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app.py
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app.py
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import duckdb
import faicons as fa
import plotly.express as px
from shiny import reactive, render, req
from shiny.express import input, ui
from shinywidgets import render_plotly
import query
from explain_plot import explain_plot
# Load data and compute static values
from shared import app_dir, tips
greeting = """
You can use this sidebar to filter and sort the data based on the columns available in the `tips` table. Here are some examples of the kinds of questions you can ask me:
1. Filtering: "Show only Male smokers who had Dinner on Saturday."
2. Sorting: "Show all data sorted by total_bill in descending order."
3. Answer questions about the data: "How do tip sizes compare between lunch and dinner?"
You can also say "Reset" to clear the current filter/sort, or "Help" for more usage tips.
"""
# Add page title and sidebar
ui.page_opts(title="Restaurant tipping", fillable=True)
current_query = reactive.Value("")
current_title = reactive.Value(None)
with ui.sidebar(open="desktop", width=400, style="height: 100%;", gap="3px"):
chat = ui.Chat(
"chat",
messages=[
query.system_prompt(tips, "tips"),
{"role": "assistant", "content": greeting},
],
tokenizer=None,
)
chat.ui(height="100%")
@chat.on_user_submit
async def perform_chat():
with ui.Progress() as p:
response, sql, title = await query.perform_query(
chat.messages(), query_db, lambda msg: p.set(message=msg)
)
await chat.append_message({"role": "assistant", "content": response})
if sql is not None:
current_query.set(sql)
if title is not None:
current_title.set(title)
# Add main content
ICONS = {
"user": fa.icon_svg("user", "regular"),
"wallet": fa.icon_svg("wallet"),
"currency-dollar": fa.icon_svg("dollar-sign"),
"ellipsis": fa.icon_svg("ellipsis"),
}
@render.express
def title():
_ = req(current_title(), current_query())
with ui.h3():
current_title()
with ui.pre():
current_query()
with ui.layout_columns(fill=False):
with ui.value_box(showcase=ICONS["user"]):
"Total tippers"
@render.express
def total_tippers():
tips_data().shape[0]
with ui.value_box(showcase=ICONS["wallet"]):
"Average tip"
@render.express
def average_tip():
d = tips_data()
if d.shape[0] > 0:
perc = d.tip / d.total_bill
f"{perc.mean():.1%}"
with ui.value_box(showcase=ICONS["currency-dollar"]):
"Average bill"
@render.express
def average_bill():
d = tips_data()
if d.shape[0] > 0:
bill = d.total_bill.mean()
f"${bill:.2f}"
with ui.layout_columns(col_widths=[6, 6, 12]):
with ui.card(full_screen=True):
ui.card_header("Tips data")
@render.data_frame
def table():
return render.DataGrid(tips_data())
with ui.card(full_screen=True):
with ui.card_header(class_="d-flex justify-content-between align-items-center"):
"Total bill vs tip"
with ui.span():
ui.input_action_link(
"interpret_scatter", fa.icon_svg("robot"), class_="me-3"
)
with ui.popover(title="Add a color variable", placement="top"):
ICONS["ellipsis"]
ui.input_radio_buttons(
"scatter_color",
None,
["none", "sex", "smoker", "day", "time"],
inline=True,
)
@render_plotly
def scatterplot():
color = input.scatter_color()
return px.scatter(
tips_data(),
x="total_bill",
y="tip",
color=None if color == "none" else color,
trendline="lowess",
)
@reactive.effect
@reactive.event(input.interpret_scatter)
async def interpret_scatter():
await explain_plot(chat.messages(), scatterplot.widget, query_db)
with ui.card(full_screen=True):
with ui.card_header(class_="d-flex justify-content-between align-items-center"):
"Tip percentages"
with ui.span():
ui.input_action_link(
"interpret_ridge", fa.icon_svg("robot"), class_="me-3"
)
with ui.popover(title="Add a color variable"):
ICONS["ellipsis"]
ui.input_radio_buttons(
"tip_perc_y",
"Split by:",
["sex", "smoker", "day", "time"],
selected="day",
inline=True,
)
@render_plotly
def tip_perc():
from ridgeplot import ridgeplot
dat = tips_data()
yvar = input.tip_perc_y()
uvals = dat[yvar].unique()
samples = [[dat.percent[dat[yvar] == val]] for val in uvals]
plt = ridgeplot(
samples=samples,
labels=uvals,
bandwidth=0.01,
colorscale="viridis",
# Prevent a divide-by-zero error that row-index is susceptible to
colormode="row-index" if len(uvals) > 1 else "mean-minmax",
)
plt.update_layout(
legend=dict(
orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5
)
)
return plt
@reactive.effect
@reactive.event(input.interpret_ridge)
async def interpret_ridge():
await explain_plot(chat.messages(), tip_perc.widget, query_db)
ui.include_css(app_dir / "styles.css")
# --------------------------------------------------------
# Reactive calculations and effects
# --------------------------------------------------------
@reactive.calc
def tips_data():
if current_query() == "":
return tips
return duckdb.query(current_query()).df()
def query_db(query: str):
return duckdb.query(query).to_df().to_json(orient="records")