{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e9aad829",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bafd2df",
   "metadata": {},
   "source": [
    "# Dogs VS Cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a4f8058",
   "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": 3,
   "id": "62993ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "PILImage mode=RGB size=192x191"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('dog.jpg')\n",
    "im.thumbnail((192, 192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62e068da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "056e131f",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 154 ms, sys: 23.2 ms, total: 177 ms\n",
      "Wall time: 55.4 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('False', TensorBase(0), TensorBase([1.0000e+00, 2.4092e-07]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eddde493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in module time:\n",
      "\n",
      "NAME\n",
      "    time - This module provides various functions to manipulate time values.\n",
      "\n",
      "DESCRIPTION\n",
      "    There are two standard representations of time.  One is the number\n",
      "    of seconds since the Epoch, in UTC (a.k.a. GMT).  It may be an integer\n",
      "    or a floating point number (to represent fractions of seconds).\n",
      "    The Epoch is system-defined; on Unix, it is generally January 1st, 1970.\n",
      "    The actual value can be retrieved by calling gmtime(0).\n",
      "    \n",
      "    The other representation is a tuple of 9 integers giving local time.\n",
      "    The tuple items are:\n",
      "      year (including century, e.g. 1998)\n",
      "      month (1-12)\n",
      "      day (1-31)\n",
      "      hours (0-23)\n",
      "      minutes (0-59)\n",
      "      seconds (0-59)\n",
      "      weekday (0-6, Monday is 0)\n",
      "      Julian day (day in the year, 1-366)\n",
      "      DST (Daylight Savings Time) flag (-1, 0 or 1)\n",
      "    If the DST flag is 0, the time is given in the regular time zone;\n",
      "    if it is 1, the time is given in the DST time zone;\n",
      "    if it is -1, mktime() should guess based on the date and time.\n",
      "\n",
      "CLASSES\n",
      "    builtins.tuple(builtins.object)\n",
      "        struct_time\n",
      "    \n",
      "    class struct_time(builtins.tuple)\n",
      "     |  struct_time(iterable=(), /)\n",
      "     |  \n",
      "     |  The time value as returned by gmtime(), localtime(), and strptime(), and\n",
      "     |  accepted by asctime(), mktime() and strftime().  May be considered as a\n",
      "     |  sequence of 9 integers.\n",
      "     |  \n",
      "     |  Note that several fields' values are not the same as those defined by\n",
      "     |  the C language standard for struct tm.  For example, the value of the\n",
      "     |  field tm_year is the actual year, not year - 1900.  See individual\n",
      "     |  fields' descriptions for details.\n",
      "     |  \n",
      "     |  Method resolution order:\n",
      "     |      struct_time\n",
      "     |      builtins.tuple\n",
      "     |      builtins.object\n",
      "     |  \n",
      "     |  Methods defined here:\n",
      "     |  \n",
      "     |  __reduce__(...)\n",
      "     |      Helper for pickle.\n",
      "     |  \n",
      "     |  __repr__(self, /)\n",
      "     |      Return repr(self).\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Static methods defined here:\n",
      "     |  \n",
      "     |  __new__(*args, **kwargs) from builtins.type\n",
      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Data descriptors defined here:\n",
      "     |  \n",
      "     |  tm_gmtoff\n",
      "     |      offset from UTC in seconds\n",
      "     |  \n",
      "     |  tm_hour\n",
      "     |      hours, range [0, 23]\n",
      "     |  \n",
      "     |  tm_isdst\n",
      "     |      1 if summer time is in effect, 0 if not, and -1 if unknown\n",
      "     |  \n",
      "     |  tm_mday\n",
      "     |      day of month, range [1, 31]\n",
      "     |  \n",
      "     |  tm_min\n",
      "     |      minutes, range [0, 59]\n",
      "     |  \n",
      "     |  tm_mon\n",
      "     |      month of year, range [1, 12]\n",
      "     |  \n",
      "     |  tm_sec\n",
      "     |      seconds, range [0, 61])\n",
      "     |  \n",
      "     |  tm_wday\n",
      "     |      day of week, range [0, 6], Monday is 0\n",
      "     |  \n",
      "     |  tm_yday\n",
      "     |      day of year, range [1, 366]\n",
      "     |  \n",
      "     |  tm_year\n",
      "     |      year, for example, 1993\n",
      "     |  \n",
      "     |  tm_zone\n",
      "     |      abbreviation of timezone name\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Data and other attributes defined here:\n",
      "     |  \n",
      "     |  n_fields = 11\n",
      "     |  \n",
      "     |  n_sequence_fields = 9\n",
      "     |  \n",
      "     |  n_unnamed_fields = 0\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.tuple:\n",
      "     |  \n",
      "     |  __add__(self, value, /)\n",
      "     |      Return self+value.\n",
      "     |  \n",
      "     |  __contains__(self, key, /)\n",
      "     |      Return key in self.\n",
      "     |  \n",
