{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1683818911690,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "6ExFyyjhbUK_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gradio"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "spyder 4.2.5 requires pyqt5<5.13, which is not installed.\n",
      "spyder 4.2.5 requires pyqtwebengine<5.13, which is not installed.\n",
      "sphinx 4.0.1 requires Jinja2<3.0,>=2.3, but you have jinja2 3.1.2 which is incompatible.\n",
      "sphinx 4.0.1 requires MarkupSafe<2.0, but you have markupsafe 2.0.1 which is incompatible.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Downloading gradio-3.29.0-py3-none-any.whl (17.3 MB)\n",
      "     ---------------------------------------- 17.3/17.3 MB 3.4 MB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (1.22.3)\n",
      "Requirement already satisfied: pillow in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (9.2.0)\n",
      "Requirement already satisfied: requests in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (2.28.1)\n",
      "Collecting huggingface-hub>=0.13.0\n",
      "  Using cached huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
      "Collecting aiofiles\n",
      "  Downloading aiofiles-23.1.0-py3-none-any.whl (14 kB)\n",
      "Collecting aiohttp\n",
      "  Downloading aiohttp-3.8.4-cp38-cp38-win_amd64.whl (324 kB)\n",
      "     -------------------------------------- 324.5/324.5 kB 1.8 MB/s eta 0:00:00\n",
      "Requirement already satisfied: pandas in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (1.4.4)\n",
      "Requirement already satisfied: pyyaml in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (6.0)\n",
      "Collecting ffmpy\n",
      "  Downloading ffmpy-0.3.0.tar.gz (4.8 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Collecting pygments>=2.12.0\n",
      "  Using cached Pygments-2.15.1-py3-none-any.whl (1.1 MB)\n",
      "Collecting altair>=4.2.0\n",
      "  Downloading altair-5.0.0-py3-none-any.whl (477 kB)\n",
      "     -------------------------------------- 477.4/477.4 kB 5.9 MB/s eta 0:00:00\n",
      "Collecting gradio-client>=0.2.1\n",
      "  Downloading gradio_client-0.2.3-py3-none-any.whl (287 kB)\n",
      "     -------------------------------------- 287.9/287.9 kB 4.4 MB/s eta 0:00:00\n",
      "Collecting markdown-it-py[linkify]>=2.0.0\n",
      "  Downloading markdown_it_py-2.2.0-py3-none-any.whl (84 kB)\n",
      "     ---------------------------------------- 84.5/84.5 kB 2.4 MB/s eta 0:00:00\n",
      "Requirement already satisfied: markupsafe in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (2.0.1)\n",
      "Collecting python-multipart\n",
      "  Downloading python_multipart-0.0.6-py3-none-any.whl (45 kB)\n",
      "     ---------------------------------------- 45.7/45.7 kB 1.1 MB/s eta 0:00:00\n",
      "Requirement already satisfied: typing-extensions in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (4.3.0)\n",
      "Collecting httpx\n",
      "  Downloading httpx-0.24.0-py3-none-any.whl (75 kB)\n",
      "     ---------------------------------------- 75.3/75.3 kB 4.1 MB/s eta 0:00:00\n",
      "Collecting pydub\n",
      "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
      "Collecting semantic-version\n",
      "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
      "Collecting uvicorn>=0.14.0\n",
      "  Downloading uvicorn-0.22.0-py3-none-any.whl (58 kB)\n",
      "     ---------------------------------------- 58.3/58.3 kB 3.0 MB/s eta 0:00:00\n",
      "Requirement already satisfied: matplotlib in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (3.3.4)\n",
      "Requirement already satisfied: pydantic in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (1.8.2)\n",
      "Requirement already satisfied: websockets>=10.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (11.0.3)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio) (3.1.2)\n",
      "Collecting fastapi\n",
      "  Downloading fastapi-0.95.1-py3-none-any.whl (56 kB)\n",
      "     ---------------------------------------- 57.0/57.0 kB 1.5 MB/s eta 0:00:00\n",
      "Collecting orjson\n",
      "  Downloading orjson-3.8.12-cp38-none-win_amd64.whl (194 kB)\n",
      "     -------------------------------------- 195.0/195.0 kB 6.0 MB/s eta 0:00:00\n",
      "Collecting mdit-py-plugins<=0.3.3\n",
      "  Downloading mdit_py_plugins-0.3.3-py3-none-any.whl (50 kB)\n",
      "     -------------------------------------- 50.5/50.5 kB 129.0 kB/s eta 0:00:00\n",
