From 32c56272fd4d3c7cd9ecbe2e42c70cb2a39034a3 Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Fri, 28 Jul 2023 11:07:23 -0700
Subject: [PATCH 1/9] Initial model-score example

---
 examples/model-score/.gitignore          |     1 +
 examples/model-score/app.py              |   198 +
 examples/model-score/fake_auc_scores.csv | 18001 +++++++++++++++++++++
 examples/model-score/scoredata.py        |    60 +
 4 files changed, 18260 insertions(+)
 create mode 100644 examples/model-score/.gitignore
 create mode 100644 examples/model-score/app.py
 create mode 100644 examples/model-score/fake_auc_scores.csv
 create mode 100644 examples/model-score/scoredata.py

diff --git a/examples/model-score/.gitignore b/examples/model-score/.gitignore
new file mode 100644
index 000000000..1269488f7
--- /dev/null
+++ b/examples/model-score/.gitignore
@@ -0,0 +1 @@
+data
diff --git a/examples/model-score/app.py b/examples/model-score/app.py
new file mode 100644
index 000000000..03b7e97ab
--- /dev/null
+++ b/examples/model-score/app.py
@@ -0,0 +1,198 @@
+import sqlite3
+
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import scoredata
+from shinywidgets import output_widget, register_widget, render_widget
+
+import shiny.experimental as x
+from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
+
+# TODO: Make an option to switch between dynamic and static
+# TODO: Refresh rate option
+# TODO: Add threshold lines (or bg shading) to plot
+# TODO: Change "score" to "auc"
+# TODO: Talk to Julia and Isabel for suggestions
+
+scoredata.begin()
+
+con = sqlite3.connect(scoredata.SQLITE_DB_URI, uri=True)
+
+
+def last_modified(con):
+    return con.execute("select max(timestamp) from auc_scores").fetchone()[0]
+
+
+# @reactive.poll calls a cheap query (`last_modified()`) every 1 second to check if the
+# expensive query should be run and downstream calculations should be updated
+@reactive.poll(lambda: last_modified(con))
+def df():
+    tbl = pd.read_sql(
+        "select * from auc_scores order by timestamp desc, model desc limit ?",
+        con,
+        params=[150],
+    )
+    # Treat timestamp as a continuous variable
+    tbl["timestamp"] = pd.to_datetime(tbl["timestamp"]).dt.strftime("%H:%M:%S")
+    # Reverse order of rows
+    tbl = tbl.iloc[::-1]
+
+    return tbl
+
+
+with reactive.isolate():
+    model_names = df()["model"].unique().tolist()
+colors = ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0"]
+colormap_models = {model: colors[i] for i, model in enumerate(model_names)}
+
+
+app_ui = x.ui.page_sidebar(
+    x.ui.sidebar(
+        ui.input_checkbox_group("models", "Models", model_names, selected=model_names),
+    ),
+    ui.h1("Model monitoring dashboard"),
+    # ui.tags.link(
+    #     rel="stylesheet",
+    #     href="https://rstudio.github.io/shiny-python-workshop-2023/site_libs/bootstrap/bootstrap.min.css",
+    # ),
+    x.ui.output_ui("cards"),
+    x.ui.card(output_widget("plot_timeseries"), class_="mt-3"),
+    x.ui.card(output_widget("plot_dist"), class_="mt-3"),
+    fillable=False,
+)
+
+
+def server(input: Inputs, output: Outputs, session: Session):
+    @reactive.Calc
+    def filtered_df():
+        data = df()
+        # Filter the rows so we only include the desired models
+        return data[data["model"].isin(input.models())]
+
+    @output
+    @render.ui
+    def cards():
+        data = filtered_df()
+        models = data["model"].unique().tolist()
+        scores_by_model = {
+            x: data[data["model"] == x].iloc[-1]["score"] for x in models
+        }
+        # Round scores to 2 decimal places
+        scores_by_model = {x: round(y, 2) for x, y in scores_by_model.items()}
+
+        return x.ui.layout_column_wrap(
+            "150px",
+            *[
+                x.ui.value_box(
+                    model,
+                    ui.h2(score),
+                    theme_color="success"
+                    if score > 0.85
+                    else "warning"
+                    if score > 0.65
+                    else "danger",
+                )
+                for model, score in scores_by_model.items()
+            ],
+            fixed_width=True,
+        )
+
+    # Streaming plotly
+
+    @output
+    @render_widget
+    @reactive.event(input.models)
+    def plot_timeseries():
+        # This plotly output will re-render whenever the set of selected models changes,
+        # but not when the data changes. Instead, for data changes, we will
+        # incrementally update only the traces. This is because the plotly plot
+        # sometimes jumps around a bit when the entire plot is redrawn.
+
+        # Create the initial (for this set of selected models) figure. Normally, changes
+        # in filtered_df would cause a re-render of the whole plot, but the fact that we
+        # decorated this function with @reactive.event(input.models) means that only
+        # changes to input.models() can trigger re-rendering.
+        fig = px.line(
+            filtered_df(),
+            x="timestamp",
+            y="score",
+            labels=dict(score="auc"),
+            color="model",
+            color_discrete_map=colormap_models,
+        )
+
+        fig.update_yaxes(range=[0, 1], fixedrange=True)
+        fig.update_xaxes(fixedrange=True, tickangle=60, dtick="M5")
+        f = go.FigureWidget(fig)
+
+        # Create a new reactive Effect *for this figure instance* (hence, it's nested
+        # within the plot_timeseries function). This effect will re-run whenever its own
+        # reactive dependencies change, i.e. filtered_df().
+        @reactive.Effect
+        def update_plotly_data():
+            # Create a new plotly figure with the updated data, that we'll use to update
+            # the existing figure. It's important that we use the same arguments as the
+            # existing figure, so that all the traces line up.
+            f_new = px.line(
+                filtered_df(),
+                x="timestamp",
+                y="score",
+                labels=dict(score="auc"),
+                color="model",
+                color_discrete_map=colormap_models,
+            )
+            # In one batch, update all the traces in the existing figure with the new
+            # ones.
+            with f.batch_update():
+                for old, new in zip(f.data, f_new.data):
+                    old.update(new)
+
+        # Stop updates when this version of the plot is no longer up-to-date
+        reactive.get_current_context().on_invalidate(update_plotly_data.destroy)
+
+        return f
+
+    @output
+    @render_widget
+    @reactive.event(input.models)
+    def plot_dist():
+        # This works exactly like plot_timeseries, but with a different plot.
+
+        fig = px.histogram(
+            filtered_df(),
+            facet_row="model",
+            nbins=20,
+            x="score",
+            labels=dict(score="auc"),
+            color="model",
+            color_discrete_map=colormap_models,
+        )
+        # From https://plotly.com/python/facet-plots/#customizing-subplot-figure-titles
+        fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
+
+        fig.update_xaxes(range=[0, 1], fixedrange=True)
+        fig.layout["height"] = 550
+
+        f = go.FigureWidget(fig)
+
+        @reactive.Effect
+        def update_plotly_data():
+            f_new = px.histogram(
+                filtered_df(),
+                facet_row="model",
+                nbins=20,
+                x="score",
+                color="model",
+                color_discrete_map=colormap_models,
+            )
+            with f.batch_update():
+                for old, new in zip(f.data, f_new.data):
+                    old.update(new)
+
+        reactive.get_current_context().on_invalidate(update_plotly_data.destroy)
+
+        return f
+
+
+app = App(app_ui, server)
diff --git a/examples/model-score/fake_auc_scores.csv b/examples/model-score/fake_auc_scores.csv
new file mode 100644
index 000000000..9ca27eff0
--- /dev/null
+++ b/examples/model-score/fake_auc_scores.csv
@@ -0,0 +1,18001 @@
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+3591,model_5,0.7062080053612582
+3592,model_1,0.024248367766849722
+3592,model_2,0.5249530170019706
+3592,model_3,0.8913275753194461
+3592,model_4,0.28397948424331854
+3592,model_5,0.698231922206468
+3593,model_1,0.03242812573909762
+3593,model_2,0.5411705884058019
+3593,model_3,0.8828425312368193
+3593,model_4,0.35016481880564254
+3593,model_5,0.7296155236428595
+3594,model_1,0.03355226882733407
+3594,model_2,0.52815684265457
+3594,model_3,0.8749413948925207
+3594,model_4,0.2879966211784632
+3594,model_5,0.7135974853998042
+3595,model_1,0.02424920670688155
+3595,model_2,0.5367351430375119
+3595,model_3,0.86999405210372
+3595,model_4,0.28838658555876473
+3595,model_5,0.7297279706457621
+3596,model_1,0.015708145312964942
+3596,model_2,0.5410964112263162
+3596,model_3,0.8763838160224253
+3596,model_4,0.222783450302668
+3596,model_5,0.7547288196627042
+3597,model_1,0.009713283265009549
+3597,model_2,0.5466696390882124
+3597,model_3,0.8679979804763565
+3597,model_4,0.20677597040776172
+3597,model_5,0.7853092468110836
+3598,model_1,0.0023904038732871676
+3598,model_2,0.5567030990961929
+3598,model_3,0.8589486499503249
+3598,model_4,0.18129915649537004
+3598,model_5,0.7626491317525498
+3599,model_1,0
+3599,model_2,0.5511631208565075
+3599,model_3,0.8501390792941669
+3599,model_4,0.19711042383685715
+3599,model_5,0.7924951405962921
+3600,model_1,2.971672499552363e-4
+3600,model_2,0.5371111162565638
+3600,model_3,0.8457182715693483
+3600,model_4,0.17436588771175598
+3600,model_5,0.8222023223293937
diff --git a/examples/model-score/scoredata.py b/examples/model-score/scoredata.py
new file mode 100644
index 000000000..fe0c62671
--- /dev/null
+++ b/examples/model-score/scoredata.py
@@ -0,0 +1,60 @@
+import datetime
+import sqlite3
+import threading
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+here = Path(__file__).parent
+auc_scores = pd.read_csv(here / "fake_auc_scores.csv")
+auc_scores.set_index("second", inplace=True)
+(here / "data").mkdir(exist_ok=True)
+
+SQLITE_DB_URI = f"file:{here / 'data' / 'auc_scores.sqlite'}"
+
+
+def thread_main(barrier: threading.Barrier):
+    with sqlite3.connect(SQLITE_DB_URI, uri=True, timeout=30) as con:
+        con.execute("PRAGMA journal_mode=WAL")
+        con.execute("drop table if exists auc_scores")
+
+        now = datetime.datetime.now()
+        position = now.minute * 60 + now.second + 1
+
+        # Simulate 100 seconds of historical data
+        offset_secs = -np.arange(100) - 1
+        abs_secs = (position + offset_secs) % (60 * 60) + 1
+        initial_scores = auc_scores.loc[abs_secs]
+        timestamps = pd.DataFrame(
+            {
+                "timestamp": now + pd.to_timedelta(offset_secs, unit="s"),
+                "second": abs_secs,
+            }
+        ).set_index("second", inplace=False)
+        initial_scores = initial_scores.join(timestamps, how="left")
+        initial_scores.to_sql("auc_scores", con, index=False, if_exists="append")
+
+        barrier.wait()
+
+        while True:
+            new_data = auc_scores.loc[position].copy()
+            # del new_data["second"]
+            new_data["timestamp"] = datetime.datetime.now()
+            new_data.to_sql("auc_scores", con, index=False, if_exists="append")
+            position = (position % (60 * 60)) + 1
+            time.sleep(1)
+
+
+def begin():
+    barrier = threading.Barrier(2)
+    threading.Thread(target=thread_main, args=[barrier], daemon=True).start()
+    barrier.wait()
+
+
+if __name__ == "__main__":
+    begin()
+    time.sleep(10)
+    con2 = sqlite3.connect(SQLITE_DB_URI, uri=True)
+    print(pd.read_sql("select * from auc_scores", con2))