      "     |  __eq__(self, value, /)\n",
      "     |      Return self==value.\n",
      "     |  \n",
      "     |  __ge__(self, value, /)\n",
      "     |      Return self>=value.\n",
      "     |  \n",
      "     |  __getattribute__(self, name, /)\n",
      "     |      Return getattr(self, name).\n",
      "     |  \n",
      "     |  __getitem__(self, key, /)\n",
      "     |      Return self[key].\n",
      "     |  \n",
      "     |  __getnewargs__(self, /)\n",
      "     |  \n",
      "     |  __gt__(self, value, /)\n",
      "     |      Return self>value.\n",
      "     |  \n",
      "     |  __hash__(self, /)\n",
      "     |      Return hash(self).\n",
      "     |  \n",
      "     |  __iter__(self, /)\n",
      "     |      Implement iter(self).\n",
      "     |  \n",
      "     |  __le__(self, value, /)\n",
      "     |      Return self<=value.\n",
      "     |  \n",
      "     |  __len__(self, /)\n",
      "     |      Return len(self).\n",
      "     |  \n",
      "     |  __lt__(self, value, /)\n",
      "     |      Return self<value.\n",
      "     |  \n",
      "     |  __mul__(self, value, /)\n",
      "     |      Return self*value.\n",
      "     |  \n",
      "     |  __ne__(self, value, /)\n",
      "     |      Return self!=value.\n",
      "     |  \n",
      "     |  __rmul__(self, value, /)\n",
      "     |      Return value*self.\n",
      "     |  \n",
      "     |  count(self, value, /)\n",
      "     |      Return number of occurrences of value.\n",
      "     |  \n",
      "     |  index(self, value, start=0, stop=9223372036854775807, /)\n",
      "     |      Return first index of value.\n",
      "     |      \n",
      "     |      Raises ValueError if the value is not present.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Class methods inherited from builtins.tuple:\n",
      "     |  \n",
      "     |  __class_getitem__(...) from builtins.type\n",
      "     |      See PEP 585\n",
      "\n",
      "FUNCTIONS\n",
      "    asctime(...)\n",
      "        asctime([tuple]) -> string\n",
      "        \n",
      "        Convert a time tuple to a string, e.g. 'Sat Jun 06 16:26:11 1998'.\n",
      "        When the time tuple is not present, current time as returned by localtime()\n",
      "        is used.\n",
      "    \n",
      "    clock_getres(...)\n",
      "        clock_getres(clk_id) -> floating point number\n",
      "        \n",
      "        Return the resolution (precision) of the specified clock clk_id.\n",
      "    \n",
      "    clock_gettime(...)\n",
      "        clock_gettime(clk_id) -> float\n",
      "        \n",
      "        Return the time of the specified clock clk_id.\n",
      "    \n",
      "    clock_gettime_ns(...)\n",
      "        clock_gettime_ns(clk_id) -> int\n",
      "        \n",
      "        Return the time of the specified clock clk_id as nanoseconds.\n",
      "    \n",
      "    clock_settime(...)\n",
      "        clock_settime(clk_id, time)\n",
      "        \n",
      "        Set the time of the specified clock clk_id.\n",
      "    \n",
      "    clock_settime_ns(...)\n",
      "        clock_settime_ns(clk_id, time)\n",
      "        \n",
      "        Set the time of the specified clock clk_id with nanoseconds.\n",
      "    \n",
      "    ctime(...)\n",
      "        ctime(seconds) -> string\n",
      "        \n",
      "        Convert a time in seconds since the Epoch to a string in local time.\n",
      "        This is equivalent to asctime(localtime(seconds)). When the time tuple is\n",
      "        not present, current time as returned by localtime() is used.\n",
      "    \n",
      "    get_clock_info(...)\n",
      "        get_clock_info(name: str) -> dict\n",
      "        \n",
      "        Get information of the specified clock.\n",
      "    \n",
      "    gmtime(...)\n",
      "        gmtime([seconds]) -> (tm_year, tm_mon, tm_mday, tm_hour, tm_min,\n",
      "                               tm_sec, tm_wday, tm_yday, tm_isdst)\n",
      "        \n",
      "        Convert seconds since the Epoch to a time tuple expressing UTC (a.k.a.\n",
      "        GMT).  When 'seconds' is not passed in, convert the current time instead.\n",
      "        \n",
      "        If the platform supports the tm_gmtoff and tm_zone, they are available as\n",
      "        attributes only.\n",
      "    \n",
      "    localtime(...)\n",
      "        localtime([seconds]) -> (tm_year,tm_mon,tm_mday,tm_hour,tm_min,\n",
      "                                  tm_sec,tm_wday,tm_yday,tm_isdst)\n",
      "        \n",
      "        Convert seconds since the Epoch to a time tuple expressing local time.\n",
      "        When 'seconds' is not passed in, convert the current time instead.\n",
      "    \n",
      "    mktime(...)\n",
      "        mktime(tuple) -> floating point number\n",
      "        \n",
      "        Convert a time tuple in local time to seconds since the Epoch.\n",
      "        Note that mktime(gmtime(0)) will not generally return zero for most\n",
      "        time zones; instead the returned value will either be equal to that\n",
      "        of the timezone or altzone attributes on the time module.\n",
      "    \n",
      "    monotonic(...)\n",
      "        monotonic() -> float\n",
      "        \n",
      "        Monotonic clock, cannot go backward.\n",
      "    \n",
