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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\RAVI\\AppData\\Roaming\\Python\\Python39\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from fastai.vision.all import *\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pathlib"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def new_path(cls, *args, **kwargs):\n",
" \n",
" cls = pathlib.WindowsPath\n",
" self = cls._from_parts(args)\n",
" if not self._flavour.is_supported:\n",
" raise NotImplementedError(\"cannot instantiate %r on your system\"\n",
" % (cls.__name__,))\n",
" return self\n",
"Path.__new__=new_path"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def is_cat(x): return x[0].isupper() \n",
"\n",
"learn = load_learner('model.pkl')\n",
"categories = ('Dog', 'Cat')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def classify_image(img):\n",
" pred,idx,probs = learn.predict(img)\n",
" return dict(zip(categories, map(float, probs)))\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7860/\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/plain": [
"(<gradio.routes.App at 0x1be650336a0>, 'http://127.0.0.1:7860/', None)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"image = gr.Image(shape=(192,192))\n",
"label = gr.Label()\n",
"examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n",
"\n",
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
"intf.launch(inline=False)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.4 64-bit",
"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": "3.9.4"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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