Spaces:
Running
Running
File size: 5,276 Bytes
883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d 97e9ddc 883f80d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
import os
import gradio as gr
from .compute import run_model
from .utils import load_ct_to_numpy
from .utils import load_pred_volume_to_numpy
from .utils import nifti_to_glb
class WebUI:
def __init__(
self,
model_name: str = None,
cwd: str = "/home/user/app/",
share: int = 1,
):
# global states
self.images = []
self.pred_images = []
# @TODO: This should be dynamically set based on chosen volume size
self.nb_slider_items = 300
self.model_name = model_name
self.cwd = cwd
self.share = share
self.class_name = "airways" # default
self.class_names = {
"airways": "CT_Airways",
}
self.result_names = {
"airways": "Airway",
}
# define widgets not to be rendered immediantly, but later on
self.slider = gr.Slider(
1,
self.nb_slider_items,
value=1,
step=1,
label="Which 2D slice to show",
)
self.volume_renderer = gr.Model3D(
clear_color=[0.0, 0.0, 0.0, 0.0],
label="3D Model",
visible=True,
elem_id="model-3d",
).style(height=512)
def set_class_name(self, value):
print("Changed task to:", value)
self.class_name = value
def combine_ct_and_seg(self, img, pred):
return (img, [(pred, self.class_name)])
def upload_file(self, file):
return file.name
def process(self, mesh_file_name):
path = mesh_file_name.name
run_model(
path,
model_path=os.path.join(self.cwd, "resources/models/"),
task=self.class_names[self.class_name],
name=self.result_names[self.class_name],
)
nifti_to_glb("prediction.nii.gz")
self.images = load_ct_to_numpy(path)
self.pred_images = load_pred_volume_to_numpy("./prediction.nii.gz")
return "./prediction.obj"
def get_img_pred_pair(self, k):
k = int(k) - 1
out = [gr.AnnotatedImage.update(visible=False)] * self.nb_slider_items
out[k] = gr.AnnotatedImage.update(
self.combine_ct_and_seg(self.images[k], self.pred_images[k]),
visible=True,
)
return out
def run(self):
css = """
#model-3d {
height: 512px;
}
#model-2d {
height: 512px;
margin: auto;
}
#upload {
height: 120px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Row():
file_output = gr.File(file_count="single", elem_id="upload")
file_output.upload(self.upload_file, file_output, file_output)
model_selector = gr.Dropdown(
list(self.class_names.keys()),
label="Task",
info="Which task to perform - one model for"
"each brain tumor type and brain extraction",
multiselect=False,
size="sm",
)
model_selector.input(
fn=lambda x: self.set_class_name(x),
inputs=model_selector,
outputs=None,
)
run_btn = gr.Button("Run analysis").style(
full_width=False, size="lg"
)
run_btn.click(
fn=lambda x: self.process(x),
inputs=file_output,
outputs=self.volume_renderer,
)
with gr.Row():
gr.Examples(
examples=[
os.path.join(self.cwd, "test_thorax_CT.nii.gz"),
],
inputs=file_output,
outputs=file_output,
fn=self.upload_file,
cache_examples=True,
)
with gr.Row():
with gr.Box():
with gr.Column():
image_boxes = []
for i in range(self.nb_slider_items):
visibility = True if i == 1 else False
t = gr.AnnotatedImage(
visible=visibility, elem_id="model-2d"
).style(
color_map={self.class_name: "#ffae00"},
height=512,
width=512,
)
image_boxes.append(t)
self.slider.input(
self.get_img_pred_pair, self.slider, image_boxes
)
self.slider.render()
with gr.Box():
self.volume_renderer.render()
# sharing app publicly -> share=True:
# https://gradio.app/sharing-your-app/
# inference times > 60 seconds -> need queue():
# https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
demo.queue().launch(
server_name="0.0.0.0", server_port=7860, share=self.share
)
|