Update app.py

app.py

@@ -1,151 +1,154 @@
-from pathlib import Path
-from typing import List, Dict, Tuple
-import matplotlib.colors as mpl_colors
-
-import pandas as pd
-import seaborn as sns
-import shinyswatch
-
-from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
-
-sns.set_theme()
-
-www_dir = Path(__file__).parent.resolve() / "www"
-
-df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
-numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
-species: List[str] = df["Species"].unique().tolist()
-species.sort()
-
-app_ui = ui.page_fillable(
-    shinyswatch.theme.minty(),
-    ui.layout_sidebar(
-        ui.sidebar(
-            # Artwork by @allison_horst
-            ui.input_selectize(
-                "xvar",
-                "X variable",
-                numeric_cols,
-                selected="Bill Length (mm)",
-            ),
-            ui.input_selectize(
-                "yvar",
-                "Y variable",
-                numeric_cols,
-                selected="Bill Depth (mm)",
-            ),
-            ui.input_checkbox_group(
-                "species", "Filter by species", species, selected=species
-            ),
-            ui.hr(),
-            ui.input_switch("by_species", "Show species", value=True),
-            ui.input_switch("show_margins", "Show marginal plots", value=True),
-        ),
-        ui.output_ui("value_boxes"),
-        ui.output_plot("scatter", fill=True),
-        ui.help_text(
-            "Artwork by ",
-            ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
-            class_="text-end",
-        ),
-    ),
-)
-
-
-def server(input: Inputs, output: Outputs, session: Session):
-    @reactive.Calc
-    def filtered_df() -> pd.DataFrame:
-        """Returns a Pandas data frame that includes only the desired rows"""
-
-        # This calculation "req"uires that at least one species is selected
-        req(len(input.species()) > 0)
-
-        # Filter the rows so we only include the desired species
-        return df[df["Species"].isin(input.species())]
-
-    @output
-    @render.plot
-    def scatter():
-        """Generates a plot for Shiny to display to the user"""
-
-        # The plotting function to use depends on whether margins are desired
-        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
-
-        plotfunc(
-            data=filtered_df(),
-            x=input.xvar(),
-            y=input.yvar(),
-            palette=palette,
-            hue="Species" if input.by_species() else None,
-            hue_order=species,
-            legend=False,
-        )
-
-    @output
-    @render.ui
-    def value_boxes():
-        df = filtered_df()
-
-        def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
-            return ui.value_box(
-                title,
-                count,
-                {"class_": "pt-1 pb-0"},
-                showcase=ui.fill.as_fill_item(
-                    ui.tags.img(
-                        {"style": "object-fit:contain;"},
-                        src=showcase_img,
-                    )
-                ),
-                theme_color=None,
-                style=f"background-color: {bgcol};",
-            )
-
-        if not input.by_species():
-            return penguin_value_box(
-                "Penguins",
-                len(df.index),
-                bg_palette["default"],
-                # Artwork by @allison_horst
-                showcase_img="penguins.png",
-            )
-
-        value_boxes = [
-            penguin_value_box(
-                name,
-                len(df[df["Species"] == name]),
-                bg_palette[name],
-                # Artwork by @allison_horst
-                showcase_img=f"{name}.png",
-            )
-            for name in species
-            # Only include boxes for _selected_ species
-            if name in input.species()
-        ]
-
-        return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))
-
-
-# "darkorange", "purple", "cyan4"
-colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
-colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
-
-palette: Dict[str, Tuple[float, float, float]] = {
-    "Adelie": colors[0],
-    "Chinstrap": colors[1],
-    "Gentoo": colors[2],
-    "default": sns.color_palette()[0],  # type: ignore
-}
-
-bg_palette = {}
-# Use `sns.set_style("whitegrid")` to help find approx alpha value
-for name, col in palette.items():
-    # Adjusted n_colors until `axe` accessibility did not complain about color contrast
-    bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore
-
-
-app = App(
-    app_ui,
-    server,
-    static_assets=str(www_dir),
-)
+# from pathlib import Path
+# from typing import List, Dict, Tuple
+# import matplotlib.colors as mpl_colors
+
+# import pandas as pd
