| from icevision.all import * | |
| import gradio as gr | |
| learn = load_learner('fasterRCNNKangaroo.pth') | |
| 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))} | |
| gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128,128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['00001.jpg','00002.jpg']).launch(share=False) |