rasa-moodbot-demo-complete-executed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kO9wt2g3okLS"
   },
   "source": [
    "# Conversational AI with Rasa\n",
    "![alt text](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTaX3LNhGcAe1HnPZSuWS0oH6af0LJHXcH7If1sQgLCFAT1chNGFg)\n",
    "\n",
    "This notebook is going to be the basis for my workshop at the PyData 2018 Amsterdam workshop. If you have any questions, please let me know!\n",
    "\n",
    "You'll build a relatively simple bot, that just asks you about your mood and tries to cheer you up if you're feeling a bit down. \n",
    "\n",
    "The tutorial consists of three parts:\n",
    "\n",
    "\n",
    "*   Part 0: Installation and preparations\n",
    "*   Part 1: You'll start with a basic bot that can only understand natural language but no dialogues.\n",
    "*   Part 2: You'll add the abilitiy to understand multiturn dialogues.\n",
    "*   Part 3: I'll give you further resources so you can extend this simple demo.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 0: Installation\n",
    "\n",
    "### Let's start with jupyter configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import logging, io, json, warnings\n",
    "logging.basicConfig(level=\"INFO\")\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "def pprint(o):\n",
    "    # small helper to make dict dumps a bit prettier\n",
    "    print(json.dumps(o, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y4miuS-TqYcn"
   },
   "source": [
    "### Installation of Rasa\n",
    "First you'll have to install Rasa Core and NLU in this notebook if you already have them installed in your env, you can just skip this. But make sure you also have the spacy model installed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 3607
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 58315,
     "status": "ok",
     "timestamp": 1521575035242,
     "user": {
      "displayName": "Ricardo Wölker",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "115384113157413623365"
     },
     "user_tz": 0
    },
    "id": "X_2-sKgo_qyl",
    "outputId": "b947c702-75fa-406d-d21d-a691dcb5236d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[33mRetrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102bb6b38>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-core/\u001b[0m\n",
      "\u001b[33mRetrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102bb6f60>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-core/\u001b[0m\n",
      "\u001b[33mRetrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102bb6fd0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-core/\u001b[0m\n",
      "\u001b[33mRetrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102bb6cf8>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-core/\u001b[0m\n",
      "Requirement already up-to-date: rasa_core==0.9.0a7 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (0.9.0a7)\n",
      "\u001b[33mRetrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102bfa128>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-nlu/\u001b[0m\n",
      "\u001b[33mRetrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102bfa898>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-nlu/\u001b[0m\n",
      "\u001b[33mRetrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102bfa908>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-nlu/\u001b[0m\n",
      "\u001b[33mRetrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102c0b470>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-nlu/\u001b[0m\n",
      "\u001b[33mRetrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x102c0b0f0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)': /simple/rasa-nlu/\u001b[0m\n",
      "Requirement already up-to-date: rasa_nlu[spacy] in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (0.12.3)\n",
      "Requirement not upgraded as not directly required: six~=1.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (1.11.0)\n",
      "Requirement not upgraded as not directly required: h5py~=2.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (2.7.1)\n",
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      "Requirement not upgraded as not directly required: networkx~=2.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (2.1)\n",
      "Requirement not upgraded as not directly required: ConfigArgParse~=0.13.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (0.13.0)\n",
      "Requirement not upgraded as not directly required: fakeredis~=0.10.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (0.10.3)\n",
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      "Requirement not upgraded as not directly required: python-telegram-bot~=10.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (10.1.0)\n",
      "Requirement not upgraded as not directly required: fbmessenger~=5.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (5.1.0)\n",
      "Requirement not upgraded as not directly required: flask-cors~=3.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_core==0.9.0a7) (3.0.4)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement not upgraded as not directly required: boto3 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from rasa_nlu[spacy]) (1.7.26)\n",
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      "Requirement not upgraded as not directly required: ujson>=1.35 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (1.35)\n",
      "Requirement not upgraded as not directly required: thinc<6.11.0,>=6.10.1 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (6.10.2)\n",
      "Requirement not upgraded as not directly required: preshed<2.0.0,>=1.0.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (1.0.0)\n",
      "Requirement not upgraded as not directly required: dill<0.3,>=0.2 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.2.7.1)\n",
      "Requirement not upgraded as not directly required: plac<1.0.0,>=0.9.6 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.9.6)\n",
