input task button component entry

components/inputs/input-task-button/app-core.py

@@ -0,0 +1,34 @@
+## file: app.py
+from time import sleep
+
+from shiny import App, reactive, render, ui
+
+app_ui = ui.page_fluid(
+    ui.row(
+        ui.column(
+            6,
+            ui.input_task_button(  # <<
+                "task_button",  # <<
+                "Increase Number slowly",  # <<
+            ),  # <<
+        ),
+        ui.column(6, ui.output_text("counter")),
+    )
+)
+
+
+def server(input, output, session):
+    count = reactive.value(0)
+
+    @reactive.effect  # <<
+    @reactive.event(input.task_button)  # <<
+    def _():
+        sleep(1)
+        count.set(count() + 1)
+
+    @render.text
+    def counter():
+        return f"{count()}"
+
+
+app = App(app_ui, server)

components/inputs/input-task-button/app-detail-preview.py

@@ -0,0 +1,28 @@
+## file: app.py
+from time import sleep
+from shiny import App, reactive, render, ui
+
+app_ui = ui.page_fluid(
+    ui.row(
+        ui.column(6, ui.input_task_button("task_button", "Increase Number slowly")),
+        ui.column(6, ui.output_text("counter").add_class("display-5 mb-0")),
+        {"class": "vh-100 justify-content-center align-items-center px-5"},
+    ).add_class("text-center")
+)
+
+
+def server(input, output, session):
+    count = reactive.value(0)
+
+    @reactive.effect
+    @reactive.event(input.task_button)
+    def _():
+        sleep(1)
+        count.set(count() + 1)
+
+    @render.text
+    def counter():
+        return f"{count()}"
+
+
+app = App(app_ui, server)

components/inputs/input-task-button/app-express.py

@@ -0,0 +1,22 @@
+from time import sleep
+
+from shiny import reactive
+from shiny.express import input, render, ui
+
+with ui.sidebar():
+    ui.input_task_button("task_button", "Increase Number slowly")  # <<
+
+
+@render.text
+def counter():
+    return f"{count()}"
+
+
+count = reactive.value(0)
+
+
+@reactive.effect  # <<
+@reactive.event(input.task_button)  # <<
+def _():
+    sleep(1)
+    count.set(count() + 1)

components/inputs/input-task-button/app-preview.py

@@ -0,0 +1,22 @@
+import asyncio
+from shiny import App, Inputs, Outputs, Session, reactive, ui
+
+app_ui = ui.page_fluid(
+    ui.input_task_button("btn", "Fit Model"),
+)
+
+
+def server(input: Inputs, output: Outputs, session: Session):
+
+    @ui.bind_task_button(button_id="btn")
+    @reactive.extended_task
+    async def long_calculation():
+        await asyncio.sleep(1)
+
+    @reactive.effect
+    @reactive.event(input.btn)
+    def btn_click():
+        long_calculation()
+
+
+app = App(app_ui, server)

