RI-DASH is a subset of our company page. The purpose of this repository is to show the community that the top UI/UX standards can be achieved using the DASH framework. We recommend checking out our portfolio tab to see the full demo[1][2].
Figure 1: Analysis of the most discriminating genes for thyroid and kidney cancer.
Figure 2: Visualization of automatic information retrieval. Expression of crucial insights from the data.
Figure 3: Contextual chatbot. The user can ask questions about the relevant publication and the chatbot will answer them.
To ensure a successful local run I recommend using docker. Run the terminal in the repository root. Next, use the following commands to create a container.
docker-compose build
docker-compose upAdd your own OpenAI API-KEY in .env file, in order to use the contextual chatbot.
$ tree
.
│ # All the graphics, stylesheets, and scripts used in the project
├── assets
│
│ # cache for chroma database - used in contextual chatbot
├── cache
│
│ # results of the genomic analysis - used for creating the plots
├── data
│
│ # chroma database - stores semantic embeddings of analyzed publications chunks
├── database
│
│ # body of each displayed page
├── pages
│
│ # all the functionality and callbacks for pages
├── utils
│
│ # stores environmental variables
├── .env
│
│ # main file; run this to start the app
└── app.py