      "Requirement not upgraded as not directly required: murmurhash<0.29,>=0.28 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.28.0)\n",
      "Requirement not upgraded as not directly required: cymem<1.32,>=1.30 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (1.31.2)\n",
      "Requirement not upgraded as not directly required: python-crfsuite>=0.8.3 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from sklearn-crfsuite; extra == \"spacy\"->rasa_nlu[spacy]) (0.9.5)\n",
      "Requirement not upgraded as not directly required: tabulate in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from sklearn-crfsuite; extra == \"spacy\"->rasa_nlu[spacy]) (0.8.2)\n",
      "Requirement not upgraded as not directly required: markdown>=2.6.8 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow<1.9,>=1.7->rasa_core==0.9.0a7) (2.6.11)\n",
      "Requirement not upgraded as not directly required: bleach==1.5.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow<1.9,>=1.7->rasa_core==0.9.0a7) (1.5.0)\n",
      "Requirement not upgraded as not directly required: html5lib==0.9999999 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow<1.9,>=1.7->rasa_core==0.9.0a7) (0.9999999)\n",
      "Requirement not upgraded as not directly required: MarkupSafe>=0.23 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from Jinja2>=2.10->flask~=1.0->rasa_core==0.9.0a7) (1.0)\n",
      "Requirement not upgraded as not directly required: docutils>=0.10 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from botocore<1.11.0,>=1.10.26->boto3->rasa_nlu[spacy]) (0.14)\n",
      "Requirement not upgraded as not directly required: Automat>=0.3.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from Twisted>=15.5->klein->rasa_nlu[spacy]) (0.6.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement not upgraded as not directly required: zope.interface>=4.4.2 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from Twisted>=15.5->klein->rasa_nlu[spacy]) (4.5.0)\n",
      "Requirement not upgraded as not directly required: constantly>=15.1 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from Twisted>=15.5->klein->rasa_nlu[spacy]) (15.1.0)\n",
      "Requirement not upgraded as not directly required: hyperlink>=17.1.1 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from Twisted>=15.5->klein->rasa_nlu[spacy]) (18.0.0)\n",
      "Requirement not upgraded as not directly required: msgpack-python in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from thinc<6.11.0,>=6.10.1->spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.5.6)\n",
      "Requirement not upgraded as not directly required: msgpack-numpy==0.4.1 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from thinc<6.11.0,>=6.10.1->spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.4.1)\n",
      "Requirement not upgraded as not directly required: cytoolz<0.9,>=0.8 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from thinc<6.11.0,>=6.10.1->spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.8.2)\n",
      "Requirement not upgraded as not directly required: wrapt in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from thinc<6.11.0,>=6.10.1->spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (1.10.11)\n",
      "Requirement not upgraded as not directly required: attrs in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from Automat>=0.3.0->Twisted>=15.5->klein->rasa_nlu[spacy]) (18.1.0)\n",
      "Requirement not upgraded as not directly required: toolz>=0.8.0 in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (from cytoolz<0.9,>=0.8->thinc<6.11.0,>=6.10.1->spacy>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.9.0)\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/urllib/request.py\", line 1318, in do_open\n",
      "    encode_chunked=req.has_header('Transfer-encoding'))\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\", line 1239, in request\n",
      "    self._send_request(method, url, body, headers, encode_chunked)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\", line 1285, in _send_request\n",
      "    self.endheaders(body, encode_chunked=encode_chunked)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\", line 1234, in endheaders\n",
      "    self._send_output(message_body, encode_chunked=encode_chunked)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\", line 1026, in _send_output\n",
      "    self.send(msg)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\", line 964, in send\n",
      "    self.connect()\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\", line 1392, in connect\n",
      "    super().connect()\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\", line 936, in connect\n",
      "    (self.host,self.port), self.timeout, self.source_address)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socket.py\", line 704, in create_connection\n",
      "    for res in getaddrinfo(host, port, 0, SOCK_STREAM):\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socket.py\", line 745, in getaddrinfo\n",
      "    for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\n",
      "socket.gaierror: [Errno 8] nodename nor servname provided, or not known\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n",
      "    \"__main__\", mod_spec)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/runpy.py\", line 85, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"/Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/spacy/__main__.py\", line 31, in <module>\n",
      "    plac.call(commands[command], sys.argv[1:])\n",
      "  File \"/Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/plac_core.py\", line 328, in call\n",
      "    cmd, result = parser.consume(arglist)\n",
      "  File \"/Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/plac_core.py\", line 207, in consume\n",
      "    return cmd, self.func(*(args + varargs + extraopts), **kwargs)\n",