      "Requirement already satisfied: toolz in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from altair>=4.2.0->gradio) (0.12.0)\n",
      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from altair>=4.2.0->gradio) (4.16.0)\n",
      "Requirement already satisfied: fsspec in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio-client>=0.2.1->gradio) (2022.10.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from gradio-client>=0.2.1->gradio) (21.3)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.13.0->gradio) (4.64.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.13.0->gradio) (3.6.0)\n",
      "Collecting mdurl~=0.1\n",
      "  Downloading mdurl-0.1.2-py3-none-any.whl (10.0 kB)\n",
      "Collecting linkify-it-py<3,>=1\n",
      "  Downloading linkify_it_py-2.0.2-py3-none-any.whl (19 kB)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from pandas->gradio) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from pandas->gradio) (2.8.2)\n",
      "Collecting h11>=0.8\n",
      "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
      "     ---------------------------------------- 58.3/58.3 kB 1.5 MB/s eta 0:00:00\n",
      "Requirement already satisfied: click>=7.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from uvicorn>=0.14.0->gradio) (8.0.4)\n",
      "Collecting frozenlist>=1.1.1\n",
      "  Downloading frozenlist-1.3.3-cp38-cp38-win_amd64.whl (34 kB)\n",
      "Collecting yarl<2.0,>=1.0\n",
      "  Downloading yarl-1.9.2-cp38-cp38-win_amd64.whl (61 kB)\n",
      "     ---------------------------------------- 61.8/61.8 kB 1.1 MB/s eta 0:00:00\n",
      "Collecting aiosignal>=1.1.2\n",
      "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from aiohttp->gradio) (2.1.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from aiohttp->gradio) (21.4.0)\n",
      "Collecting multidict<7.0,>=4.5\n",
      "  Downloading multidict-6.0.4-cp38-cp38-win_amd64.whl (28 kB)\n",
      "Collecting async-timeout<5.0,>=4.0.0a3\n",
      "  Using cached async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
      "Collecting starlette<0.27.0,>=0.26.1\n",
      "  Downloading starlette-0.26.1-py3-none-any.whl (66 kB)\n",
      "     -------------------------------------- 66.9/66.9 kB 717.4 kB/s eta 0:00:00\n",
      "Collecting httpcore<0.18.0,>=0.15.0\n",
      "  Downloading httpcore-0.17.0-py3-none-any.whl (70 kB)\n",
      "     -------------------------------------- 70.6/70.6 kB 974.2 kB/s eta 0:00:00\n",
      "Requirement already satisfied: sniffio in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from httpx->gradio) (1.2.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from httpx->gradio) (2022.12.7)\n",
      "Requirement already satisfied: idna in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from httpx->gradio) (3.4)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from matplotlib->gradio) (3.0.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from matplotlib->gradio) (1.4.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from matplotlib->gradio) (0.11.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from requests->gradio) (1.26.12)\n",
      "Requirement already satisfied: colorama in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from click>=7.0->uvicorn>=0.14.0->gradio) (0.4.5)\n",
      "Requirement already satisfied: anyio<5.0,>=3.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio) (3.5.0)\n",
      "Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (1.3.10)\n",
      "Requirement already satisfied: importlib-resources>=1.4.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (5.2.0)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.18.0)\n",
      "Collecting uc-micro-py\n",
      "  Downloading uc_micro_py-1.0.2-py3-none-any.whl (6.2 kB)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas->gradio) (1.16.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in c:\\users\\kfarr\\anaconda3\\lib\\site-packages (from importlib-resources>=1.4.0->jsonschema>=3.0->altair>=4.2.0->gradio) (3.8.0)\n",
      "Building wheels for collected packages: ffmpy\n",
      "  Building wheel for ffmpy (setup.py): started\n",
      "  Building wheel for ffmpy (setup.py): finished with status 'done'\n",
      "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4693 sha256=0ebeacbbffce76bf78266617d53a78a428ccfa19d0fbe150b5e1b6ebf554720a\n",
      "  Stored in directory: c:\\users\\kfarr\\appdata\\local\\pip\\cache\\wheels\\ff\\5b\\59\\913b443e7369dc04b61f607a746b6f7d83fb65e2e19fcc958d\n",