From 25abb2af146f70b46696ab04bd941de899e69abf Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Fri, 28 Jul 2023 14:55:39 -0700
Subject: [PATCH 2/9] More features, drastically simplify plotly streaming

---
 examples/model-score/app.py              | 183 +++++++++++------------
 examples/model-score/plotly_streaming.py |  45 ++++++
 examples/model-score/scoredata.py        |  20 +--
 3 files changed, 140 insertions(+), 108 deletions(-)
 create mode 100644 examples/model-score/plotly_streaming.py

diff --git a/examples/model-score/app.py b/examples/model-score/app.py
index 03b7e97ab..3093919b1 100644
--- a/examples/model-score/app.py
+++ b/examples/model-score/app.py
@@ -2,19 +2,22 @@
 
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
 import scoredata
-from shinywidgets import output_widget, register_widget, render_widget
+from plotly_streaming import render_plotly_streaming
+from shinywidgets import output_widget
 
 import shiny.experimental as x
-from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
+from shiny import App, Inputs, Outputs, Session, reactive, render, ui
 
 # TODO: Make an option to switch between dynamic and static
-# TODO: Refresh rate option
-# TODO: Add threshold lines (or bg shading) to plot
-# TODO: Change "score" to "auc"
 # TODO: Talk to Julia and Isabel for suggestions
 
+THRESHOLD_MID = 0.85
+THRESHOLD_MID_COLOR = "rgb(0, 137, 26)"
+THRESHOLD_LOW = 0.5
+THRESHOLD_LOW_COLOR = "rgb(193, 0, 0)"
+
+# Start a background thread that writes fake data to the SQLite database every second
 scoredata.begin()
 
 con = sqlite3.connect(scoredata.SQLITE_DB_URI, uri=True)
@@ -25,7 +28,13 @@ def last_modified(con):
 
 
 # @reactive.poll calls a cheap query (`last_modified()`) every 1 second to check if the
-# expensive query should be run and downstream calculations should be updated
+# expensive query (`df()`) should be run and downstream calculations should be updated.
+#
+# By declaring this function at the top-level of the script instead of in the server
+# function, all sessions are sharing the same reactive poll, so the expensive query is
+# only run once no matter how many users are connected.
+
+
 @reactive.poll(lambda: last_modified(con))
 def df():
     tbl = pd.read_sql(
@@ -41,38 +50,64 @@ def df():
     return tbl
 
