{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#|default_exp demo"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Basic setup, once again..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"from fastai.vision.all import *\n",
"import pathlib\n",
"import gradio as gr\n",
"plt = platform.system()\n",
"if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Test with own images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"albani = PILImage.create('albani2.jpg')\n",
"\n",
"albani.thumbnail((192, 192))\n",
"albani"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn = load_learner(Path('./resnet18-albani.pkl'))\n",
"\n",
"learn.predict(albani)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Gradio demo"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7863\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/plain": []
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
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"data": {
"text/html": [],
"text/plain": [
""
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"metadata": {},
"output_type": "display_data"
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"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
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"data": {
"text/html": [
"\n",
"\n"
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"text/plain": [
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"metadata": {},
"output_type": "display_data"
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"metadata": {},
"output_type": "display_data"
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"\n",
"\n"
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""
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"metadata": {},
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"data": {
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""
]
},
"metadata": {},
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}
],
"source": [
"#|export\n",
"\n",
"learn = load_learner(Path('./resnet18-albani.pkl'))\n",
"categories = ('God Øl', 'Dårlig Øl')\n",
"\n",
"def classify_image(img):\n",
" pred,idx,probs = learn.predict(img)\n",
" return dict(zip(categories, map(float, probs)))\n",
"\n",
"description = \"\"\"\n",
" ## Er du i tvivl om at den øl du sidder med i hånden lige, nu er god?\n",
" ### Tvivl ej 68 års invotation inden for machine learning skal nok fortælle dig om øllen er god eller ej\n",
"\"\"\"\n",
"image = gr.Image(shape=(192, 192))\n",
"label = gr.Label()\n",
"examples = ['albani.jpg', 'albani2.jpg', 'albani3.jpg', 'heineken.jpg', 'carlsberg.jpg']\n",
"\n",
"iface = gr.Interface(\n",
" fn=classify_image, \n",
" inputs=image, \n",
" outputs=label, \n",
" examples=examples, \n",
" description=description,\n",
")\n",
"\n",
"iface.launch(inline=False)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Build for huggingface"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Successfully saved requirements file in ./requirements.txt\n"
]
}
],
"source": [
"from nbdev.export import nb_export\n",
"\n",
"nb_export('model-test.ipynb', '.')\n",
"! pipreqs . --force\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "fastai",
"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": "3.10.8"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "b977fc7f635931fb2de4a5e9c6503b6f91cf50b058799eb3f69da04d3aca2546"
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"nbformat": 4,
"nbformat_minor": 2
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