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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
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},
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"id": "GAEn7ZfhcSLC",
"outputId": "0eb4e49b-e0bc-41ab-f75a-d404141a1191",
"slideshow": {
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},
"tags": []
},
"outputs": [
{
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"text": [
"Collecting gradio\n",
" Obtaining dependency information for gradio from https://files.pythonhosted.org/packages/d8/3c/fcfa9f2f9b97e9c38ccb30eb3edef8acbb284487bc7d8f5b98daf01cf757/gradio-4.12.0-py3-none-any.whl.metadata\n",
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"\u001b[?25hBuilding wheels for collected packages: ffmpy\n",
" Building wheel for ffmpy (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=795f3c4dd1d184ba6162b37602d4e538cac01d79ee2a96270555a769e029a0ec\n",
" Stored in directory: /home/agastya/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
"Successfully built ffmpy\n",
"Installing collected packages: pydub, ffmpy, websockets, tomlkit, semantic-version, python-multipart, orjson, h11, fsspec, aiofiles, uvicorn, starlette, huggingface-hub, httpcore, httpx, fastapi, gradio-client, altair, gradio\n",
"Successfully installed aiofiles-23.2.1 altair-5.2.0 fastapi-0.108.0 ffmpy-0.3.1 fsspec-2023.12.2 gradio-4.12.0 gradio-client-0.8.0 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 huggingface-hub-0.20.1 orjson-3.9.10 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.32.0.post1 tomlkit-0.12.0 uvicorn-0.25.0 websockets-11.0.3\n"
]
}
],
"source": [
"!pip install gradio nbdev"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting nbdev\n",
" Obtaining dependency information for nbdev from https://files.pythonhosted.org/packages/45/44/2148be2391edd62b8c88c2114f380141b72276901a53d590be9f769a68e4/nbdev-2.3.13-py3-none-any.whl.metadata\n",
" Downloading nbdev-2.3.13-py3-none-any.whl.metadata (10 kB)\n",
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"Collecting ghapi>=1.0.3 (from nbdev)\n",
" Obtaining dependency information for ghapi>=1.0.3 from https://files.pythonhosted.org/packages/60/71/064560201c434c22c9f395878ba0f21f9ede8169e61c1a109f26a377bd5e/ghapi-1.0.4-py3-none-any.whl.metadata\n",
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"\u001b[?25hInstalling collected packages: watchdog, astunparse, ghapi, execnb, ipywidgets, nbdev\n",
" Attempting uninstall: ipywidgets\n",
" Found existing installation: ipywidgets 8.1.1\n",
" Uninstalling ipywidgets-8.1.1:\n",
" Successfully uninstalled ipywidgets-8.1.1\n",
"Successfully installed astunparse-1.6.3 execnb-0.1.5 ghapi-1.0.4 ipywidgets-8.0.4 nbdev-2.3.13 watchdog-3.0.0\n"
]
}
],
"source": [
"!pip install nbdev"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "S2uYQd3pexuE",
"outputId": "fbb28fa7-14a7-4446-ed63-75cd98bf6dc2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m719.8/719.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hMounted at /content/gdrive\n"
]
}
],
"source": [
"# Required for fetching files from gDrive\n",
"! [ -e /content ] && pip install -Uqq fastbook\n",
"import fastbook\n",
"fastbook.setup_book()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "Ivzb1m1Ub2y5"
},
"outputs": [],
"source": [
"#|default_exp app"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "xL1Hpue4cDms"
},
"outputs": [],
"source": [
"#|export\n",
"# importing dependencies\n",
"from fastai.vision.all import *\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"editable": true,
"id": "nc2O48TScW_7",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"#|export\n",
"# creating inference\n",
"learn = load_learner('content/bearClassifier.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"editable": true,
"id": "dGC3UmI4c3QY",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"#|export\n",
"categories = ('Black', 'Grizzly', 'Teddy')\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": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 646
},
"editable": true,
"id": "jLrBazuoddxB",
"outputId": "7665b29e-7489-469f-ac00-dbd90d9bbe0c",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
"\n",
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"Running on public URL: https://d10d07b14c3e9bde1d.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://d10d07b14c3e9bde1d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#|export\n",
"# gradio interface Setup\n",
"image = gr.Image()\n",
"label = gr.Label()\n",
"examples = ['content/bear1.jpg', 'content/bear2.jpg', 'content/bear3.jpg', 'content/bear4.jpg']\n",
"\n",
"intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples).launch()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 131
},
"id": "gevfj4iakJLP",
"outputId": "6bf44a0f-8cee-4618-b0b6-35c4f51d12dc"
},
"outputs": [],
"source": [
"import nbdev\n",
"# doc(nbdev.export.nb_export)\n",
"nbdev.export.nb_export('02_BearClassifierDeployment.ipynb', './')"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|