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from statistics import mode
import gradio as gr
import unidecode
import requests
import string
import json
# Load data from files
with open('answers_en.txt', encoding='utf-8') as fp:
answers = [line.strip() for line in fp]
with open('keys_en.json', 'r') as fp:
vocabulary = json.load(fp)
with open('keys_en.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://nkluge-correa.github.io/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() |