Whiz Bot Guide
==============

We start by importing the basic bot from ``botiverse.bots`` and gui for
testing.

.. code:: ipython3

    from botiverse.bots import WhizBot
    from botiverse import chat_gui

The Whiz Bot is much like the basic bot except that it is capable of
using multinigual embeddings and sequential models which means better
performance and multi-linguality at the cose of more training time. In
this we train on an Arabic dataset similar to the one we used with the
basic bot.

Dataset Sample
^^^^^^^^^^^^^^

.. code:: json

     {
       "tag": "برامج",
       "patterns": [
         "ما هي البرامج التي تقدمها الجامعة؟",
         "ما هي المقررات المتاحة؟", 
         "أخبرني عن البرامج الأكاديمية",
         "هل يمكنك تقديم معلومات عن التخصصات؟" 
       ],
       "responses": [
         "...تقدم جامعتنا مجموعة واسعة من البرامج في",
         "...نقدم برامج أكاديمية متنوعة تشمل مجالات دراسية مختلفة"
       ]
     }

Initiate Chatbot
~~~~~~~~~~~~~~~~

We start by initiating the whiz bot. Although it supports two different
models (``linear`` and ``GRU``); each of those has its own
representation ``BERT`` and ``BytePairOneHotEncoding`` respectively (for
the latter, ``repr`` is passed as ``GRU``)

bot = WhizBot(repr=‘BERT’)

Read the Data
~~~~~~~~~~~~~

We read the data similar to how we did with the basic bot

.. code:: ipython3

    bot.read_data('./dataset_ar.json')

Train the chatbot
~~~~~~~~~~~~~~~~~

We train the chatbot where we can also supply the number of epochs and
batch size.

.. code:: ipython3

    bot.train(epochs=10, batch_size=32)


.. parsed-literal::

    Train Acc: 0.93: 100%|██████████| 240/240 [00:01<00:00, 220.21it/s]


Infer
~~~~~

Finally, we can infer given real data as usual

.. code:: ipython3

    bot.infer("ما هي الدورات المتاحة؟")




.. parsed-literal::

    "Hello! Welcome to our university's website."



Deploy the Chatbot
~~~~~~~~~~~~~~~~~~

And deploy the model if needed.

.. code:: ipython3

    chat_gui("Whiz Bot", bot.infer)