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import gradio as gr |
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from prediction import run_image_prediction |
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import torch |
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import torchvision.transforms as T |
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from celle.utils import process_image |
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from PIL import Image |
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from matplotlib import pyplot as plt |
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def gradio_demo(model_name, sequence_input, nucleus_image, protein_image): |
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model = f"CELL-E_2-Image_Prediction/models/{model_name}.ckpt" |
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config = f"CELL-E_2-Image_Prediction/models/{model_name}.yaml" |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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if "Finetuned" in model_name: |
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dataset = "OpenCell" |
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else: |
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dataset = "HPA" |
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nucleus_image = process_image(nucleus_image, dataset, "nucleus") |
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if protein_image: |
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protein_image = process_image(protein_image, dataset, "protein") |
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protein_image = protein_image > torch.median(protein_image) |
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protein_image = protein_image[0, 0] |
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protein_image = protein_image * 1.0 |
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else: |
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protein_image = torch.ones((256, 256)) |
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threshold, heatmap = run_image_prediction( |
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sequence_input=sequence_input, |
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nucleus_image=nucleus_image, |
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model_ckpt_path=model, |
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model_config_path=config, |
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device=device, |
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) |
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plt.imshow(heatmap.cpu(), cmap="rainbow", interpolation="bicubic") |
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plt.axis("off") |
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plt.savefig("temp.png", bbox_inches="tight", dpi=256) |
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heatmap = Image.open("temp.png") |
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return ( |
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T.ToPILImage()(nucleus_image[0, 0]), |
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T.ToPILImage()(protein_image), |
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T.ToPILImage()(threshold), |
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heatmap, |
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) |
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with gr.Blocks(theme='gradio/soft') as demo: |
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gr.Markdown("Select the prediction model.") |
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gr.Markdown( |
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"CELL-E_2_HPA_480 is a good general purpose model for various cell types using ICC-IF." |
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) |
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gr.Markdown( |
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"CELL-E_2_HPA_Finetuned_480 is finetuned on OpenCell and is good more live-cell predictions on HEK cells." |
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) |
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with gr.Row(): |
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model_name = gr.Dropdown( |
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["CELL-E_2-HPA_480", "CELL-E_2-HPA_Finetuned_480"], |
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value="CELL-E_2-HPA_480", |
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label="Model Name", |
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) |
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with gr.Row(): |
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gr.Markdown( |
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"Input the desired amino acid sequence. GFP is shown below by default." |
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) |
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with gr.Row(): |
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sequence_input = gr.Textbox( |
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value="MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK", |
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label="Sequence", |
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) |
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with gr.Row(): |
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gr.Markdown( |
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"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)" |
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) |
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gr.Markdown("The protein image is optional and is just used for display.") |
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with gr.Row().style(equal_height=True): |
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nucleus_image = gr.Image( |
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type="pil", |
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label="Nucleus Image", |
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image_mode="L", |
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) |
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protein_image = gr.Image(type="pil", label="Protein Image (Optional)") |
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with gr.Row(): |
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gr.Markdown("Image predictions are show below.") |
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with gr.Row().style(equal_height=True): |
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nucleus_image_crop = gr.Image(type="pil", label="Nucleus Image", image_mode="L") |
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protein_threshold_image = gr.Image( |
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type="pil", label="Protein Threshold Image", image_mode="L" |
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) |
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predicted_threshold_image = gr.Image( |
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type="pil", label="Predicted Threshold image", image_mode="L" |
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) |
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predicted_heatmap = gr.Image(type="pil", label="Predicted Heatmap") |
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with gr.Row(): |
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button = gr.Button("Run Model") |
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inputs = [model_name, sequence_input, nucleus_image, protein_image] |
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outputs = [ |
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nucleus_image_crop, |
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protein_threshold_image, |
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predicted_threshold_image, |
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predicted_heatmap, |
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] |
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button.click(gradio_demo, inputs, outputs) |
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demo.launch(enable_queue=True) |
