Spaces:
Runtime error
Runtime error
Commit
·
f72940a
1
Parent(s):
740d542
Gets notebook code exports working. Prepares app.py for Spaces
Browse files- app.py +41 -0
- lesson_2.ipynb +189 -323
app.py
ADDED
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# AUTOGENERATED! DO NOT EDIT! File to edit: lesson_2.ipynb.
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# %% auto 0
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__all__ = ['dls', 'labels', 'interface', 'predict']
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# %% lesson_2.ipynb 4
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from fastai.vision.all import *
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from fastai.collab import *
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if os.path.isfile('export.pkl'):
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learn: Learner = load_learner('export.pkl')
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else:
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path = untar_data(URLs.PETS)
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dls = ImageDataLoaders.from_name_re(path, get_image_files(path/'images'), pat='(.+)_\d+.jpg', item_tfms=Resize(460), batch_tfms=aug_transforms(size=224, min_scale=0.75))
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learn = vision_learner(dls, models.resnet50, metrics=accuracy)
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learn.fine_tune(1)
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learn.path = Path('.')
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learn.export()
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# %% lesson_2.ipynb 5
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dls: DataLoaders = learn.dls
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labels = dls.vocab
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def predict(image):
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image = PILImage.create(image)
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prediction, prediction_index, probabilities = learn.predict(image)
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return { labels[i]: float(probabilities[i]) for i in range(len(labels))}
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# %% lesson_2.ipynb 7
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import gradio
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interface = gradio.Interface(
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fn=predict,
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inputs=gradio.Image(height=512, width=512),
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outputs=gradio.Label(num_top_classes=3),
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title="Dog Breed Classifier",
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description="Upload a photo of a dog and we'll tell you the likely breeds",
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article="This is Roman, he's a Red Nosed Pitbull (aka. American Pit-Bull Terrier) from New Orleans.\n\nTo try out the classifier, tap on his image to load him into the input box above and then press submit.",
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examples=['roman.png']
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)
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interface.launch(share=True)
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lesson_2.ipynb
CHANGED
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a8bd8621-65ae-4cb2-bdac-12c3b97a0de5",
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"2) Add \"#|default_exp [file_name]\" to the top of your notebook\n",
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"3) Add \"#|export\" to the top of each cell you want to export\n",
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"4) run:\n",
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" import nbdev\n",
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"cell_type": "
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"id": "533b4754-10d9-44bc-9894-a50523aec091",
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"metadata": {},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 2,
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"id": "
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"metadata": {},
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"outputs": [
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{
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"name": "
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: execnb>=0.1.4 in /usr/local/lib/python3.9/site-packages (from nbdev) (0.1.5)\n",
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"Requirement already satisfied: astunparse in /usr/local/lib/python3.9/site-packages (from nbdev) (1.6.3)\n",
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"Requirement already satisfied: ipython in /usr/local/lib/python3.9/site-packages (from execnb>=0.1.4->nbdev) (8.18.1)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.9/site-packages (from fastcore>=1.5.27->nbdev) (23.0)\n",
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"\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n",
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"\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
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"source": [
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"metadata": {},
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{
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"ename": "NameError",
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"evalue": "name 'mx' is not defined",
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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;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#|export\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mmx\u001b[49m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'mx' is not defined"
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"source": [
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"#|export\n",
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"jupyterlab_server 2.25.4\n",
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"jupyterlab_widgets 3.0.10\n",
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"kaggle 1.6.6\n",