      "  File \"/Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/spacy/cli/download.py\", line 30, in download\n",
      "    shortcuts = get_json(about.__shortcuts__, \"available shortcuts\")\n",
      "  File \"/Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/spacy/cli/download.py\", line 55, in get_json\n",
      "    data = url_read(url)\n",
      "  File \"/Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/spacy/compat.py\", line 82, in url_read\n",
      "    file_ = url_open(url)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/urllib/request.py\", line 223, in urlopen\n",
      "    return opener.open(url, data, timeout)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/urllib/request.py\", line 526, in open\n",
      "    response = self._open(req, data)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/urllib/request.py\", line 544, in _open\n",
      "    '_open', req)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/urllib/request.py\", line 504, in _call_chain\n",
      "    result = func(*args)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/urllib/request.py\", line 1361, in https_open\n",
      "    context=self._context, check_hostname=self._check_hostname)\n",
      "  File \"/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/urllib/request.py\", line 1320, in do_open\n",
      "    raise URLError(err)\n",
      "urllib.error.URLError: <urlopen error [Errno 8] nodename nor servname provided, or not known>\n",
      "\n",
      "\u001b[93m    Linking successful\u001b[0m\n",
      "    /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/en_core_web_md\n",
      "    -->\n",
      "    /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages/spacy/data/en\n",
      "\n",
      "    You can now load the model via spacy.load('en')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "python = sys.executable\n",
    "\n",
    "# In your environment run:\n",
    "!{python} -m pip install -U rasa_core==0.9.0a7 rasa_nlu[spacy];\n",
    "\n",
    "# as well as install a language model:\n",
    "!{python} -m spacy download en_core_web_md;\n",
    "!{python} -m spacy link --force en_core_web_md en;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if the correct versions are installed (should be rasa_nlu: 0.12.3 rasa_core: 0.9.0a7) and the spacy model is available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rasa_nlu: 0.12.3 rasa_core: 0.9.0a7\n",
      "Loading spaCy language model...\n",
      "Hello world!\n"
     ]
    }
   ],
   "source": [
    "import rasa_nlu\n",
    "import rasa_core\n",
    "import spacy\n",
    "\n",
    "print(\"rasa_nlu: {} rasa_core: {}\".format(rasa_nlu.__version__, rasa_core.__version__))\n",
    "print(\"Loading spaCy language model...\")\n",
    "print(spacy.load(\"en\")(\"Hello world!\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some additional Tools needed\n",
    "To do some of the visualizations you will also need graphviz. If you don't have graphviz installed, and this doesn't work: don't worry. I'll show you the graph and besides that visualization everything else will work.\n",
    "\n",
    "Try installing with anyone of these (or adapt to your operating system):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: apt-get: command not found\n",
      "\u001b[33mWarning:\u001b[0m graphviz 2.40.1 is already installed and up-to-date\n",
      "To reinstall 2.40.1, run `brew reinstall graphviz`\n"
     ]
    }
   ],
   "source": [
    "!apt-get -qq install -y graphviz libgraphviz-dev pkg-config;\n",
    "!brew install graphviz;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and another python package and we are ready to go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pygraphviz in /Users/tmbo/.virtualenvs/pydata/lib/python3.6/site-packages (1.3.1)\r\n"
     ]
    }
   ],
   "source": [
    "!{python} -m pip install pygraphviz;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cNa5KHl1nzhT"
   },
   "source": [
    "## Part 1: Add natural language understanding\n",
    "\n",
    "First thing our bot is going to learn is how to understand user messages. To do that, you're going to build a first version of your language understanding model with Rasa NLU. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GAlnWyGRwcwO"
   },
   "source": [
    "### Language Understanding\n",
    "\n",
    "\n",
    "Lets create some training data here, grouping user messages by their `intent`s. The intent describes what the messages *mean*. [More information about the data format](https://nlu.rasa.com/dataformat.html#markdown-format). This is the training data for our NLU model, one example per line. Entities are labeled using the markdown link syntex: `[entity value](entity_type)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 864,
     "status": "ok",
     "timestamp": 1521380057973,
     "user": {
      "displayName": "Alexander Weidauer",
      "photoUrl": "//lh6.googleusercontent.com/-j4xnI5_PFWA/AAAAAAAAAAI/AAAAAAAAAAo/wPT2w5Bl3xg/s50-c-k-no/photo.jpg",
      "userId": "100444450157165144272"
     },
     "user_tz": -60
    },
    "id": "V6KHWT2zeEh5",
    "outputId": "bab4845d-4ebc-4b9b-f8d9-2f701504a9c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing 'nlu_md' (str) to file 'nlu.md'.\n"
     ]
    }
   ],
   "source": [
    "nlu_md = \"\"\"\n",
    "## intent:greet\n",
    "- hey I am [Peter](PERSON)\n",
    "- hello there I am [Hans](PERSON)\n",
    "- hi I am [Tom](PERSON)\n",
    "- hello there\n",
    "- good morning\n",
    "- good evening\n",
    "- moin\n",
    "- hey there\n",
    "- let's go\n",
    "- hey dude\n",
    "- goodmorning\n",
    "- goodevening\n",
    "- good afternoon\n",
    "\n",
    "## intent:goodbye\n",
    "- cu\n",
    "- good by\n",
    "- cee you later\n",
    "- good night\n",
    "- good afternoon\n",
    "- bye\n",
    "- goodbye\n",
    "- have a nice day\n",
    "- see you around\n",
    "- bye bye\n",
    "- see you later\n",
    "\n",
    "## intent:mood_affirm\n",
    "- yes\n",
    "- indeed\n",
