{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0fa79b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "489a7e57",
   "metadata": {},
   "source": [
    "# Dogs v Cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0ff3631",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x): return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84312fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "im = PILImage.create('dog.jpg')\n",
    "im.thumbnail((192, 192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0e23187",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c19e825",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d678926b",
   "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": null,
   "id": "13fbf93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76c0b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "image = gr.inputs.Image(shape=(192, 192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['/kaggle/input/dogvcat/dog.jpeg', '/kaggle/input/dogvcat/cat.webp', '/kaggle/input/dogvcat/cat1.jpeg']\n",
    "\n",
    "# create gradio interface\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "465ea051",
   "metadata": {},
   "source": [
    "# export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5809c2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import a package to convert notebook into py file\n",
    "import nbdev\n",
    "nbdev.export.nb_export('app.ipynb', 'app')"
   ]
  }
 ],
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  "vscode": {
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