      "    monotonic_ns(...)\n",
      "        monotonic_ns() -> int\n",
      "        \n",
      "        Monotonic clock, cannot go backward, as nanoseconds.\n",
      "    \n",
      "    perf_counter(...)\n",
      "        perf_counter() -> float\n",
      "        \n",
      "        Performance counter for benchmarking.\n",
      "    \n",
      "    perf_counter_ns(...)\n",
      "        perf_counter_ns() -> int\n",
      "        \n",
      "        Performance counter for benchmarking as nanoseconds.\n",
      "    \n",
      "    process_time(...)\n",
      "        process_time() -> float\n",
      "        \n",
      "        Process time for profiling: sum of the kernel and user-space CPU time.\n",
      "    \n",
      "    process_time_ns(...)\n",
      "        process_time() -> int\n",
      "        \n",
      "        Process time for profiling as nanoseconds:\n",
      "        sum of the kernel and user-space CPU time.\n",
      "    \n",
      "    sleep(...)\n",
      "        sleep(seconds)\n",
      "        \n",
      "        Delay execution for a given number of seconds.  The argument may be\n",
      "        a floating point number for subsecond precision.\n",
      "    \n",
      "    strftime(...)\n",
      "        strftime(format[, tuple]) -> string\n",
      "        \n",
      "        Convert a time tuple to a string according to a format specification.\n",
      "        See the library reference manual for formatting codes. When the time tuple\n",
      "        is not present, current time as returned by localtime() is used.\n",
      "        \n",
      "        Commonly used format codes:\n",
      "        \n",
      "        %Y  Year with century as a decimal number.\n",
      "        %m  Month as a decimal number [01,12].\n",
      "        %d  Day of the month as a decimal number [01,31].\n",
      "        %H  Hour (24-hour clock) as a decimal number [00,23].\n",
      "        %M  Minute as a decimal number [00,59].\n",
      "        %S  Second as a decimal number [00,61].\n",
      "        %z  Time zone offset from UTC.\n",
      "        %a  Locale's abbreviated weekday name.\n",
      "        %A  Locale's full weekday name.\n",
      "        %b  Locale's abbreviated month name.\n",
      "        %B  Locale's full month name.\n",
      "        %c  Locale's appropriate date and time representation.\n",
      "        %I  Hour (12-hour clock) as a decimal number [01,12].\n",
      "        %p  Locale's equivalent of either AM or PM.\n",
      "        \n",
      "        Other codes may be available on your platform.  See documentation for\n",
      "        the C library strftime function.\n",
      "    \n",
      "    strptime(...)\n",
      "        strptime(string, format) -> struct_time\n",
      "        \n",
      "        Parse a string to a time tuple according to a format specification.\n",
      "        See the library reference manual for formatting codes (same as\n",
      "        strftime()).\n",
      "        \n",
      "        Commonly used format codes:\n",
      "        \n",
      "        %Y  Year with century as a decimal number.\n",
      "        %m  Month as a decimal number [01,12].\n",
      "        %d  Day of the month as a decimal number [01,31].\n",
      "        %H  Hour (24-hour clock) as a decimal number [00,23].\n",
      "        %M  Minute as a decimal number [00,59].\n",
      "        %S  Second as a decimal number [00,61].\n",
      "        %z  Time zone offset from UTC.\n",
      "        %a  Locale's abbreviated weekday name.\n",
      "        %A  Locale's full weekday name.\n",
      "        %b  Locale's abbreviated month name.\n",
      "        %B  Locale's full month name.\n",
      "        %c  Locale's appropriate date and time representation.\n",
      "        %I  Hour (12-hour clock) as a decimal number [01,12].\n",
      "        %p  Locale's equivalent of either AM or PM.\n",
      "        \n",
      "        Other codes may be available on your platform.  See documentation for\n",
      "        the C library strftime function.\n",
      "    \n",
      "    thread_time(...)\n",
      "        thread_time() -> float\n",
      "        \n",
      "        Thread time for profiling: sum of the kernel and user-space CPU time.\n",
      "    \n",
      "    thread_time_ns(...)\n",
      "        thread_time() -> int\n",
      "        \n",
      "        Thread time for profiling as nanoseconds:\n",
      "        sum of the kernel and user-space CPU time.\n",
      "    \n",
      "    time(...)\n",
      "        time() -> floating point number\n",
      "        \n",
      "        Return the current time in seconds since the Epoch.\n",
      "        Fractions of a second may be present if the system clock provides them.\n",
      "    \n",
      "    time_ns(...)\n",
      "        time_ns() -> int\n",
      "        \n",
      "        Return the current time in nanoseconds since the Epoch.\n",
      "    \n",
      "    tzset(...)\n",
      "        tzset()\n",
      "        \n",
      "        Initialize, or reinitialize, the local timezone to the value stored in\n",
      "        os.environ['TZ']. The TZ environment variable should be specified in\n",
      "        standard Unix timezone format as documented in the tzset man page\n",
      "        (eg. 'US/Eastern', 'Europe/Amsterdam'). Unknown timezones will silently\n",
