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)