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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|default_exp app \n",
"\n",
"# This notebook uses nbdev (https://github.com/fastai/nbdev/) to use these \n",
"# special comments starting with `#|` and create the `app.py` output."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tree leaf classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"from fastai.vision.all import *\n",
"import gradio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"learn = load_learner('model.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"im = PILImage.create('images/ash.jpg')\n",
"im.thumbnail((224, 224))\n",
"im"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%time learn.predict(im)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn.dls.vocab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"\n",
"categories = ('ash', 'chestnut', 'ginkgo biloba', 'silver maple', 'willow oak')\n",
"\n",
"def classify_image(img):\n",
" pred, idx, probs = learn.predict(img)\n",
" # Change each probability to a float, since Gradio doesn't support Tensors or NumPy\n",
" return dict(zip(categories, map(float, probs)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"classify_image(im)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# gradio interface"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"image = gradio.Image(shape=(192, 192))\n",
"label = gradio.Label()\n",
"examples = ['images/ash.jpg', 'images/chestnut.jpg', 'images/ginkgo_biloba.jpg',\n",
" 'images/silver_maple.jpg', 'images/willow_oak.jpg']\n",
"# More useful args\n",
"title = \"Tree leaf classifier demo\"\n",
"description = \"A tree leaf classifier demo, trained on images downloaded from DuckDuckGo. Created as a demo of HuggingFace Spaces and Gradio.\"\n",
"article = \"<p>From this blog post: <a href='https://briansigafoos.com/ml-quick-start' target='_blank'>Machine Learning quick start by Brian Sigafoos</a></p>\"\n",
"interpretation = 'default'\n",
"\n",
"interface = gradio.Interface(fn=classify_image, inputs=image, outputs=label,\n",
" examples=examples, title=title, description=description,\n",
" article=article, interpretation=interpretation)\n",
"interface.launch(inline=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# export"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nbdev.export import nb_export\n",
"\n",
"nb_export('app.ipynb', './')\n",
"print('Export successful')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
},
"vscode": {
"interpreter": {
"hash": "eff2759d08249ab8aebc36f9602f3021ae9774f8f0203a4a83a5ad2ff4836f90"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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