{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "962a300d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0ebd943",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x):\n",
    "    return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df77cf28",
   "metadata": {},
   "outputs": [],
   "source": [
    "im = PILImage.create('dog.jpeg')\n",
    "im.thumbnail((192,192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f86a507",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5debae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47327aef",
   "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": 7,
   "id": "f370a4a2",
   "metadata": {},
   "outputs": [],
   "source": [
    " classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9494a05a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "image = gr.Image(shape = (192,192))\n",
    "label = gr.Label()\n",
    "examples = ['dog.jpeg', 'cat.jpeg', 'dunno.png']\n",
    "\n",
    "intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)\n",
    "intf.launch(inline = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1188f972",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install nbdev\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7795680",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nbdev\n",
    "notebook_name = 'app.ipynb'\n",
    "export_destination = '.' # the root directory\n",
    "nbdev.export.nb_export(notebook_name, export_destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48fb9eef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc910548",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.12.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}