      "Successfully built ffmpy\n",
      "Installing collected packages: pydub, ffmpy, uc-micro-py, semantic-version, python-multipart, pygments, orjson, multidict, mdurl, h11, frozenlist, async-timeout, aiofiles, yarl, uvicorn, starlette, markdown-it-py, linkify-it-py, huggingface-hub, httpcore, aiosignal, mdit-py-plugins, httpx, fastapi, altair, aiohttp, gradio-client, gradio\n",
      "  Attempting uninstall: pygments\n",
      "    Found existing installation: Pygments 2.11.2\n",
      "    Uninstalling Pygments-2.11.2:\n",
      "      Successfully uninstalled Pygments-2.11.2\n",
      "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 altair-5.0.0 async-timeout-4.0.2 fastapi-0.95.1 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.29.0 gradio-client-0.2.3 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 huggingface-hub-0.14.1 linkify-it-py-2.0.2 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 mdurl-0.1.2 multidict-6.0.4 orjson-3.8.12 pydub-0.25.1 pygments-2.15.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.2 uvicorn-0.22.0 yarl-1.9.2\n"
     ]
    }
   ],
   "source": [
    "#|default_exp app\n",
    "!pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from contextlib import contextmanager\n",
    "import pathlib\n",
    "\n",
    "@contextmanager\n",
    "def set_posix_windows():\n",
    "    posix_backup = pathlib.PosixPath\n",
    "    try:\n",
    "        pathlib.PosixPath = pathlib.WindowsPath\n",
    "        yield\n",
    "    finally:\n",
    "        pathlib.PosixPath = posix_backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1683819919471,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "wi-KWPXhbXg9"
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "import nbdev as nb\n",
    "import pathlib\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1683819199624,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "aCXsLYU8bkho",
    "outputId": "e777cb72-16fa-4e29-ea00-50265acfe12f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "PILImage mode=RGB size=224x149"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('grizzly.jpg')\n",
    "im.thumbnail((224,224))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1683819539238,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "zBU5VmuWcfcW"
   },
   "outputs": [],
   "source": [
    "EXPORT_PATH = pathlib.Path(\"export.pkl\")\n",
    "\n",
    "with set_posix_windows():\n",
    "    learn = load_learner(EXPORT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "cannot instantiate 'PosixPath' on your system",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[1;32mIn [43]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#export\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m learn \u001b[38;5;241m=\u001b[39m \u001b[43mload_learner\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mexport.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\fastai\\learner.py:446\u001b[0m, in \u001b[0;36mload_learner\u001b[1;34m(fname, cpu, pickle_module)\u001b[0m\n\u001b[0;32m    444\u001b[0m distrib_barrier()\n\u001b[0;32m    445\u001b[0m map_loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m cpu \u001b[38;5;28;01melse\u001b[39;00m default_device()\n\u001b[1;32m--> 446\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: res \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmap_loc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpickle_module\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \n\u001b[0;32m    448\u001b[0m     e\u001b[38;5;241m.\u001b[39margs \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCustom classes or functions exported with your `Learner` not available in namespace.\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mRe-declare/import before loading:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;241m.\u001b[39margs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py:712\u001b[0m, in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[0;32m    710\u001b[0m             opened_file\u001b[38;5;241m.\u001b[39mseek(orig_position)\n\u001b[0;32m    711\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mload(opened_file)\n\u001b[1;32m--> 712\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    713\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _legacy_load(opened_file, map_location, pickle_module, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpickle_load_args)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py:1049\u001b[0m, in \u001b[0;36m_load\u001b[1;34m(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)\u001b[0m\n\u001b[0;32m   1047\u001b[0m unpickler \u001b[38;5;241m=\u001b[39m UnpicklerWrapper(data_file, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpickle_load_args)\n\u001b[0;32m   1048\u001b[0m unpickler\u001b[38;5;241m.