{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5519ca86",
   "metadata": {},
   "source": [
    "# About this notebook\n",
    "This notebook is to demonstrate how we are going to use the model.pkl from which we trained on the [Kaggle notebook](https://www.kaggle.com/code/tunglinwood/saving-a-basic-fastai-model/edit) and then exported. And then we use this notebook to demonstrate what we got from the model and identify with example picture downloaded from fastai image dataset and test it with trained model. Then finally, export to the app.py in which we can deploy with Gradio."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d855f875",
   "metadata": {},
   "source": [
    "The `#|default_exp app` is to tell the export function to export those cells tagged with `|#export` into a `app.py` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18acb717",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03241176",
   "metadata": {},
   "source": [
    "I add a `requirements.txt` and execute `pip install -r requirements.txt` to install `fastai`, `gradio`, and `nbdev` in order to get this notebook to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44eb0ad3",
   "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": "markdown",
   "id": "eeb3b907",
   "metadata": {},
   "source": [
    "**Warm Note:** Given the dataset of image is fairly large(8.1GB), try not to run it locally. Train the model on Kaggle and download the model, instead. Afterall, this notebook is just to demonstrate how the model was trained on [Kaggle Notebook](https://www.kaggle.com/code/tunglinwood/saving-a-basic-fastai-model/edit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d838c0b3",
   "metadata": {},
   "outputs": [
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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      "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m path \u001b[38;5;241m=\u001b[39m untar_data(URLs\u001b[38;5;241m.\u001b[39mPETS)\u001b[38;5;241m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimages\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m      3\u001b[0m dls \u001b[38;5;241m=\u001b[39m ImageDataLoaders\u001b[38;5;241m.\u001b[39mfrom_name_func(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      4\u001b[0m     get_image_files(path), valid_pct\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,\n\u001b[0;32m      5\u001b[0m     label_func\u001b[38;5;241m=\u001b[39mis_cat,\n\u001b[0;32m      6\u001b[0m     item_tfms\u001b[38;5;241m=\u001b[39mResize(\u001b[38;5;241m192\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msquish\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\fastai\\data\\external.py:136\u001b[0m, in \u001b[0;36muntar_data\u001b[1;34m(url, archive, data, c_key, force_download, base)\u001b[0m\n\u001b[0;32m    134\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownload `url` using `FastDownload.get`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    135\u001b[0m d \u001b[38;5;241m=\u001b[39m FastDownload(fastai_cfg(), module\u001b[38;5;241m=\u001b[39mfastai\u001b[38;5;241m.\u001b[39mdata, archive\u001b[38;5;241m=\u001b[39marchive, data\u001b[38;5;241m=\u001b[39mdata, base\u001b[38;5;241m=\u001b[39mbase)\n\u001b[1;32m--> 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mget(url, force\u001b[38;5;241m=\u001b[39mforce_download, extract_key\u001b[38;5;241m=\u001b[39mc_key)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\fastdownload\\core.py:117\u001b[0m, in \u001b[0;36mFastDownload.get\u001b[1;34m(self, url, extract_key, force)\u001b[0m\n\u001b[0;32m    115\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_path(extract_key, urldest(url, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39march_path()))\n\u001b[0;32m    116\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mexists(): \u001b[38;5;28;01mreturn\u001b[39;00m data\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdownload(url, force\u001b[38;5;241m=\u001b[39mforce)\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextract(url, extract_key\u001b[38;5;241m=\u001b[39mextract_key, force\u001b[38;5;241m=\u001b[39mforce)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\fastdownload\\core.py:92\u001b[0m, in \u001b[0;36mFastDownload.download\u001b[1;34m(self, url, force)\u001b[0m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownload `url` to archive path, unless exists and `self.check` fails and not `force`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     91\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39march_path()\u001b[38;5;241m.\u001b[39mmkdir(exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, parents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m download_and_check(url, urldest(url, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39march_path()), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodule, force)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\fastdownload\\core.py:61\u001b[0m, in \u001b[0;36mdownload_and_check\u001b[1;34m(url, fpath, fmod, force)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m check(fmod, url, fpath): \u001b[38;5;28;01mreturn\u001b[39;00m fpath\n\u001b[0;32m     60\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading a new version of this dataset...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 61\u001b[0m res \u001b[38;5;241m=\u001b[39m download_url(url, fpath)\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m check(fmod, url, fpath): \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloaded file is corrupt or not latest version\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\fastdownload\\core.py:19\u001b[0m, in \u001b[0;36mdownload_url\u001b[1;34m(url, dest, timeout, show_progress)\u001b[0m\n\u001b[0;32m     17\u001b[0m     pbar\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;241m=\u001b[39m tsize\n\u001b[0;32m     18\u001b[0m     pbar\u001b[38;5;241m.