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| from pathlib import Path | |
| from typing import List, Dict, Tuple | |
| import pandas as pd | |
| import seaborn as sns | |
| import shinyswatch | |
| import run | |
| import os | |
| os.environ['MPLCONFIGDIR'] = "/code/configs" | |
| import matplotlib.pyplot as plt | |
| 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_plot("plot_output", fill=True), | |
| ), | |
| ) | |
| def server(input: Inputs, output: Outputs, session: Session): | |
| def image(): | |
| if input.image_input(): | |
| src = input.image_input()[0]['datapath'] | |
| img = {"src": src, "width": "500px"} | |
| return img | |
| return None | |
| def plot_output(): | |
| if input.image_input(): | |
| src = input.image_input()[0]['datapath'] | |
| prob, prediction = run.pred(src) | |
| fig, axes = plt.subplots(1, 2, figsize=(60, 30)) | |
| im = axes[0].imshow(prob, cmap='viridis') | |
| axes[0].set_title("Probability Map") | |
| cbar = fig.colorbar(im, ax=axes[0]) | |
| axes[1].imshow(prediction, cmap='gray') | |
| axes[1].set_title("Prediction") | |
| for ax in axes: | |
| ax.set_xticks([]) | |
| ax.set_yticks([]) | |
| ax.set_xticklabels([]) | |
| ax.set_yticklabels([]) | |
| return fig | |
| return None | |
| app = App( | |
| app_ui, | |
| server, | |
| static_assets=str(www_dir), | |
| ) | |