    "- of course\n",
    "- that sounds good\n",
    "- correct\n",
    "\n",
    "## intent:mood_deny\n",
    "- no\n",
    "- never\n",
    "- I don't think so\n",
    "- don't like that\n",
    "- no way\n",
    "- not really\n",
    "\n",
    "## intent:mood_great\n",
    "- perfect\n",
    "- very good\n",
    "- great\n",
    "- amazing\n",
    "- feeling like a king\n",
    "- wonderful\n",
    "- I am feeling very good\n",
    "- I am great\n",
    "- I am amazing\n",
    "- I am going to save the world\n",
    "- super\n",
    "- extremely good\n",
    "- so so perfect\n",
    "- so good\n",
    "- so perfect\n",
    "\n",
    "## intent:mood_unhappy\n",
    "- my day was horrible\n",
    "- I am sad\n",
    "- I don't feel very well\n",
    "- I am disappointed\n",
    "- super sad\n",
    "- I'm so sad\n",
    "- sad\n",
    "- very sad\n",
    "- unhappy\n",
    "- bad\n",
    "- very bad\n",
    "- awful\n",
    "- terrible\n",
    "- not so good\n",
    "- not very good\n",
    "- extremly sad\n",
    "- so saad\n",
    "- so sad\n",
    "\"\"\"\n",
    "\n",
    "%store nlu_md > nlu.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To properly train your NLU model, you also need to define what is part of that model. Rasa NLU uses a similar pipeline concept as sklearn does. All the components that are listed in the pipeline will be trained one after another and everyone of them contributes its part to the structured data extraction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing 'config' (str) to file 'config.yml'.\n"
     ]
    }
   ],
   "source": [
    "config = \"\"\"\n",
    "language: \"en\"\n",
    "\n",
    "pipeline:\n",
    "- name: \"nlp_spacy\"                   # loads the spacy language model\n",
    "- name: \"tokenizer_spacy\"             # splits the sentence into tokens\n",
    "- name: \"ner_spacy\"                   # uses the pretrained spacy NER model\n",
    "- name: \"intent_featurizer_spacy\"     # transform the sentence into a vector representation\n",
    "- name: \"intent_classifier_sklearn\"   # uses the vector representation to classify using SVM\n",
    "\"\"\" \n",
    "\n",
    "%store config > config.yml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1xeXgpdwzOAl"
   },
   "source": [
    "### Train the Rasa NLU Model\n",
    "\n",
    "You're going to train a model to recognise these intents, so that when you send a message like \"hello\" to your bot, it will recognise this as a `\"greet\"` intent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2821,
     "status": "ok",
     "timestamp": 1521380062879,
     "user": {
      "displayName": "Alexander Weidauer",
      "photoUrl": "//lh6.googleusercontent.com/-j4xnI5_PFWA/AAAAAAAAAAI/AAAAAAAAAAo/wPT2w5Bl3xg/s50-c-k-no/photo.jpg",
      "userId": "100444450157165144272"
     },
     "user_tz": -60
    },
    "id": "qHsAH49OePcM",
    "outputId": "f840c42d-acf2-4fbf-b439-e5a239ffb32b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rasa_nlu.training_data.loading:Training data format of nlu.md is md\n",
      "INFO:rasa_nlu.training_data.training_data:Training data stats: \n",
      "\t- intent examples: 68 (6 distinct intents)\n",
      "\t- Found intents: 'mood_deny', 'greet', 'mood_great', 'goodbye', 'mood_unhappy', 'mood_affirm'\n",
      "\t- entity examples: 3 (1 distinct entities)\n",
      "\t- found entities: 'PERSON'\n",
      "\n",
      "INFO:rasa_nlu.utils.spacy_utils:Trying to load spacy model with name 'en'\n",
      "INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.\n",
      "INFO:rasa_nlu.model:Starting to train component nlp_spacy\n",
      "INFO:rasa_nlu.model:Finished training component.\n",
      "INFO:rasa_nlu.model:Starting to train component tokenizer_spacy\n",
      "INFO:rasa_nlu.model:Finished training component.\n",
      "INFO:rasa_nlu.model:Starting to train component ner_spacy\n",
      "INFO:rasa_nlu.model:Finished training component.\n",
      "INFO:rasa_nlu.model:Starting to train component intent_featurizer_spacy\n",
      "INFO:rasa_nlu.model:Finished training component.\n",
      "INFO:rasa_nlu.model:Starting to train component intent_classifier_sklearn\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s finished\n",
      "INFO:rasa_nlu.model:Finished training component.\n",
      "INFO:rasa_nlu.model:Successfully saved model into '/Users/tmbo/lastmile/bot-ai/rasa-demo-pydata18/models/nlu/default/current'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 6 candidates, totalling 12 fits\n"
     ]
    }
   ],
   "source": [
    "from rasa_nlu.training_data import load_data\n",
    "from rasa_nlu.config import RasaNLUModelConfig\n",
    "from rasa_nlu.model import Trainer\n",
    "from rasa_nlu import config\n",
    "\n",
    "# loading the nlu training samples\n",
    "training_data = load_data(\"nlu.md\")\n",
    "\n",
    "# trainer to educate our pipeline\n",
    "trainer = Trainer(config.load(\"config.yml\"))\n",
    "\n",
    "# train the model!\n",
    "interpreter = trainer.train(training_data)\n",
    "\n",
    "# store it for future use\n",
    "model_directory = trainer.persist(\"./models/nlu\", fixed_model_name=\"current\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1xeXgpdwzOAl"
   },
   "source": [
    "### Use & evaluate the NLU model\n",
    "Let's play around with the model and shoot it some messages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2821,
     "status": "ok",
     "timestamp": 1521380062879,
     "user": {
      "displayName": "Alexander Weidauer",
      "photoUrl": "//lh6.googleusercontent.com/-j4xnI5_PFWA/AAAAAAAAAAI/AAAAAAAAAAo/wPT2w5Bl3xg/s50-c-k-no/photo.jpg",
      "userId": "100444450157165144272"
     },
     "user_tz": -60
    },
    "id": "qHsAH49OePcM",
    "outputId": "f840c42d-acf2-4fbf-b439-e5a239ffb32b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"intent\": {\n",
      "    \"name\": \"mood_great\",\n",
      "    \"confidence\": 0.5465564226725814\n",
      "  },\n",
