commented 1 week ago

I can enhance the UI/UX design, and add features such as the user will be aware of which ML model they are using beforehand, and also add the feature "the graphical report of the provided data". I can even add the use of docker to containerize the whole project which can eventually help the developer and operator simultaneously.

By incorporating this we can do this project on any system regardless of which OS kernel it is used.

I will be sharing a screenshot of my previous project for you to look over.

If you like my idea, kindly let me know.

yashasvini121 commented 6 days ago

For now, please focus on the containerization task, as the UI/UX issues are being handled by other contributors. Make sure to include setup.sh and setup.ps1 files.

@RandomSummer assigned!

Also mention estimated time you need for this task.

RandomSummer commented 4 days ago

I have completed all my analysis and operation. Now submitting the proposed blueprint for the Docker Architecture, to make things work, I need some time for testing for routing and networking purposes.

Screenshot 2024-10-08 092135

Few bugs: -> To successfully run the App. python version should be specified (Python 3.11) as (Python 3.12 and 3.13 doesn't support a few features yet like ydata-profiling etc)

Suggestions: -> I would like a specific OS kernel in Docker that the users won't need to download, it will run automatically. -> Just download and run the image, and everything will be fine.

yashasvini121 commented 14 hours ago

This project does not utilize a database, and there's no need to complicate matters by maintaining separate models. Instead, please create a straightforward Docker Compose file that handles the pip installations and subsequently runs the project.

Additionally, update the README file to include instructions on how to run the project using Docker