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| # AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb. | |
| # %% auto 0 | |
| __all__ = ['plt', 'learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image'] | |
| # %% ../app.ipynb 2 | |
| import sys | |
| import subprocess | |
| subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio==3.50']) | |
| subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'fastai']) | |
| subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'ipywidgets']) | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| import pathlib as pl | |
| plt = platform.system() | |
| def is_cat(x): return x[0].isupper() | |
| # %% ../app.ipynb 4 | |
| if plt == 'Linux' : pl.WindowsPath = pl.PosixPath | |
| learn = load_learner('model.pkl') | |
| # %% ../app.ipynb 6 | |
| categories = ('Dog', 'Cat') | |
| def classify_image(img): | |
| pred,idx,probs = learn.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| # %% ../app.ipynb 8 | |
| image = gr.Image(height=192, width = 192) | |
| label = gr.Label() | |
| examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg'] | |
| intf = gr.Interface(fn=classify_image, inputs = image, outputs=label, examples = examples) | |
| intf.launch(inline=False) | |