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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'fastai'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn [2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfastai\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mvision\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mall\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'fastai'"
]
}
],
"source": [
"from fastai.vision.all import *\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn = load_learner('export.pkl')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labels = learn.dls.vocab\n",
"def predict(img):\n",
" img = PILImage.create(img)\n",
" pred,pred_idx,probs = learn.predict(img)\n",
" return {labels[i]: float(probs[i]) for i in range(len(labels))}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.12 ('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": "3.9.12"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "b781f973879b6653a182b86dce637bbad8607b90046e75a81b5febd27741eaed"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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