Spaces:
Running
Running
| # import gradio as gr | |
| # def greet(name): | |
| # return "Hello " + name + "!!" | |
| # iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| # iface.launch() | |
| __all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf'] | |
| import gradio as gr | |
| from fastai.vision.all import * | |
| from pathlib import Path | |
| model_path = Path('models') | |
| image_path = Path('images') | |
| cloud_categories = ( | |
| 'Cirrus', | |
| 'Cirrostratus', | |
| 'Cirrocumulus', | |
| 'Altostratus', | |
| 'Altocumulus', | |
| 'Stratus', | |
| 'Stratocumulus', | |
| 'Nimbostratus', | |
| 'Cumulus', | |
| 'Cumulonimbus', | |
| 'Lenticular' | |
| ) | |
| cloud_examples = [image_path / f"{c}.jpg" for c in cloud_categories] | |
| cloud_learner = load_learner(model_path/'cloudmodel.pkl') | |
| def is_cat(x): return x[0].isupper() | |
| learn = load_learner('model.pkl') | |
| categories = ('Dog', 'Cat') | |
| def classify_image(img): | |
| pred, idx, probs = learn.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| image= gr.inputs.Image(shape=(192, 192)) | |
| label = gr.outputs.Label() | |
| examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg'] | |
| intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) | |
| intf.launch(inline=False) |