 
-with reactive.isolate():
-    model_names = df()["model"].unique().tolist()
-colors = ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0"]
-colormap_models = {model: colors[i] for i, model in enumerate(model_names)}
+model_colors = {
+    "model_1": "#7fc97f",
+    "model_2": "#beaed4",
+    "model_3": "#fdc086",
+    "model_4": "#ffff99",
+    "model_5": "#386cb0",
+}
+model_names = list(model_colors.keys())
 
 
 app_ui = x.ui.page_sidebar(
     x.ui.sidebar(
+        ui.input_selectize(
+            "refresh",
+            "Refresh interval",
+            {
+                0: "Realtime",
+                5: "5 seconds",
+                30: "30 seconds",
+                60 * 5: "5 minutes",
+                60 * 15: "15 minutes",
+            },
+        ),
         ui.input_checkbox_group("models", "Models", model_names, selected=model_names),
     ),
-    ui.h1("Model monitoring dashboard"),
-    # ui.tags.link(
-    #     rel="stylesheet",
-    #     href="https://rstudio.github.io/shiny-python-workshop-2023/site_libs/bootstrap/bootstrap.min.css",
-    # ),
-    x.ui.output_ui("cards"),
-    x.ui.card(output_widget("plot_timeseries"), class_="mt-3"),
-    x.ui.card(output_widget("plot_dist"), class_="mt-3"),
+    ui.div(
+        ui.h1("Model monitoring dashboard"),
+        ui.p(
+            x.ui.output_ui("value_boxes"),
+        ),
+        x.ui.card(output_widget("plot_timeseries")),
+        x.ui.card(output_widget("plot_dist")),
+        style="max-width: 800px;",
+    ),
     fillable=False,
 )
 
 
 def server(input: Inputs, output: Outputs, session: Session):
+    @reactive.Calc
+    def throttled_df():
+        refresh = int(input.refresh())
+        if refresh == 0:
+            return df()
+        else:
+            reactive.invalidate_later(refresh)
+            with reactive.isolate():
+                return df()
+
     @reactive.Calc
     def filtered_df():
-        data = df()
+        data = throttled_df()
         # Filter the rows so we only include the desired models
         return data[data["model"].isin(input.models())]
 
     @output
     @render.ui
-    def cards():
+    def value_boxes():
         data = filtered_df()
         models = data["model"].unique().tolist()
         scores_by_model = {
@@ -82,15 +117,15 @@ def cards():
         scores_by_model = {x: round(y, 2) for x, y in scores_by_model.items()}
 
         return x.ui.layout_column_wrap(
-            "150px",
+            "135px",
             *[
                 x.ui.value_box(
                     model,
                     ui.h2(score),
                     theme_color="success"
-                    if score > 0.85
+                    if score > THRESHOLD_MID
                     else "warning"
-                    if score > 0.65
+                    if score > THRESHOLD_LOW
                     else "danger",
                 )
                 for model, score in scores_by_model.items()
@@ -98,67 +133,37 @@ def cards():
             fixed_width=True,
         )
 
-    # Streaming plotly
-
     @output
-    @render_widget
-    @reactive.event(input.models)
+    @render_plotly_streaming(recreate_when=input.models)
     def plot_timeseries():
-        # This plotly output will re-render whenever the set of selected models changes,
-        # but not when the data changes. Instead, for data changes, we will
-        # incrementally update only the traces. This is because the plotly plot
-        # sometimes jumps around a bit when the entire plot is redrawn.
-
-        # Create the initial (for this set of selected models) figure. Normally, changes
-        # in filtered_df would cause a re-render of the whole plot, but the fact that we
-        # decorated this function with @reactive.event(input.models) means that only
-        # changes to input.models() can trigger re-rendering.
         fig = px.line(
             filtered_df(),
             x="timestamp",
             y="score",
             labels=dict(score="auc"),
             color="model",
-            color_discrete_map=colormap_models,
+            color_discrete_map=model_colors,
+        )
+
+        fig.add_hline(
+            THRESHOLD_LOW,
+            line_dash="dash",
+            line=dict(color=THRESHOLD_LOW_COLOR, width=2),
+        )
+        fig.add_hline(
+            THRESHOLD_MID,
+            line_dash="dash",
+            line=dict(color=THRESHOLD_MID_COLOR, width=2),
         )
 
         fig.update_yaxes(range=[0, 1], fixedrange=True)
         fig.update_xaxes(fixedrange=True, tickangle=60, dtick="M5")
-        f = go.FigureWidget(fig)
-
-        # Create a new reactive Effect *for this figure instance* (hence, it's nested
-        # within the plot_timeseries function). This effect will re-run whenever its own
-        # reactive dependencies change, i.e. filtered_df().
-        @reactive.Effect
-        def update_plotly_data():
-            # Create a new plotly figure with the updated data, that we'll use to update
-            # the existing figure. It's important that we use the same arguments as the
-            # existing figure, so that all the traces line up.
-            f_new = px.line(
-                filtered_df(),
-                x="timestamp",
-                y="score",
-                labels=dict(score="auc"),
-                color="model",
-                color_discrete_map=colormap_models,
-            )
-            # In one batch, update all the traces in the existing figure with the new
-            # ones.
-            with f.batch_update():
-                for old, new in zip(f.data, f_new.data):
-                    old.update(new)
-
-        # Stop updates when this version of the plot is no longer up-to-date
-        reactive.get_current_context().on_invalidate(update_plotly_data.destroy)
-
-        return f
+
+        return fig
 
     @output
-    @render_widget
-    @reactive.event(input.models)
+    @render_plotly_streaming(recreate_when=input.models)
     def plot_dist():
-        # This works exactly like plot_timeseries, but with a different plot.
-
         fig = px.histogram(
             filtered_df(),
             facet_row="model",
@@ -166,33 +171,27 @@ def plot_dist():
             x="score",
             labels=dict(score="auc"),
             color="model",
-            color_discrete_map=colormap_models,
+            color_discrete_map=model_colors,
+        )
+
+        fig.add_vline(
+            THRESHOLD_LOW,
+            line_dash="dash",
+            line=dict(color=THRESHOLD_LOW_COLOR, width=2),
+        )
+        fig.add_vline(
+            THRESHOLD_MID,
+            line_dash="dash",
+            line=dict(color=THRESHOLD_MID_COLOR, width=2),
         )
+
         # From https://plotly.com/python/facet-plots/#customizing-subplot-figure-titles
         fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
 
         fig.update_xaxes(range=[0, 1], fixedrange=True)
-        fig.layout["height"] = 550
-
-        f = go.FigureWidget(fig)
-
-        @reactive.Effect
-        def update_plotly_data():
-            f_new = px.histogram(
-                filtered_df(),
-                facet_row="model",
-                nbins=20,
-                x="score",
-                color="model",
-                color_discrete_map=colormap_models,
-            )
-            with f.batch_update():
-                for old, new in zip(f.data, f_new.data):
-                    old.update(new)
-
-        reactive.get_current_context().on_invalidate(update_plotly_data.destroy)
-
-        return f
+        fig.layout.height = 500
+
+        return fig
 
 
 app = App(app_ui, server)
diff --git a/examples/model-score/plotly_streaming.py b/examples/model-score/plotly_streaming.py
new file mode 100644
index 000000000..209e3dc7a
--- /dev/null
+++ b/examples/model-score/plotly_streaming.py
@@ -0,0 +1,45 @@
+import functools
+
+import plotly.graph_objects as go
+from shinywidgets import render_widget
+
+from shiny import reactive
+
+
+def render_plotly_streaming(fn=None, *, recreate_when=lambda: None):
+    """Custom decorator for Plotly streaming plots. This is similar to
+    shinywidgets.render_widget, except:
+
+    1. You return simply a Figure, not FigureWidget.
+    2. On reactive invalidation, the figure is updated in-place, rather than recreated
+       from scratch.
+    """
+
+    if fn is not None:
+        return render_plotly_streaming(recreate_when=recreate_when)(fn)
+
+    def decorator(func):
+        @render_widget
+        @functools.wraps(func)
+        def wrapper():
+            recreate_when()
+
+            with reactive.isolate():
+                fig = func()
+                widget = go.FigureWidget(fig)
+
+            @reactive.Effect
+            def update_plotly_data():
+                f_new = func()
+                with widget.batch_update():
+                    widget.update_layout(f_new.layout)
+                    for old, new in zip(widget.data, f_new.data):
+                        old.update(new)
+
+            reactive.get_current_context().on_invalidate(update_plotly_data.destroy)
+
+            return widget
+
+        return wrapper
+
+    return decorator
diff --git a/examples/model-score/scoredata.py b/examples/model-score/scoredata.py
index fe0c62671..6761af460 100644
--- a/examples/model-score/scoredata.py
+++ b/examples/model-score/scoredata.py
@@ -1,7 +1,6 @@
+import asyncio
 import datetime
 import sqlite3
-import threading
-import time
 from pathlib import Path
 
 import numpy as np
@@ -15,7 +14,7 @@
 SQLITE_DB_URI = f"file:{here / 'data' / 'auc_scores.sqlite'}"
 