components/inputs/input-task-button/app-variation-extended-task-core.py

@@ -0,0 +1,92 @@
+import asyncio  # <<
+from typing import List
+
+import seaborn as sns
+from shiny import App, Inputs, Outputs, Session, reactive, render, ui
+from sklearn.compose import ColumnTransformer
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder
+
+diamonds = sns.load_dataset("diamonds")
+
+app_ui = ui.page_sidebar(
+    ui.sidebar(
+        ui.input_selectize(
+            "predictors",
+            "Choose predictors",
+            ["carat", "color", "cut", "clarity"],
+            selected="carat",
+            multiple=True,
+        ),
+        ui.input_task_button("btn", "Fit Model"),
+        ui.input_switch("show", "Show Data Sample", True),
+    ),
+    ui.value_box("Mean Square Error", ui.output_ui("mse"), max_height=125),
+    ui.output_data_frame("diamonds_df"),
+)
+
+
+def server(input: Inputs, output: Outputs, session: Session):
+    @ui.bind_task_button(button_id="btn")  # <<
+    @reactive.extended_task  # <<
+    async def calc_model_mse(colnames: List[str]) -> float:  # <<
+        await asyncio.sleep(5)
+
+        predictors = diamonds[colnames]
+        response = diamonds["price"]
+
+        selected_cat_cols = [
+            x for x in colnames if x in ["color", "cut", "clarity"]
+        ]
+
+        # categorical variables selected one-hot encode / dummy variable encode
+        if selected_cat_cols:
+            categorical_transformer = Pipeline(
+                steps=[("encoder", OneHotEncoder(drop="first"))]
+            )
+            preprocessor = ColumnTransformer(
+                transformers=[
+                    ("cat", categorical_transformer, selected_cat_cols),
+                ]
+            )
+            steps = [
+                ("preprocessor", preprocessor),
+                ("regressor", LinearRegression()),
+            ]
+
+        # no categorical column selected, so no one-hot/dummy encoding needed
+        # we are doing this so lr is always a Pipeline object
+        else:
+            steps = [
+                ("regressor", LinearRegression()),
+            ]
+
+        # create pipeline
+        lr = Pipeline(steps=steps)
+
+        # fit the model
+        lr = lr.fit(X=predictors, y=response)
+
+        # predict on itself
+        pred = lr.predict(predictors)
+
+        return mean_squared_error(predictors, pred)
+
+    @reactive.effect  # <<
+    @reactive.event(input.btn)  # <<
+    def btn_click():  # <<
+        calc_model_mse(list(input["predictors"]()))  # <<
+
+    @render.text
+    def mse():
+        return f"{calc_model_mse.result():.2f}"
+
+    @render.data_frame
+    def diamonds_df():
+        if input["show"]():
+            return render.DataTable(diamonds.sample(5))
+
+
+app = App(app_ui, server)

components/inputs/input-task-button/app-variation-extended-task-express.py

@@ -0,0 +1,90 @@
+import asyncio  # <<
+from typing import List
+
+import seaborn as sns
+from shiny import reactive
+from shiny.express import input, render, ui
+from sklearn.compose import ColumnTransformer
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder
+
+diamonds = sns.load_dataset("diamonds")
+
+
+with ui.sidebar():
+    ui.input_selectize(
+        "predictors",
+        "Choose predictors",
+        ["carat", "color", "cut", "clarity"],
+        selected="carat",
+        multiple=True,
+    )
+    ui.input_task_button("btn", "Fit Model")  # <<
+    ui.input_switch("show", "Show Data Sample", True)
+
+with ui.value_box(max_height=125):
+    "Mean Square Error"
+
+    @render.ui
+    def mse():
+        return f"{calc_model_mse.result():.2f}"
+
+
+@render.data_frame
+def diamonds_df():
+    if input["show"]():
+        return render.DataTable(diamonds.sample(5))
+
+
+@ui.bind_task_button(button_id="btn")  # <<
+@reactive.extended_task  # <<
+async def calc_model_mse(colnames: List[str]) -> float:  # <<
+    await asyncio.sleep(5)
+
+    predictors = diamonds[colnames]
+    response = diamonds["price"]
+
+    selected_cat_cols = [
+        x for x in colnames if x in ["color", "cut", "clarity"]
+    ]
+
+    # categorical variables selected one-hot encode / dummy variable encode
+    if selected_cat_cols:
+        categorical_transformer = Pipeline(
+            steps=[("encoder", OneHotEncoder(drop="first"))]
+        )
+        preprocessor = ColumnTransformer(
+            transformers=[
+                ("cat", categorical_transformer, selected_cat_cols),
+            ]
+        )
+        steps = [
+            ("preprocessor", preprocessor),
+            ("regressor", LinearRegression()),
+        ]
+
+    # no categorical column selected, so no one-hot/dummy encoding needed
+    # we are doing this so lr is always a Pipeline object
+    else:
+        steps = [
+            ("regressor", LinearRegression()),
+        ]
+
+    # create pipeline
+    lr = Pipeline(steps=steps)
+
+    # fit the model
+    lr = lr.fit(X=predictors, y=response)
+
+    # predict on itself
+    pred = lr.predict(predictors)
+
+    return mean_squared_error(predictors, pred)
+
+
+@reactive.effect  # <<
+@reactive.event(input.btn)  # <<
+def btn_click():  # <<
+    calc_model_mse(list(input["predictors"]()))  # <<