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import gradio as gr
import pickle
# from datasets import load_from_disk
from plaid.containers.sample import Sample
# import pyvista as pv
import pyrender
import trimesh
import matplotlib.pyplot as plt
import os
# switch to "osmesa" or "egl" before loading pyrender
os.environ["PYOPENGL_PLATFORM"] = "egl"
import numpy as np
# FOLDER = "plot"
# dataset = load_from_disk("Rotor37")
field_names_train = ["Temperature", "Pressure", "Density"]#pickle.loads(dataset[0]["sample"]).get_field_names()
field_names_test = []
def sample_info(sample_id_str, fieldn):
sample_id = int(sample_id_str)
plaid_sample = Sample.load_from_dir(f"Rotor37/dataset/samples/sample_"+str(sample_id_str).zfill(9))
# generate mesh
sphere = trimesh.creation.icosphere(subdivisions=4, radius=0.8)
sphere.vertices+=1e-2*np.random.randn(*sphere.vertices.shape)
mesh = pyrender.Mesh.from_trimesh(sphere, smooth=False)
# compose scene
scene = pyrender.Scene(ambient_light=[.1, .1, .3], bg_color=[0, 0, 0])
camera = pyrender.PerspectiveCamera( yfov=np.pi / 3.0)
light = pyrender.DirectionalLight(color=[1,1,1], intensity=2e3)
scene.add(mesh, pose= np.eye(4))
scene.add(light, pose= np.eye(4))
c = 2**-0.5
scene.add(camera, pose=[[ 1, 0, 0, 0],
[ 0, c, -c, -2],
[ 0, c, c, 2],
[ 0, 0, 0, 1]])
# render scene
r = pyrender.OffscreenRenderer(512, 512)
color, _ = r.render(scene)
plt.figure(figsize=(8,8))
plt.imshow(color)
plt.savefig("test.png")
return str__, "test.png"
if __name__ == "__main__":
with gr.Blocks() as demo:
d1 = gr.Slider(0, 999, value=0, label="Training sample id", info="Choose between 0 and 999")
d2 = gr.Dropdown(field_names_train, value=field_names_train[0], label="Field name")
output1 = gr.Text(label="Training sample info")
output2 = gr.Image(label="Training sample visualization")
# d1.input(update_second, d1, d2)
d1.input(sample_info, [d1, d2], [output1, output2])
d2.input(sample_info, [d1, d2], [output1, output2])
demo.launch()
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