 
-def thread_main(barrier: threading.Barrier):
+async def async_main():
     with sqlite3.connect(SQLITE_DB_URI, uri=True, timeout=30) as con:
         con.execute("PRAGMA journal_mode=WAL")
         con.execute("drop table if exists auc_scores")
@@ -36,25 +35,14 @@ def thread_main(barrier: threading.Barrier):
         initial_scores = initial_scores.join(timestamps, how="left")
         initial_scores.to_sql("auc_scores", con, index=False, if_exists="append")
 
-        barrier.wait()
-
         while True:
             new_data = auc_scores.loc[position].copy()
             # del new_data["second"]
             new_data["timestamp"] = datetime.datetime.now()
             new_data.to_sql("auc_scores", con, index=False, if_exists="append")
             position = (position % (60 * 60)) + 1
-            time.sleep(1)
+            await asyncio.sleep(1)
 
 
 def begin():
-    barrier = threading.Barrier(2)
-    threading.Thread(target=thread_main, args=[barrier], daemon=True).start()
-    barrier.wait()
-
-
-if __name__ == "__main__":
-    begin()
-    time.sleep(10)
-    con2 = sqlite3.connect(SQLITE_DB_URI, uri=True)
-    print(pd.read_sql("select * from auc_scores", con2))
+    asyncio.create_task(async_main())

From 6c8f665dcb1f82764cd88c5a9fb36fc0c12e08d8 Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Sat, 29 Jul 2023 01:58:01 -0700
Subject: [PATCH 3/9] Add custom time range option

---
 examples/model-score/app.py              | 128 +++++++++++++----
 examples/model-score/plotly_streaming.py |  57 +++++++-
 examples/model-score/requirements.in     |   4 +
 examples/model-score/requirements.txt    | 174 +++++++++++++++++++++++
 examples/model-score/scoredata.py        |   4 +-
 5 files changed, 332 insertions(+), 35 deletions(-)
 create mode 100644 examples/model-score/requirements.in
 create mode 100644 examples/model-score/requirements.txt

diff --git a/examples/model-score/app.py b/examples/model-score/app.py
index 3093919b1..0ca3753b0 100644
--- a/examples/model-score/app.py
+++ b/examples/model-score/app.py
@@ -1,4 +1,5 @@
 import sqlite3
+from datetime import datetime, timedelta, timezone
 
 import pandas as pd
 import plotly.express as px
@@ -43,13 +44,27 @@ def df():
         params=[150],
     )
     # Treat timestamp as a continuous variable
-    tbl["timestamp"] = pd.to_datetime(tbl["timestamp"]).dt.strftime("%H:%M:%S")
+    tbl["timestamp"] = pd.to_datetime(tbl["timestamp"], utc=True)
+    tbl["time"] = tbl["timestamp"].dt.strftime("%H:%M:%S")
     # Reverse order of rows
     tbl = tbl.iloc[::-1]
 
     return tbl
 
 
+def read_time_period(from_time, to_time):
+    tbl = pd.read_sql(
+        "select * from auc_scores where timestamp between ? and ? order by timestamp, model",
+        con,
+        params=[from_time, to_time],
+    )
+    # Treat timestamp as a continuous variable
+    tbl["timestamp"] = pd.to_datetime(tbl["timestamp"], utc=True)
+    tbl["time"] = tbl["timestamp"].dt.strftime("%H:%M:%S")
+
+    return tbl
+
+
 model_colors = {
     "model_1": "#7fc97f",
     "model_2": "#beaed4",
@@ -60,37 +75,79 @@ def df():
 model_names = list(model_colors.keys())
 
 
-app_ui = x.ui.page_sidebar(
-    x.ui.sidebar(
-        ui.input_selectize(
-            "refresh",
-            "Refresh interval",
-            {
-                0: "Realtime",
-                5: "5 seconds",
-                30: "30 seconds",
-                60 * 5: "5 minutes",
-                60 * 15: "15 minutes",
-            },
+def app_ui(req):
+    end_time = datetime.now(timezone.utc)
+    start_time = end_time - timedelta(minutes=1)
+
+    return x.ui.page_sidebar(
+        x.ui.sidebar(
+            ui.input_checkbox_group(
+                "models", "Models", model_names, selected=model_names
+            ),
+            ui.input_radio_buttons(
+                "timeframe",
+                "Timeframe",
+                ["Latest", "Specific timeframe"],
+                selected="Latest",
+            ),
+            ui.panel_conditional(
+                "input.timeframe === 'Latest'",
+                ui.input_selectize(
+                    "refresh",
+                    "Refresh interval",
+                    {
+                        0: "Realtime",
+                        5: "5 seconds",
+                        30: "30 seconds",
+                        60 * 5: "5 minutes",
+                        60 * 15: "15 minutes",
+                    },
+                ),
+            ),
+            ui.panel_conditional(
+                "input.timeframe !== 'Latest'",
+                ui.input_slider(
+                    "timerange",
+                    "Time range",
+                    min=start_time,
+                    max=end_time,
+                    value=[start_time, end_time],
+                    step=timedelta(seconds=1),
+                    time_format="%H:%M:%S",
+                ),
+            ),
         ),
-        ui.input_checkbox_group("models", "Models", model_names, selected=model_names),
-    ),
-    ui.div(
-        ui.h1("Model monitoring dashboard"),
-        ui.p(
-            x.ui.output_ui("value_boxes"),
+        ui.div(
+            ui.h1("Model monitoring dashboard"),
+            ui.p(
+                x.ui.output_ui("value_boxes"),
+            ),
+            x.ui.card(output_widget("plot_timeseries")),
+            x.ui.card(output_widget("plot_dist")),
+            style="max-width: 800px;",
         ),
-        x.ui.card(output_widget("plot_timeseries")),
-        x.ui.card(output_widget("plot_dist")),
-        style="max-width: 800px;",
-    ),
-    fillable=False,
-)
+        fillable=False,
+    )
 
 
 def server(input: Inputs, output: Outputs, session: Session):
+    @reactive.Effect
+    def update_time_range():
+        reactive.invalidate_later(5)
+        min_time, max_time = pd.to_datetime(
+            con.execute(
+                "select min(timestamp), max(timestamp) from auc_scores"
+            ).fetchone(),
+            utc=True,
+        )
+        ui.update_slider(
+            "timerange",
+            min=min_time.replace(tzinfo=timezone.utc),
+            max=max_time.replace(tzinfo=timezone.utc),
+        )
+
     @reactive.Calc
-    def throttled_df():
+    def recent_df():
         refresh = int(input.refresh())
         if refresh == 0:
             return df()
@@ -99,12 +156,22 @@ def throttled_df():
             with reactive.isolate():
                 return df()
 
+    @reactive.Calc
+    def timeframe_df():
+        start, end = input.timerange()
+        return read_time_period(start, end)
+
     @reactive.Calc
     def filtered_df():
-        data = throttled_df()
+        data = recent_df() if input.timeframe() == "Latest" else timeframe_df()
+
         # Filter the rows so we only include the desired models
         return data[data["model"].isin(input.models())]
 
