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bokeh_tabs.py
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bokeh_tabs.py
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# copied work
import bokeh
import bokeh.models
import markdown
import streamlit as st
def main():
tabs = bokeh.models.Tabs(
tabs=[
layout_panel(),
widgets_tables_panel(),
]
)
st.bokeh_chart(tabs)
def _chart():
circle_chart = bokeh.plotting.figure(sizing_mode="stretch_both")
circle_chart.circle(
[1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5
)
return circle_chart
def layout_panel():
text = """
## My Vision on the Bokeh Streamlit Integration
I believe the integration with Bokeh **can give Streamlit Super Powers** if improved slightly.
For example
- Wrapping the Bokeh api into a more **Streamlit like Api** like `st.bokeh.datatable(my_dataframe)`
- Enabling **Python Callbacks** for advanced interactivity. I have a gut feeling it's easy to integrate because
- Streamlit and Bokeh are both Tornado Applications.
- There are already a lot of tutorials on integrations with Flask, Django and Jupyter Notebooks.
I believe the integration with Bokeh also can have a downside.
Personally I find the Bokeh documentation and api hard to learn, navigate and use.
And I start spending a lot of time on layout and formatting because I can!
Is it **Pandoras Box** that I've opened?
I believe it should be considered whether the improved integration should be with Bokeh or
[Panel](https://github.com/holoviz/panel). Panel could provide integration to the full suite of
PyViz tools and more advanced layouts and widgets.
I will be adding more examples when I get the time.
"""
return bokeh.models.Panel(child=_markdown(text), title="Vision")
def widgets_tables_panel():
text = """
## My Vision on the Bokeh Streamlit Integration
I believe the integration with Bokeh **can give Streamlit Super Powers** if improved slightly.
For example
- Wrapping the Bokeh api into a more **Streamlit like Api** like `st.bokeh.datatable(my_dataframe)`
- Enabling **Python Callbacks** for advanced interactivity. I have a gut feeling it's easy to integrate because
- Streamlit and Bokeh are both Tornado Applications.
- There are already a lot of tutorials on integrations with Flask, Django and Jupyter Notebooks.
I believe the integration with Bokeh also can have a downside.
Personally I find the Bokeh documentation and api hard to learn, navigate and use.
And I start spending a lot of time on layout and formatting because I can!
Is it **Pandoras Box** that I've opened?
I believe it should be considered whether the improved integration should be with Bokeh or
[Panel](https://github.com/holoviz/panel). Panel could provide integration to the full suite of
PyViz tools and more advanced layouts and widgets.
I will be adding more examples when I get the time.
"""
return bokeh.models.Panel(child=_markdown(text), title="Vision")
def vision_panel():
text = """
## My Vision on the Bokeh Streamlit Integration
I believe the integration with Bokeh **can give Streamlit Super Powers** if improved slightly.
For example
- Wrapping the Bokeh api into a more **Streamlit like Api** like `st.bokeh.datatable(my_dataframe)`
- Enabling **Python Callbacks** for advanced interactivity. I have a gut feeling it's easy to integrate because
- Streamlit and Bokeh are both Tornado Applications.
- There are already a lot of tutorials on integrations with Flask, Django and Jupyter Notebooks.
I believe the integration with Bokeh also can have a downside.
Personally I find the Bokeh documentation and api hard to learn, navigate and use.
And I start spending a lot of time on layout and formatting because I can!
Is it **Pandoras Box** that I've opened?
I believe it should be considered whether the improved integration should be with Bokeh or
[Panel](https://github.com/holoviz/panel). Panel could provide integration to the full suite of
PyViz tools and more advanced layouts and widgets.
I will be adding more examples when I get the time.
"""
return bokeh.models.Panel(child=_markdown(text), title="Vision")
def _markdown(text):
return bokeh.models.widgets.markups.Div(
text=markdown.markdown(text), sizing_mode="stretch_width"
)
main()