✨ Code-base structure

The project is coded using a simple and intuitive structure presented below:

< PROJECT ROOT >
   |
   |-- core/                               # Implements app configuration
   |    |-- settings.py                    # Defines Global Settings
   |    |-- wsgi.py                        # Start the app in production
   |    |-- urls.py                        # Define URLs served by all apps/nodes
   |
   |-- apps/
   |    |
   |    |-- home/                          # A simple app that serves HTML files 
   |    |    |-- views.py                  # Serves HTML pages for authenticated users
   |    |    |-- urls.py                   # Defines some simple routes  
   |    |
   |    |-- authentication/                # Handles auth routes (login and register)
   |    |    |-- urls.py                   # Define authentication routes  
   |    |    |-- views.py                  # Handles login and registration  
   |    |    |-- forms.py                  # Define auth forms (login and register) 
   |    |
   |    |-- endpoints/                     # Information about the endpoints / Django REST API Information
   |    |    |-- urls.py                   # Defines routes to access the models
   |    |    |-- views.py                  # Defines how models are accessed in REST API
   |    |    |-- serializers.py            # Defines how database objects are mapped in request
   |    |    |-- models.py                 # Database models to store information about algorithms and requests in the database
   |    |
   |    |-- ML/                            # Storage location for all ML related code (für Q1 oder REST?)
   |    |    |-- income_classifier/                      # keeps the income classifiers
   |    |    |     |-- random_forest.py                  # Implement ML algorithm code here  (nochmal gucken was da drinnen ist)
   |    |    |
   |    |    |-- research/                               # Beinhaltet Jupyter Notebook und joblib/pickle Dateien daraus
   |    |    |     |-- RF Beispiel Algorithmus.ipynb      # Jupyter Notebook Datei
   |    |    |     |-- encoders.joblib                         
   |    |    |     |-- train_mode.joblib
   |    |    |     |-- extra_trees.joblib
   |    |    |     |-- random_forest.joblib
   |    |    |   
   |    |    |-- tests.py                  # Test case to check if RF algorithm is working as expected 
   |    |    |-- registry.py               # To connect ML code with the server code
   |    |
   |    |-- quality1/                      # App for Quality RF Eingangsparameter
   |    |    |-- urls.py                   #  1:8000/quality1/ defined as url
   |    |    |-- views.py                  #  Defines how models are accessed
   |    |    |-- models.py                 # Model Parameters mit Qualitätsparametern
   |    |
   |    |-- explainer/                     # django_plotly_dash Erweiterung für Explainer Dashboard
   |    |    |-- dash_apps/                #  
   |    |    |    |-- finished_apps        #  Location to save finished dah_apps
   |    |    |
   |    |    |-- urls.py
   |    |    |-- views.py
   |    |    |-- models.py
   |    |    |-- routing.py
   |    |
   |    |-- static/
   |    |    |-- <css, JS, images>         # CSS files, Javascripts files
   |    |
   |    |-- templates/                     # Templates used to render pages
   |         |-- includes/                 # HTML chunks and components
   |         |    |-- navigation.html      # Top menu component
   |         |    |-- sidebar.html         # Sidebar component
   |         |    |-- footer.html          # App Footer
   |         |    |-- scripts.html         # Scripts common to all pages
   |         |
   |         |-- layouts/                   # Master pages
   |         |    |-- base-fullscreen.html  # Used by Authentication pages
   |         |    |-- base.html             # Used by common pages
   |         |
   |         |-- accounts/                  # Authentication pages
   |         |    |-- login.html            # Login page
   |         |    |-- register.html         # Register page
   |         |
   |         |-- home/                      # UI Kit Pages
   |              |-- index.html            # Index page
   |              |-- 404-page.html         # 404 page
   |              |-- *.html                # All other pages
   |
   |-- requirements.txt                     # Development modules - SQLite storage
   |
   |-- .env                                 # Inject Configuration via Environment
   |-- manage.py                            # Start the app - Django default start script
   |
   |-- ************************************************************************

Einbindung ML Modell in Endpoints

Jupyter Notebook:

Der ML-Algorithmus besteht nicht nur aus den Variablen rf und et (mit Modellgewichten)

• Vorverarbeitungsvariablen train_mode und encoders müssen auch gespeichert werden

• save preprocessing objects and RF algorithmen:

• pickle.dump(train_mode, "./train_mode.pkl", compress=True)

• pickle.dump(encoders, "./encoders.pkl", compress=True)

• pickle.dump(rf, "./random_forest.pkl", compress=True)

• pickle.dump(et, "./extra_trees.pkl", compress=True)

Anpassung des ML Codes und hinzufügen der App:

• Passe apps / ML / income_classifier / random_forest.py Datei an deinen ML-Code an

• (https://www.deploymachinelearning.com/serve-ml-models/)

• Add ML app to INSTALLED_APPS in core / settings.py

Testen des RF Algorithmus

• Passe apps / ML / tests.py Datei an ML Modell an
• run python manage.py test apps.ml.tests

ML Code mit Server Code verbinden

• Hierfür wird ML registry object in apps / ML / registry.py Datei erstellt
• Enthällt Informationen über verfügbare Algorithmen und deren entsprechende Endpunkte

Create a new .env file using sample env.sample

T

How to use it

Set Up for Unix, MacOS

Install modules via VENV

$ virtualenv env
$ source env/bin/activate
$ pip3 install -r requirements.txt

Set Up Database

$ python manage.py makemigrations
$ python manage.py migrate

Start the app

$ python manage.py runserver
// OR with https
$ python manage.py runsslserver 

At this point, the app runs at http://127.0.0.1:8000/.

Set Up for Windows

Install modules via VENV (windows)

$ virtualenv env
$ .\env\Scripts\activate
$ pip3 install -r requirements.txt

Set Up Database

$ python manage.py makemigrations
$ python manage.py migrate

Start the app

$ python manage.py runserver

At this point, the app runs at http://127.0.0.1:8000/.

Datta Able Django

Open-source Django Dashboard generated by AppSeed op top of a modern design. Datta Able Bootstrap Lite is the most stylised Bootstrap 4 Lite Admin Template, around all other Lite/Free admin templates in the market. It comes with high feature-rich pages and components with fully developer-centric code.

• Datta Able Django - Product page
• Complete documentation - Learn how to use and update the product

Built with App Generator, timestamp: 2022-10-05 07:49

• ✅ Up-to-date dependencies
• ✅ Database: sqlite
• ✅ Authentication, Session Based, OAuth via Github
• ✅ Dark Mode (persistent)
• ✅ Docker

---

Datta Able Django - Open-source starter generated by AppSeed Generator.