\u001b[39mupdate(count\u001b[38;5;241m*\u001b[39mbsize)\n\u001b[1;32m---> 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m urlsave(url, dest, reporthook\u001b[38;5;241m=\u001b[39mprogress \u001b[38;5;28;01mif\u001b[39;00m show_progress \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, timeout\u001b[38;5;241m=\u001b[39mtimeout)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\fastcore\\net.py:184\u001b[0m, in \u001b[0;36murlsave\u001b[1;34m(url, dest, reporthook, headers, timeout)\u001b[0m\n\u001b[0;32m    182\u001b[0m dest \u001b[38;5;241m=\u001b[39m urldest(url, dest)\n\u001b[0;32m    183\u001b[0m dest\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39mmkdir(parents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 184\u001b[0m nm,msg \u001b[38;5;241m=\u001b[39m urlretrieve(url, dest, reporthook, headers\u001b[38;5;241m=\u001b[39mheaders, timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m    185\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m nm\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\fastcore\\net.py:161\u001b[0m, in \u001b[0;36murlretrieve\u001b[1;34m(url, filename, reporthook, data, headers, timeout)\u001b[0m\n\u001b[0;32m    159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reporthook: reporthook(blocknum, bs, size)\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 161\u001b[0m     block \u001b[38;5;241m=\u001b[39m fp\u001b[38;5;241m.\u001b[39mread(bs)\n\u001b[0;32m    162\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m block: \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m    163\u001b[0m     read \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(block)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\http\\client.py:473\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    470\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength:\n\u001b[0;32m    471\u001b[0m     \u001b[38;5;66;03m# clip the read to the \"end of response\"\u001b[39;00m\n\u001b[0;32m    472\u001b[0m     amt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength\n\u001b[1;32m--> 473\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mread(amt)\n\u001b[0;32m    474\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m s \u001b[38;5;129;01mand\u001b[39;00m amt:\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[0;32m    476\u001b[0m     \u001b[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n\u001b[0;32m    477\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m    705\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 706\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m    708\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\ssl.py:1314\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1310\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m   1311\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1312\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[0;32m   1313\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[1;32m-> 1314\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread(nbytes, buffer)\n\u001b[0;32m   1315\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\ssl.py:1166\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m   1164\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1165\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1166\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[0;32m   1167\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1168\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "path = untar_data(URLs.PETS)/'images'\n",
    "\n",
    "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, method='squish'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c107f724",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
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     "metadata": {},
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   ],
   "source": [
    "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
    "learn.fine_tune(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bed928f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.PETS)/'images'\n",
    "\n",
    "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": "7e56b200",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    /* 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-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "    }\n",
       "</style>\n"
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
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       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.010825</td>\n",
       "      <td>00:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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-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": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>0.013532</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.038955</td>\n",
       "      <td>0.018082</td>\n",
       "      <td>0.006089</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.016343</td>\n",
       "      <td>0.018480</td>\n",
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       "      <td>00:24</td>\n",
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       "  </tbody>\n",
       "</table>"
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      "text/plain": [
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     "metadata": {},
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    }
   ],
   "source": [
    "learn = vision_learner(dls, resnet18, metrics=error_rate).to_fp16()\n",
    "learn.fine_tune(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5171c7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3295ef11",
   "metadata": {},
   "outputs": [
    {
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",
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",
      "text/plain": [
       "PILImage mode=RGB size=192x191"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('dog.jpg')\n",
    "im.thumbnail((192,192))\n",
    "im"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "610559b8",
   "metadata": {},
   "source": [
    "**HERE** we are acutally load the learner that we had in the model.pkl and they are basically pointing the same learner referred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae2bc6ac",
   "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)",