      "        fall back to UTC. If the TZ environment variable is not set, the local\n",
      "        timezone is set to the systems best guess of wallclock time.\n",
      "        Changing the TZ environment variable without calling tzset *may* change\n",
      "        the local timezone used by methods such as localtime, but this behaviour\n",
      "        should not be relied on.\n",
      "\n",
      "DATA\n",
      "    CLOCK_MONOTONIC = 6\n",
      "    CLOCK_MONOTONIC_RAW = 4\n",
      "    CLOCK_PROCESS_CPUTIME_ID = 12\n",
      "    CLOCK_REALTIME = 0\n",
      "    CLOCK_THREAD_CPUTIME_ID = 16\n",
      "    CLOCK_UPTIME_RAW = 8\n",
      "    altzone = -7200\n",
      "    daylight = 1\n",
      "    timezone = -3600\n",
      "    tzname = ('CET', 'CEST')\n",
      "\n",
      "FILE\n",
      "    (built-in)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8014d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### Learner.predict\n",
       "\n",
       ">      Learner.predict (item, rm_type_tfms=None, with_input=False)\n",
       "\n",
       "Prediction on `item`, fully decoded, loss function decoded and probabilities\n",
       "\n",
       "<a href=\"https://docs.fast.ai/learner.html#learner.predict\" target=\"_blank\" rel=\"noreferrer noopener\">Show in docs</a>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "doc(learn.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ace54947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__call__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__func__',\n",
       " '__ge__',\n",
       " '__get__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__self__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(learn.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c00c9cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict.__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25725dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<bound method Learner.predict of <fastai.learner.Learner object at 0x147843a60>>'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(learn.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47ade92a",
   "metadata": {},
   "outputs": [],
   "source": [
    "?learn.predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03d6eca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "??learn.predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "161ca9ce",
   "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": 23,
   "id": "63e19e64",
   "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': 0.9999997615814209, 'Cat': 2.4092108219520014e-07}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c467f8bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/choucri/mambaforge/lib/python3.9/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",
      "/Users/choucri/mambaforge/lib/python3.9/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "/Users/choucri/mambaforge/lib/python3.9/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",
      "/Users/choucri/mambaforge/lib/python3.9/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 0x14df13af0>, 'http://127.0.0.1:7862/', None)"
      ]
     },
     "execution_count": 27,
     "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"
    },
    {
     "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/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",
    "image = gr.inputs.Image(shape=(192, 192))\n",
    "label = gr.outputs.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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538af359",
   "metadata": {},
   "source": [
    "# Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "27597eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/choucri/.zshenv:source:12: no such file or directory: /Users/choucri/.cargo/env\n",
      "Could not access repo: choucrifahed/fastai2 to find your default branch - `main assumed.\n",
      "Edit `settings.ini` if this is incorrect.\n",
      "In the future, you can allow nbdev to see private repos by setting the environment variable GITHUB_TOKEN as described here:\n",
      "https://nbdev.fast.ai/cli.html#Using-nbdev_new-with-private-repos\n",
      "\n",
      "\u001b[90m# settings.ini\u001b[39m\n",
      "\u001b[94mlib_name\u001b[39m = fastai2\u001b[90m # Automatically inferred from git\u001b[39m\n",
      "\u001b[94muser\u001b[39m = choucrifahed\u001b[90m # Automatically inferred from git\u001b[39m\n",
      "\u001b[94mbranch\u001b[39m = main\u001b[90m # Automatically inferred from git\u001b[39m\n",
      "\u001b[94mauthor\u001b[39m = Choucri FAHED\u001b[90m # Automatically inferred from git\u001b[39m\n",
      "\u001b[94mauthor_email\u001b[39m = choucri.fahed@mines-nancy.org\u001b[90m # Automatically inferred from git\u001b[39m\n",
      "\u001b[94mkeywords\u001b[39m = nbdev\u001b[90m # Automatically inferred from git\u001b[39m\n",
      "\u001b[94mdescription\u001b[39m = \u001b[90m # Automatically inferred from git\u001b[39m\n",
      "\u001b[94mrepo\u001b[39m = fastai2\u001b[90m # Automatically inferred from git\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "!nbdev_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce0a6c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nbdev import nbdev_export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5aa406b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'notebook2script' in dir(nbdev.export)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8567b690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.7'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbdev.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "caccb695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/choucri/.zshenv:source:12: no such file or directory: /Users/choucri/.cargo/env\n",
