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
from transformers import SamModel, SamConfig, SamProcessor
import torch

from PIL import Image
import io

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 tif_bytes_to_pil_image(tif_bytes):
  # Create a BytesIO object from the TIFF bytes
  bytes_io = io.BytesIO(tif_bytes)

  # Open the BytesIO object as an Image
  image = Image.open(bytes_io)

  return image

def server(input: Inputs, output: Outputs, session: Session):
    @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
            
    def process_image():
        """Processes the uploaded image, loads the model, and evaluates to get predictions"""
        # Load the uploaded image
        uploaded_image_bytes = input.tile_image()[0].read()

        # Convert the uploaded TIFF bytes to a PIL Image object
        uploaded_image = tif_bytes_to_pil_image(uploaded_image_bytes)

        # Perform any preprocessing steps on the image as needed
        
        # Example: Convert the image to the required input format for the model
        # image_array = preprocess_image(uploaded_image)

        # 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_state_dict = torch.load(str(dir / "checkpoint.pth"), map_location=torch.device('cpu'))
        model.load_state_dict(model_state_dict)
        
        # set the device to cuda if available, otherwise use cpu
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model.to(device)
        
        # Evaluate the image with the model
        # Example: predictions = model.predict(image_array)
        
        # Return the processed result (replace 'result' with the actual processed result)
        return "Processed result"

    @reactive.Calc
    def processed_result():
        """Processes the image when uploaded"""
        if input.tile_image() is not None:
            return process_image()
        else:
            return None

    @output
    @render.text
    def processed_output():
        """Displays the predictions of the uploaded image"""
        return processed_result()
    



    
    
    @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),
)