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import streamlit as st
import pyvista as pv
from dcgan import DCGAN3D_G
import torch
import requests
import time
import numpy as np
import streamlit.components.v1 as components
url = "https://github.com/LukasMosser/PorousMediaGan/blob/master/checkpoints/berea/berea_generator_epoch_24.pth?raw=true"
# If repo is private - we need to add a token in header:
resp = requests.get(url)
with open('berea_generator_epoch_24.pth', 'wb') as f:
f.write(resp.content)
time.sleep(5)
st.text(resp.status_code)
pv.set_plot_theme("document")
pl = pv.Plotter(shape=(1, 1),
window_size=(800, 800))
netG = DCGAN3D_G(64, 512, 1, 32, 1)
netG.load_state_dict(torch.load("berea_generator_epoch_24.pth", map_location=torch.device('cpu')))
z = torch.randn(1, 512, 1, 1, 1)
with torch.no_grad():
X = netG(z)
st.image((X[0, 0, 32].numpy()+1)/2, output_format="png")
img = (X[0, 0].numpy()+1)/2
a = 0.9
# create a uniform grid to sample the function with
x_min, y_min, z_min = 0, 0, 0
grid = pv.UniformGrid(
dims=img.shape,
spacing=(1, 1, 1),
origin=(x_min, y_min, z_min),
)
x, y, z = grid.points.T
# sample and plot
values = img.flatten()
grid.point_data['my_array'] = values
slices = grid.slice_orthogonal()
pl.add_mesh(slices, cmap="gray")
pl.export_html('pyvista.html')
st.header("test html import")
view_width = 800
view_height = 800
HtmlFile = open("pyvista.html", 'r', encoding='utf-8')
source_code = HtmlFile.read()
components.html(source_code, width=view_width, height=view_height)
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