{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "4b40cb3a-544a-4b23-8c00-431cb7133130",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python 3.11.5\n"
]
}
],
"source": [
"%%bash\n",
"python --version"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "93e1afb8-f78c-4862-9d56-a06a3559b4d1",
"metadata": {},
"outputs": [],
"source": [
"#|default_exp app"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2adf2fa8-199b-48e4-a91c-9a093032480c",
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"from fastai.vision.all import *\n",
"import gradio as gr\n",
"import timm"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f0218bf1-1836-4d7a-8d47-33584471f28b",
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"learn = load_learner('model.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "168ac2e4-f83b-4ce0-8f23-00999eb5d556",
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"categories = learn.dls.vocab\n",
"\n",
"def classify_image(img):\n",
" pred,idx,probs = learn.predict(img)\n",
" return dict(zip(categories, map(float,probs)))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d343a0d3-40fd-4502-a86b-cb3bac9fdf7f",
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"examples = ['images/unicycle.jpeg', 'images/bicycle.jpeg', 'images/tricycle.png']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "645eb0ee-b7e5-4ec4-a42e-9f43a163a3a5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"images/unicycle.jpeg is a tricycle\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"images/bicycle.jpeg is a bicycle\n"
]
},
{
"ename": "UnidentifiedImageError",
"evalue": "cannot identify image file 'images/tricycle.png'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mUnidentifiedImageError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m examples:\n\u001b[0;32m----> 2\u001b[0m image \u001b[38;5;241m=\u001b[39m PILImage\u001b[38;5;241m.\u001b[39mcreate(example)\n\u001b[1;32m 3\u001b[0m res_dict \u001b[38;5;241m=\u001b[39m classify_image(image)\n\u001b[1;32m 4\u001b[0m top_prob_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(res_dict, key\u001b[38;5;241m=\u001b[39mres_dict\u001b[38;5;241m.\u001b[39mget)\n",
"File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/fastai/vision/core.py:125\u001b[0m, in \u001b[0;36mPILBase.create\u001b[0;34m(cls, fn, **kwargs)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fn,\u001b[38;5;28mbytes\u001b[39m): fn \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO(fn)\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fn,Image\u001b[38;5;241m.\u001b[39mImage): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(fn)\n\u001b[0;32m--> 125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(load_image(fn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmerge(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_open_args, kwargs)))\n",
"File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/fastai/vision/core.py:98\u001b[0m, in \u001b[0;36mload_image\u001b[0;34m(fn, mode)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_image\u001b[39m(fn, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 97\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOpen and load a `PIL.Image` and convert to `mode`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 98\u001b[0m im \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mopen(fn)\n\u001b[1;32m 99\u001b[0m im\u001b[38;5;241m.\u001b[39mload()\n\u001b[1;32m 100\u001b[0m im \u001b[38;5;241m=\u001b[39m im\u001b[38;5;241m.\u001b[39m_new(im\u001b[38;5;241m.\u001b[39mim)\n",
"File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/PIL/Image.py:3280\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m 3278\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(message)\n\u001b[1;32m 3279\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot identify image file \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (filename \u001b[38;5;28;01mif\u001b[39;00m filename \u001b[38;5;28;01melse\u001b[39;00m fp)\n\u001b[0;32m-> 3280\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnidentifiedImageError(msg)\n",
"\u001b[0;31mUnidentifiedImageError\u001b[0m: cannot identify image file 'images/tricycle.png'"
]
}
],
"source": [
"for example in examples:\n",
" image = PILImage.create(example)\n",
" res_dict = classify_image(image)\n",
" top_prob_key = max(res_dict, key=res_dict.get)\n",
" print(example + ' is a '+ top_prob_key)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "156a1fa0-e124-4a18-b411-367e7926afa4",
"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/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#|export\n",
"\n",
"image = gr.Image()\n",
"label = gr.Label()\n",
"\n",
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
"intf.launch()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2894f2be-e453-4795-8a16-2aa4770aa16d",
"metadata": {},
"outputs": [],
"source": [
"import nbdev"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "10568397-2167-4c39-8120-436e577b452d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Export successful\n"
]
}
],
"source": [
"nbdev.export.nb_export('notebook.ipynb', '')\n",
"print('Export successful')"
]
}
],
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}