      "aiohttp==3.8.1\n",
      "aiosignal==1.2.0\n",
      "analytics-python==1.4.0\n",
      "anyio==3.6.1\n",
      "appnope @ file:///home/conda/feedstock_root/build_artifacts/appnope_1649077682618/work\n",
      "argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1640817743617/work\n",
      "argon2-cffi-bindings @ file:///Users/runner/miniforge3/conda-bld/argon2-cffi-bindings_1649500366121/work\n",
      "asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1618968359944/work\n",
      "astunparse @ file:///home/conda/feedstock_root/build_artifacts/astunparse_1610696312422/work\n",
      "async-timeout==4.0.2\n",
      "attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1659291887007/work\n",
      "backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work\n",
      "backoff==1.10.0\n",
      "backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work\n",
      "bcrypt==3.2.2\n",
      "beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1649463573192/work\n",
      "bleach @ file:///home/conda/feedstock_root/build_artifacts/bleach_1656355450470/work\n",
      "blis @ file:///Users/runner/miniforge3/conda-bld/cython-blis_1656314587498/work\n",
      "brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1648854211613/work\n",
      "catalogue @ file:///Users/runner/miniforge3/conda-bld/catalogue_1649412218237/work\n",
      "certifi==2022.6.15\n",
      "cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1656782892895/work\n",
      "charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1655906222726/work\n",
      "click @ file:///Users/runner/miniforge3/conda-bld/click_1651215220522/work\n",
      "colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1655412516417/work\n",
      "conda==4.13.0\n",
      "conda-package-handling @ file:///Users/runner/miniforge3/conda-bld/conda-package-handling_1649385102057/work\n",
      "cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1657174062636/work\n",
      "cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1635519461629/work\n",
      "cymem @ file:///Users/runner/miniforge3/conda-bld/cymem_1649412235004/work\n",
      "dataclasses @ file:///home/conda/feedstock_root/build_artifacts/dataclasses_1628958434797/work\n",
      "debugpy @ file:///Users/runner/miniforge3/conda-bld/debugpy_1649586394624/work\n",
      "decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work\n",
      "defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work\n",
      "entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work\n",
      "execnb @ file:///Users/jhoward/mambaforge/conda-bld/execnb_1659833056911/work\n",
      "executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1658852325129/work\n",
      "fastai @ file:///Users/jhoward/mambaforge/conda-bld/fastai_1659569901484/work\n",
      "fastapi==0.79.0\n",
      "fastcore @ file:///home/jhoward/mambaforge/conda-bld/fastcore_1660183297681/work\n",
      "fastdownload @ file:///home/jhoward/mambaforge/conda-bld/fastdownload_1657219113869/work\n",
      "fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/python-fastjsonschema_1658064924516/work/dist\n",
      "fastprogress @ file:///home/jhoward/mambaforge/conda-bld/fastprogress_1658473398631/work\n",
      "ffmpy==0.3.0\n",
      "flit_core @ file:///home/conda/feedstock_root/build_artifacts/flit-core_1645629044586/work/source/flit_core\n",
      "fonttools @ file:///Users/runner/miniforge3/conda-bld/fonttools_1657249442212/work\n",
      "frozenlist==1.3.1\n",
      "fsspec==2022.7.1\n",
      "ghapi @ file:///Users/jhoward/mambaforge/conda-bld/ghapi_1658603874045/work\n",
      "gradio==3.1.4\n",
      "h11==0.12.0\n",
      "httpcore==0.15.0\n",
      "httpx==0.23.0\n",
      "idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1642433548627/work\n",
      "importlib-metadata @ file:///Users/runner/miniforge3/conda-bld/importlib-metadata_1653252883293/work\n",
      "importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1658604161399/work\n",
      "ipykernel @ file:///Users/runner/miniforge3/conda-bld/ipykernel_1657295113967/work\n",
      "ipython @ file:///Users/runner/miniforge3/conda-bld/ipython_1653755021573/work\n",
      "ipython-genutils==0.2.0\n",
      "ipywidgets @ file:///home/conda/feedstock_root/build_artifacts/ipywidgets_1655973868664/work\n",
      "jedi @ file:///Users/runner/miniforge3/conda-bld/jedi_1649067175439/work\n",
      "Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1651774399431/work\n",
      "joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1633637554808/work\n",
      "jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1659525086692/work\n",
      "jupyter @ file:///Users/runner/miniforge3/conda-bld/jupyter_1637233342265/work\n",
      "jupyter-client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1654730843242/work\n",
      "jupyter-console @ file:///home/conda/feedstock_root/build_artifacts/jupyter_console_1655961255101/work\n",
      "jupyter_core @ file:///Users/runner/miniforge3/conda-bld/jupyter_core_1658332409400/work\n",
