app.py

from pathlib import Path
from typing import List, Dict, Tuple
import matplotlib.colors as mpl_colors
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import shinyswatch
import run
import PIL

from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
from transformers import SamModel, SamConfig, SamProcessor
import torch

sns.set_theme()

www_dir = Path(__file__).parent.resolve() / "www"

app_ui = ui.page_fillable(
    shinyswatch.theme.minty(),
    ui.layout_sidebar(
        ui.sidebar(
            ui.input_file("image_input", "Upload image: ", multiple=True),
        ),
        ui.output_image("image"),
        # ui.output_image("image_output"),
        ui.output_plot("plot"),
        # ui.output_image("prediction"),
        # ui.output_image("prob")
    ),
)


def server(input: Inputs, output: Outputs, session: Session):
    @output
    @render.image
    def image():
        here = Path(__file__).parent
        if input.image_input():
            src = input.image_input()[0]['datapath']
            img = {"src": src, "width": "500px"} 
            return img
        return None

    @output
    @render.image
    def image_output():
        here = Path(__file__).parent
        if input.image_input():
            src = input.image_input()[0]['datapath']
            img = {"src": src, "width": "500px"} 
            x = run.pred(src)
            print(x)
            return img
        return None

    @render.plot(alt="A histogram")  
    def plot(): 
        if input.image_input():
            new_image = input.image_input()[0]['datapath']

            pred_prob, pred_prediction = run.pred(new_image)
            
            fig, axes = plt.subplots(1, 3, figsize=(15, 5))

            axes[0].imshow(new_image, cmap='gray')
            axes[0].set_title("Image")

            im = axes[1].imshow(pred_prob) 
            axes[1].set_title("Probability Map")
            cbar = fig.colorbar(im, ax=axes[1])

            axes[2].imshow(pred_prediction, cmap='gray')
            axes[2].set_title("Prediction")

            for ax in axes:
                ax.set_xticks([])
                ax.set_yticks([])
                ax.set_xticklabels([])
                ax.set_yticklabels([])

        return fig  


app = App(
    app_ui,
    server,
    static_assets=str(www_dir),
)