{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|default_exp app"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"from fastai.vision.all import *\n",
"import gradio as gr\n",
"\n",
"def is_cat(x: str): return x[0].isupper()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"im = PILImage.create(\"dog.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"learn = load_learner(\"model.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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": [
"('True', TensorBase(1), TensorBase([2.0341e-14, 1.0000e+00]))"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.predict(\"cat.jpg\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## this is how we will get the answer"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"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": [
"{'dog': 2.0341067629640712e-14, 'cat': 1.0}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred, idx, probs = learn.predict(\"cat.jpg\")\n",
"a = [\"dog\", \"cat\"]\n",
"dict(zip(a,map(float, probs)))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"categories = (\"Dog\", \"Cat\")\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": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/danielcodex/.local/lib/python3.9/site-packages/gradio/inputs.py:256: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
" warnings.warn(\n",
"/home/danielcodex/.local/lib/python3.9/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n",
"/home/danielcodex/.local/lib/python3.9/site-packages/gradio/outputs.py:196: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
" warnings.warn(\n",
"/home/danielcodex/.local/lib/python3.9/site-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
" warnings.warn(value)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7861\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/plain": []
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#|export\n",
"image = gr.inputs.Image(shape=(192, 192))\n",
"label = gr.outputs.Label()\n",
"examples = [\"dog.jpg\", \"cat.jpg\"]\n",
"\n",
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
"intf.launch(inline=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"export successful\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import nbdev\n",
"nbdev.export.nb_export(\"learning.ipynb\", \"app\")\n",
"print(\"export successful\")\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from fastdownload import download_url\n",
"from duckduckgo_search import ddg_images\n",
"\n",
"test = L(ddg_images(\"dog\", max_results=1)).itemgot(\"image\")\n",
"# download_url(test[0], \"dog.jpg\", show_progress=False)\n",
"\n",
"cat = L(ddg_images(\"cat\", max_results=1)).itemgot(\"image\")\n",
"# download_url(cat[0], \"cat.jpg\", show_progress=False)\n",
"\n",
"# dunno = L(ddg_images(\"dunno\", max_results=20)).itemgot(\"image\")\n",
"# download_url(dunno[3], \"dunno.jpg\", show_progress=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.5"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "36cf16204b8548560b1c020c4e8fb5b57f0e4c58016f52f2d4be01e192833930"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}