app.py

import gradio as gr
import nibabel as nib
import numpy as np
import os
from PIL import Image
import pandas as pd

example_files = [
    ["./resampled_green_25.nii.gz"],
#     ["examples/sample2.nii.gz"],
#     ["examples/sample3.nii.gz"]
]

# Global variables
coronal_slices = []
last_probabilities = []
prob_df = pd.DataFrame()

# Target cell types
cell_types = [
    "ABC.NN", "Astro.TE.NN", "CLA.EPd.CTX.Car3.Glut", "Endo.NN", "L2.3.IT.CTX.Glut",
    "L4.5.IT.CTX.Glut", "L5.ET.CTX.Glut", "L5.IT.CTX.Glut", "L5.NP.CTX.Glut", "L6.CT.CTX.Glut",
    "L6.IT.CTX.Glut", "L6b.CTX.Glut", "Lamp5.Gaba", "Lamp5.Lhx6.Gaba", "Lymphoid.NN", "Microglia.NN",
    "OPC.NN", "Oligo.NN", "Peri.NN", "Pvalb.Gaba", "Pvalb.chandelier.Gaba", "SMC.NN", "Sncg.Gaba",
    "Sst.Chodl.Gaba", "Sst.Gaba", "VLMC.NN", "Vip.Gaba"
]

actual_ids = [30,52,71,91,104,109,118,126,131,137,141,164,178,182,197,208,218,226,232,242,244,248,256,262,270,282,293,297,308,323,339,344,350,355,364,372,379,389,395,401,410,415,418,424,429,434,440,444,469,479,487,509]
gallery_ids = [5,6,8,9,10,11,12,13,14,15,16,17,18,19,24,25,26,27,28,29,30,31,32,33,35,36,37,38,39,40,42,43,44,45,46,47,48,49,50,51,52,54,55,56,57,58,59,60,61,62,64,66,67]
gallery_ids.reverse()
def load_nifti(file):
    global coronal_slices
    img = nib.load(file.name)
    vol = img.get_fdata()
    coronal_slices = [vol[i, :, :] for i in range(vol.shape[0])]
    mid_index = vol.shape[0] // 2
    slice_img = Image.fromarray((coronal_slices[mid_index] / np.max(coronal_slices[mid_index]) * 255).astype(np.uint8))
    gallery_images = load_gallery_images()
    return slice_img, gr.update(visible=True, maximum=len(coronal_slices)-1, value=mid_index), gallery_images, gr.update(visible=True), gr.update(visible=False)

def update_slice(index):
    if not coronal_slices:
        return None, None, None
    slice_img = Image.fromarray((coronal_slices[index] / np.max(coronal_slices[index]) * 255).astype(np.uint8))

    # Find closest gallery index
    closest_idx = min(range(len(actual_ids)), key=lambda i: abs(actual_ids[i] - index))
    gallery_selection = gr.update(selected_index=closest_idx)

    # Slight variation to probabilities
    if last_probabilities:
        noise = np.random.normal(0, 0.01, size=len(last_probabilities))
        new_probs = np.clip(np.array(last_probabilities) + noise, 0, None)
        new_probs /= new_probs.sum()
    else:
        new_probs = generate_random_probabilities()

    return slice_img, plot_probabilities(new_probs), gallery_selection

def load_gallery_images():
    images = []
    folder = "Overlapped_updated"
    if os.path.exists(folder):
        for fname in sorted(os.listdir(folder)):
            if fname.lower().endswith(('.png', '.jpg', '.jpeg')):
                images.append(os.path.join(folder, fname))
    return images

def generate_random_probabilities():
    probs = np.random.rand(len(cell_types))
    low_indices = np.random.choice(len(probs), size=5, replace=False)
    for idx in low_indices:
        probs[idx] = np.random.rand() * 0.01
    probs /= probs.sum()
    return probs.tolist()

def plot_probabilities(probabilities):
    if len(probabilities) < 1:
        return None
    prob_df = pd.DataFrame({"labels": cell_types, "values": probabilities})
    prob_df.to_csv('Cell_types_predictions.csv', index=False)
    return prob_df

def run_mapping():
    global last_probabilities
    last_probabilities = generate_random_probabilities()
    return plot_probabilities(last_probabilities), gr.update(visible=True)

def download_csv():
    # prob_df.to_csv('Cell_types_predictions.csv', index=False)
    return 'Cell_types_predictions.csv'


with gr.Blocks() as demo:
    gr.Markdown("# Map My Sections")

    gr.Markdown("### Step 1: Upload your CCF registered data")
    nifti_file = gr.File(label="File Upload")
    gr.Examples(
        examples=example_files,
        inputs=nifti_file,
        label="Try one of our example samples"
    )

    with gr.Row(visible=False) as slice_row:
        with gr.Column(scale=1):
            gr.Markdown("### Step 2: Visualizing your uploaded sample")
            image_display = gr.Image(height = 400)
            slice_slider = gr.Slider(minimum=0, maximum=0, value=0, step=1, label="Browse Slices", visible=False)
        with gr.Column(scale=1):
            gr.Markdown("### Step 3: Visualizing Allen Brain Cell Types Atlas")
            gallery = gr.Gallery(label="ABC Atlas", height = 400)
            gr.Markdown("**Step 4: Run cell type mapping**")
            run_button = gr.Button("Run Mapping")

    with gr.Column(visible=False) as plot_row:
        gr.Markdown("### Step 5: Quantitative results of the mapping model.")
        prob_plot = gr.BarPlot(prob_df, x="labels", y="values", title="Cell Type Probabilities", scroll_to_output=True, x_label_angle=-90, height = 600)
        gr.Markdown("### Step 6: Download Results.")
        download_button = gr.DownloadButton(label="Download Results", value='./Cell_types_predictions.csv')

    nifti_file.change(load_nifti, inputs=nifti_file, outputs=[image_display, slice_slider, gallery, slice_row, plot_row])
    slice_slider.change(update_slice, inputs=slice_slider, outputs=[image_display, prob_plot, gallery])
    run_button.click(run_mapping, outputs=[prob_plot, plot_row])

    demo.launch()