      "jupyterlab-pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1649936611996/work\n",
      "jupyterlab-widgets @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_widgets_1655961217661/work\n",
      "kiwisolver @ file:///Users/runner/miniforge3/conda-bld/kiwisolver_1657953144922/work\n",
      "langcodes @ file:///home/conda/feedstock_root/build_artifacts/langcodes_1636741340529/work\n",
      "libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1654092243459/work/libmambapy\n",
      "linkify-it-py==1.0.3\n",
      "lxml @ file:///Users/runner/miniforge3/conda-bld/lxml_1656759436279/work\n",
      "mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1654092243459/work/mamba\n",
      "markdown-it-py==2.1.0\n",
      "MarkupSafe @ file:///Users/runner/miniforge3/conda-bld/markupsafe_1648737704633/work\n",
      "matplotlib @ file:///Users/runner/miniforge3/conda-bld/matplotlib-suite_1651609662464/work\n",
      "matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1631080358261/work\n",
      "mdit-py-plugins==0.3.0\n",
      "mdurl==0.1.1\n",
      "mistune @ file:///Users/runner/miniforge3/conda-bld/mistune_1635844789148/work\n",
      "monotonic==1.6\n",
      "multidict==6.0.2\n",
      "munkres==1.1.4\n",
      "murmurhash @ file:///Users/runner/miniforge3/conda-bld/murmurhash_1651135548935/work\n",
      "nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1656688109017/work\n",
      "nbconvert @ file:///home/conda/feedstock_root/build_artifacts/nbconvert-meta_1660222578365/work\n",
      "nbdev @ file:///home/jhoward/mambaforge/conda-bld/nbdev_1660425476454/work\n",
      "nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1651607001005/work\n",
      "nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1648959695634/work\n",
      "notebook @ file:///home/conda/feedstock_root/build_artifacts/notebook_1654636967533/work\n",
      "numpy @ file:///Users/runner/miniforge3/conda-bld/numpy_1657483922417/work\n",
      "orjson==3.7.11\n",
      "packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1637239678211/work\n",
      "pandas==1.4.3\n",
      "pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work\n",
      "paramiko==2.11.0\n",
      "parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1638334955874/work\n",
      "pathy @ file:///home/conda/feedstock_root/build_artifacts/pathy_1656568808184/work\n",
      "pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1602535608087/work\n",
      "pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work\n",
      "Pillow @ file:///Users/runner/miniforge3/conda-bld/pillow_1657007226315/work\n",
      "pkgutil_resolve_name @ file:///home/conda/feedstock_root/build_artifacts/pkgutil-resolve-name_1633981968097/work\n",
      "ply==3.11\n",
      "preshed @ file:///Users/runner/miniforge3/conda-bld/preshed_1649427230883/work\n",
      "prometheus-client @ file:///home/conda/feedstock_root/build_artifacts/prometheus_client_1649447152425/work\n",
      "prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1656332401605/work\n",
      "psutil @ file:///Users/runner/miniforge3/conda-bld/psutil_1653089238227/work\n",
      "ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl\n",
      "pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work\n",
      "pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1649384988412/work\n",
      "pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work\n",
      "pycryptodome==3.15.0\n",
      "pydantic @ file:///Users/runner/miniforge3/conda-bld/pydantic_1660286522600/work\n",
      "pydub==0.25.1\n",
      "Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1650904496387/work\n",
      "PyNaCl==1.5.0\n",
      "pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1643496850550/work\n",
      "pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work\n",
      "PyQt5==5.15.7\n",
      "PyQt5-sip==12.11.0\n",
      "pyrsistent @ file:///Users/runner/miniforge3/conda-bld/pyrsistent_1649013453068/work\n",
      "PySocks @ file:///Users/runner/miniforge3/conda-bld/pysocks_1648857336012/work\n",
      "python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work\n",
      "python-multipart==0.0.5\n",
      "pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1660317639764/work\n",
      "PyYAML @ file:///Users/runner/miniforge3/conda-bld/pyyaml_1648757420486/work\n",
      "pyzmq @ file:///Users/runner/miniforge3/conda-bld/pyzmq_1660329100211/work\n",
      "qtconsole @ file:///home/conda/feedstock_root/build_artifacts/qtconsole-base_1654465674650/work\n",
      "QtPy @ file:///home/conda/feedstock_root/build_artifacts/qtpy_1660233836213/work\n",
      "requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1656534056640/work\n",
      "rfc3986==1.5.0\n",
      "ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1653464482706/work\n",
      "scikit-learn @ file:///Users/runner/miniforge3/conda-bld/scikit-learn_1659726144131/work\n",
      "scipy==1.9.0\n",
      "Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1628511208346/work\n",
      "shellingham @ file:///home/conda/feedstock_root/build_artifacts/shellingham_1659638615822/work\n",
      "sip @ file:///Users/runner/miniforge3/conda-bld/sip_1655764940129/work\n",
      "six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work\n",
      "smart-open @ file:///home/conda/feedstock_root/build_artifacts/smart_open_1630238320325/work\n",
      "sniffio==1.2.0\n",
