{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "UySFk1vPKxb_"
},
"outputs": [],
"source": [
"#|default_exp app"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gT0wxrhGKIxL"
},
"source": [
"# Bearify"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "Fg2er2rQLApV"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\utkar\\prod_apps\\Bearify\\bear_env\\lib\\site-packages\\tqdm\\auto.py:21: 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": [
"#|export\n",
"from fastai.vision.all import *\n",
"import gradio as gr\n",
"\n",
"def which_bear(x): pass"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 209
},
"id": "vBBjPghILOjq",
"outputId": "caa4c037-3d1e-43ae-a8e2-0f9c79198a2d"
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'C:\\\\content\\\\teddy.jpg'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m im \u001b[38;5;241m=\u001b[39m \u001b[43mPILImage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/content/teddy.jpg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m im\u001b[38;5;241m.\u001b[39mthumbnail((\u001b[38;5;241m192\u001b[39m,\u001b[38;5;241m192\u001b[39m))\n\u001b[0;32m 3\u001b[0m im\n",
"File \u001b[1;32m~\\prod_apps\\Bearify\\bear_env\\lib\\site-packages\\fastai\\vision\\core.py:125\u001b[0m, in \u001b[0;36mPILBase.create\u001b[1;34m(cls, fn, **kwargs)\u001b[0m\n\u001b[0;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[0;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[1;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[1;32m~\\prod_apps\\Bearify\\bear_env\\lib\\site-packages\\fastai\\vision\\core.py:98\u001b[0m, in \u001b[0;36mload_image\u001b[1;34m(fn, mode)\u001b[0m\n\u001b[0;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[0;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[1;32m---> 98\u001b[0m im \u001b[38;5;241m=\u001b[39m \u001b[43mImage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 99\u001b[0m im\u001b[38;5;241m.\u001b[39mload()\n\u001b[0;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[1;32m~\\prod_apps\\Bearify\\bear_env\\lib\\site-packages\\PIL\\Image.py:3277\u001b[0m, in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m 3274\u001b[0m filename \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mrealpath(os\u001b[38;5;241m.\u001b[39mfspath(fp))\n\u001b[0;32m 3276\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filename:\n\u001b[1;32m-> 3277\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3278\u001b[0m exclusive_fp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 3280\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\content\\\\teddy.jpg'"
]
}
],
"source": [
"im = PILImage.create('/content/teddy.jpg')\n",
"im.thumbnail((192,192))\n",
"im"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "Ko1vxtuzACNo"
},
"outputs": [],
"source": [
"learn = load_learner('/content/bear_model.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "N4lUOFyom35W",
"outputId": "d363cb16-e67f-4829-a776-8af408671170"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"('teddy', tensor(2), tensor([4.8331e-05, 7.1999e-05, 9.9988e-01]))"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.predict(im)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "k8MzL29fm5wO"
},
"outputs": [],
"source": [
"categories = ('Teddy', 'Black', 'Grizzly')\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": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"id": "R_dNtPRtoPER",
"outputId": "95b072b8-736f-424d-98dd-2a99e5078bef"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'Teddy': 4.833127968595363e-05,\n",
" 'Black': 7.199876563390717e-05,\n",
" 'Grizzly': 0.9998795986175537}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classify_image(im)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 211
},
"id": "Uc2M0zOEoR6b",
"outputId": "08c190d2-b5ad-43d1-aa00-f4c452152024"
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'gradio' has no attribute 'inputs'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m| \u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m192\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m192\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mintf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclassify_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mintf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'gradio' has no attribute 'inputs'"
]
}
],
"source": [
"image = gr.inputs.Image(shape = (192,192))\n",
"labels = gr.outputs.Label()\n",
"\n",
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=labels)\n",
"intf.launch(inline=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bqK_vxTfpqBj"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "bear_env",
"language": "python",
"name": "bear_env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|