Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

docs(layout_columns): Add example app #903

Merged
merged 6 commits into from
Dec 18, 2023
Merged
Show file tree
Hide file tree
Changes from 4 commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
33 changes: 33 additions & 0 deletions shiny/api-examples/layout_columns/app.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,33 @@
from model_plots import * # model plots and cards

from shiny import App, Inputs, Outputs, Session, render, ui

app_ui = ui.page_fluid(
ui.panel_title(ui.h2("Model Dashboard")),
ui.markdown("Using `ui.layout_columns()` for the layout."),
ui.layout_columns(
card_loss,
card_acc,
card_feat,
col_widths=(5, 7, 12),
# row_heights=(2, 3),
# height="700px",
),
)


def server(input: Inputs, output: Outputs, session: Session):
@render.plot
def loss_over_time():
return plot_loss_over_time()

@render.plot
def accuracy_over_time():
return plot_accuracy_over_time()

@render.plot
def feature_importance():
return plot_feature_importance()


app = App(app_ui, server)
56 changes: 56 additions & 0 deletions shiny/api-examples/layout_columns/model_plots.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,56 @@
import matplotlib.pyplot as plt
import numpy as np

from shiny import ui


def plot_loss_over_time():
epochs = np.arange(1, 101)
loss = 1000 / np.sqrt(epochs) + np.random.rand(100) * 25

fig = plt.figure(figsize=(10, 6))
plt.plot(epochs, loss)
plt.xlabel("Epochs")
plt.ylabel("Loss")
return fig


def plot_accuracy_over_time():
epochs = np.arange(1, 101)
accuracy = np.sqrt(epochs) / 12 + np.random.rand(100) * 0.15
accuracy = [np.min([np.max(accuracy[:i]), 1]) for i in range(1, 101)]

fig = plt.figure(figsize=(10, 6))
plt.plot(epochs, accuracy)
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
return fig


def plot_feature_importance():
features = ["Product Category", "Price", "Brand", "Rating", "Number of Reviews"]
importance = np.random.rand(5)

fig = plt.figure(figsize=(10, 6))
plt.barh(features, importance)
plt.xlabel("Importance")
return fig


card_loss = ui.card(
ui.card_header("Loss Over Time"),
ui.output_plot("loss_over_time"),
full_screen=True,
)

card_acc = ui.card(
ui.card_header("Accuracy Over Time"),
ui.output_plot("accuracy_over_time"),
full_screen=True,
)

card_feat = ui.card(
ui.card_header("Feature Importance"),
ui.output_plot("feature_importance"),
full_screen=True,
)
Loading