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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
@output
@render.plot
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([])
else:
print("no image received")
fig, _ = plt.subplots() # Create an empty figure if no image received
fig.text(0.5, 0.5, "No image received", ha='center', va='center', fontsize=14)
return fig
app = App(
app_ui,
server,
static_assets=str(www_dir),
) |