+    @reactive.Calc
+    def filtered_model_names():
+        return filtered_df()["model"].unique()
+
     @output
     @render.ui
     def value_boxes():
@@ -134,11 +201,11 @@ def value_boxes():
         )
 
     @output
-    @render_plotly_streaming(recreate_when=input.models)
+    @render_plotly_streaming(recreate_key=filtered_model_names)
     def plot_timeseries():
         fig = px.line(
             filtered_df(),
-            x="timestamp",
+            x="time",
             y="score",
             labels=dict(score="auc"),
             color="model",
@@ -162,7 +229,7 @@ def plot_timeseries():
         return fig
 
     @output
-    @render_plotly_streaming(recreate_when=input.models)
+    @render_plotly_streaming(recreate_key=filtered_model_names)
     def plot_dist():
         fig = px.histogram(
             filtered_df(),
@@ -188,6 +255,7 @@ def plot_dist():
         # From https://plotly.com/python/facet-plots/#customizing-subplot-figure-titles
         fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
 
+        fig.update_yaxes(matches=None)
         fig.update_xaxes(range=[0, 1], fixedrange=True)
         fig.layout.height = 500
 
diff --git a/examples/model-score/plotly_streaming.py b/examples/model-score/plotly_streaming.py
index 209e3dc7a..5315ea7ad 100644
--- a/examples/model-score/plotly_streaming.py
+++ b/examples/model-score/plotly_streaming.py
@@ -1,4 +1,5 @@
 import functools
+import json
 
 import plotly.graph_objects as go
 from shinywidgets import render_widget
@@ -6,23 +7,54 @@
 from shiny import reactive
 
 
-def render_plotly_streaming(fn=None, *, recreate_when=lambda: None):
+# Return a hash of an arbitrary object, including nested dicts, lists, and numpy/pandas
+# data structures. Uses json.dumps() internally.
+def _hash_anything(obj):
+    return hash(json.dumps(obj, sort_keys=True, default=_to_json_repr))
+
+
+def _to_json_repr(obj):
+    # If has to_json(), use that; make sure it's callable
+    if hasattr(obj, "to_json") and callable(obj.to_json):
+        return json.loads(obj.to_json())
+    if hasattr(obj, "to_list") and callable(obj.to_list):
+        return obj.to_list()
+    if hasattr(obj, "tolist") and callable(obj.tolist):
+        return obj.tolist()
+    if hasattr(obj, "to_dict") and callable(obj.to_dict):
+        return obj.to_dict()
+    raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
+
+
+def render_plotly_streaming(fn=None, *, recreate_key=lambda: None):
     """Custom decorator for Plotly streaming plots. This is similar to
     shinywidgets.render_widget, except:
 
     1. You return simply a Figure, not FigureWidget.
     2. On reactive invalidation, the figure is updated in-place, rather than recreated
        from scratch.
+
+    Parameters
+    ----------
+    recreate_key : callable, optional
+        A function that returns a hashable object. If the value returned by this
+        function changes, the plot will be recreated from scratch. This is useful for
+        changes that render_plotly_streaming can't handle well, such as changing the
+        number of traces in a plot.
     """
 
     if fn is not None:
-        return render_plotly_streaming(recreate_when=recreate_when)(fn)
+        return render_plotly_streaming(recreate_key=recreate_key)(fn)
 
     def decorator(func):
+        @deduplicate
+        def recreate_trigger():
+            return _hash_anything(recreate_key())
+
         @render_widget
         @functools.wraps(func)
         def wrapper():
-            recreate_when()
+            recreate_trigger()
 
             with reactive.isolate():
                 fig = func()
@@ -43,3 +75,22 @@ def update_plotly_data():
         return wrapper
 
     return decorator
+
+
+def deduplicate(func):
+    with reactive.isolate():
+        rv = reactive.Value(func())
+
+    @reactive.Effect
+    def update():
+        x = func()
+        with reactive.isolate():
+            if x != rv():
+                rv.set(x)
+
+    @reactive.Calc
+    @functools.wraps(func)
+    def wrapper():
+        return rv()
+
+    return wrapper
diff --git a/examples/model-score/requirements.in b/examples/model-score/requirements.in
new file mode 100644
index 000000000..1a5617b60
--- /dev/null
+++ b/examples/model-score/requirements.in
@@ -0,0 +1,4 @@
+pandas
+plotly
+shiny
+shinywidgets
diff --git a/examples/model-score/requirements.txt b/examples/model-score/requirements.txt
new file mode 100644
index 000000000..17d358c9b
--- /dev/null
+++ b/examples/model-score/requirements.txt
@@ -0,0 +1,174 @@
+#
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
+#
+#    pip-compile
+#
+anyio==3.7.1
+    # via
+    #   starlette
+    #   watchfiles
+appdirs==1.4.4
+    # via shiny
+appnope==0.1.3
+    # via
+    #   ipykernel
+    #   ipython
+asgiref==3.7.2
+    # via shiny
+asttokens==2.2.1
+    # via stack-data
+backcall==0.2.0
+    # via ipython
+click==8.1.6
+    # via
+    #   shiny
+    #   uvicorn
+comm==0.1.3
+    # via ipykernel
+contextvars==2.4
+    # via shiny
+debugpy==1.6.7
+    # via ipykernel
+decorator==5.1.1
+    # via ipython
+exceptiongroup==1.1.2
+    # via anyio
+executing==1.2.0
+    # via stack-data
+h11==0.14.0
+    # via uvicorn
+htmltools==0.2.1
+    # via shiny
+idna==3.4
+    # via anyio
+immutables==0.19
+    # via contextvars
+ipykernel==6.25.0
+    # via ipywidgets
+ipython==8.14.0
+    # via
+    #   ipykernel
+    #   ipywidgets
+ipywidgets==8.0.7
+    # via shinywidgets
+jedi==0.18.2
+    # via ipython
+jupyter-client==8.3.0
+    # via ipykernel
+jupyter-core==5.3.1
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   shinywidgets
+jupyterlab-widgets==3.0.8
+    # via ipywidgets
+linkify-it-py==2.0.2
+    # via shiny
+markdown-it-py==3.0.0
+    # via
+    #   mdit-py-plugins
+    #   shiny
+matplotlib-inline==0.1.6
+    # via
+    #   ipykernel
+    #   ipython
+mdit-py-plugins==0.4.0
+    # via shiny
+mdurl==0.1.2
+    # via markdown-it-py
+nest-asyncio==1.5.7
+    # via ipykernel
+numpy==1.25.1
+    # via pandas
+packaging==23.1
+    # via
+    #   htmltools
+    #   ipykernel
+    #   plotly
+pandas==2.0.3
+    # via -r requirements.in
+parso==0.8.3
+    # via jedi
+pexpect==4.8.0
+    # via ipython
+pickleshare==0.7.5
+    # via ipython
+platformdirs==3.9.1
+    # via jupyter-core
+plotly==5.15.0
+    # via -r requirements.in
+prompt-toolkit==3.0.39
+    # via ipython
+psutil==5.9.5
+    # via ipykernel
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.2
+    # via stack-data
+pygments==2.15.1
+    # via ipython
+python-dateutil==2.8.2
+    # via
+    #   jupyter-client
+    #   pandas
+    #   shinywidgets
+python-multipart==0.0.6
+    # via shiny
+pytz==2023.3
+    # via pandas
+pyzmq==25.1.0
+    # via
+    #   ipykernel
+    #   jupyter-client
+shiny==0.4.0
+    # via
+    #   -r requirements.in
+    #   shinywidgets
+shinywidgets==0.2.1
+    # via -r requirements.in
+six==1.16.0
+    # via
+    #   asttokens
+    #   python-dateutil
+sniffio==1.3.0
+    # via anyio
+stack-data==0.6.2
+    # via ipython
+starlette==0.31.0
+    # via shiny
+tenacity==8.2.2
+    # via plotly
+tornado==6.3.2
+    # via
+    #   ipykernel
+    #   jupyter-client
+traitlets==5.9.0
+    # via
+    #   comm
+    #   ipykernel
+    #   ipython
+    #   ipywidgets
+    #   jupyter-client
+    #   jupyter-core
+    #   matplotlib-inline
+typing-extensions==4.7.1
+    # via
+    #   asgiref
+    #   htmltools
+    #   shiny
+    #   uvicorn
+tzdata==2023.3
+    # via pandas
+uc-micro-py==1.0.2
+    # via linkify-it-py
+uvicorn==0.23.1
+    # via shiny
+watchfiles==0.19.0
+    # via shiny
+wcwidth==0.2.6
+    # via prompt-toolkit
+websockets==11.0.3
+    # via shiny
+widgetsnbextension==4.0.8
+    # via ipywidgets
diff --git a/examples/model-score/scoredata.py b/examples/model-score/scoredata.py
index 6761af460..0eb1a0719 100644
--- a/examples/model-score/scoredata.py
+++ b/examples/model-score/scoredata.py
@@ -19,7 +19,7 @@ async def async_main():
         con.execute("PRAGMA journal_mode=WAL")
         con.execute("drop table if exists auc_scores")
 
