{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp app.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x): return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PILImage mode=RGB size=186x192"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('dog.jpg')\n",
    "im.thumbnail((192,192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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([1.0000e+00, 6.9666e-08]))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "categories = ('Dog', 'Cat')\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": 48,
   "metadata": {},
   "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': 0.9999998807907104, 'Cat': 6.966574517264235e-08}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/clarice/miniconda3/envs/fastai/lib/python3.8/site-packages/gradio/inputs.py:256: 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",
      "/home/clarice/miniconda3/envs/fastai/lib/python3.8/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "/home/clarice/miniconda3/envs/fastai/lib/python3.8/site-packages/gradio/outputs.py:196: 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",
      "/home/clarice/miniconda3/envs/fastai/lib/python3.8/site-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": [
      "Running on local URL:  http://127.0.0.1:7862/\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App at 0x7f67a8a85d00>, 'http://127.0.0.1:7862/', None)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| export\n",
    "image = gr.inputs.Image(shape=(192,192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['dog.jpg', 'cat.jpg', 'dunno.png']\n",
    "\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'extra_files'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/clarice/Documents/Git/minimal/app.ipynb Cell 9\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/clarice/Documents/Git/minimal/app.ipynb#ch0000008?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnbdev\u001b[39;00m \u001b[39mimport\u001b[39;00m nbdev_export\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/clarice/Documents/Git/minimal/app.ipynb#ch0000008?line=1'>2</a>\u001b[0m nbdev_export(\u001b[39m'\u001b[39;49m\u001b[39mapp.ipynb\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastcore/script.py:107\u001b[0m, in \u001b[0;36mcall_parse.<locals>._f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[39m@wraps\u001b[39m(func)\n\u001b[1;32m    105\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_f\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    106\u001b[0m     mod \u001b[39m=\u001b[39m inspect\u001b[39m.\u001b[39mgetmodule(inspect\u001b[39m.\u001b[39mcurrentframe()\u001b[39m.\u001b[39mf_back)\n\u001b[0;32m--> 107\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m mod: \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    108\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m SCRIPT_INFO\u001b[39m.\u001b[39mfunc \u001b[39mand\u001b[39;00m mod\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m==\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m__main__\u001b[39m\u001b[39m\"\u001b[39m: SCRIPT_INFO\u001b[39m.\u001b[39mfunc \u001b[39m=\u001b[39m func\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\n\u001b[1;32m    109\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(sys\u001b[39m.\u001b[39margv)\u001b[39m>\u001b[39m\u001b[39m1\u001b[39m \u001b[39mand\u001b[39;00m sys\u001b[39m.\u001b[39margv[\u001b[39m1\u001b[39m]\u001b[39m==\u001b[39m\u001b[39m'\u001b[39m\u001b[39m'\u001b[39m: sys\u001b[39m.\u001b[39margv\u001b[39m.\u001b[39mpop(\u001b[39m1\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/nbdev/doclinks.py:144\u001b[0m, in \u001b[0;36mnbdev_export\u001b[0;34m(path, recursive, symlinks, file_re, folder_re, skip_file_glob, skip_file_re)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExport notebooks in `path` to Python modules\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    143\u001b[0m \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39menviron\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mIN_TEST\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m0\u001b[39m): \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m--> 144\u001b[0m files \u001b[39m=\u001b[39m nbglob(path\u001b[39m=\u001b[39;49mpath, recursive\u001b[39m=\u001b[39;49mrecursive, file_re\u001b[39m=\u001b[39;49mfile_re, \n\u001b[1;32m    145\u001b[0m                folder_re\u001b[39m=\u001b[39;49mfolder_re, skip_file_glob\u001b[39m=\u001b[39;49mskip_file_glob, skip_file_re\u001b[39m=\u001b[39;49mskip_file_re, symlinks\u001b[39m=\u001b[39;49msymlinks)\n\u001b[1;32m    146\u001b[0m \u001b[39mfor\u001b[39;00m f \u001b[39min\u001b[39;00m files: nb_export(f)\n\u001b[1;32m    147\u001b[0m add_init(get_config()\u001b[39m.\u001b[39mpath(\u001b[39m'\u001b[39m\u001b[39mlib_path\u001b[39m\u001b[39m'\u001b[39m))\n",
      "File \u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/nbdev/doclinks.py:127\u001b[0m, in \u001b[0;36mnbglob\u001b[0;34m(path, skip_folder_re, file_glob, recursive, key, as_path, **kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFind all files in a directory matching an extension given a `config_key`.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    126\u001b[0m path \u001b[39m=\u001b[39m Path(path \u001b[39mor\u001b[39;00m config_key(key))\n\u001b[0;32m--> 127\u001b[0m \u001b[39mif\u001b[39;00m recursive \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: recursive\u001b[39m=\u001b[39mstr2bool(config_key(\u001b[39m'\u001b[39;49m\u001b[39mrecursive\u001b[39;49m\u001b[39m'\u001b[39;49m, path\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m))\n\u001b[1;32m    128\u001b[0m res \u001b[39m=\u001b[39m globtastic(path, file_glob\u001b[39m=\u001b[39mfile_glob, skip_folder_re\u001b[39m=\u001b[39mskip_folder_re, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    129\u001b[0m \u001b[39mreturn\u001b[39;00m res\u001b[39m.\u001b[39mmap(Path) \u001b[39mif\u001b[39;00m as_path \u001b[39melse\u001b[39;00m res\n",
      "File \u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/nbdev/read.py:120\u001b[0m, in \u001b[0;36mconfig_key\u001b[0;34m(c, default, path, missing_ok)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[39mif\u001b[39;00m missing_ok \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    119\u001b[0m     warn(\u001b[39m\"\u001b[39m\u001b[39m`missing_ok` is no longer used. Don\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt pass it to `config_key` to silence this warning.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 120\u001b[0m cfg \u001b[39m=\u001b[39m get_config()\n\u001b[1;32m    121\u001b[0m res \u001b[39m=\u001b[39m cfg\u001b[39m.\u001b[39mpath(c, default) \u001b[39mif\u001b[39;00m path \u001b[39melse\u001b[39;00m cfg\u001b[39m.\u001b[39mget(c, default)\n\u001b[1;32m    122\u001b[0m \u001b[39mif\u001b[39;00m res \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m`\u001b[39m\u001b[39m{\u001b[39;00mc\u001b[39m}\u001b[39;00m\u001b[39m` not specified in \u001b[39m\u001b[39m{\u001b[39;00m_nbdev_config_name\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/nbdev/read.py:112\u001b[0m, in \u001b[0;36mget_config\u001b[0;34m(cfg_name, path)\u001b[0m\n\u001b[1;32m    110\u001b[0m cfg_file \u001b[39m=\u001b[39m _nbdev_config_file(cfg_name, path)\n\u001b[1;32m    111\u001b[0m extra_files \u001b[39m=\u001b[39m _xdg_config_paths(cfg_name)\n\u001b[0;32m--> 112\u001b[0m cfg \u001b[39m=\u001b[39m Config(cfg_file\u001b[39m.\u001b[39;49mparent, cfg_file\u001b[39m.\u001b[39;49mname, extra_files\u001b[39m=\u001b[39;49mextra_files)\n\u001b[1;32m    113\u001b[0m \u001b[39mreturn\u001b[39;00m apply_defaults(cfg)\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'extra_files'"
     ]
    }
   ],
   "source": [
    "from nbdev import nbdev_export\n",
    "nbdev_export('app.ipynb')"
   ]
  }
 ],
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