RobioAI/DeepLearning

1from pathlib import Path2from typing import List, Dict, Tuple3import matplotlib.colors as mpl_colors4 5import pandas as pd6import seaborn as sns7import shinyswatch8 9from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui10 11sns.set_theme()12 13www_dir = Path(__file__).parent.resolve() / "www"14 15df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")16numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()17species: List[str] = df["Species"].unique().tolist()18species.sort()19 20app_ui = ui.page_fillable(21    shinyswatch.theme.minty(),22    ui.layout_sidebar(23        ui.sidebar(24            # Artwork by @allison_horst25            ui.input_selectize(26                "xvar",27                "X variable",28                numeric_cols,29                selected="Bill Length (mm)",30            ),31            ui.input_selectize(32                "yvar",33                "Y variable",34                numeric_cols,35                selected="Bill Depth (mm)",36            ),37            ui.input_checkbox_group(38                "species", "Filter by species", species, selected=species39            ),40            ui.hr(),41            ui.input_switch("by_species", "Show species", value=True),42            ui.input_switch("show_margins", "Show marginal plots", value=True),43        ),44        ui.output_ui("value_boxes"),45        ui.output_plot("scatter", fill=True),46        ui.help_text(47            "Artwork by ",48            ui.a("@allison_horst", href="https://twitter.com/allison_horst"),49            class_="text-end",50        ),51    ),52)53 54 55def server(input: Inputs, output: Outputs, session: Session):56    @reactive.Calc57    def filtered_df() -> pd.DataFrame:58        """Returns a Pandas data frame that includes only the desired rows"""59 60        # This calculation "req"uires that at least one species is selected61        req(len(input.species()) > 0)62 63        # Filter the rows so we only include the desired species64        return df[df["Species"].isin(input.species())]65 66    @output67    @render.plot68    def scatter():69        """Generates a plot for Shiny to display to the user"""70 71        # The plotting function to use depends on whether margins are desired72        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot73 74        plotfunc(75            data=filtered_df(),76            x=input.xvar(),77            y=input.yvar(),78            palette=palette,79            hue="Species" if input.by_species() else None,80            hue_order=species,81            legend=False,82        )83 84    @output85    @render.ui86    def value_boxes():87        df = filtered_df()88 89        def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):90            return ui.value_box(91                title,92                count,93                {"class_": "pt-1 pb-0"},94                showcase=ui.fill.as_fill_item(95                    ui.tags.img(96                        {"style": "object-fit:contain;"},97                        src=showcase_img,98                    )99                ),100                theme_color=None,101                style=f"background-color: {bgcol};",102            )103 104        if not input.by_species():105            return penguin_value_box(106                "Penguins",107                len(df.index),108                bg_palette["default"],109                # Artwork by @allison_horst110                showcase_img="penguins.png",111            )112 113        value_boxes = [114            penguin_value_box(115                name,116                len(df[df["Species"] == name]),117                bg_palette[name],118                # Artwork by @allison_horst119                showcase_img=f"{name}.png",120            )121            for name in species122            # Only include boxes for _selected_ species123            if name in input.species()124        ]125 126        return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))127 128 129# "darkorange", "purple", "cyan4"130colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]131colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]132 133palette: Dict[str, Tuple[float, float, float]] = {134    "Adelie": colors[0],135    "Chinstrap": colors[1],136    "Gentoo": colors[2],137    "default": sns.color_palette()[0],  # type: ignore138}139 140bg_palette = {}141# Use `sns.set_style("whitegrid")` to help find approx alpha value142for name, col in palette.items():143    # Adjusted n_colors until `axe` accessibility did not complain about color contrast144    bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore145 146 147app = App(148    app_ui,149    server,150    static_assets=str(www_dir),151)152