      "  \"entities\": [],\n",
      "  \"intent_ranking\": [\n",
      "    {\n",
      "      \"name\": \"mood_great\",\n",
      "      \"confidence\": 0.5465564226725814\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"mood_unhappy\",\n",
      "      \"confidence\": 0.14016152690443984\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"goodbye\",\n",
      "      \"confidence\": 0.10480766689923886\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"mood_affirm\",\n",
      "      \"confidence\": 0.08457208252121914\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"greet\",\n",
      "      \"confidence\": 0.0724611464633465\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"mood_deny\",\n",
      "      \"confidence\": 0.05144115453917394\n",
      "    }\n",
      "  ],\n",
      "  \"text\": \"doing great\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "pprint(interpreter.parse(\"doing great\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of evaluating it by hand, the model can also be evaluated on a test data set (though for simplicity we are going to use the same for test and train):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.\n",
      "INFO:rasa_nlu.training_data.loading:Training data format of nlu.md is md\n",
      "INFO:rasa_nlu.training_data.training_data:Training data stats: \n",
      "\t- intent examples: 68 (6 distinct intents)\n",
      "\t- Found intents: 'mood_deny', 'greet', 'mood_great', 'goodbye', 'mood_unhappy', 'mood_affirm'\n",
      "\t- entity examples: 3 (1 distinct entities)\n",
      "\t- found entities: 'PERSON'\n",
      "\n",
      "INFO:rasa_nlu.evaluate:Intent evaluation results:\n",
      "INFO:rasa_nlu.evaluate:Intent Evaluation: Only considering those 68 examples that have a defined intent out of 68 examples\n",
      "INFO:rasa_nlu.evaluate:F1-Score:  0.9852163087457204\n",
      "INFO:rasa_nlu.evaluate:Precision: 0.986344537815126\n",
      "INFO:rasa_nlu.evaluate:Accuracy:  0.9852941176470589\n",
      "INFO:rasa_nlu.evaluate:Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     goodbye       1.00      0.91      0.95        11\n",
      "       greet       0.93      1.00      0.96        13\n",
      " mood_affirm       1.00      1.00      1.00         5\n",
      "   mood_deny       1.00      1.00      1.00         6\n",
      "  mood_great       1.00      1.00      1.00        15\n",
      "mood_unhappy       1.00      1.00      1.00        18\n",
      "\n",
      " avg / total       0.99      0.99      0.99        68\n",
      "\n",
      "INFO:rasa_nlu.evaluate:Confusion matrix, without normalization: \n",
      "[[10  1  0  0  0  0]\n",
      " [ 0 13  0  0  0  0]\n",
      " [ 0  0  5  0  0  0]\n",
      " [ 0  0  0  6  0  0]\n",
      " [ 0  0  0  0 15  0]\n",
      " [ 0  0  0  0  0 18]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rasa_nlu.evaluate:Entity evaluation results:\n",
      "INFO:rasa_nlu.evaluate:Evaluation for entity extractor: ner_spacy \n",
      "INFO:rasa_nlu.evaluate:F1-Score:  0.970857463104801\n",
      "INFO:rasa_nlu.evaluate:Precision: 1.0\n",
      "INFO:rasa_nlu.evaluate:Accuracy:  0.9433962264150944\n",
      "INFO:rasa_nlu.evaluate:Classification report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       DATE       0.00      0.00      0.00         0\n",
      "     PERSON       1.00      1.00      1.00         3\n",
      "       TIME       0.00      0.00      0.00         0\n",
      "  no_entity       1.00      0.94      0.97       156\n",
      "\n",
      "avg / total       1.00      0.94      0.97       159\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from rasa_nlu.evaluate import run_evaluation\n",
    "\n",
    "run_evaluation(\"nlu.md\", model_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tK5os3OinphP"
   },
   "source": [
    "# Part 2: Adding dialogue capabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BBF6Nqi9scQE"
   },
   "source": [
    "### Writing Stories\n",
    "\n",
    "A good place to start is by writing a few stories. These are example conversations that Rasa Core will learn from. \n",
    "\n",
    "The format works like this:\n",
    "\n",
    "A story starts with `##` and you can give it a name. \n",
    "lines that start with `*` are messages sent by the user. Although you don't write the *actual* message, but rather the intent (and the entities) that represent what the user *means*. If you don't know about intents and entities, don't worry! We will talk about them more later. \n",
    "Lines that start with `-` are *actions* taken by your bot. In this case all of our actions are just messages sent back to the user, like `utter_greet`, but in general an action can do anything, including calling an API and interacting with the outside world. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 568,
     "status": "ok",
     "timestamp": 1521575049982,
     "user": {
      "displayName": "Ricardo Wölker",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "115384113157413623365"
     },
     "user_tz": 0
    },
    "id": "zamaNW4RsrLe",
    "outputId": "6203e4c3-46a3-4c43-add6-95a70d4a6d50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing 'stories_md' (str) to file 'stories.md'.\n"
     ]
    }
   ],
   "source": [
    "stories_md = \"\"\"\n",
    "## happy path               <!-- name of the story - just for debugging -->\n",
    "* greet              \n",
    "  - utter_greet\n",
    "* mood_great               <!-- user utterance, in format intent[entities] -->\n",
    "  - utter_happy\n",
    "* mood_affirm\n",
    "  - utter_happy\n",
    "* mood_affirm\n",
    "  - utter_goodbye\n",
    "  \n",
    "## sad path 1               <!-- this is already the start of the next story -->\n",
    "* greet\n",
    "  - utter_greet             <!-- action the bot should execute -->\n",
    "* mood_unhappy\n",
    "  - action_retrieve_image\n",
    "  - utter_cheer_up\n",
    "  - utter_did_that_help\n",
    "* mood_affirm\n",
