| import gradio as gr | |
| import skimage | |
| from fastai.learner import load_learner | |
| from fastai.vision.all import * | |
| from huggingface_hub import hf_hub_download | |
| learn = load_learner( | |
| hf_hub_download("strickvl/redaction-classifier-fastai", "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 Image Classifier" | |
| description = "A classifier trained on publicly released redacted (and unredacted) FOIA documents, using [fastai](https://github.com/fastai/fastai)." | |
| with open("article.md") as f: | |
| article = f.read() | |
| examples = [ | |
| "test1.jpg", | |
| "test2.jpg", | |
| "test3.jpg", | |
| "test4.jpg", | |
| "test5.jpg", | |
| ] | |
| interpretation = "default" | |
| enable_queue = True | |
| theme = "default" | |
| allow_flagging = "never" | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.inputs.Image(shape=(1024, 1024)), | |
| outputs=gr.outputs.Label(num_top_classes=3), | |
| title=title, | |
| description=description, | |
| article=article, | |
| theme=theme, | |
| allow_flagging=allow_flagging, | |
| examples=examples, | |
| interpretation=interpretation, | |
| ) | |
| demo.launch( | |
| cache_examples=True, | |
| enable_queue=enable_queue, | |
| ) | |