I am creating a plot and then updating a dataframe everytime a new bar is pulled from the broker. I am actually replacing the dataframe with a fresh 400 bars (really only 1 should be added to the end) so that the plot is always 400 bars long. I am having issues updating a reloading my static html plot. How can i add a callback or function to reload the window whenever I save a new fig to the document?

This plot function is called from another module that is actually pulling new bar data from the broker. I am thinking about just running through this below function again with the new dataframe and instead calling save(fig) instead of show(fig) but then again how can i tell the document that a new saved version is available and to reload the current page. I do not want a new browser instance to be pulled up everytime I update a dataframe.

EDIT: Code has been updated to work, sorry!

import numpy as np
import os

from functools import partial
from itertools import cycle

from typing import Dict
# from data_objects import Indicator

from bokeh.palettes import Category10
from bokeh.layouts import gridplot
from bokeh.transform import factor_cmap
from bokeh.models import (
    Range1d,
    ColumnDataSource,
    CustomJSTickFormatter,
    DatetimeTickFormatter,
    HoverTool,
    NumeralTickFormatter,
    CrosshairTool,
    WheelZoomTool,
    Span,
    CustomJS
)
from bokeh.plotting import figure, show, curdoc, save
from bokeh.io.state import curstate
from bokeh.io import output_file
from bokeh.colors.named import (
    lime as BULL_COLOR,
    tomato as BEAR_COLOR
)
OHLCV_LIST = ['Open', 'High', 'Low', 'Close', 'Volume']

def _bokeh_reset(filename=None):
    curstate().reset()
    if filename:
        if not filename.endswith('.html'):
            filename += '.html'
        output_file(filename, title=filename)
    else:
        output_file('default.html', title='')


def plot(df: pd.DataFrame, indicators: Dict[str, str]=None, update_plot=False):

    # reset the current state and create an html output
    _bokeh_reset('test_plot')

    COLORS = [BEAR_COLOR, BULL_COLOR]
    BAR_WIDTH = .8

    is_datetime_index = isinstance(df.index, pd.DatetimeIndex)

    # copy dataframe without indicators
    plot_df = df[list(OHLCV_LIST)].copy(deep=False)

    # get indicators for plot
    indicator_columns = df.columns.difference(plot_df.columns)

    plot_df.index.name = None  # Provides source name @index
    plot_df['datetime'] = plot_df.index  # Save original, maybe datetime index
    plot_df = plot_df.reset_index(drop=True)
    index = plot_df.index

    # Initialize Bokeh figure function
    new_bokeh_figure = partial(
        figure,
        x_axis_type='linear',
        width=500,
        height=400,
        tools="xpan,xwheel_zoom,box_zoom,undo,redo,reset,save",
        active_drag='xpan',
        sizing_mode='stretch_both',
        active_scroll='xwheel_zoom')
    
    pad = (index[-1] - index[0]) / 20
    _kwargs = dict(x_range=Range1d(index[0], index[-1],
                                min_interval=10,
                                bounds=(index[0] - pad,
                                        index[-1] + pad))) if index.size > 1 else {}
    # create bokeh figure instance
    fig_ohlc = new_bokeh_figure(**_kwargs)
    # used for volume and can be extended in the future
    figs_below_ohlc = []

    # create source object
    source = ColumnDataSource(plot_df)
    source.add((plot_df.Close >= plot_df.Open).values.astype(np.uint8).astype(str), 'inc')
    # map colors
    inc_cmap = factor_cmap('inc', COLORS, ['0', '1'])

    def new_indicator_figure(**kwargs):
        kwargs.setdefault('height', 150)
        fig = new_bokeh_figure(x_range=fig_ohlc.x_range,
                               active_scroll='xwheel_zoom',
                               active_drag='xpan',
                               sizing_mode='stretch_width',
                               **kwargs)
        fig.xaxis.visible = False
        fig.yaxis.minor_tick_line_color = None
        return fig
        
    # create function to add candlesticks for OHLC
    def _plot_ohlc():
        """Main OHLC bars"""
        fig_ohlc.segment('index', 'High', 'index', 'Low', source=source, color="black")
        fig_ohlc.vbar('index', BAR_WIDTH, 'Open', 'Close', source=source,
                          line_color="black", fill_color=inc_cmap)
        
    def _plot_volume_section():
        """Volume section"""
        fig = new_indicator_figure(y_axis_label="Volume")
        fig.xaxis.formatter = fig_ohlc.xaxis[0].formatter
        fig.xaxis.visible = True
        fig_ohlc.xaxis.visible = False  # Show only Volume's xaxis
        fig.vbar('index', BAR_WIDTH, 'Volume', source=source, color='red')
        return fig


    fig_volume = _plot_volume_section()
    figs_below_ohlc.append(fig_volume)
    ohlc_bars = _plot_ohlc()
    plots = [fig_ohlc] + figs_below_ohlc # where figs_below holds the volume figure

    fig = gridplot(
        plots,
        ncols=1,
        toolbar_location='right',
        toolbar_options=dict(logo=None),
        merge_tools=True,
        sizing_mode='stretch_both'
    )
    show(fig)
    return fig