    "  - utter_happy\n",
    "\n",
    "## sad path 2\n",
    "* greet\n",
    "  - utter_greet\n",
    "* mood_unhappy\n",
    "  - action_retrieve_image\n",
    "  - utter_cheer_up\n",
    "  - utter_did_that_help\n",
    "* mood_deny\n",
    "  - utter_goodbye\n",
    "  \n",
    "## strange user\n",
    "* mood_affirm\n",
    "  - utter_happy\n",
    "* mood_affirm\n",
    "  - utter_unclear\n",
    "\n",
    "## say goodbye\n",
    "* goodbye\n",
    "  - utter_goodbye\n",
    "\n",
    "## fallback\n",
    "- utter_unclear\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "%store stories_md > stories.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fs3nOUzBsqrG"
   },
   "source": [
    "### Defining a Domain\n",
    "\n",
    "The domain specifies the universe that your bot lives in. You should list all of the intents and actions that show up in your stories. \n",
    "This is also the place to write templates, which contain the messages your bot can send back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 465,
     "status": "ok",
     "timestamp": 1521575053606,
     "user": {
      "displayName": "Ricardo Wölker",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "115384113157413623365"
     },
     "user_tz": 0
    },
    "id": "WB6eIXVkAOSX",
    "outputId": "566b7c42-3772-4754-df55-19874543108c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing 'domain_yml' (str) to file 'domain.yml'.\n"
     ]
    }
   ],
   "source": [
    "domain_yml = \"\"\"\n",
    "intents:\n",
    "- greet\n",
    "- goodbye\n",
    "- mood_affirm\n",
    "- mood_deny\n",
    "- mood_great\n",
    "- mood_unhappy\n",
    "\n",
    "slots:\n",
    "  img_api_response:\n",
    "    type: unfeaturized\n",
    "\n",
    "actions:\n",
    "- utter_greet\n",
    "- utter_cheer_up\n",
    "- utter_did_that_help\n",
    "- utter_happy\n",
    "- utter_goodbye\n",
    "- utter_unclear\n",
    "- __main__.ApiAction\n",
    "\n",
    "templates:\n",
    "  utter_greet:\n",
    "  - text: \"Hey! How are you?\"\n",
    "\n",
    "  utter_cheer_up:\n",
    "  - text: \"Here is something to cheer you up: {img_api_response}\"\n",
    "\n",
    "  utter_did_that_help:\n",
    "  - text: \"Did that help you?\"\n",
    "\n",
    "  utter_unclear:\n",
    "  - text: \"I am not sure what you are aiming for.\"\n",
    "  \n",
    "  utter_happy:\n",
    "  - text: \"Great carry on!\"\n",
    "\n",
    "  utter_goodbye:\n",
    "  - text: \"Bye\"\n",
    "\"\"\"\n",
    "\n",
    "%store domain_yml > domain.yml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Custom API methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, you not only want to send back messages to the user, but you also want to call an API or run some code. YOu can create custom actions that will be called once the bots ML model predicts them. You'll use that to fetch a random image from a remote server (we are not actually fetching it here, but we could ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasa_core.actions import Action\n",
    "from rasa_core.events import SlotSet\n",
    "\n",
    "import requests\n",
    "\n",
    "class ApiAction(Action):\n",
    "    def name(self):\n",
    "        return \"action_retrieve_image\"\n",
    "\n",
    "    def run(self, dispatcher, tracker, domain):\n",
    "        dispatcher.utter_message(\"looking for a good img\")\n",
    "        url = \"https://picsum.photos/200/300/?random\"\n",
    "        return [SlotSet(\"img_api_response\", url)]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qWALQbCdwGxK"
   },
   "source": [
    "### Pro Tip: Visualising the Training Data\n",
    "\n",
    "You can visualise the stories to get a sense of how the conversations go. This is usually a good way to see if there are any stories which don't make sense\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 895
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2808,
     "status": "ok",
     "timestamp": 1521380138202,
     "user": {
      "displayName": "Alexander Weidauer",
      "photoUrl": "//lh6.googleusercontent.com/-j4xnI5_PFWA/AAAAAAAAAAI/AAAAAAAAAAo/wPT2w5Bl3xg/s50-c-k-no/photo.jpg",
      "userId": "100444450157165144272"
     },
     "user_tz": -60
    },
    "id": "zl6vtV72BDaV",
    "outputId": "2468f1bf-3fd0-42f0-9338-8f48c8cad3dd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:apscheduler.scheduler:Scheduler started\n",
      "Processed Story Blocks: 100%|██████████| 6/6 [00:00<00:00, 646.27it/s, # trackers=1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from rasa_core.agent import Agent\n",
    "\n",
    "agent = Agent('domain.yml')\n",
    "agent.visualize(\"stories.md\", \"story_graph.png\", max_history=2)\n",
    "Image(filename=\"story_graph.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zQxY6m6kvlj3"
   },
   "source": [
    "### Training your Dialogue Model\n",
    "\n",
    "Now comes the fun part! We're going to show Rasa Core the stories we wrote above, and train a model on these examples. \n",
    "In this case, the model is a neural network implemented in Keras which learns to predict which action to take next. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 14025
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12554,
     "status": "ok",
     "timestamp": 1521575068686,
     "user": {
      "displayName": "Ricardo Wölker",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "115384113157413623365"
     },
     "user_tz": 0
    },
    "id": "PKV49d7tAxmS",
    "outputId": "ccb26bea-5be8-48f7-a88e-d70a6e4a1733"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "Processed Story Blocks: 100%|██████████| 6/6 [00:00<00:00, 503.49it/s, # trackers=1]\n",
      "Processed Story Blocks: 100%|██████████| 6/6 [00:00<00:00, 352.16it/s, # trackers=6]\n",
      "Processed Story Blocks: 100%|██████████| 6/6 [00:00<00:00, 111.44it/s, # trackers=20]\n",
      "Processed actions: 143it [00:00, 540.36it/s, # examples=143]\n",
