yigewu
commited on
Commit
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Parent(s):
eaf12c0
finished app.py
Browse files- app.ipynb +0 -21
- app.py +24 -4
- app/app.py +0 -27
app.ipynb
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@@ -381,27 +381,6 @@
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"# *export*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'notebook2script' from 'nbdev.export' (/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/nbdev/export.py)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn [27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnbdev\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexport\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notebook2script\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'notebook2script' from 'nbdev.export' (/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/nbdev/export.py)"
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]
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}
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],
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"source": [
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"from nbdev.export import notebook2script"
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]
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},
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"cell_type": "code",
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"execution_count": 28,
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"# *export*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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app.py
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import gradio as gr
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
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# %% auto 0
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__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
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# %% ../app.ipynb 1
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from fastai.vision.all import *
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import gradio as gr
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# %% ../app.ipynb 7
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from fastcore.all import *
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learn = load_learner('donut_model2.pkl')
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# %% ../app.ipynb 9
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categories = ('donut', 'scone')
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def classify_image(img):
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pred,idx,probs = learn.predict(img)
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return dict(zip(categories, map(float, probs)))
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# %% ../app.ipynb 11
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image = gr.inputs.Image(shape = (192,192))
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label = gr.outputs.Label()
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examples = ['donut.jpg', 'scone.jpg']
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intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)
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intf.launch(inline=False, share=True)
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app/app.py
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
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# %% auto 0
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__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
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# %% ../app.ipynb 1
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from fastai.vision.all import *
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import gradio as gr
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# %% ../app.ipynb 7
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from fastcore.all import *
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learn = load_learner('donut_model2.pkl')
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# %% ../app.ipynb 9
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categories = ('donut', 'scone')
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def classify_image(img):
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pred,idx,probs = learn.predict(img)
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return dict(zip(categories, map(float, probs)))
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# %% ../app.ipynb 11
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image = gr.inputs.Image(shape = (192,192))
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label = gr.outputs.Label()
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examples = ['donut.jpg', 'scone.jpg']
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intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)
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intf.launch(inline=False, share=True)
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