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"keras 2.15.0\n",
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"libclang 16.0.6\n",
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"lxml 5.1.0\n",
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"Markdown 3.5.1\n",
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"MarkupSafe 2.1.3\n",
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"matplotlib-inline 0.1.6\n",
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"mistune 3.0.2\n",
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"ml-dtypes 0.2.0\n",
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"mypy-extensions 1.0.0\n",
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"nbdev 2.3.13\n",
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"nbformat 5.10.3\n",
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"nest-asyncio 1.6.0\n",
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"notebook 7.1.2\n",
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"notebook_shim 0.2.4\n",
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"numpy 1.26.2\n",
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"oauthlib 3.2.2\n",
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"opencv-python 4.9.0.80\n",
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"opt-einsum 3.3.0\n",
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"overrides 7.7.0\n",
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"packaging 23.0\n",
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"pathspec 0.11.1\n",
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"pexpect 4.9.0\n",
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"pip 24.0\n",
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"platformdirs 3.1.1\n",
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"prometheus_client 0.20.0\n",
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"simpleai 0.8.3\n",
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"tensorboard 2.15.1\n",
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"tinycss2 1.2.1\n",
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"uri-template 1.3.0\n",
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"urllib3 2.1.0\n",
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"wcwidth 0.2.13\n",
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|
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"Note: you may need to restart the kernel to use updated packages.\n"
|
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]
|
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}
|
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],
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"source": [
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"pip
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"outputs": [
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{
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"ename": "NameError",
|
| 389 |
-
"evalue": "name 'untar_data' is not defined",
|
| 390 |
-
"output_type": "error",
|
| 391 |
-
"traceback": [
|
| 392 |
-
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
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-
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
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-
"Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mall\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m----> 2\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[43muntar_data\u001b[49m(URLs\u001b[38;5;241m.\u001b[39mPETS)\n\u001b[1;32m 3\u001b[0m dls \u001b[38;5;241m=\u001b[39m ImageDataLoaders\u001b[38;5;241m.\u001b[39mfrom_name_re(path, get_image_files(path\u001b[38;5;241m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimages\u001b[39m\u001b[38;5;124m'\u001b[39m), pat\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(.+)_\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124md+.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m, item_tfms\u001b[38;5;241m=\u001b[39mResize(\u001b[38;5;241m460\u001b[39m), batch_tfms\u001b[38;5;241m=\u001b[39maug_transforms(size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m224\u001b[39m, min_scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.75\u001b[39m))\n\u001b[1;32m 4\u001b[0m learn \u001b[38;5;241m=\u001b[39m vision_learner(dls, models\u001b[38;5;241m.\u001b[39mresnet50, metrics\u001b[38;5;241m=\u001b[39maccuracy)\n",
|
| 395 |
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"\u001b[0;31mNameError\u001b[0m: name 'untar_data' is not defined"
|
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]
|
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}
|
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],
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"source": [
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"
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| 404 |
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|
| 405 |
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"learn.path = Path('.')\n",
|
| 406 |
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"learn.export()"
|
| 407 |
]
|
| 408 |
}
|
| 409 |
],
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| 1 |
{
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| 2 |
"cells": [
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| 3 |
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{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"id": "2904c251",
|
| 7 |
+
"metadata": {},
|
| 8 |
+
"outputs": [],
|
| 9 |
+
"source": [
|
| 10 |
+
"#|default_exp app"
|
| 11 |
+
]
|
| 12 |
+
},
|
| 13 |
+
{
|
| 14 |
+
"cell_type": "markdown",
|
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"id": "8f566fe5",
|
| 16 |
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"metadata": {},
|
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"source": [
|
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+
"# Export instructions"
|
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+
]
|
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+
},
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{
|
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"cell_type": "markdown",
|
| 23 |
"id": "a8bd8621-65ae-4cb2-bdac-12c3b97a0de5",
|
|
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|
| 28 |
"2) Add \"#|default_exp [file_name]\" to the top of your notebook\n",
|
| 29 |
"3) Add \"#|export\" to the top of each cell you want to export\n",