      "soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1658207591808/work\n",
      "spacy @ file:///Users/runner/miniforge3/conda-bld/spacy_1658931811656/work\n",
      "spacy-legacy @ file:///home/conda/feedstock_root/build_artifacts/spacy-legacy_1645713043381/work\n",
      "spacy-loggers @ file:///home/conda/feedstock_root/build_artifacts/spacy-loggers_1648794027183/work\n",
      "srsly @ file:///Users/runner/miniforge3/conda-bld/srsly_1658344498536/work\n",
      "stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1655315839047/work\n",
      "starlette==0.19.1\n",
      "terminado @ file:///Users/runner/miniforge3/conda-bld/terminado_1652790663188/work\n",
      "thinc @ file:///Users/runner/miniforge3/conda-bld/thinc_1657300632800/work\n",
      "threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1643647933166/work\n",
      "tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1637612658783/work\n",
      "toml @ file:///home/conda/feedstock_root/build_artifacts/toml_1604308577558/work\n",
      "torch==1.12.1\n",
      "torchvision==0.13.1\n",
      "tornado @ file:///Users/runner/miniforge3/conda-bld/tornado_1656938113699/work\n",
      "tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1649051611147/work\n",
      "traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1655411388954/work\n",
      "typer @ file:///home/conda/feedstock_root/build_artifacts/typer_1657029164904/work\n",
      "typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1656706066251/work\n",
      "uc-micro-py==1.0.1\n",
      "unicodedata2 @ file:///Users/runner/miniforge3/conda-bld/unicodedata2_1649111971119/work\n",
      "urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1658789158161/work\n",
      "uvicorn==0.18.2\n",
      "wasabi @ file:///home/conda/feedstock_root/build_artifacts/wasabi_1658931821849/work\n",
      "wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1600965781394/work\n",
      "webencodings==0.5.1\n",
      "widgetsnbextension @ file:///home/conda/feedstock_root/build_artifacts/widgetsnbextension_1655939017940/work\n",
      "yarl==1.8.1\n",
      "zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1659400682470/work\n"
     ]
    }
   ],
   "source": [
    "!pip freeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aaf2007a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "#### nbdev_export\n",
       "\n",
       ">      nbdev_export (path:str=None, recursive:bool=None, symlinks:bool=True,\n",
       ">                    file_re:str=None, folder_re:str=None,\n",
       ">                    skip_file_glob:str=None, skip_file_re:str='^[_.]')\n",
       "\n",
       "Export notebooks in `path` to Python modules\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| path | str | None | Path or filename |\n",
       "| recursive | bool | None | Search subfolders |\n",
       "| symlinks | bool | True | Follow symlinks? |\n",
       "| file_re | str | None | Only include files matching regex |\n",
       "| folder_re | str | None | Only enter folders matching regex |\n",
       "| skip_file_glob | str | None | Skip files matching glob |\n",
       "| skip_file_re | str | ^[_.] | Skip files matching regex |\n",
       "\n",
       "<a href=\"https://nbdev.fast.ai/doclinks.html#nbdev_export\" target=\"_blank\" rel=\"noreferrer noopener\">Show in docs</a>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "doc(nbdev_export)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "46cd05ba",
   "metadata": {},
   "outputs": [
    {
     "ename": "InterpolationMissingOptionError",
     "evalue": "Bad value substitution: option 'doc_host' in section 'DEFAULT' contains an interpolation key 'user' which is not a valid option name. Raw value: 'https://%(user)s.github.io'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInterpolationMissingOptionError\u001b[0m           Traceback (most recent call last)",
      "Input \u001b[0;32mIn [63]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnbdev_export\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/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[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    106\u001b[0m     mod \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39mgetmodule(inspect\u001b[38;5;241m.\u001b[39mcurrentframe()\u001b[38;5;241m.\u001b[39mf_back)\n\u001b[0;32m--> 107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mod: \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    108\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m SCRIPT_INFO\u001b[38;5;241m.\u001b[39mfunc \u001b[38;5;129;01mand\u001b[39;00m mod\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m: SCRIPT_INFO\u001b[38;5;241m.\u001b[39mfunc \u001b[38;5;241m=\u001b[39m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(sys\u001b[38;5;241m.\u001b[39margv)\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m sys\u001b[38;5;241m.\u001b[39margv[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m: sys\u001b[38;5;241m.\u001b[39margv\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/nbdev/doclinks.py:148\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    146\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m files: nb_export(f)\n\u001b[1;32m    147\u001b[0m add_init(get_config()\u001b[38;5;241m.