KVE repair dashboard

This is a digital framework to track KVE repairs and scans.

Installation

Install git from here: https://git-scm.com/downloads

Open a comand line in the desired folder and clone the repository:

git clone https://github.com/Pugens/composite-repair-dashboard

Install libraries using pip:

pip install -r requirements.txt

Then run the app.py file with this command:

python app.py

Optional:

Install conda from here to manage python environments: https://docs.conda.io/en/latest/miniconda.html

And run the following commands when you have conda installed:

conda env create -f environment.yml
conda activate composite_repair_dashboard

Dev links:

Advanced guide on dash:

• https://towardsdatascience.com/create-a-professional-dasbhoard-with-dash-and-css-bootstrap-e1829e238fc5

Pdf with python code:

• https://community.plotly.com/t/displaying-a-pdf-file-on-plotly-dash-dashboard-in-python/33493/10
• https://blog.alivate.com.au/poppler-windows/

Upload files:

• https://docs.faculty.ai/user-guide/apps/examples/dash_file_upload_download.html
• https://github.com/aegis1980/dash-obj-in-3dmesh

Possible limitations:

• The app is not responsive, so it will not work on mobile devices.
• The app is not optimized for large datasets, so it will not work well with large datasets.
• The app is not optimized for large files, so it will not work well with large files.
• The app is not optimized for large images, so it will not work well with large images.
• The app is not optimized for large pdfs, so it will not work well with large pdfs.
• The app is not optimized for large videos, so it will not work well with large videos.

5 challenges when using Plotly Dash for web apps

Inspiration:

[Gallery of dashboard built with dash] (https://dash.gallery/Portal/?search=image)