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| import gradio as gr | |
| from PIL import Image, ImageOps | |
| import numpy as np | |
| from tibblingai import wta | |
| import matplotlib.pyplot as plt # needed for plt.imread | |
| def load_sample_images(): | |
| sample_paths = ["sample1.jpg", "sample2.jpg"] # Must be in the same folder as your script | |
| original_imgs = [Image.open(path) for path in sample_paths] | |
| processed_imgs = [process_image(path) for path in sample_paths] | |
| return gr.update(visible=True, value=original_imgs), gr.update(visible=True, value=processed_imgs) | |
| # Image processing function (you can replace this) | |
| def process_image(img: Image.Image) -> Image.Image: | |
| # Read image using matplotlib | |
| img_np = plt.imread(img) | |
| if img_np.ndim == 3 and img_np.shape[2] > 3: | |
| img_np = img_np[:, :, :3] | |
| # Convert to grayscale for original image display | |
| gray_img = ImageOps.grayscale(Image.fromarray((img_np * 255).astype(np.uint8))) | |
| # Run your WTA processing (dummy if not available) | |
| # Replace this line with actual WTA processing | |
| img_tensor = wta.wta(img_np).numpy() # Assuming returns shape (1, H, W) | |
| # Convert processed image to inferno colormap | |
| inferno_colored = plt.cm.inferno(img_tensor[0]) | |
| inferno_img = Image.fromarray((inferno_colored[:, :, :3] * 255).astype(np.uint8)) | |
| return gray_img, inferno_img | |
| # Function to process uploaded images | |
| def process_images(images): | |
| if not isinstance(images, list): | |
| images = [images] | |
| original_imgs = [] | |
| processed_imgs = [] | |
| for img in images: | |
| original_gray_scale_img, inferno_img = process_image(img) | |
| original_imgs.append(original_gray_scale_img) | |
| processed_imgs.append(inferno_img) | |
| return gr.update(visible=True, value=original_imgs), gr.update(visible=True, value=processed_imgs) | |
| # Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## Upload Image(s) for Processing") | |
| # File component (no preview shown here) | |
| file_input = gr.File(file_types=["image"],file_count="multiple", label="Upload Images") | |
| with gr.Row(): | |
| original_gallery = gr.Gallery(label="Original Images", visible=False) | |
| processed_gallery = gr.Gallery(label="Processed Images", visible=False) | |
| file_input.change(fn=process_images, inputs=file_input, outputs=[original_gallery, processed_gallery]) | |
| with gr.Row(): | |
| with gr.Column(scale=0, min_width=300): | |
| load_examples_btn = gr.Button("Run WTA on sample images") | |
| load_examples_btn.click(fn=load_sample_images, outputs=[original_gallery, processed_gallery]) | |
| # 🆕 Examples section | |
| # examples = gr.Examples( | |
| # examples=[["sample1.jpg"], ["sample2.jpg"]], | |
| # inputs=file_input, | |
| # label="Example Images" | |
| # ) | |
| # example_files = [["sample1.jpg", "sample2.jpg"], ["sample2.jpg"]] | |
| # gr.Examples(examples=example_files, inputs=[file_input], label="Try one of our example samples") | |
| demo.launch(debug=True) | |