This is the Github Page for our SDC Project.
Pipline Status
Builds:
Deployment:
Build with:
Deployed to:
The Dataset is from Kaggle. It represents car accidents in the UK over a time-frame of about 10 years.
The current deployed version can be visited under: http://sdc-project.azurewebsites.net. It gets automatically built and pushed on every new commit.
Below is a schematic of our pipline for deployment:
And here a little overview of the infrastructure used in deployment:
The structure of this repo is the following:
Folder | Details |
---|---|
/backend | contains all the files for our backend |
/frontend | contains all our frontend files |
/dash_app | contains the dash app, which is integrated into our frontend |
/data | empty folder for local usage, see /data/README.md |
/etc | contains additional content for the project (for example pics) |
/notebooks | contains all the notebooks used for data analysis etc. |
First you'll need to install node.js. Then you can build the frontend. Inside the /frontend/sdc-frontend/ folder run:
npm install
and (for local testing, for production substitute local for prod)
npm run build:local
Sidenote: Since env-cmd is used this command will not work on standard Windows cmd or Powershell. Execute the following commands prior to solve this:
npm update
npm install dotenv --save
To build all docker containers run (after substituting user and token) in the root project folder:
docker compose build --build-arg kaggle_user=user --build-arg kaggle_token=token
After building you can start the project simply via:
docker compose up
And point your browser to localhost
Error | Details | Solution |
---|---|---|
401 | The credentials are wrong. | - |
403 | The credentials are wrong. | - |
404 | The dataset cannot be found on kaggle. | Run the build again. |
416 | The contigent for downloading the dataset was already used. | The process needs to be run again the next day. |
Error | Details | Solution |
---|---|---|
env-cmd | env-cmd is used and does not work. | run commands found under Build |
- Descriptive Analysis
- Train ML model (serverity ~ .)
- Create Dash App
- Update Frontend
- Create Flask Backend
- Create Github Actions
- Change Layout Dash App
- Update Dash App
- Add Filters to Dash App 1
- Add Filters to Dash App 2
- Setup Azure AppServices
- Update backend with model