{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "41971c73-6b80-487e-acdc-836991e62c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a14bf09-3459-4b81-9e2f-f0430591efa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from sys import platform\n",
    "import pathlib\n",
    "if platform == 'linux':\n",
    "    pathlib.WindowsPath = pathlib.PosixPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12171d9b-9822-461a-9c16-3925a275e859",
   "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": 4,
   "id": "5f678c6f-a7a8-4da2-845c-2c82f056e890",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.PETS)/'images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a2dd42f-3fae-4bcf-a7a9-932dedb19684",
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = ImageDataLoaders.from_name_func('.',\n",
    "                                     get_image_files(path), valid_pct=0.2, seed=42,\n",
    "                                     label_func=is_cat,\n",
    "                                     item_tfms=Resize(192))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09e8e2ad-30eb-45bc-aa4b-9fe5a256287a",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
    "learn.fine_tune(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b601d058-559a-4246-b4bb-2ad490d826f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b01bcd5-5c99-4531-9f93-70dc32238dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77bd55bf-2d8a-4ca0-bf94-ff9078eb124b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "categories = ('Dog', 'Cat')\n",
    "\n",
    "def classify(img):\n",
    "    pred,idx,probs = learn.predict(img)\n",
    "    return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc00d8a5-a4fe-42ec-9daa-cce8ab31400b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App at 0x241de99cfc8>, 'http://127.0.0.1:7860/', None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|export\n",
    "image = gr.inputs.Image(shape=(192, 192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['dog.jpg', 'cat.jpg']\n",
    "\n",
    "intf = gr.Interface(fn=classify, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7cf96e37-8c63-4494-84c5-d91b9ff96e2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Export successful\n"
     ]
    }
   ],
   "source": [
    "import nbdev\n",
    "nbdev.export.nb_export('app.ipynb', 'app')\n",
    "print('Export successful')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c513736-d2b5-48d4-89da-746a8b0047cf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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