|
import os |
|
import gradio as gr |
|
from tools.predict import single_prediction |
|
|
|
KAGGLE_NOTEBOOK = "[](https://www.kaggle.com/code/mmenendezg/mobilevit-fluorescent-neuronal-cells/notebook)" |
|
GITHUB_REPOSITORY = "[](https://github.com/mmenendezg/mobilevit-fluorescent-cells)" |
|
HF_SPACE = "[](https://huggingface.co/spaces/mmenendezg/mobilevit-fluorescent-neuronal-cells)" |
|
|
|
|
|
demo = gr.Blocks() |
|
with demo: |
|
gr.Markdown( |
|
f""" |
|
# Fluorescent Neuronal Cells Segmentation |
|
|
|
This model extracts a segmentation mask of the neuronal cells on an image. |
|
|
|
{KAGGLE_NOTEBOOK} |
|
|
|
{GITHUB_REPOSITORY} |
|
|
|
{HF_SPACE} |
|
""" |
|
) |
|
with gr.Tab("Image Segmentation"): |
|
with gr.Row(): |
|
with gr.Column(): |
|
uploaded_image = gr.Image( |
|
label="Neuronal Cells Image", |
|
sources=["upload", "clipboard"], |
|
type="pil", |
|
height=550, |
|
) |
|
with gr.Column(): |
|
mask_image = gr.Image(label="Segmented Neurons", height=550) |
|
with gr.Row(): |
|
classify_btn = gr.Button("Segment the image", variant="primary") |
|
clear_btn = gr.ClearButton(components=[uploaded_image, mask_image]) |
|
classify_btn.click( |
|
fn=single_prediction, inputs=uploaded_image, outputs=[mask_image] |
|
) |
|
gr.Examples( |
|
examples=[ |
|
os.path.join(os.path.dirname(__file__), "examples/example_1.png"), |
|
os.path.join(os.path.dirname(__file__), "examples/example_2.png"), |
|
], |
|
inputs=uploaded_image, |
|
) |
|
demo.launch(show_error=True) |
|
|
