| import gradio as gr |
| from fastai.vision.all import * |
| import skimage |
|
|
| learn = load_learner('model.pkl') |
|
|
| categories = ('CYST', 'FA', 'IDC') |
|
|
| def classify_image(img): |
| pred,idx,probs = learn.predict(img) |
| return dict(zip(categories, map(float,probs))) |
|
|
| title = "Ultrasound Tumor Classifier" |
| description = "An ultrasound tumor classifier trained on a small dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces." |
|
|
| image = gr.inputs.Image() |
| label = gr.outputs.Label() |
| examples = ['CYST.png', 'FA.png', 'IDC.png'] |
| interpretation='default' |
|
|
| intf = gr.Interface(fn=classify_image, inputs=image, outputs=label,title=title,description=description, examples=examples, interpretation=interpretation) |
| intf.launch(inline=False) |
|
|
