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import gradio as gr
from huggingface_hub import hf_hub_download
from prediction import run_sequence_prediction
import torch
import torchvision.transforms as T
from celle.utils import process_image
from PIL import Image
from matplotlib import pyplot as plt
def gradio_demo(model_name, sequence_input, image):
model = hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="model.ckpt")
config = hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="config.yaml")
hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="nucleus_vqgan.yaml")
hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="threshold_vqgan.yaml")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if "Finetuned" in model_name:
dataset = "OpenCell"
else:
dataset = "HPA"
nucleus_image = image['image']
protein_image = image['mask']
nucleus_image = process_image(nucleus_image, dataset, "nucleus")
protein_image = process_image(protein_image, dataset, "nucleus")
protein_image = 1.0*(protein_image > .5)
print(f'{nucleus_image=}')
print(f'{protein_image.shape=}')
threshold, heatmap = run_sequence_prediction(
sequence_input=sequence_input,
nucleus_image=nucleus_image,
protein_image=protein_image,
model_ckpt_path=model,
model_config_path=config,
device=device,
)
protein_image = protein_image[0, 0]
protein_image = protein_image * 1.0
# Plot the heatmap
plt.imshow(heatmap.cpu(), cmap="rainbow", interpolation="bicubic")
plt.axis("off")
# Save the plot to a temporary file
plt.savefig("temp.png", bbox_inches="tight", dpi=256)
# Open the temporary file as a PIL image
heatmap = Image.open("temp.png")
return (
T.ToPILImage()(nucleus_image[0, 0]),
T.ToPILImage()(protein_image),
T.ToPILImage()(threshold),
heatmap,
)
with gr.Blocks() as demo:
gr.Markdown("Select the prediction model.")
gr.Markdown(
"CELL-E_2_HPA_2560 is a good general purpose model for various cell types using ICC-IF."
)
gr.Markdown(
"CELL-E_2_OpenCell_2560 is trained on OpenCell and is good more live-cell predictions on HEK cells."
)
with gr.Row():
model_name = gr.Dropdown(
["CELL-E_2_HPA_2560", "CELL-E_2_OpenCell_2560"],
value="CELL-E_2_HPA_2560",
label="Model Name",
)
with gr.Row():
gr.Markdown(
"Input the desired amino acid sequence. GFP is shown below by default."
)
with gr.Row():
sequence_input = gr.Textbox(
value="MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK",
label="Sequence",
)
with gr.Row():
gr.Markdown(
"Uploading a nucleus image is necessary. A random crop of 256 x 256 will be applied if larger. We provide default images in [images](https://huggingface.co/spaces/HuangLab/CELL-E_2/tree/main/images)"
)
gr.Markdown("The protein image is optional and is just used for display.")
with gr.Row().style(equal_height=True):
nucleus_image = gr.Image(
source="upload",
tool="sketch",
label="Nucleus Image",
line_color="white",
interactive=True,
image_mode="L",
type="pil"
)
with gr.Row():
gr.Markdown("Image predictions are show below.")
with gr.Row().style(equal_height=True):
predicted_sequence = gr.Textbox(
label="Predicted Sequence",
)
with gr.Row():
button = gr.Button("Run Model")
inputs = [model_name, sequence_input, nucleus_image]
outputs = [predicted_sequence]
button.click(gradio_demo, inputs, outputs)
demo.launch(share=True)
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