appl.ipynb

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   ],
   "source": [
    "#|default_exp app\n",
    "! pip install -q gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3268b74d",
   "metadata": {
    "id": "3268b74d"
   },
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   "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": 7,
   "id": "4t2PFd6WWzS9",
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     "height": 37
    },
    "id": "4t2PFd6WWzS9",
    "outputId": "bfcdfaef-b763-4aa4-dcd5-fc2bda3e3003"
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    "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'))"
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      "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
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       "<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",
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       "      <td>0</td>\n",
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   "source": [
    "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
    "learn.fine_tune(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "l9NBqA_vNyim",
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    "id": "l9NBqA_vNyim"
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   "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))"
   ]
  },
  {
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   "execution_count": 10,
   "id": "S8aN5mepglPI",
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      "/usr/local/lib/python3.8/dist-packages/torch/amp/autocast_mode.py:202: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n",
      "/usr/local/lib/python3.8/dist-packages/torch/cuda/amp/grad_scaler.py:118: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.\n",
      "  warnings.warn(\"torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.\")\n"
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       "  <thead>\n",
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       "  <thead>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.024219</td>\n",
       "      <td>0.022904</td>\n",
       "      <td>0.008119</td>\n",
       "      <td>18:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = vision_learner(dls, resnet18, metrics=error_rate).to_fp16()\n",
    "learn.fine_tune(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "-yoGYFncgqEo",
   "metadata": {
    "id": "-yoGYFncgqEo"
   },
   "outputs": [],
   "source": [
    "learn.export('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4hdXuc99yno-",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166
    },
    "id": "4hdXuc99yno-",
    "outputId": "edcef38a-2a28-43bb-c806-eb9c3d7f054e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "PILImage mode=RGB size=192x149"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('dog.jfif')\n",
    "im.thumbnail((192,192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "tfzQ9z8Byscl",
   "metadata": {
    "id": "tfzQ9z8Byscl"
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "_V3OLNZwy2JG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "_V3OLNZwy2JG",
    "outputId": "c59255d3-6049-4ea7-b2c1-213652009c2c"
   },
   "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": [
       "('False', TensorBase(0), TensorBase([0.9818, 0.0182]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "kRe4jKhe0K3O",
   "metadata": {
    "id": "kRe4jKhe0K3O"
   },
   "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": 18,
   "id": "jVYIjJAb0n6F",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "jVYIjJAb0n6F",
    "outputId": "a8f14296-7e47-4c30-b4e3-1a46b20c7ad7"
   },
   "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": [
       "{'Dog': 0.9818421602249146, 'Cat': 0.01815781183540821}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "EuWEpo4J0qgq",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EuWEpo4J0qgq",
    "outputId": "eacd459e-6528-4f50-b27c-b74249867d0e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "/usr/local/lib/python3.8/dist-packages/gradio/outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
      "  warnings.warn(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
      "Running on public URL: https://66be1ba3-06e8-4bfb.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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|export\n",
    "image = gr.inputs.Image(shape=(192, 192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['dog.jfif', 'cat.jfif', 'dunno.jfif']\n",
    "\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False, share=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4os35LyR363m",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4os35LyR363m",
    "outputId": "18100c46-a139-4983-bfec-ec8fb93d22f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: nbdev in /usr/local/lib/python3.8/dist-packages (2.3.11)\n",
      "Requirement already satisfied: astunparse in /usr/local/lib/python3.8/dist-packages (from nbdev) (1.6.3)\n",
      "Requirement already satisfied: fastcore>=1.5.27 in /usr/local/lib/python3.8/dist-packages (from nbdev) (1.5.28)\n",
      "Requirement already satisfied: watchdog in /usr/local/lib/python3.8/dist-packages (from nbdev) (2.2.1)\n",
      "Requirement already satisfied: execnb>=0.1.4 in /usr/local/lib/python3.8/dist-packages (from nbdev) (0.1.4)\n",
      "Requirement already satisfied: PyYAML in /usr/local/lib/python3.8/dist-packages (from nbdev) (6.0)\n",
      "Requirement already satisfied: asttokens in /usr/local/lib/python3.8/dist-packages (from nbdev) (2.2.1)\n",
      "Requirement already satisfied: ghapi>=1.0.3 in /usr/local/lib/python3.8/dist-packages (from nbdev) (1.0.3)\n",
      "Requirement already satisfied: ipython in /usr/local/lib/python3.8/dist-packages (from execnb>=0.1.4->nbdev) (7.9.0)\n",
