how can I export this interactive plot to view in a browser without jupyter?

Question

I have this interactive plot in python:

import ipywidgets as widgets
import plotly.graph_objects as go 
from numpy import linspace


def leaf_plot(sense, spec):
    fig = go.Figure()

    x = linspace(0,1,101)
    x[0] += 1e-16
    x[-1] -= 1e-16

    positive =  sense*x/(sense*x + (1-spec)*(1-x)) 
                                                    #probability a person is infected, given a positive test result, 
                                                    #P(p|pr) = P(pr|p)*P(p)/P(pr)
                                                    #        = P(pr|p)*P(p)/(P(pr|p)*P(p) + P(pr|n)*P(n))
                                                    #        =   sense*P(p)/(  sense*P(p) +(1-spec)*P(n))
    negative =  1-spec*(1-x)/((1-sense)*x + spec*(1-x))

    fig.add_trace(
        go.Scatter(x=x, y  = positive, name="Positive",marker=dict( color='red'))
    )

    fig.add_trace(
        go.Scatter(x=x, y  = negative, 
                   name="Negative", 
                   mode = 'lines+markers',
                   marker=dict( color='green'))
    )
 
    fig.update_xaxes(title_text = "Base Rate")
    fig.update_yaxes(title_text = "Post-test Probability")
    fig.show()

sense_ = widgets.FloatSlider(
    value=0.5,
    min=0,
    max=1.0,
    step=0.01,
    description='Sensitivity:',
    disabled=False,
    continuous_update=False,
    orientation='horizontal',
    readout=True,
    readout_format='.2f',
)

spec_ = widgets.FloatSlider(
    value=0.5,
    min=0,
    max=1.0,
    step=0.01,
    description='Specificity:',
    disabled=False,
    continuous_update=False,
    orientation='horizontal',
    readout=True,
    readout_format='.2f',
)
ui = widgets.VBox([sense_, spec_])

out = widgets.interactive_output(leaf_plot, {'sense': sense_, 'spec': spec_})

display(ui, out)

How can I export this so that it can be viewed as a standalone web page in a browser, say as HTML, while retaining the interactivity, as e.g. in https://gabgoh.github.io/COVID/index.html ?

Using plotly's fig.write_html() option I get a standalone web page, but this way I lose the sliders.

With some modification, plotly allows at most for a single slider (the ipywidgets are not included in the plotly figure object).

Plus, in plotly, the said slider basically controls the visibility of pre-calculated traces (see e.g. https://plotly.com/python/sliders/), which restricts the interactivity (sometimes the parameter space is huge).

What's the best way to go?

(I don't necessarily need to stick with plotly/ipywidgets)

Answer 1

you need to rework things a bit, but you can achieve what you want with dash and Heroku.

first you need to modify leaf_plot() to return a figure object.

from numpy import linspace


def leaf_plot(sense, spec):
    fig = go.Figure()

    x = linspace(0,1,101)
    x[0] += 1e-16
    x[-1] -= 1e-16

    positive = sense*x/(sense*x + (1-spec)*(1-x)) 
    negative = 1-spec*(1-x)/((1-sense)*x + spec*(1-x))

    fig.add_trace(
        go.Scatter(x=x, y  = positive, name="Positive",marker=dict( color='red'))
    )

    fig.add_trace(
        go.Scatter(x=x, y  = negative, 
                   name="Negative", 
                   mode = 'lines+markers',
                   marker=dict( color='green'))
    )
    
    fig.update_layout(
    xaxis_title="Base rate",
    yaxis_title="After-test probability",
    )

    return fig

Then write the dash app:

from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output


# Build App
app = JupyterDash(__name__)
app.layout = html.Div([
    html.H1("Interpreting Test Results"),
    dcc.Graph(id='graph'),
    html.Label([
        "sensitivity",
        dcc.Slider(
            id='sensitivity-slider',
            min=0,
            max=1,
            step=0.01,
            value=0.5,
            marks = {i: '{:5.2f}'.format(i) for i in linspace(1e-16,1-1e-16,11)}
        ),
    ]),
    html.Label([
        "specificity",
        dcc.Slider(
            id='specificity-slider',
            min=0,
            max=1,
            step=0.01,
            value=0.5,
            marks = {i: '{:5.2f}'.format(i) for i in linspace(1e-16,1-1e-16,11)}
        ),
    ]),
])

# Define callback to update graph
@app.callback(
    Output('graph', 'figure'),
    Input("sensitivity-slider", "value"),
    Input("specificity-slider", "value")
)
def update_figure(sense, spec):
    return leaf_plot(sense, spec)

# Run app and display result inline in the notebook
app.run_server()

If you execute this in a jupyter notebook, you will only be able to access your app locally.

If you want to publish, you can try Heroku