app.py

from PIL import Image
import numpy as np
import io
from huggingface_hub import hf_shiny as hf

# Define function to process uploaded image
def process_image(image):
    # Convert image to numpy array
    img_array = np.array(image)
    patch_size = 128
    step = 128
    all_img_patches = []
    for i in range(0, img_array.shape[0] - patch_size + 1, step):
        for j in range(0, img_array.shape[1] - patch_size + 1, step):
            single_patch_img = img_array[i:i + patch_size, j:j + patch_size]
            all_img_patches.append(single_patch_img)
    images = np.array(all_img_patches)
    # This is just a example to process the image
    processed_images = []
    for img in images:
        processed_image = np.mean(img, axis=-1)  # Example: Convert to grayscale
        processed_images.append(processed_image)
    processed_images = np.array(processed_images)
    return processed_images
# Define Shiny app
app = hf.start()
@app.streamlit_app(
    title="Sidewalk Segmentation App",
    uploaders={"Upload an image": ["jpg", "jpeg", "png"]}
)
def main(uploaded_image):
    # Convert uploaded image to PIL Image
    image = Image.open(io.BytesIO(uploaded_image.read()))

    # Display uploaded image
    app.image(image, caption="Uploaded Image")

    # Process uploaded image
    with app.spinner("Processing..."):
        segmentation_result = process_image(image)

    # Display segmentation result
    app.image(segmentation_result, caption="Sidewalk Segmentation Result")

if __name__ == "__main__":
    app.run()