{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -Uqq fastbook\n",
    "!pip install gradio\n",
    "!pip install nbdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learner = load_learner('model.pkl')\n",
    "\n",
    "categories = ('Bird', 'Drone')\n",
    "\n",
    "def calssify_images(img):\n",
    "    pred, idx, probs = learner.predict(img)\n",
    "    return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "image = gr.inputs.Image(shape = (192, 192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['BirdExample1.jpg', 'BirdExample2.jpg', 'DroneExample1.jpg', 'DroneExample2.jpg']\n",
    "\n",
    "intf = gr.Interface(fn = calssify_images, inputs = image, outputs = label, examples = examples)\n",
    "intf.launch(inline = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Export successful\n"
     ]
    }
   ],
   "source": [
    "import nbdev\n",
    "nbdev.export.nb_export('app.ipynb', './')\n",
    "print('Export successful')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.8.12 ('datasci-env-py38')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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