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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#| default_exp app"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -Uqq fastbook\n",
"!pip install gradio\n",
"!pip install nbdev"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"from fastai.vision.all import *\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"learner = load_learner('model.pkl')\n",
"\n",
"categories = ('Bird', 'Drone')\n",
"\n",
"def calssify_images(img):\n",
" pred, idx, probs = learner.predict(img)\n",
" return dict(zip(categories, map(float, probs)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"image = gr.inputs.Image(shape = (192, 192))\n",
"label = gr.outputs.Label()\n",
"examples = ['BirdExample1.jpg', 'BirdExample2.jpg', 'DroneExample1.jpg', 'DroneExample2.jpg']\n",
"\n",
"intf = gr.Interface(fn = calssify_images, inputs = image, outputs = label, examples = examples)\n",
"intf.launch(inline = False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Export successful\n"
]
}
],
"source": [
"import nbdev\n",
"nbdev.export.nb_export('app.ipynb', './')\n",
"print('Export successful')"
]
}
],
"metadata": {
"interpreter": {
"hash": "2376d2b9915f38786098b2b3250c4b9f66c08129e4576f9e739de38b6074d39d"
},
"kernelspec": {
"display_name": "Python 3.8.12 ('datasci-env-py38')",
"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": "3.8.12"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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