      "Requirement already satisfied: pip in /usr/local/lib/python3.8/dist-packages (from fastcore>=1.5.27->nbdev) (22.0.4)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from fastcore>=1.5.27->nbdev) (23.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from asttokens->nbdev) (1.15.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.8/dist-packages (from astunparse->nbdev) (0.38.4)\n",
      "Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (0.2.0)\n",
      "Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (2.6.1)\n",
      "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (2.0.10)\n",
      "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (0.18.2)\n",
      "Requirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (0.7.5)\n",
      "Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (4.8.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (57.4.0)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (4.4.2)\n",
      "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.8/dist-packages (from ipython->execnb>=0.1.4->nbdev) (5.7.1)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->ipython->execnb>=0.1.4->nbdev) (0.8.3)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->execnb>=0.1.4->nbdev) (0.2.6)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->ipython->execnb>=0.1.4->nbdev) (0.7.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install nbdev\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "q2br-1jO4cRC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "id": "q2br-1jO4cRC",
    "outputId": "446f526d-94b6-4e84-c0ab-6eec25102e1b"
   },
   "outputs": [
    {
     "ename": "ImportError",
     "errorDetails": {
      "actions": [
       {
        "action": "open_url",
        "actionText": "Open Examples",
        "url": "/notebooks/snippets/importing_libraries.ipynb"
       }
      ]
     },
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-92ee83ce556b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnbdev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnotebook2script\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'notebook2script' from 'nbdev.export' (/usr/local/lib/python3.8/dist-packages/nbdev/export.py)",
      "",
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from nbdev.export import notebook2script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "u6lIoSN34qBp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "id": "u6lIoSN34qBp",
    "outputId": "c2ba58bc-7edd-4521-ecf6-c1309005e696"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-d953437cef2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnbdev\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnbdev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb_export\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'appl.ipynb'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'app'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Export successful'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/nbdev/export.py\u001b[0m in \u001b[0;36mnb_export\u001b[0;34m(nbname, lib_path, procs, debug, mod_maker, name)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlib_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlib_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0mexp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExportModuleProc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0mnb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNBProcessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m     \u001b[0mnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcells\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/nbdev/process.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path, procs, nb, debug, rm_directives, process)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;34m\"Process cells and nbdev comments in a notebook\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrm_directives\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnb\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlang\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnb_lang\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcell\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcells\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirectives_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_directives\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremove\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrm_directives\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/execnb/nbio.py\u001b[0m in \u001b[0;36mread_nb\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;34m\"Return notebook at `path`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict2nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_read_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'path_'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/execnb/nbio.py\u001b[0m in \u001b[0;36m_read_json\u001b[0;34m(self, encoding, errors)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# %% ../nbs/01_nbio.ipynb 6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_read_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# %% ../nbs/01_nbio.ipynb 13\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/pathlib.py\u001b[0m in \u001b[0;36mread_text\u001b[0;34m(self, encoding, errors)\u001b[0m\n\u001b[1;32m   1234\u001b[0m         \u001b[0mOpen\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtext\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mclose\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m         \"\"\"\n\u001b[0;32m-> 1236\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1237\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/pathlib.