      "INFO:rasa_core.policies.memoization:Memorized 143 unique action examples.\n",
      "INFO:rasa_core.policies.keras_policy:Fitting model with 143 total samples and a validation split of 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "masking_1 (Masking)          (None, 5, 15)             0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 32)                6144      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 9)                 297       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 9)                 0         \n",
      "=================================================================\n",
      "Total params: 6,441\n",
      "Trainable params: 6,441\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/400\n",
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     ]
    },
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      "143/143 [==============================] - 0s 267us/step - loss: 0.0313 - acc: 0.9860\n",
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      "143/143 [==============================] - 0s 274us/step - loss: 0.0514 - acc: 0.9930\n",
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      "143/143 [==============================] - 0s 266us/step - loss: 0.0274 - acc: 1.0000\n",
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      "143/143 [==============================] - 0s 269us/step - loss: 0.0268 - acc: 0.9930\n",
      "Epoch 359/400\n",
      "143/143 [==============================] - 0s 268us/step - loss: 0.0305 - acc: 1.0000\n",
      "Epoch 360/400\n",
      "143/143 [==============================] - 0s 266us/step - loss: 0.0451 - acc: 0.9860\n",
      "Epoch 361/400\n",
      "143/143 [==============================] - 0s 263us/step - loss: 0.0340 - acc: 1.0000\n",
      "Epoch 362/400\n",
      "143/143 [==============================] - 0s 268us/step - loss: 0.0170 - acc: 1.0000\n",
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      "Epoch 378/400\n",
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      "Epoch 379/400\n",
      "143/143 [==============================] - 0s 263us/step - loss: 0.0368 - acc: 0.9930\n",
      "Epoch 380/400\n",
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      "Epoch 381/400\n",
      "143/143 [==============================] - 0s 268us/step - loss: 0.0367 - acc: 0.9930\n",
      "Epoch 382/400\n",
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      "143/143 [==============================] - 0s 259us/step - loss: 0.0293 - acc: 0.9930\n",
      "Epoch 384/400\n",
      "143/143 [==============================] - 0s 252us/step - loss: 0.0413 - acc: 0.9860\n",
      "Epoch 385/400\n",
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      "Epoch 387/400\n",
      "143/143 [==============================] - 0s 254us/step - loss: 0.0158 - acc: 1.0000\n",
      "Epoch 388/400\n",
      "143/143 [==============================] - 0s 254us/step - loss: 0.0625 - acc: 0.9860\n",
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      "Epoch 390/400\n",
      "143/143 [==============================] - 0s 253us/step - loss: 0.0258 - acc: 1.0000\n",
      "Epoch 391/400\n",
      "143/143 [==============================] - 0s 256us/step - loss: 0.0293 - acc: 0.9930\n",
      "Epoch 392/400\n",
      "143/143 [==============================] - 0s 266us/step - loss: 0.0198 - acc: 1.0000\n",
      "Epoch 393/400\n",
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      "Epoch 394/400\n",
      "143/143 [==============================] - 0s 261us/step - loss: 0.0398 - acc: 0.9930\n",
      "Epoch 395/400\n",
      "143/143 [==============================] - 0s 265us/step - loss: 0.0374 - acc: 0.9930\n",
      "Epoch 396/400\n",
      "143/143 [==============================] - 0s 277us/step - loss: 0.0266 - acc: 0.9930\n",
      "Epoch 397/400\n",
      "143/143 [==============================] - 0s 260us/step - loss: 0.0399 - acc: 0.9930\n",
      "Epoch 398/400\n",
      "143/143 [==============================] - 0s 257us/step - loss: 0.0206 - acc: 1.0000\n",
      "Epoch 399/400\n",
      "143/143 [==============================] - 0s 265us/step - loss: 0.0130 - acc: 1.0000\n",
      "Epoch 400/400\n",
      "143/143 [==============================] - 0s 256us/step - loss: 0.0439 - acc: 0.9860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rasa_core.policies.keras_policy:Done fitting keras policy model\n",
      "INFO:rasa_core.agent:Model directory models/dialogue exists and contains old model files. All files will be overwritten.\n",
      "INFO:rasa_core.agent:Persisted model to '/Users/tmbo/lastmile/bot-ai/rasa-demo-pydata18/models/dialogue'\n"
     ]
    }
   ],
   "source": [
    "from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy\n",
    "from rasa_core.agent import Agent\n",
    "\n",
    "# this will catch predictions the model isn't very certain about\n",
    "# there is a threshold for the NLU predictions as well as the action predictions\n",
    "fallback = FallbackPolicy(fallback_action_name=\"utter_unclear\",\n",
    "                          core_threshold=0.2,\n",
    "                          nlu_threshold=0.6)\n",
    "\n",
    "agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy(), fallback])\n",
    "\n",
    "# loading our neatly defined training dialogues\n",
    "training_data = agent.load_data('stories.md')\n",
    "\n",
    "agent.train(\n",
    "    training_data,\n",
    "    validation_split=0.0,\n",
    "    epochs=400\n",
    ")\n",
    "\n",
    "agent.persist('models/dialogue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FSjCEdvlzfSR"
   },
   "source": [
    "### Starting up the bot (with NLU)\n",
    "\n",
    "Now that we've trained the dialogue **and** language understanding models and saved them, we can start up an `Agent` which will handle conversations for us. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1799,
     "status": "ok",
     "timestamp": 1521380071327,
     "user": {
