app.py

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

import os
# os.environ["TRANSFORMERS_CACHE"] = "./hf_cache"
from transformers import SamModel, SamConfig, SamProcessor
import torch

sns.set_theme()

dir = Path(__file__).resolve().parent
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(
            ui.input_file("tile_image", "Choose TIFF File", accept=[".tif"], multiple=False),
            # 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_image("uploaded_image"),  # display the uploaded TIFF sidewalk tile image
        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):
    # Load the model configuration
    model_config = SamConfig.from_pretrained("facebook/sam-vit-base")
    processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
    
    # Create an instance of the model architecture with the loaded configuration
    model = SamModel(config=model_config)
    # Update the model by loading the weights from saved file.
    model.load_state_dict(torch.load(str(dir / "checkpoint.pth")))
    
    # set the device to cuda if available, otherwise use cpu
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)
    
    @reactive.Calc
    def uploaded_image_path() -> str:
        """Returns the path to the uploaded image"""
        if input.tile_image() is not None:
            return input.tile_image()[0]['datapath']  # Assuming multiple=False
        else:
            return ""  # No image uploaded

    @render.image
    def uploaded_image():
        """Displays the uploaded image"""
        img_src = uploaded_image_path()
        if img_src:
            img: ImgData = {"src": str(dir / uploaded_image_path()), "width": "100px"}
            return img
        else:
            return None  # Return an empty string if no image is uploaded
    
    @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),
)