\u001b[39mpath(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlib_path\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m--> 148\u001b[0m \u001b[43mbuild_modidx\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/nbdev/doclinks.py:117\u001b[0m, in \u001b[0;36mbuild_modidx\u001b[0;34m()\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m contextlib\u001b[38;5;241m.\u001b[39msuppress(\u001b[38;5;167;01mFileNotFoundError\u001b[39;00m): _fn\u001b[38;5;241m.\u001b[39munlink()\n\u001b[1;32m    116\u001b[0m cfg \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m--> 117\u001b[0m doc_func \u001b[38;5;241m=\u001b[39m partial(_doc_link, urljoin(\u001b[43mcfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdoc_host\u001b[49m,cfg\u001b[38;5;241m.\u001b[39mdoc_baseurl))\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m globtastic(dest, file_glob\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.py\u001b[39m\u001b[38;5;124m\"\u001b[39m, skip_folder_re\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m.ipynb_checkpoints\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m Path(file)\u001b[38;5;241m.\u001b[39mname[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m!=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m: DocLinks(file, doc_func, _fn)\u001b[38;5;241m.\u001b[39mbuild_index()\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/fastcore/foundation.py:268\u001b[0m, in \u001b[0;36mConfig.__getattr__\u001b[0;34m(self, k)\u001b[0m\n\u001b[0;32m--> 268\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m,k):   \u001b[38;5;28;01mreturn\u001b[39;00m stop(\u001b[38;5;167;01mAttributeError\u001b[39;00m(k)) \u001b[38;5;28;01mif\u001b[39;00m k\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124md\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39md \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/fastcore/foundation.py:270\u001b[0m, in \u001b[0;36mConfig.get\u001b[0;34m(self, k, default)\u001b[0m\n\u001b[0;32m--> 270\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(\u001b[38;5;28mself\u001b[39m,k,default\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43md\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefault\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/configparser.py:1306\u001b[0m, in \u001b[0;36mSectionProxy.get\u001b[0;34m(self, option, fallback, raw, vars, _impl, **kwargs)\u001b[0m\n\u001b[1;32m   1304\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _impl:\n\u001b[1;32m   1305\u001b[0m     _impl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parser\u001b[38;5;241m.\u001b[39mget\n\u001b[0;32m-> 1306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moption\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mraw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mvars\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mvars\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1307\u001b[0m \u001b[43m             \u001b[49m\u001b[43mfallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfallback\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/configparser.py:799\u001b[0m, in \u001b[0;36mRawConfigParser.get\u001b[0;34m(self, section, option, raw, vars, fallback)\u001b[0m\n\u001b[1;32m    797\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m value\n\u001b[1;32m    798\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 799\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_interpolation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbefore_get\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moption\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m                                          \u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/configparser.py:395\u001b[0m, in \u001b[0;36mBasicInterpolation.before_get\u001b[0;34m(self, parser, section, option, value, defaults)\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbefore_get\u001b[39m(\u001b[38;5;28mself\u001b[39m, parser, section, option, value, defaults):\n\u001b[1;32m    394\u001b[0m     L \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 395\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_interpolate_some\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparser\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moption\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(L)\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.9/configparser.py:434\u001b[0m, in \u001b[0;36mBasicInterpolation._interpolate_some\u001b[0;34m(self, parser, option, accum, rest, section, map, depth)\u001b[0m\n\u001b[1;32m    432\u001b[0m     v \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m[var]\n\u001b[1;32m    433\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m--> 434\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m InterpolationMissingOptionError(\n\u001b[1;32m    435\u001b[0m         option, section, rawval, var) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m    436\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m v:\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interpolate_some(parser, option, accum, v,\n\u001b[1;32m    438\u001b[0m                            section, \u001b[38;5;28mmap\u001b[39m, depth \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[0;31mInterpolationMissingOptionError\u001b[0m: Bad value substitution: option 'doc_host' in section 'DEFAULT' contains an interpolation key 'user' which is not a valid option name. Raw value: 'https://%(user)s.github.io'"
     ]
    }
   ],
   "source": [
    "nbdev_export('.')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}