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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "0eee85c0",
"metadata": {},
"outputs": [],
"source": [
"#| default_exp app"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "598c11f4",
"metadata": {},
"outputs": [],
"source": [
"!pip install gradio > /dev/null"
]
},
{
"cell_type": "markdown",
"id": "1f836217",
"metadata": {},
"source": [
"### Ecommerece Image classification app"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "76333c52",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"from fastai.vision.all import *\n",
"import gradio as gr\n",
"# ['jeans', 'sofa', 'tshirt', 'tv']\n",
"\n",
"def is_jeans(x): return x[0].isupper()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "09b9fe0b",
"metadata": {},
"outputs": [],
"source": [
"im = PILImage.create('jeans.jpeg')\n",
"im.thumbnail((192,192))\n",
"im"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ab0e0e6f",
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"learn = load_learner('export.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "97559e61",
"metadata": {},
"outputs": [],
"source": [
"%time\n",
"learn.predict(im)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4e112978",
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"categories = ('Jeans', 'Sofa', 'Tshirt', 'Tv')\n",
"\n",
"def classify_images(img):\n",
" pred, idx, probs = learn.predict(img)\n",
" return dict(zip(categories, map(float, probs)))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d8d09015",
"metadata": {},
"outputs": [],
"source": [
"classify_images(im)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8530628e",
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"image = gr.components.Image(shape=(192,192))\n",
"label = gr.components.Label()\n",
"examples = ['jeans.jpeg', 'sofa.jpeg', 'tshirt.jpg', 'tv.jpeg']\n",
"\n",
"intf = gr.Interface(fn=classify_images, inputs=image, outputs=label, examples=examples)\n",
"intf.launch(inline=False)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9487e868",
"metadata": {},
"outputs": [],
"source": [
"import nbdev\n",
"nbdev.export.nb_export('app.ipynb', './')\n",
"print('Export successful')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a97c4bb5",
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|