| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| imported_learn = load_learner("model.pkl") | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred, pred_idx, probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| title = "Redacted Document Classifier" | |
| description = "A classifier trained on publicly released redacted (and unredacted) FOIA documents, using fastai." | |
| article = "<p style='text-align: center'><a href='https://mlops.systems/fastai/redactionmodel/computervision/datalabelling/2021/09/06/redaction-classification-chapter-2.html' target='_blank'>Blog post</a></p>" | |
| examples = [ | |
| "test1.jpg", | |
| "test2.jpg", | |
| "test3.jpg", | |
| "test4.jpg", | |
| "test5.jpg", | |
| ] | |
| interpretation = "default" | |
| # interpretation='shap' | |
| enable_queue = True | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.inputs.Image(shape=(512, 512)), | |
| outputs=gr.outputs.Label(num_top_classes=3), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| interpretation=interpretation, | |
| enable_queue=enable_queue, | |
| ).launch() | |