      "displayName": "Alexander Weidauer",
      "photoUrl": "//lh6.googleusercontent.com/-j4xnI5_PFWA/AAAAAAAAAAI/AAAAAAAAAAo/wPT2w5Bl3xg/s50-c-k-no/photo.jpg",
      "userId": "100444450157165144272"
     },
     "user_tz": -60
    },
    "id": "Ywxs0ljHgkLm",
    "outputId": "e11a8abd-70f6-41c5-ccd1-a7cba7146ce9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.\n"
     ]
    }
   ],
   "source": [
    "from rasa_core.agent import Agent\n",
    "agent = Agent.load('models/dialogue', interpreter=model_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5MnGuFRpzzBh"
   },
   "source": [
    "### Talking to the Bot (with NLU)\n",
    "\n",
    "We can start talking to the bot in natural language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1340
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 57365,
     "status": "error",
     "timestamp": 1521380132537,
     "user": {
      "displayName": "Alexander Weidauer",
      "photoUrl": "//lh6.googleusercontent.com/-j4xnI5_PFWA/AAAAAAAAAAI/AAAAAAAAAAo/wPT2w5Bl3xg/s50-c-k-no/photo.jpg",
      "userId": "100444450157165144272"
     },
     "user_tz": -60
    },
    "id": "t_4zHDsgkbno",
    "outputId": "57473fce-7184-45d1-f2b2-470bfc84244e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your bot is ready to talk! Type your messages here or send 'stop'\n",
      "hey\n",
      "Hey! How are you?\n",
      "not so well\n",
      "looking for a good img\n",
      "Here is something to cheer you up: https://picsum.photos/200/300/?random\n",
      "Did that help you?\n",
      "yes, thanks\n",
      "Great carry on!\n",
      "stop\n"
     ]
    }
   ],
   "source": [
    "print(\"Your bot is ready to talk! Type your messages here or send 'stop'\")\n",
    "while True:\n",
    "    a = input()\n",
    "    if a == 'stop':\n",
    "        break\n",
    "    responses = agent.handle_message(a)\n",
    "    for response in responses:\n",
    "        print(response[\"text\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of the dialogue model\n",
    "As with the NLU model, instead of just subjectively testing the model, we can also evaluate the model on a dataset. You'll be using the training data set again, but usually you'd use a test data set separate from the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processed Story Blocks: 100%|██████████| 6/6 [00:00<00:00, 425.08it/s, # trackers=1]\n",
      "Processed Story Blocks: 100%|██████████| 6/6 [00:00<00:00, 31895.85it/s]\n",
      "Processed Story Blocks: 100%|██████████| 6/6 [00:00<00:00, 6145.50it/s]\n",
      "INFO:rasa_core.evaluate:Evaluating 6 stories\n",
      "Progress:\n",
      "100%|██████████| 6/6 [00:00<00:00, 25.05it/s]\n",
      "INFO:rasa_nlu.evaluate:F1-Score:  1.0\n",
      "INFO:rasa_nlu.evaluate:Precision: 1.0\n",
      "INFO:rasa_nlu.evaluate:Accuracy:  1.0\n",
      "INFO:rasa_nlu.evaluate:Classification report: \n",
      "                       precision    recall  f1-score   support\n",
      "\n",
      "        action_listen       1.00      1.00      1.00        13\n",
      "action_retrieve_image       1.00      1.00      1.00         2\n",
      "       utter_cheer_up       1.00      1.00      1.00         2\n",
      "  utter_did_that_help       1.00      1.00      1.00         2\n",
      "        utter_goodbye       1.00      1.00      1.00         3\n",
      "          utter_greet       1.00      1.00      1.00         3\n",
      "          utter_happy       1.00      1.00      1.00         4\n",
      "        utter_unclear       1.00      1.00      1.00         1\n",
      "\n",
      "          avg / total       1.00      1.00      1.00        30\n",
      "\n",
      "INFO:rasa_nlu.evaluate:Confusion matrix, without normalization: \n",
      "[[13  0  0  0  0  0  0  0]\n",
      " [ 0  2  0  0  0  0  0  0]\n",
      " [ 0  0  2  0  0  0  0  0]\n",
      " [ 0  0  0  2  0  0  0  0]\n",
      " [ 0  0  0  0  3  0  0  0]\n",
      " [ 0  0  0  0  0  3  0  0]\n",
      " [ 0  0  0  0  0  0  4  0]\n",
      " [ 0  0  0  0  0  0  0  1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from rasa_core.evaluate import run_story_evaluation\n",
    "\n",
    "run_story_evaluation(\"stories.md\", \"models/dialogue\", \n",
    "                     nlu_model_path=None, \n",
    "                     max_stories=None, \n",
    "                     out_file_plot=\"story_eval.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive learning\n",
    "Unfortunately, this doesn't work in jupyter yet. Hence, we going to do this on the command line. [The repository](https://github.com/tmbo/rasa-demo-pydata18)  contains a makefile with the instructions to run the interactive learning. So go ahead and run `make interactive` on the commandline (make sure your shell is in the righht virtual env)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m7VUXU6Cn5R-"
   },
   "source": [
    "# Part 3: Next steps\n",
    "\n",
    "Great job! You've built your first bot that uses Machine Learning to manage dialogues. As a next step, we'd suggest you start building your own bot. Here are a few useful links:\n",
    "\n",
    "\n",
    "\n",
    "*   Install the Rasa Stack on your machine [here](https://core.rasa.ai/installation.html)\n",
    "*   Checkout more of the Docs: [NLU](https://nlu.rasa.com) and [Core](https://core.rasa.com)\n",
    "*   Connect to the community in our [Gitter Chat](https://gitter.im/RasaHQ/rasa_core)\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Any feedback for this tutorial?* Please shoot me a mail at tom@rasa.com"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "y4miuS-TqYcn",
    "BBF6Nqi9scQE",
    "Fs3nOUzBsqrG",
    "5MnGuFRpzzBh"
   ],
   "default_view": {},
   "name": "Building a Simple Bot with Rasa Stack - Tutorial",
   "provenance": [
    {
     "file_id": "1GutDkDXmfU-nRzNH7Pxxx8YpdvLUw9LO",
     "timestamp": 1521183725373
    }
   ],
   "toc_visible": true,
   "version": "0.3.2",
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