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import faicons as fa
import plotly.express as px
# Load data and compute static values
from shared import app_dir, bot_data
from shinywidgets import render_plotly
from shiny import reactive, render
from shiny.express import input, ui
data_rng = (min(bot_data.interactions), max(bot_data.interactions))
# Add page title and sidebar
ui.page_opts(title="MyBot Interactions", fillable=True)
with ui.sidebar(open="desktop"):
ui.input_slider(
"interactions",
"Number of Interactions",
min=data_rng[0],
max=data_rng[1],
value=data_rng,
)
ui.input_checkbox_group(
"time_period",
"Time Period",
["Morning", "Afternoon", "Evening", "Night"],
selected=["Morning", "Afternoon", "Evening", "Night"],
inline=True,
)
ui.input_action_button("reset", "Reset filter")
# Add main content
ICONS = {
"user": fa.icon_svg("user", "regular"),
"chat": fa.icon_svg("comments"),
"interaction": fa.icon_svg("exchange-alt"),
"ellipsis": fa.icon_svg("ellipsis"),
}
with ui.layout_columns(fill=False):
with ui.value_box(showcase=ICONS["user"]):
"Total Users"
@render.express
def total_users():
bot_data_filtered().shape[0]
with ui.value_box(showcase=ICONS["chat"]):
"Average Interactions per User"
@render.express
def average_interactions():
d = bot_data_filtered()
if d.shape[0] > 0:
avg_interactions = d.interactions.mean()
f"{avg_interactions:.1f}"
with ui.value_box(showcase=ICONS["interaction"]):
"Total Interactions"
@render.express
def total_interactions():
d = bot_data_filtered()
if d.shape[0] > 0:
total_interactions = d.interactions.sum()
f"{total_interactions}"
with ui.layout_columns(col_widths=[6, 6, 12]):
with ui.card(full_screen=True):
ui.card_header("Interaction Data")
@render.data_frame
def table():
return render.DataGrid(bot_data_filtered())
with ui.card(full_screen=True):
with ui.card_header(class_="d-flex justify-content-between align-items-center"):
"Interactions Over Time"
with ui.popover(title="Add a color variable", placement="top"):
ICONS["ellipsis"]
ui.input_radio_buttons(
"scatter_color",
None,
["none", "user_type", "time_period"],
inline=True,
)
@render_plotly
def scatterplot():
color = input.scatter_color()
return px.scatter(
bot_data_filtered(),
x="time",
y="interactions",
color=None if color == "none" else color,
trendline="lowess",
)
with ui.card(full_screen=True):
with ui.card_header(class_="d-flex justify-content-between align-items-center"):
"Interaction Types"
with ui.popover(title="Add a color variable"):
ICONS["ellipsis"]
ui.input_radio_buttons(
"interaction_type",
"Split by:",
["user_type", "time_period"],
selected="user_type",
inline=True,
)
@render_plotly
def interaction_types():
from ridgeplot import ridgeplot
dat = bot_data_filtered()
yvar = input.interaction_type()
uvals = dat[yvar].unique()
samples = [[dat.interactions[dat[yvar] == val]] for val in uvals]
plt = ridgeplot(
samples=samples,
labels=uvals,
bandwidth=0.01,
colorscale="viridis",
colormode="row-index",
)
plt.update_layout(
legend=dict(
orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5
)
)
return plt
ui.include_css(app_dir / "styles.css")
# --------------------------------------------------------
# Reactive calculations and effects
# --------------------------------------------------------
@reactive.calc
def bot_data_filtered():
interactions = input.interactions()
idx1 = bot_data.interactions.between(interactions[0], interactions[1])
idx2 = bot_data.time_period.isin(input.time_period())
return bot_data[idx1 & idx2]
@reactive.effect
@reactive.event(input.reset)
def _():
ui.update_slider("interactions", value=data_rng)
ui.update_checkbox_group("time_period", selected=["Morning", "Afternoon", "Evening", "Night"]) |