app.py

from statistics import mode
import urllib.request
import gradio as gr
import unidecode
import string
import json

urllib.request.urlretrieve(
    'https://drive.google.com/uc?export=download&id=1TXD41vfqNWA6UNDQ73WhtJdZivCJatn-',
    'bot_questions_tags.json'
)

answers = ['Hello! My name is Ai.ra, and I am an artificial intelligence (AI). More specifically, I am an NLP (Natural Language Processing) model trained in conversation (a chatbot!).',
 'You can ask me things about "Artificial Intelligence," "Machine Learning," "AI Safety," or "AI Ethics."',
 "I don't have that kind of property hahaha I am software!",
 "AIRES (AI Robotics Ethics Society) is a society focused on educating the leaders and developers of tomorrow's Artificial Intelligence (AI) to ensure that AI is created ethically and responsibly.",
 'Aron Hui is the president/founder of AIRES.',
 'What "intelligence" is, remains an open question. However, not to leave you in the lurch, I will define "intelligence" as follows: "Intelligence is the ability of an agent to achieve goals in a wide range of environments."',
 'There is no consensus in the literature on what "AI" is (a corollary of not having a robust definition of what "intelligence\'\' is). However, we can say that AI is the intelligence demonstrated by machines, as opposed to the natural intelligence possessed by animals and humans.',
 'General Intelligence, or Universal Intelligence, can be defined as the ability to efficiently achieve goals in a wide range of domains.',
 'GOFAI ("good-old-fashioned-ai"), or symbolic artificial intelligence, is the term used to refer to methods of developing AI systems based on high-level symbolic (interpretable) representations, logic, and search.',
 'A multi-agent system (MAS "Multi-Agent Systems") is a computer system composed of multiple interacting intelligent agents.',
 'Machine Learning (ML) is a field of research dedicated to understanding and building methods that "learn", i.e., methods that use information/data to improve performance on some tasks.']


with open('bot_questions_tags.json') as json_file:
    dictionary = json.load(json_file)


def generate_ngrams(text, WordsToCombine):
    """
    Generates n-grams of length WordsToCombine from the input text.

    Args:
        text: A string representing the input text
        WordsToCombine: An integer representing the
            size of the n-grams to be generated

    Returns:
        A list of n-grams generated from the input text, where each
        n-gram is a list of WordsToCombine words
    """
    words = text.split()
    output = []
    for i in range(len(words) - WordsToCombine+1):
        output.append(words[i:i+WordsToCombine])
    return output


def make_keys(text, WordsToCombine):
    """
    Given a text and a number of words to combine, returns
    a list of keys that correspond to all possible combinations
    of n-grams (sequences of n consecutive words) in the text.

    Args:
        - text (str): The input text.
        - WordsToCombine (int): The number of words to combine.

    Returns:
        - sentences (list of str): A list of all the keys, which are
        the n-grams in the text.
    """
    gram = generate_ngrams(text, WordsToCombine)
    sentences = []
    for i in range(0, len(gram)):
        sentence = ' '.join(gram[i])
        sentences.append(sentence)
    return sentences


def chat(message, history):
    """
    A function that generates a response to a user input message
    based on a pre-built dictionary of responses.

    Args:
        message (str): A string representing the user's input message.
        history (list): A list of tuples containing previous
        messages and responses.

    Returns:
        tuple: A tuple containing two lists of tuples. The first list is
        the original history with the user's input message and the bot's
        response appended as a tuple. The second list is an updated history
        with the same tuples.
    """
    history = history or []
    text = message.lower()
    sentences = []
    values = []
    new_text = text.translate(str.maketrans('', '', string.punctuation))
    new_text = unidecode.unidecode(new_text)

    if len(new_text.split()) == 1:
        if new_text in dictionary.keys():
            l = [dictionary[new_text]] * 100
            values.append(l)
        new_text = new_text + ' ' + new_text

    else:
        if new_text in dictionary.keys():
            l = [dictionary[new_text]] * 100
            values.append(l)

    for i in range(1, len(new_text.split()) + 1):
        sentence = make_keys(new_text, i)
        sentences.append(sentence)

    for i in range(len(sentences)):
        attention = sentences[i]
        for i in range(len(attention)):
            if attention[i] in dictionary.keys():
                l = [dictionary[attention[i]]] * i
                values.append(l)

    if len([item for sublist in values for item in sublist]) == 0:
        bot_input_ids = "I'm sorry, either I didn't understand the question, or it is not part of my domain of expertise... :( Try asking it in another way or using other words. Maybe then I can help you!"
        history.append((message, bot_input_ids))
        return history, history

    else:
        values = [item for sublist in values for item in sublist]
        prediction = mode(values)
        bot_input_ids = answers[int(prediction)-1]
        history.append((message, bot_input_ids))
        return history, history

title = "Basic Chatbot - By Teeny-Tiny Castle 🏰"

head = (
  "<center>"
  "<img src='https://d2vrvpw63099lz.cloudfront.net/do-i-need-a-chatbot/header-chat-box.png' width=400>"
  "This is an example of a rules-based closed domain chatbot. It knows a couple of answers to questions related to AI."
  "<br>"
  "</center>"
)

ref = (
"<center>"
"To see its full version (ML style) of this bot, go to <a href='https://playground.airespucrs.org/aira'>this link</a>."
"</center>")

# create a chat interface
chatbot = gr.Chatbot()

demo = gr.Interface(
    chat,
    ["text", "state"],
    [chatbot, "state"],
    allow_flagging="never",
    title=title,
    description=head,
    article=ref
)

demo.launch()