      "Cell \u001b[1;32mIn[19], line 2\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 load_learner(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\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 torch\u001b[38;5;241m.\u001b[39mload(fname, map_location\u001b[38;5;241m=\u001b[39mmap_loc, pickle_module\u001b[38;5;241m=\u001b[39mpickle_module)\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;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\torch\\serialization.py:1026\u001b[0m, in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[0;32m   1024\u001b[0m             \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m   1025\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mUnpicklingError(UNSAFE_MESSAGE \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1026\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m _load(opened_zipfile,\n\u001b[0;32m   1027\u001b[0m                      map_location,\n\u001b[0;32m   1028\u001b[0m                      pickle_module,\n\u001b[0;32m   1029\u001b[0m                      overall_storage\u001b[38;5;241m=\u001b[39moverall_storage,\n\u001b[0;32m   1030\u001b[0m                      \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpickle_load_args)\n\u001b[0;32m   1031\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mmap:\n\u001b[0;32m   1032\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmmap can only be used with files saved with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1033\u001b[0m                        \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`torch.save(_use_new_zipfile_serialization=True), \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1034\u001b[0m                        \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplease torch.save your checkpoint with this option in order to use mmap.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\torch\\serialization.py:1438\u001b[0m, in \u001b[0;36m_load\u001b[1;34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[0m\n\u001b[0;32m   1436\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   1437\u001b[0m unpickler\u001b[38;5;241m.\u001b[39mpersistent_load \u001b[38;5;241m=\u001b[39m persistent_load\n\u001b[1;32m-> 1438\u001b[0m result \u001b[38;5;241m=\u001b[39m unpickler\u001b[38;5;241m.\u001b[39mload()\n\u001b[0;32m   1440\u001b[0m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_validate_loaded_sparse_tensors()\n\u001b[0;32m   1441\u001b[0m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_log_api_usage_metadata(\n\u001b[0;32m   1442\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch.load.metadata\u001b[39m\u001b[38;5;124m\"\u001b[39m, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mserialization_id\u001b[39m\u001b[38;5;124m\"\u001b[39m: zip_file\u001b[38;5;241m.\u001b[39mserialization_id()}\n\u001b[0;32m   1443\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\pathlib.py:873\u001b[0m, in \u001b[0;36mPath.__new__\u001b[1;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[0;32m    871\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)\n\u001b[0;32m    872\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--> 873\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    874\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    875\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('model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871f7476",
   "metadata": {},
   "source": [
    "And then we call the predict function to the learner to predict the given image. And it is going to return (Boolean, The tensor of the firstimage, probability([probability of first category, and the other])).\n",
    "**Note:** Unfortunately, Gradio doesn't manage tensor for now, so we dont need the tensor in the result of prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6e0bf9da",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'learn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m learn\u001b[38;5;241m.\u001b[39mpredict(im)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'learn' is not defined"
     ]
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0419ed3a",
   "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": 16,
   "id": "762dec00",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'im' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m classify_image(im)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'im' is not defined"
     ]
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0518a30a",
   "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": []
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\queueing.py\", line 527, in process_events\n",
      "    response = await route_utils.call_process_api(\n",
      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\route_utils.py\", line 261, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\blocks.py\", line 1786, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\blocks.py\", line 1338, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
      "    return await get_async_backend().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\utils.py\", line 759, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\Yuan Tao\\AppData\\Local\\Temp\\ipykernel_7672\\1180065428.py\", line 5, in classify_image\n",
      "    pred,idx,probs = learn.predict(img)\n",
      "                     ^^^^^\n",
      "NameError: name 'learn' is not defined\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\queueing.py\", line 527, in process_events\n",
      "    response = await route_utils.call_process_api(\n",
      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\route_utils.py\", line 261, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\blocks.py\", line 1786, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\blocks.py\", line 1338, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
      "    return await get_async_backend().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\Yuan Tao\\anaconda3\\envs\\tunglinenv\\Lib\\site-packages\\gradio\\utils.py\", line 759, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\Yuan Tao\\AppData\\Local\\Temp\\ipykernel_7672\\1180065428.py\", line 5, in classify_image\n",
      "    pred,idx,probs = learn.predict(img)\n",
      "                     ^^^^^\n",
      "NameError: name 'learn' is not defined\n"
     ]
    }
   ],
   "source": [
    "#|export\n",
    "image = gr.Image()\n",
    "label = gr.Label()\n",
    "examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n",
    "\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1e90ce",
   "metadata": {},
   "source": [
    "## end -"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85daa01a",
   "metadata": {},
   "source": [
    "There is a migration of notebooktoscript function of nbdev.export so below is an working update to export."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "82774c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Export successfully!\n"
     ]
    }
   ],
   "source": [
    "import nbdev\n",
    "import nbdev.export\n",
    "nbdev.export.nb_export('app.ipynb', 'app')\n",
    "print('Export successfully!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51e177ff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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