\u001b[39mpersistent_load \u001b[38;5;241m=\u001b[39m persistent_load\n\u001b[1;32m-> 1049\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43munpickler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1051\u001b[0m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_validate_loaded_sparse_tensors()\n\u001b[0;32m   1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\pathlib.py:1043\u001b[0m, in \u001b[0;36mPath.__new__\u001b[1;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1041\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_from_parts(args, init\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1042\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flavour\u001b[38;5;241m.\u001b[39mis_supported:\n\u001b[1;32m-> 1043\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot instantiate \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m on your system\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1044\u001b[0m                               \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,))\n\u001b[0;32m   1045\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init()\n\u001b[0;32m   1046\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: cannot instantiate 'PosixPath' on your system"
     ]
    }
   ],
   "source": [
    "#|export\n",
    "learn = load_learner('export.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "executionInfo": {
     "elapsed": 516,
     "status": "ok",
     "timestamp": 1683819549420,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "BBeAEYq1crv_",
    "outputId": "445ff1c7-445f-44a6-8347-925ffa2dd58f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('grizzly', tensor(1), tensor([7.0926e-06, 9.9998e-01, 1.2630e-05]))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "executionInfo": {
     "elapsed": 433,
     "status": "ok",
     "timestamp": 1683819607950,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "Kp7Z3S0gd0xa"
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "categories = learn.dls.vocab\n",
    "\n",
    "def classify_img(img):\n",
    "  pred, idx, probs = learn.predict(img)\n",
    "  return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "executionInfo": {
     "elapsed": 646,
     "status": "ok",
     "timestamp": 1683819616934,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "RF3yGacdeDMR",
    "outputId": "8edc2222-41ec-4c99-9f5a-ae351dba07a4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'black': 7.092581654433161e-06,\n",
       " 'grizzly': 0.9999803304672241,\n",
       " 'teddy': 1.2629865523194894e-05}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_img(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1683819726037,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "BXAZAYDReFHs"
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "image = gr.components.Image(shape=(192,192))\n",
    "label = gr.components.Label()\n",
    "examples = ['grizzly.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5492,
     "status": "ok",
     "timestamp": 1683819878681,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "v3KH5DY8eWBt",
    "outputId": "083fb312-514f-40bb-a870-8cc2ffdef621"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "Running on public URL: https://9333585e7694f22a92.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#|export\n",
    "intf = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False, share=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 590,
     "status": "ok",
     "timestamp": 1683820118484,
     "user": {
      "displayName": "kyle farrugia",
      "userId": "00748281885532264716"
     },
     "user_tz": -60
    },
    "id": "yiUXHrwGeqye",
    "outputId": "69b7ba54-4e6e-4fcc-f8aa-5fff9b319180"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Export successful\n"
     ]
    }
   ],
   "source": [
    "nb.export.nb_export('app.ipynb')\n",
    "print('Export successful')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DHR4gDl8f7Uv"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNY+PpOVflTp+nrnnX2++0G",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
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}