|
| 30 |
"4) run:\n",
|
| 31 |
+
"```python\n",
|
| 32 |
" import nbdev\n",
|
| 33 |
+
" notebook_name = \"lesson_2.ipynb\"\n",
|
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+
" export_destination = \".\" # the root directory\n",
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" nbdev.export.nb_export(notebook_name, export_destination)\n",
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+
"```"
|
| 37 |
]
|
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},
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{
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"cell_type": "markdown",
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"id": "6f087d39-106b-46a2-8623-f4429e2885f1",
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"metadata": {},
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"source": [
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+
"# Code"
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| 45 |
]
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| 46 |
},
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| 47 |
{
|
| 48 |
"cell_type": "code",
|
| 49 |
"execution_count": 2,
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+
"id": "746c335b-5825-4032-9ee9-834d947ba01d",
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"metadata": {},
|
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"outputs": [
|
| 53 |
{
|
| 54 |
+
"name": "stderr",
|
| 55 |
"output_type": "stream",
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| 56 |
"text": [
|
| 57 |
+
"/Users/charlesfeinn/miniforge-pypy3/envs/default/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'dlopen(/Users/charlesfeinn/miniforge-pypy3/envs/default/lib/python3.9/site-packages/torchvision/image.so, 0x0006): Symbol not found: __ZN3c1017RegisterOperatorsD1Ev\n",
|
| 58 |
+
" Referenced from: <CC4BC91F-8B6A-3F9A-B9EB-A2B9D578E202> /Users/charlesfeinn/miniforge-pypy3/envs/default/lib/python3.9/site-packages/torchvision/image.so\n",
|
| 59 |
+
" Expected in: <B36C132B-E822-3FDA-9BEC-DD9DB8197D06> /Users/charlesfeinn/miniforge-pypy3/envs/default/lib/python3.9/site-packages/torch/lib/libtorch_cpu.dylib'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
|
| 60 |
+
" warn(\n"
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]
|
| 62 |
}
|
| 63 |
],
|
| 64 |
"source": [
|
| 65 |
+
"#|export\n",
|
| 66 |
+
"from fastai.vision.all import *\n",
|
| 67 |
+
"from fastai.collab import *\n",
|
| 68 |
+
"\n",
|
| 69 |
+
"if os.path.isfile('export.pkl'):\n",
|
| 70 |
+
" learn: Learner = load_learner('export.pkl')\n",
|
| 71 |
+
"else:\n",
|
| 72 |
+
" path = untar_data(URLs.PETS)\n",
|
| 73 |
+
" dls = ImageDataLoaders.from_name_re(path, get_image_files(path/'images'), pat='(.+)_\\d+.jpg', item_tfms=Resize(460), batch_tfms=aug_transforms(size=224, min_scale=0.75))\n",
|
| 74 |
+
" learn = vision_learner(dls, models.resnet50, metrics=accuracy)\n",
|
| 75 |
+
" learn.fine_tune(1)\n",
|
| 76 |
+
" learn.path = Path('.')\n",
|
| 77 |
+
" learn.export()"
|
| 78 |
]
|
| 79 |
},
|
| 80 |
{
|
| 81 |
"cell_type": "code",
|
| 82 |
+
"execution_count": 3,
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"id": "d670bc7e",
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"def predict(image):\n",
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" image = PILImage.create(image)\n",
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}
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],
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"source": [
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"metadata": {},
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"outputs": [
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"/Users/charlesfeinn/miniforge-pypy3/envs/default/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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"name": "stdout",
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"text": [
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"Running on local URL: http://127.0.0.1:7860\n",
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"Running on public URL: https://95de38d55a9ae84b0c.gradio.live\n",
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"\n",
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| 203 |
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"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
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"source": [
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"#|export\n",
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"import gradio\n",
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"interface = gradio.Interface(\n",
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" fn=predict, \n",
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" inputs=gradio.Image(height=512, width=512), \n",
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" outputs=gradio.Label(num_top_classes=3),\n",
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" title=\"Dog Breed Classifier\",\n",
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" description=\"Upload a photo of a dog and we'll tell you the likely breeds\",\n",
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" article=\"This is Roman, he's a Red Nosed Pitbull (aka. American Pit-Bull Terrier) from New Orleans.\\n\\nTo try out the classifier, tap on his image to load him into the input box above and then press submit.\",\n",
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" examples=['roman.png']\n",
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| 253 |
"Note: you may need to restart the kernel to use updated packages.\n"
|
| 254 |
]
|
| 255 |
}
|
| 256 |
],
|
| 257 |
"source": [
|
| 258 |
+
"%pip install jupyterlab-quarto"
|
| 259 |
]
|
| 260 |
},
|
| 261 |
{
|
| 262 |
"cell_type": "code",
|
| 263 |
+
"execution_count": 7,
|
| 264 |
+
"id": "342a044e",
|
| 265 |
"metadata": {},
|
| 266 |
+
"outputs": [],
|
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| 267 |
"source": [
|
| 268 |
+
"import nbdev\n",
|
| 269 |
+
"\n",
|
| 270 |
+
"notebook_name = \"lesson_2.ipynb\"\n",
|
| 271 |
+
"export_destination = \".\" # the root directory\n",
|
| 272 |
+
"nbdev.export.nb_export(notebook_name, export_destination)"
|
|
|
|
|
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|
| 273 |
]
|
| 274 |
}
|
| 275 |
],
|