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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|default_exp app"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"from fastai.vision.all import *\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"learn = load_learner('model.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from fastbook import *\n",
"urls = search_images_ddg('banjo',1)\n",
"from fastdownload import download_url\n",
"dest = 'banjo.jpg'\n",
"download_url(urls[0], dest, show_progress=False)\n",
"\n",
"im = Image.open(dest)\n",
"im.to_thumb(256,256)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img = PILImage.create('banjo.jpg')\n",
"img.thumbnail((224,224))\n",
"img"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn.predict(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"categories = ('didgeridoo','tambourine','xylophone','acordian','alphorn','bagpipes','banjo','bongo drum','casaba','castanets','clarinet','clavichord','concertina','drums','dulcimer','flute','guiro','guitar','harmonica','harp','marakas','ocarina','piano','saxaphone','sitar','steel drum','trombone','trumpet','tuba','violin')\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": null,
"metadata": {},
"outputs": [],
"source": [
"classify_image(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|export\n",
"image = gr.inputs.Image(shape=(224,224))\n",
"label=gr.outputs.Label()\n",
"examples=['banjo.jpg']\n",
"intf = gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples)\n",
"intf.launch(inline=False)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nbdev.export import nb_export\n",
"nb_export('app.ipynb')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.10.10"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|