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, mode, buffering, encoding, errors, newline)\u001b[0m\n\u001b[1;32m   1220\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_closed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1221\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1222\u001b[0;31m         return io.open(self, mode, buffering, encoding, errors, newline,\n\u001b[0m\u001b[1;32m   1223\u001b[0m                        opener=self._opener)\n\u001b[1;32m   1224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/pathlib.py\u001b[0m in \u001b[0;36m_opener\u001b[0;34m(self, name, flags, mode)\u001b[0m\n\u001b[1;32m   1076\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0o666\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m         \u001b[0;31m# A stub for the opener argument to built-in open()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1079\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_raw_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0o777\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'appl.ipynb'"
     ]
    }
   ],
   "source": [
    "import nbdev\n",
    "nbdev.export.nb_export('appl.ipynb', 'app')\n",
    "print('Export successful')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "rDKLNSV_5PCq",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rDKLNSV_5PCq",
    "outputId": "0c8b7433-9348-489f-aa76-ae6021bd4416"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting notebook2script\n",
      "  Downloading notebook2script-0.2.1-py3-none-any.whl (62 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.6/62.6 KB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: click>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from notebook2script) (7.1.2)\n",
      "Requirement already satisfied: nbconvert>=5.6.1 in /usr/local/lib/python3.8/dist-packages (from notebook2script) (5.6.1)\n",
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      "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=3.6.0->domdf-python-tools>=2.8.1->notebook2script) (3.12.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.16->ipython>=7.14.0->notebook2script) (0.8.3)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.4->nbconvert>=5.6.1->notebook2script) (2.0.1)\n",
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      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<3.1.0,>=3.0.30->ipython>=7.14.0->notebook2script) (0.2.6)\n",
      "Collecting ruamel.yaml.clib>=0.2.6\n",
      "  Downloading ruamel.yaml.clib-0.2.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (555 kB)\n",
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      "Collecting pure-eval\n",
      "  Downloading pure_eval-0.2.2-py3-none-any.whl (11 kB)\n",
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      "  Downloading executing-1.2.0-py2.py3-none-any.whl (24 kB)\n",
      "Requirement already satisfied: toolz>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from attr-utils>=0.5.5->formate>=0.4.5->yapf-isort>=0.5.5->notebook2script) (0.12.0)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=4.4->nbconvert>=5.6.1->notebook2script) (0.19.3)\n",
      "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=4.4->nbconvert>=5.6.1->notebook2script) (5.10.2)\n",
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      "  Downloading colorful-0.5.5-py2.py3-none-any.whl (201 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.4/201.4 KB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hBuilding wheels for collected packages: wrapt\n",
      "  Building wheel for wrapt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for wrapt: filename=wrapt-1.12.1-cp38-cp38-linux_x86_64.whl size=78577 sha256=f7dd7460b7927a0c137f8e678118c179b329b5c994a5aebd821afa3d7842c170\n",
      "  Stored in directory: /root/.cache/pip/wheels/5f/fd/9e/b6cf5890494cb8ef0b5eaff72e5d55a70fb56316007d6dfe73\n",
      "Successfully built wrapt\n",
      "Installing collected packages: yapf, wrapt, pure-eval, mccabe, executing, colorful, ruamel.yaml.clib, prompt-toolkit, prettyprinter, natsort, mistletoe, matplotlib-inline, lazy-object-proxy, isort, deprecation, stack-data, ruamel.yaml, domdf-python-tools, deprecation-alias, astroid, pylint, pre-commit-hooks, ipython, dom-toml, consolekit, attr-utils, astatine, formate, yapf-isort, notebook2script\n",
      "  Attempting uninstall: wrapt\n",
      "    Found existing installation: wrapt 1.14.1\n",
      "    Uninstalling wrapt-1.14.1:\n",
      "      Successfully uninstalled wrapt-1.14.1\n",
      "  Attempting uninstall: prompt-toolkit\n",
      "    Found existing installation: prompt-toolkit 2.0.10\n",
      "    Uninstalling prompt-toolkit-2.0.10:\n",
      "      Successfully uninstalled prompt-toolkit-2.0.10\n",
      "  Attempting uninstall: natsort\n",
      "    Found existing installation: natsort 5.5.0\n",
      "    Uninstalling natsort-5.5.0:\n",
      "      Successfully uninstalled natsort-5.5.0\n",
      "  Attempting uninstall: ipython\n",
      "    Found existing installation: ipython 7.9.0\n",
      "    Uninstalling ipython-7.9.0:\n",
      "      Successfully uninstalled ipython-7.9.0\n",
      "\u001b[31mERROR: 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",
      "google-colab 1.0.0 requires ipython~=7.9.0, but you have ipython 8.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed astatine-0.3.2 astroid-2.5 attr-utils-0.7.0 colorful-0.5.5 consolekit-1.4.1 deprecation-2.1.0 deprecation-alias-0.3.1 dom-toml-0.6.0 domdf-python-tools-3.6.0 executing-1.2.0 formate-0.4.10 ipython-8.9.0 isort-5.6.4 lazy-object-proxy-1.9.0 matplotlib-inline-0.1.6 mccabe-0.6.1 mistletoe-1.0.1 natsort-8.2.0 notebook2script-0.2.1 pre-commit-hooks-4.4.0 prettyprinter-0.18.0 prompt-toolkit-3.0.36 pure-eval-0.2.2 pylint-2.7.1 ruamel.yaml-0.17.21 ruamel.yaml.clib-0.2.7 stack-data-0.6.2 wrapt-1.12.1 yapf-0.31.0 yapf-isort-0.6.0\n"
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     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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