+# import seaborn as sns
+# import shinyswatch
+
+# from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
+
+# sns.set_theme()
+
+# www_dir = Path(__file__).parent.resolve() / "www"
+
+# df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
+# numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
+# species: List[str] = df["Species"].unique().tolist()
+# species.sort()
+
+# app_ui = ui.page_fillable(
+#     shinyswatch.theme.minty(),
+#     ui.layout_sidebar(
+#         ui.sidebar(
+#             # Artwork by @allison_horst
+#             ui.input_selectize(
+#                 "xvar",
+#                 "X variable",
+#                 numeric_cols,
+#                 selected="Bill Length (mm)",
+#             ),
+#             ui.input_selectize(
+#                 "yvar",
+#                 "Y variable",
+#                 numeric_cols,
+#                 selected="Bill Depth (mm)",
+#             ),
+#             ui.input_checkbox_group(
+#                 "species", "Filter by species", species, selected=species
+#             ),
+#             ui.hr(),
+#             ui.input_switch("by_species", "Show species", value=True),
+#             ui.input_switch("show_margins", "Show marginal plots", value=True),
+#         ),
+#         ui.output_ui("value_boxes"),
+#         ui.output_plot("scatter", fill=True),
+#         ui.help_text(
+#             "Artwork by ",
+#             ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
+#             class_="text-end",
+#         ),
+#     ),
+# )
+
+
+# def server(input: Inputs, output: Outputs, session: Session):
+#     @reactive.Calc
+#     def filtered_df() -> pd.DataFrame:
+#         """Returns a Pandas data frame that includes only the desired rows"""
+
+#         # This calculation "req"uires that at least one species is selected
+#         req(len(input.species()) > 0)
+
+#         # Filter the rows so we only include the desired species
+#         return df[df["Species"].isin(input.species())]
+
+#     @output
+#     @render.plot
+#     def scatter():
+#         """Generates a plot for Shiny to display to the user"""
+
+#         # The plotting function to use depends on whether margins are desired
+#         plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
+
+#         plotfunc(
+#             data=filtered_df(),
+#             x=input.xvar(),
+#             y=input.yvar(),
+#             palette=palette,
+#             hue="Species" if input.by_species() else None,
+#             hue_order=species,
+#             legend=False,
+#         )
+
+#     @output
+#     @render.ui
+#     def value_boxes():
+#         df = filtered_df()
+
+#         def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
+#             return ui.value_box(
+#                 title,
+#                 count,
+#                 {"class_": "pt-1 pb-0"},
+#                 showcase=ui.fill.as_fill_item(
+#                     ui.tags.img(
+#                         {"style": "object-fit:contain;"},
+#                         src=showcase_img,
+#                     )
+#                 ),
+#                 theme_color=None,
+#                 style=f"background-color: {bgcol};",
+#             )
+
+#         if not input.by_species():
+#             return penguin_value_box(
+#                 "Penguins",
+#                 len(df.index),
+#                 bg_palette["default"],
+#                 # Artwork by @allison_horst
+#                 showcase_img="penguins.png",
+#             )
+
+#         value_boxes = [
+#             penguin_value_box(
+#                 name,
+#                 len(df[df["Species"] == name]),
+#                 bg_palette[name],
+#                 # Artwork by @allison_horst
+#                 showcase_img=f"{name}.png",
+#             )
+#             for name in species
+#             # Only include boxes for _selected_ species
+#             if name in input.species()
+#         ]
+
+#         return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))
+
+
+# # "darkorange", "purple", "cyan4"
+# colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
+# colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
+
+# palette: Dict[str, Tuple[float, float, float]] = {
+#     "Adelie": colors[0],
+#     "Chinstrap": colors[1],
+#     "Gentoo": colors[2],
+#     "default": sns.color_palette()[0],  # type: ignore
+# }
+
+# bg_palette = {}
+# # Use `sns.set_style("whitegrid")` to help find approx alpha value
+# for name, col in palette.items():
+#     # Adjusted n_colors until `axe` accessibility did not complain about color contrast
+#     bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore
+
+
+# app = App(
+#     app_ui,
+#     server,
+#     static_assets=str(www_dir),
+# )
+
+from data import get_data
+get_data()