import gradio as gr from tifffile import imread from PIL import Image from path_analysis.analyse import analyse_paths import numpy as np # Function to preview the imported image def preview_image(file1): if file1: print('Uploading image', file1.name) im = imread(file1.name) print(im.ndim, im.shape) if im.ndim>2: return Image.fromarray(np.max(im, axis=0)) else: return Image.fromarray(im) else: return None with gr.Blocks() as demo: with gr.Row(): with gr.Column(): # Inputs for cell ID, image, and path cellid_input = gr.Textbox(label="Cell ID", placeholder="Image_1") image_input = gr.File(label="Input foci image") image_preview = gr.Image(label="Max projection of foci image") image_input.change(fn=preview_image, inputs=image_input, outputs=image_preview) path_input = gr.File(label="SNT traces file") # Additional options wrapped in an accordion for better UI experience with gr.Accordion("Additional options ..."): sphere_radius = gr.Number(label="Trace sphere radius (um)", value=0.1984125, interactive=True) peak_threshold = gr.Number(label="Peak relative threshold", value=0.4, interactive=True) # Resolutions for xy and z axis with gr.Row(): xy_res = gr.Number(label='xy-yesolution (um)', value=0.0396825, interactive=True) z_res = gr.Number(label='z resolution (um)', value=0.0909184, interactive=True) # Resolutions for xy and z axis threshold_type = gr.Radio(["per-trace", "per-cell"], label="Threshold-type", value="per-trace", interactive=True) use_corrected_positions = gr.Checkbox(label="Correct foci position measurements", value=True, interactive=True) screening_distance = gr.Number(label='Screening distance (voxels)', value=10, interactive=True) # The output column showing the result of processing with gr.Column(): trace_output = gr.Image(label="Overlayed paths") image_output=gr.Gallery(label="Traced paths") plot_output=gr.Plot(label="Foci intensity traces") data_output=gr.DataFrame(label="Detected peak data")#, "Peak 1 pos", "Peak 1 int"]) data_file_output=gr.File(label="Output data file (.csv)") def process(cellid_input, image_input, path_input, sphere_radius, peak_threshold, xy_res, z_res, threshold_type, use_corrected_positions, screening_distance): config = { 'sphere_radius': sphere_radius, 'peak_threshold': peak_threshold, 'xy_res': xy_res, 'z_res': z_res, 'threshold_type': threshold_type, 'use_corrected_positions': use_corrected_positions, 'screening_distance': screening_distance, } paths, traces, fig, extracted_peaks = analyse_paths(cellid_input, image_input.name, path_input.name, config) extracted_peaks.to_csv('output.csv') print('extracted', extracted_peaks) return paths, [Image.fromarray(im) for im in traces], fig, extracted_peaks, 'output.csv' with gr.Row(): greet_btn = gr.Button("Process") greet_btn.click(fn=process, inputs=[cellid_input, image_input, path_input, sphere_radius, peak_threshold, xy_res, z_res, threshold_type, use_corrected_positions, screening_distance], outputs=[trace_output, image_output, plot_output, data_output, data_file_output], api_name="process") if __name__ == "__main__": demo.launch()