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| from fastai.vision.all import * | |
| import gradio as gr | |
| import pathlib | |
| pathlib.WindowsPath = pathlib.PosixPath | |
| learn = load_learner("export.pkl") | |
| categories = ('CT', 'MRI', "Ultrasound", "X-Ray") | |
| 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 = ['mri.jpg','ct.jpg','ultrasound.jpg', 'xray.jpg'] | |
| title = 'Medical Image Classifier' | |
| description = 'You need to know whether you are dealing with an MRI , X-Ray, CT, or Ultrasound image, and you need an answer fast? Then you have come to the right place. Upload the picture to classify it.' | |
| article = "Author: <a href=\"https://huggingface.co/archietram\">Archie Tram</a>. " | |
| intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, title=title, description=description, article=article) | |
| intf.launch(inline=False) |