from fastai.vision import * import gradio as gr from pathlib import Path from fastbook import load_learner model_path = Path('models') image_path = Path('images') learn = load_learner(model_path/'cloudmodel.pk1') categories = ( 'Cirrus', 'Cirrostratus', 'Cirrocumulus', 'Altostratus', 'Altocumulus', 'Stratus', 'Stratocumulus', 'Nimbostratus', 'Cumulus', 'Cumulonimbus', 'Lenticular' ) image = gr.inputs.Image(shape=(192,192)) label = gr.outputs.Label() learn = load_learner(model_path/'cloudmodel.pkl') examples = [image_path / f"{c}.jpg" for c in categories] def classify_image(img): pred,idx,probs = learn.predict(img) return dict(zip(categories, map(float, probs))) iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) iface.launch()