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Description

A PoC with bokeh server with a custom basic login view

STEPS

  • STEP01: clone the template bokeh repository as project name alias
$ git clone https://gsdpi@dev.azure.com/gsdpi/avib/_git/uniovi-gsdpi-bokeh-template uniovi-gsdpi-bokeh-mp-traccion
  • STEP02: create virtual env for your project and activate it
$ python3 -m venv .venv
$ source .venv/bin/activate
  • STEP03: Install default python modules in your project
(.venv)$ pip install -r requirements.txt
  • STEP04: Install extra python dependencies for your application
(.venv)$ pip install matplotlib
(.venv)$ pip install scipy
(.venv)$ pip install scikit-learn
(.venv)$ pip install openpyxl
  • STEP05: Freeze all modules used by your application
(.venv)$ pip freeze > requirements.txt

  • STEP06: Implement your bokeh application

    • You must create the principal view using a python module called main.py
    • You can use all the modules you need.
    • You must create a logout button using this code:
##########################################
# Logout Button
#########################################
button_logout = Button(label="logout", button_type="primary", sizing_mode = "scale_width")
button_logout.js_on_event("button_click", CustomJS(code=f"window.location.href='{curdoc().session_context.request.path}{curdoc().session_context.logout_url}'"))

(.venv)$ pip freeze > requirements.txt
  • STEP07: Configure Application

Edit bootstrap Bokeh Server and set these arguments:

app = "uniovi-gsdpi-bokeh-template"                                          --> App main folder bokeh application (must b ethe same as main folder)
app_prefix = "MP"                                                            --> Prefix for your bokeh application
app_port = 5006                                                              --> Port for your bokeh application
app_title = "MP traccion"                                                    --> Title for your bokeh application
app_logo = "logo_gsdpi.png"                                                  --> Logo for your bokeh application
app_background = "login_background.png"                                      --> Background for your bokeh application
cookie_secret = "gsdpi"                                                      --> Cookie secret for your bokeh application
websocket_origin = ["avispe.edv.uniovi.es:80", "localhost:" + str(app_port)] --> Web Origins Domains for your bokeh application
basic_username = "<USERNAME>"                                                --> Username credentials for your bokeh application
basic_password = "<PASSWORD>"                                                --> Password credentials for your bokeh application
login_level = logging.DEBUG                                                  --> Logging level or your bokeh application
  • STEP08: Execute application and debug
$ python boostrap.py
  • STEP09: Build the docker image

Exec this command to build:

$ docker build -t uniovi-gsdpi-bokeh-mp-traccion:1.0.0 .
  • STEP10: run the docker container locally

Exec this command to run the container:

$ docker run --rm --name uniovi-gsdpi-bokeh-mp-traccion:1.0.0 -p 5006:5006 uniovi-gsdpi-bokeh-mp-traccion:1.0.0
  • STEP11: tag image docker image to be uploaded to azure container registry
$ docker tag uniovi-gsdpi-bokeh-mp-traccion:1.0.0 avibdocker.azurecr.io/uniovi-gsdpi-bokeh-mp-traccion:1.0.0
  • STEP12: push image docker image
$ docker push avibdocker.azurecr.io/uniovi-gsdpi-bokeh-mp-traccion:1.0.0