-        now = datetime.datetime.now()
+        now = datetime.datetime.utcnow()
         position = now.minute * 60 + now.second + 1
 
         # Simulate 100 seconds of historical data
@@ -38,7 +38,7 @@ async def async_main():
         while True:
             new_data = auc_scores.loc[position].copy()
             # del new_data["second"]
-            new_data["timestamp"] = datetime.datetime.now()
+            new_data["timestamp"] = datetime.datetime.utcnow()
             new_data.to_sql("auc_scores", con, index=False, if_exists="append")
             position = (position % (60 * 60)) + 1
             await asyncio.sleep(1)

From e1de020b286cb46ee432a7d0cc9c20bf4142588b Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Mon, 31 Jul 2023 16:46:53 -0700
Subject: [PATCH 4/9] Add comments

---
 examples/model-score/app.py       | 96 ++++++++++++++++++++++---------
 examples/model-score/scoredata.py |  2 +
 2 files changed, 70 insertions(+), 28 deletions(-)

diff --git a/examples/model-score/app.py b/examples/model-score/app.py
index 0ca3753b0..7d2eca506 100644
--- a/examples/model-score/app.py
+++ b/examples/model-score/app.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import sqlite3
 from datetime import datetime, timedelta, timezone
 
@@ -10,9 +12,6 @@
 import shiny.experimental as x
 from shiny import App, Inputs, Outputs, Session, reactive, render, ui
 
-# TODO: Make an option to switch between dynamic and static
-# TODO: Talk to Julia and Isabel for suggestions
-
 THRESHOLD_MID = 0.85
 THRESHOLD_MID_COLOR = "rgb(0, 137, 26)"
 THRESHOLD_LOW = 0.5
@@ -25,26 +24,33 @@
 
 
 def last_modified(con):
+    """
+    Fast-executing call to get the timestamp of the most recent row in the database.
+    We will poll against this in absence of a way to receive a push notification when
+    our SQLite database changes.
+    """
     return con.execute("select max(timestamp) from auc_scores").fetchone()[0]
 
 
-# @reactive.poll calls a cheap query (`last_modified()`) every 1 second to check if the
-# expensive query (`df()`) should be run and downstream calculations should be updated.
-#
-# By declaring this function at the top-level of the script instead of in the server
-# function, all sessions are sharing the same reactive poll, so the expensive query is
-# only run once no matter how many users are connected.
-
-
 @reactive.poll(lambda: last_modified(con))
 def df():
+    """
+    @reactive.poll calls a cheap query (`last_modified()`) every 1 second to check if
+    the expensive query (`df()`) should be run and downstream calculations should be
+    updated.
+
+    By declaring this reactive object at the top-level of the script instead of in the
+    server function, all sessions are sharing the same object, so the expensive query is
+    only run once no matter how many users are connected.
+    """
     tbl = pd.read_sql(
         "select * from auc_scores order by timestamp desc, model desc limit ?",
         con,
         params=[150],
     )
-    # Treat timestamp as a continuous variable
+    # Convert timestamp to datetime object, which SQLite doesn't support natively
     tbl["timestamp"] = pd.to_datetime(tbl["timestamp"], utc=True)
+    # Create a short label for readability
     tbl["time"] = tbl["timestamp"].dt.strftime("%H:%M:%S")
     # Reverse order of rows
     tbl = tbl.iloc[::-1]
@@ -70,7 +76,6 @@ def read_time_period(from_time, to_time):
     "model_2": "#beaed4",
     "model_3": "#fdc086",
     "model_4": "#ffff99",
-    "model_5": "#386cb0",
 }
 model_names = list(model_colors.keys())
 
@@ -131,38 +136,41 @@ def app_ui(req):
 
 
 def server(input: Inputs, output: Outputs, session: Session):
-    @reactive.Effect
-    def update_time_range():
-        reactive.invalidate_later(5)
-        min_time, max_time = pd.to_datetime(
-            con.execute(
-                "select min(timestamp), max(timestamp) from auc_scores"
-            ).fetchone(),
-            utc=True,
-        )
-        ui.update_slider(
-            "timerange",
-            min=min_time.replace(tzinfo=timezone.utc),
-            max=max_time.replace(tzinfo=timezone.utc),
-        )
-
     @reactive.Calc
     def recent_df():
+        """
+        Returns the most recent rows from the database, at the refresh interval
+        requested by the user. If the refresh interview is 0, go at maximum speed.
+        """
         refresh = int(input.refresh())
         if refresh == 0:
             return df()
         else:
+            # This approach works well if you know that input.refresh() is likely to be
+            # a longer interval than the underlying changing data source (df()). If not,
+            # then this can cause downstream reactives to be invalidated when they
+            # didn't need to be.
             reactive.invalidate_later(refresh)
             with reactive.isolate():
                 return df()
 
     @reactive.Calc
     def timeframe_df():
+        """
+        Returns rows from the database within the specified time range. Notice that we
+        implement the business logic as a separate function (read_time_period), so it's
+        easier to reason about and test.
+        """
         start, end = input.timerange()
         return read_time_period(start, end)
 
     @reactive.Calc
     def filtered_df():
+        """
+        Return the data frame that should be displayed in the app, based on the user's
+        input. This will be either the latest rows, or a specific time range. Also
+        filter out rows for models that the user has deselected.
+        """
         data = recent_df() if input.timeframe() == "Latest" else timeframe_df()
 
