| import gradio as gr | |
| from fastai.vision.all import load_learner | |
| learner = load_learner('saved_model/beaverdam.pkl') | |
| categories = ('Beaver Dam', 'Not a Beaver Dam') | |
| def is_it_a_beaver_dam(input_img): | |
| pred, idx, probs = learner.predict(input_img) | |
| return f'{pred} {dict(zip(categories, map(float, probs)))}' | |
| demo = gr.Interface(fn=is_it_a_beaver_dam, inputs=gr.Image(shape=(200, 200)), outputs=gr.Label()) | |
| demo.launch() |