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| import gradio as gr | |
| from PIL import Image | |
| import datasets | |
| from datasets import load_dataset | |
| from huggingface_hub import delete_repo | |
| idx = 0 | |
| data_to_label = load_dataset("active-learning/to_label_samples") | |
| imgs = data_to_label["train"]["image"] | |
| def get_image(): | |
| global idx | |
| new_img = imgs[idx] | |
| idx += 1 | |
| return new_img | |
| labeled_data = [] | |
| information = """# Active Learning Demo | |
| This demo showcases Active Learning, which is great when labeling is expensive. In this demo, you will label images by choosing a digit (0-9). | |
| How does this work? | |
| * There is a large pool of unlabeled images | |
| * A model is trained with the few labeled images | |
| * We can then use the model to pick the images with the lowest confidence or with the lowest probability of corresponding to an image. These are the images for which the model is confused, so by improving them, the quality of the model can improve much more than queries for which the model was already doing well! | |
| * In this UI, you will be provided a couple of images to label | |
| * Once all the provided images are labeled, the model is retrained, and a new set of images is chosen! | |
| """ | |
| webhook_info = """## Model Retraining | |
| There are new labeled images. The model is retraining. Follow progress in [here](https://huggingface.co/spaces/active-learning/webhook). | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown(information) | |
| img_to_label = gr.Image(shape=[28,28], value=get_image()) | |
| label_dropdown = gr.Dropdown(choices=[0,1,2,3,4,5,6,7,8,9], interactive=True, value=0) | |
| save_btn = gr.Button("Save label") | |
| output_box = gr.Markdown(value=webhook_info, visible=False) | |
| reload_btn = gr.Button("Reload", visible=False) | |
| def save_data(img, label): | |
| global labeled_data | |
| global idx | |
| labeled_data.append([img, label]) | |
| if len(imgs) == idx : | |
| # Remove dataset of queries to label | |
| # datasets library does not allow pushing an empty dataset, so as a | |
| # workaround we just delete the repo | |
| delete_repo(repo_id="active-learning/to_label_samples", repo_type="dataset") | |
| # Save to dataset | |
| labeled_dataset = load_dataset("active-learning/labeled_samples")["train"] | |
| feature = datasets.Image(decode=False) | |
| for img, label in labeled_data: | |
| # Hack due to https://github.com/huggingface/datasets/issues/4796 | |
| labeled_dataset = labeled_dataset.add_item({ | |
| "image": feature.encode_example(Image.fromarray(img)), | |
| "label": label | |
| }) | |
| labeled_dataset.push_to_hub("active-learning/labeled_samples") | |
| labeled_data = [] | |
| idx = 0 | |
| return { | |
| img_to_label: gr.update(visible=False), | |
| label_dropdown: gr.update(visible=False), | |
| save_btn: gr.update(visible=False), | |
| output_box: gr.update(visible=True), | |
| reload_btn: gr.update(visible=True) | |
| } | |
| else: | |
| return { | |
| img_to_label: gr.update(value=get_image()) | |
| } | |
| def reload_data(): | |
| global data_to_label | |
| global imgs | |
| data_to_label = load_dataset("active-learning/to_label_samples") | |
| imgs = data_to_label["train"]["image"] | |
| if len(imgs) == 0: | |
| return | |
| else: | |
| return { | |
| img_to_label: gr.update(visible=True), | |
| label_dropdown: gr.update(visible=True), | |
| save_btn: gr.update(visible=True), | |
| output_box: gr.update(visible=False), | |
| reload_btn: gr.update(visible=False) | |
| } | |
| save_btn.click( | |
| save_data, | |
| inputs=[img_to_label, label_dropdown], | |
| outputs=[img_to_label, label_dropdown, save_btn, output_box, reload_btn] | |
| ) | |
| reload_btn.click( | |
| reload_data, | |
| outputs=[img_to_label, label_dropdown, save_btn, output_box, reload_btn] | |
| ) | |
| demo.launch(debug=True) |