Add more block, tags and update captions

Author: stephwills

docs/posts/20260227_bokeh_django.md

@@ -1,15 +1,13 @@
 ---
-date: 2025-12-10
+date: 2026-02-27
 authors:
   - stephwills
 categories:
-  - category_a
-  - category_b
-  - category_c
+  - Technology
 tags:
-  - tag_a
-  - tag_b
-  - tag_c
+  - Django
+  - Python
+  - Plotting
 ---
 
 # Plotting in Django with Bokeh
@@ -18,6 +16,8 @@ Last year, I was involved in developing one of the RSE team's internal projects,
 
 We built ProCAT using [Django](https://www.djangoproject.com/), the RSE team's preferred framework for creating web applications. One important design choice was selecting which library to use for generating our analytics plots, which forms the subject of today's blogpost.
 
+<!-- more -->
+
 ## Choosing a plotting library
 
 For ProCAT, the requirement was to provide interactive timeseries charts, allowing the user to zoom in, use tooltips, select the time range to be displayed and apply filters to select which data to display.
@@ -129,7 +129,7 @@ It is important to note that, to use these components, the BokehJS resources mus
 
 Following this, you should now have your Bokeh plot rendered in your Django view!
 
-![Example plot](images/bokeh_django/plot.png)
+![An initial Bokeh plot](images/bokeh_django/plot.png)
 
 ### Creating interactive widgets
 
@@ -197,7 +197,7 @@ layout = row(
 
 The layout can then be used to generate the HTML components to be added to the template, as shown above.
 
-![Example plot with widgets](images/bokeh_django/plot_with_widgets.png)
+![A plot using date picker widgets](images/bokeh_django/plot_with_widgets.png)
 
 ### Ajax data sources (plus more advanced widgets)
 
@@ -295,7 +295,7 @@ callback = CustomJS(
 
 Now, we should have a working plot using `AjaxDataSource`s with our `CheckboxButtonGroup` widget.
 
-![Example plot](images/bokeh_django/ajax_plot.png)
+![An Ajax plot with a checkbox button](images/bokeh_django/ajax_plot.png)
 
 ## Summary