         # Filter the rows so we only include the desired models
@@ -186,6 +194,8 @@ def value_boxes():
         return x.ui.layout_column_wrap(
             "135px",
             *[
+                # For each model, return a value_box with the score, colored based on
+                # how high the score is.
                 x.ui.value_box(
                     model,
                     ui.h2(score),
@@ -203,6 +213,11 @@ def value_boxes():
     @output
     @render_plotly_streaming(recreate_key=filtered_model_names)
     def plot_timeseries():
+        """
+        Returns a Plotly Figure visualization. Streams new data to the Plotly widget in
+        the browser whenever filtered_df() updates, and completely recreates the figure
+        when filtered_model_names() changes (see recreate_key=... above).
+        """
         fig = px.line(
             filtered_df(),
             x="time",
@@ -210,6 +225,11 @@ def plot_timeseries():
             labels=dict(score="auc"),
             color="model",
             color_discrete_map=model_colors,
+            # The default for render_mode is "auto", which switches between
+            # type="scatter" and type="scattergl" depending on the number of data
+            # points. Switching that value breaks streaming updates, as the type
+            # property is read-only. Setting it to "webgl" keeps the type consistent.
+            render_mode="webgl",
         )
 
         fig.add_hline(
@@ -261,5 +281,25 @@ def plot_dist():
 
         return fig
 
+    @reactive.Effect
+    def update_time_range():
+        """
+        Every 5 seconds, update the custom time range slider's min and max values to
+        reflect the current min and max values in the database.
+        """
+
+        reactive.invalidate_later(5)
+        min_time, max_time = pd.to_datetime(
+            con.execute(
+                "select min(timestamp), max(timestamp) from auc_scores"
+            ).fetchone(),
+            utc=True,
+        )
+        ui.update_slider(
+            "timerange",
+            min=min_time.replace(tzinfo=timezone.utc),
+            max=max_time.replace(tzinfo=timezone.utc),
+        )
+
 
 app = App(app_ui, server)
diff --git a/examples/model-score/scoredata.py b/examples/model-score/scoredata.py
index 0eb1a0719..ba59ed381 100644
--- a/examples/model-score/scoredata.py
+++ b/examples/model-score/scoredata.py
@@ -35,6 +35,8 @@ async def async_main():
         initial_scores = initial_scores.join(timestamps, how="left")
         initial_scores.to_sql("auc_scores", con, index=False, if_exists="append")
 
+        con.execute("create index idx_auc_scores_timestamp on auc_scores(timestamp)")
+
         while True:
             new_data = auc_scores.loc[position].copy()
             # del new_data["second"]

From d5547f6314937ccb21fca893e504de3d17fea111 Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Tue, 1 Aug 2023 12:31:31 -0700
Subject: [PATCH 5/9] Fix performance issues

Caused by plotly layout update, which isn't even needed
---
 examples/model-score/app.py              |  4 ++--
 examples/model-score/plotly_streaming.py | 12 ++++++++----
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/examples/model-score/app.py b/examples/model-score/app.py
index 7d2eca506..0df1a0903 100644
--- a/examples/model-score/app.py
+++ b/examples/model-score/app.py
@@ -211,7 +211,7 @@ def value_boxes():
         )
 
     @output
-    @render_plotly_streaming(recreate_key=filtered_model_names)
+    @render_plotly_streaming(recreate_key=filtered_model_names, update="data")
     def plot_timeseries():
         """
         Returns a Plotly Figure visualization. Streams new data to the Plotly widget in
@@ -249,7 +249,7 @@ def plot_timeseries():
         return fig
 
     @output
-    @render_plotly_streaming(recreate_key=filtered_model_names)
+    @render_plotly_streaming(recreate_key=filtered_model_names, update="data")
     def plot_dist():
         fig = px.histogram(
             filtered_df(),
diff --git a/examples/model-score/plotly_streaming.py b/examples/model-score/plotly_streaming.py
index 5315ea7ad..d409d3bfd 100644
--- a/examples/model-score/plotly_streaming.py
+++ b/examples/model-score/plotly_streaming.py
@@ -26,7 +26,9 @@ def _to_json_repr(obj):
     raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
 
 
-def render_plotly_streaming(fn=None, *, recreate_key=lambda: None):
+def render_plotly_streaming(
+    fn=None, *, recreate_key=lambda: None, update=("layout", "data")
+):
     """Custom decorator for Plotly streaming plots. This is similar to
     shinywidgets.render_widget, except:
 
@@ -64,9 +66,11 @@ def wrapper():
             def update_plotly_data():
                 f_new = func()
                 with widget.batch_update():
-                    widget.update_layout(f_new.layout)
-                    for old, new in zip(widget.data, f_new.data):
-                        old.update(new)
+                    if "layout" in update:
+                        widget.update_layout(f_new.layout)
+                    if "data" in update:
+                        for old, new in zip(widget.data, f_new.data):
+                            old.update(new)
 
             reactive.get_current_context().on_invalidate(update_plotly_data.destroy)
 

From 24467b1a33c282440218f029fefa5b4975f8df3d Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Tue, 1 Aug 2023 16:32:05 -0700
Subject: [PATCH 6/9] Replace tags.head with head_content

head_content(), not tags.head(), is how you hoist arbitrary HTML
into the <head> of the enclosing page
---
 shiny/experimental/ui/_page.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/shiny/experimental/ui/_page.py b/shiny/experimental/ui/_page.py
index c786896d8..d11683ef9 100644
--- a/shiny/experimental/ui/_page.py
+++ b/shiny/experimental/ui/_page.py
@@ -10,6 +10,7 @@
     TagChild,
     TagList,
     css,
+    head_content,
     tags,
 )
 
@@ -278,7 +279,7 @@ def page_fillable(
     )
 
     return page_bootstrap(
-        tags.head(tags.style("html { height: 100%; }")),
+        head_content(tags.style("html { height: 100%; }")),
         as_fillable_container(
             tags.body(
                 {"class": "bslib-page-fill bslib-gap-spacing", "style": style},

From d7b4b3d70dc51644a6bd669dd63b5343627170f6 Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Wed, 2 Aug 2023 13:26:40 -0700
Subject: [PATCH 7/9] Code review feedback

---
 examples/model-score/app.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/examples/model-score/app.py b/examples/model-score/app.py
index 0df1a0903..731c38613 100644
--- a/examples/model-score/app.py
+++ b/examples/model-score/app.py
@@ -71,13 +71,11 @@ def read_time_period(from_time, to_time):
     return tbl
 
 
+model_names = ["model_1", "model_2", "model_3", "model_4"]
 model_colors = {
-    "model_1": "#7fc97f",
-    "model_2": "#beaed4",
-    "model_3": "#fdc086",
-    "model_4": "#ffff99",
+    name: color
+    for name, color in zip(model_names, px.colors.qualitative.D3[0 : len(model_names)])
 }
-model_names = list(model_colors.keys())
 
 
 def app_ui(req):
@@ -103,6 +101,7 @@ def app_ui(req):
                     {
                         0: "Realtime",
                         5: "5 seconds",
+                        15: "15 seconds",
                         30: "30 seconds",
                         60 * 5: "5 minutes",
                         60 * 15: "15 minutes",
@@ -230,21 +229,24 @@ def plot_timeseries():
             # points. Switching that value breaks streaming updates, as the type
             # property is read-only. Setting it to "webgl" keeps the type consistent.
             render_mode="webgl",
+            template="simple_white",
         )
 
         fig.add_hline(
             THRESHOLD_LOW,
             line_dash="dash",
             line=dict(color=THRESHOLD_LOW_COLOR, width=2),
+            opacity=0.3,
         )
         fig.add_hline(
             THRESHOLD_MID,
             line_dash="dash",
             line=dict(color=THRESHOLD_MID_COLOR, width=2),
+            opacity=0.3,
         )
 
         fig.update_yaxes(range=[0, 1], fixedrange=True)
-        fig.update_xaxes(fixedrange=True, tickangle=60, dtick="M5")
+        fig.update_xaxes(fixedrange=True, tickangle=60)
 
         return fig
 
@@ -259,17 +261,20 @@ def plot_dist():
             labels=dict(score="auc"),
             color="model",
             color_discrete_map=model_colors,
+            template="simple_white",
         )
 
         fig.add_vline(
             THRESHOLD_LOW,
             line_dash="dash",
             line=dict(color=THRESHOLD_LOW_COLOR, width=2),
+            opacity=0.3,
         )
         fig.add_vline(
             THRESHOLD_MID,
             line_dash="dash",
             line=dict(color=THRESHOLD_MID_COLOR, width=2),
+            opacity=0.3,
         )
 
