Docker container

Dockerfile

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+# https://huggingface.co/spaces/sayakpaul/demo-docker-gradio/blob/main/Dockerfile
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY *.py /code/
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["python", "app_local.py"]

app_local.py

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+
+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(server_name="0.0.0.0", share=False)