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| import platform | |
| import pathlib | |
| plt = platform.system() | |
| pathlib.WindowsPath = pathlib.PosixPath | |
| import requests | |
| # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb. | |
| # %% auto 0 | |
| __all__ = ['learn', 'categories', 'image', 'label', 'examples', 'interface', 'classify_image'] | |
| # %% app.ipynb 1 | |
| from fastai.vision.all import * | |
| import PIL.Image | |
| PIL.Image.MAX_IMAGE_PIXELS = None | |
| from PIL import Image | |
| import gradio as gr | |
| # %% app.ipynb 2 | |
| learn = load_learner('Binarymodel.pkl') | |
| # %% app.ipynb 3 | |
| categories=('Brain','Chest','Random') | |
| def classify_image(img): | |
| pred,indx,probs=learn.predict(img) | |
| return dict(zip(categories,map(float,probs))) | |
| # %% app.ipynb 4 | |
| image=gr.inputs.Image(shape=(512,512)) | |
| label=gr.outputs.Label() | |
| examples=['1.jpg','10.jpg','2.jpeg','9.jpg','3.jpg','4.jpg','5.jpg','6.png','7.jpg', | |
| '8.jpeg','9.jpg','b.jpg','d.jpg','f.jpg','e.jpg'] | |
| interface=gr.Interface(fn=classify_image, inputs=image ,outputs=label,examples=examples) | |
| interface.launch(inline=False) | |