         # From https://plotly.com/python/facet-plots/#customizing-subplot-figure-titles
@@ -288,7 +293,7 @@ def update_time_range():
         reflect the current min and max values in the database.
         """
 
-        reactive.invalidate_later(5)
+        reactive.invalidate_later(15)
         min_time, max_time = pd.to_datetime(
             con.execute(
                 "select min(timestamp), max(timestamp) from auc_scores"

From 71bff63747089ac4bbeea63e69de158e63aa355b Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Wed, 2 Aug 2023 19:22:27 -0700
Subject: [PATCH 8/9] Change auc to accuracy

Also change db initialization routine to ensure
the sqlite database is created before the app starts
---
 examples/model-score/app.py                   | 12 ++++----
 ...uc_scores.csv => fake_accuracy_scores.csv} |  0
 examples/model-score/scoredata.py             | 30 ++++++++++++-------
 3 files changed, 25 insertions(+), 17 deletions(-)
 rename examples/model-score/{fake_auc_scores.csv => fake_accuracy_scores.csv} (100%)

diff --git a/examples/model-score/app.py b/examples/model-score/app.py
index 731c38613..a495962ed 100644
--- a/examples/model-score/app.py
+++ b/examples/model-score/app.py
@@ -29,7 +29,7 @@ def last_modified(con):
     We will poll against this in absence of a way to receive a push notification when
     our SQLite database changes.
     """
-    return con.execute("select max(timestamp) from auc_scores").fetchone()[0]
+    return con.execute("select max(timestamp) from accuracy_scores").fetchone()[0]
 
 
 @reactive.poll(lambda: last_modified(con))
@@ -44,7 +44,7 @@ def df():
     only run once no matter how many users are connected.
     """
     tbl = pd.read_sql(
-        "select * from auc_scores order by timestamp desc, model desc limit ?",
+        "select * from accuracy_scores order by timestamp desc, model desc limit ?",
         con,
         params=[150],
     )
@@ -60,7 +60,7 @@ def df():
 
 def read_time_period(from_time, to_time):
     tbl = pd.read_sql(
-        "select * from auc_scores where timestamp between ? and ? order by timestamp, model",
+        "select * from accuracy_scores where timestamp between ? and ? order by timestamp, model",
         con,
         params=[from_time, to_time],
     )
@@ -221,7 +221,7 @@ def plot_timeseries():
             filtered_df(),
             x="time",
             y="score",
-            labels=dict(score="auc"),
+            labels=dict(score="accuracy"),
             color="model",
             color_discrete_map=model_colors,
             # The default for render_mode is "auto", which switches between
@@ -258,7 +258,7 @@ def plot_dist():
             facet_row="model",
             nbins=20,
             x="score",
-            labels=dict(score="auc"),
+            labels=dict(score="accuracy"),
             color="model",
             color_discrete_map=model_colors,
             template="simple_white",
@@ -296,7 +296,7 @@ def update_time_range():
         reactive.invalidate_later(15)
         min_time, max_time = pd.to_datetime(
             con.execute(
-                "select min(timestamp), max(timestamp) from auc_scores"
+                "select min(timestamp), max(timestamp) from accuracy_scores"
             ).fetchone(),
             utc=True,
         )
diff --git a/examples/model-score/fake_auc_scores.csv b/examples/model-score/fake_accuracy_scores.csv
similarity index 100%
rename from examples/model-score/fake_auc_scores.csv
rename to examples/model-score/fake_accuracy_scores.csv
diff --git a/examples/model-score/scoredata.py b/examples/model-score/scoredata.py
index ba59ed381..4e595fe72 100644
--- a/examples/model-score/scoredata.py
+++ b/examples/model-score/scoredata.py
@@ -7,17 +7,17 @@
 import pandas as pd
 
 here = Path(__file__).parent
-auc_scores = pd.read_csv(here / "fake_auc_scores.csv")
-auc_scores.set_index("second", inplace=True)
+accuracy_scores = pd.read_csv(here / "fake_accuracy_scores.csv")
+accuracy_scores.set_index("second", inplace=True)
 (here / "data").mkdir(exist_ok=True)
 
-SQLITE_DB_URI = f"file:{here / 'data' / 'auc_scores.sqlite'}"
+SQLITE_DB_URI = f"file:{here / 'data' / 'accuracy_scores.sqlite'}"
 
 
-async def async_main():
+def init_db():
     with sqlite3.connect(SQLITE_DB_URI, uri=True, timeout=30) as con:
         con.execute("PRAGMA journal_mode=WAL")
-        con.execute("drop table if exists auc_scores")
+        con.execute("drop table if exists accuracy_scores")
 
         now = datetime.datetime.utcnow()
         position = now.minute * 60 + now.second + 1
@@ -25,7 +25,7 @@ async def async_main():
         # Simulate 100 seconds of historical data
         offset_secs = -np.arange(100) - 1
         abs_secs = (position + offset_secs) % (60 * 60) + 1
-        initial_scores = auc_scores.loc[abs_secs]
+        initial_scores = accuracy_scores.loc[abs_secs]
         timestamps = pd.DataFrame(
             {
                 "timestamp": now + pd.to_timedelta(offset_secs, unit="s"),
@@ -33,18 +33,26 @@ async def async_main():
             }
         ).set_index("second", inplace=False)
         initial_scores = initial_scores.join(timestamps, how="left")
-        initial_scores.to_sql("auc_scores", con, index=False, if_exists="append")
+        initial_scores.to_sql("accuracy_scores", con, index=False, if_exists="append")
 
-        con.execute("create index idx_auc_scores_timestamp on auc_scores(timestamp)")
+        con.execute(
+            "create index idx_accuracy_scores_timestamp on accuracy_scores(timestamp)"
+        )
 
+        return position
+
+
+async def update_db(position):
+    with sqlite3.connect(SQLITE_DB_URI, uri=True, timeout=30) as con:
         while True:
-            new_data = auc_scores.loc[position].copy()
+            new_data = accuracy_scores.loc[position].copy()
             # del new_data["second"]
             new_data["timestamp"] = datetime.datetime.utcnow()
-            new_data.to_sql("auc_scores", con, index=False, if_exists="append")
+            new_data.to_sql("accuracy_scores", con, index=False, if_exists="append")
             position = (position % (60 * 60)) + 1
             await asyncio.sleep(1)
 
 
 def begin():
-    asyncio.create_task(async_main())
+    position = init_db()
+    asyncio.create_task(update_db(position))

From 7604b6250943ea4a4062b06e28bc1069df0b75e5 Mon Sep 17 00:00:00 2001
From: Joe Cheng <joe@posit.co>
Date: Thu, 3 Aug 2023 11:36:41 -0700
Subject: [PATCH 9/9] Fix lint rule

---
 setup.cfg | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.cfg b/setup.cfg
index 4ba561815..43a58bf6d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -111,7 +111,8 @@ console_scripts =
 # F403: 'from module import *' used; unable to detect undefined names
 # F405: Name may be undefined, or defined from star imports
 # W503: Line break occurred before a binary operator
-ignore = E302, E501, F403, F405, W503
+# E203: whitespace before ':' (see https://black.readthedocs.io/en/stable/guides/using_black_with_other_tools.html#flake8)
+ignore = E302, E501, F403, F405, W503, E203
 extend_exclude = docs, .venv, venv, typings, e2e, build
 
 [isort]