import gradio as gr
from huggingface_hub import hf_hub_download
from prediction import run_image_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, nucleus_image, protein_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 = process_image(nucleus_image, dataset, "nucleus")
    if protein_image:
        protein_image = process_image(protein_image, dataset, "protein")
        protein_image = protein_image > torch.median(protein_image)
        protein_image = protein_image[0, 0]
        protein_image = protein_image * 1.0
    else:
        protein_image = torch.ones((256, 256))

    threshold, heatmap = run_image_prediction(
        sequence_input=sequence_input,
        nucleus_image=nucleus_image,
        model_ckpt_path=model,
        model_config_path=config,
        device=device,
    )

    # 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_480 is a good general purpose model for various cell types using ICC-IF."
    )
    gr.Markdown(
        "CELL-E_2_HPA_Finetuned_480 is finetuned on OpenCell and is good more live-cell predictions on HEK cells."
    )
    with gr.Row():
        model_name = gr.Dropdown(
            ["CELL-E_2_HPA_480", "CELL-E_2_HPA_Finetuned_480"],
            value="CELL-E_2_HPA_480",
            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(
            type="pil",
            label="Nucleus Image",
            image_mode="L",
        )

        protein_image = gr.Image(type="pil", label="Protein Image (Optional)")

    with gr.Row():
        gr.Markdown("Image predictions are show below.")

    with gr.Row().style(equal_height=True):
        nucleus_image_crop = gr.Image(type="pil", label="Nucleus Image", image_mode="L")

        protein_threshold_image = gr.Image(
            type="pil", label="Protein Threshold Image", image_mode="L"
        )

        predicted_threshold_image = gr.Image(
            type="pil", label="Predicted Threshold image", image_mode="L"
        )

        predicted_heatmap = gr.Image(type="pil", label="Predicted Heatmap")
    with gr.Row():
        button = gr.Button("Run Model")

        inputs = [model_name, sequence_input, nucleus_image, protein_image]

        outputs = [
            nucleus_image_crop,
            protein_threshold_image,
            predicted_threshold_image,
            predicted_heatmap,
        ]

        button.click(gradio_demo, inputs, outputs)

demo.launch(enable_queue=True)