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by: textsocial
Created: —
Category: Virtual Assistant
Viewed: 69


How to create a Twitch chatbot for Android in Java

I recently got into Android Dev with Android Studio and Java.

Since most of my projects are chat bots, i thought it would be cool to implement a Twitch chat bot that could run on my Android mobile phone. I’ve used Python requests and twitchio api to build my bot. I don’t know much about web dev and i have no front-end experience.

The main problem i think is…how do I create a high-performance bot that does not consume too much battery?

Another problem could be, should i make it more server-side? Or should i find a way to accomplish the battery problem, without a server to rely on?

What do you think?

What SDK version should i use for implementation?

I’m also looking for docs to build this project. I will appreciate any resource related to the twitchio api, and in general, to the sdk and android studio. Which i can use to make the transition from Python.

If needed…i can provide the actual code of my python twitch bot on github.

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Are you getting a very low open rate and CTR in your email marketing? Are you tired of getting high cost per lead in your Facebook advertisement? Then you probably need a Facebook Messenger Chatbot Facebook Messenger chatbots are very easy to use and automated that drives the leads at a very low cost. Facebook has over 2 billion users and Messenger has 1.3 billion active users. what’s that mean? Your customer already leveraging the platform, you just need to target them. Who can take the benefit of this Marketing? Any type of service based agency, brand or offline stores. The main benefit of this chatbot is- It’s available 24×7 for your customers, you don’t have to present all the time to your messenger inbox. It increases your response time for the Facebook page. The Work Process: Message me for a free consultationI’ll help you to decide what will be best for your businessI’ll design flow and conversation script for your businessAfter your approval, we’ll set all the flow and launch your chatbotYou can get 30 days Free post-sales support and maintenance. ALERT: This basic package only includes a simple chatbot with upto 10 Interactions and 20 keywords. If you want an advanced one, check out one of my gig “Build An Advanced Messenger Chatbot For Your Business”

by: banhumbleback
Created: —
Category: Other
Viewed: 229


Build An Advanced Messenger Chatbot For Your Business for $90

Are you getting a very low open rate and CTR in your email marketing? Are you tired of getting high cost per lead in your Facebook advertisement? Then you probably need a Facebook Messenger Chatbot Facebook Messenger chatbots are very easy to use and automated that drives the leads at a very low cost. Facebook has over 2 billion users and Messenger has 1.3 billion active users. what’s that mean? Your customer already leveraging the platform, you just need to target them. Who can take the benefit of this Marketing? Any type of service based agency, brand or offline stores. The main benefit of this chatbot is- It’s available 24×7 for your customers, you don’t have to present all the time to your messenger inbox. It increases your response time for the Facebook page. The Work Process: Message me for a free consultationI’ll help you to decide what will be best for your businessI’ll design flow and conversation script for your businessAfter your approval, we’ll set all the flow and launch your chatbotYou can get 30 days Free post-sales support and maintenance.ALERT: This is a standard package which includes an advance chatbot to collect leads and for automated customer services. If your business is small, you can check my basic package “Build A Basic Messenger Chatbot For Your Business” Thank you for trusting me

by: banhumbleback
Created: —
Category: Social Networks
Viewed: 0


Como dar um foco ao chatbot

Estou desenvolvendo um chatbot em python que objetiva simular uma conversa terapeutica.

Usei como corpus algumas simulações de diálogos que eu mesma construí e embora o bot esteja conversando, as conversas nunca fazem sentido.

# -*- codding: utf-8 -*- from chatterbot import ChatBot from chatterbot.trainers import ListTrainer import os #Aqui ficam os import do telegram  chatbot = ChatBot("Futaba")  trainer = ListTrainer(chatbot)  for arquivos in os.listdir('arquivos'):     chats = open('arquivos/' + arquivos, 'r', encoding="utf8").readlines()     trainer.train(chats)  print("Hey, meu nome é Futaba e você pode se sentir confortável para conversar comigo mesmo que conversar não seja lá a coisa mais confortável pra você.") response = chatbot.get_response("Hey!") print(response)  while True:     resq = input('Você: ')     resp = chatbot.get_response(resq)     if float(response.confidence) > 0.8:         print('Futaba: ' + str(resp))     else:         print('Como você tem se sentido?') 

Eu estipulei um nível de confiabilidade para que o bot faça uma pergunta ao usuário caso não tenha muita certeza da resposta.

Alguém sabe de alguma maneira mais assertiva de tornar o diálogo mais fluido e que faça mais sentido?

obs: Já tentei treinar com livros, legendas de filmes e frases de filósofos, mas a única coisa que parece se aproximar de dar certo é o corpus que eu mesma escrevi baseado em diálogos terapeuticos

obs2: Se alguém souber de outros lugares onde eu possa pegar exemplos de diálogos terapeuticos, ajuda.

Can anyone check this SImple chatbot application and suggest me the corrective measures?

This chatbot will take the input from csv file and give the output to the user’s question…here problem I am facing is when user asks any question, it gives tkenize output of user’s text

#Block 1 """ In this block we will import all the required libraries  """ import pandas as pd                      # importing pandas to read the csv file import nltk import re from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer from sklearn.feature_extraction.text import TfidfVectorizer import random import string from sklearn.metrics.pairwise import cosine_similarity   #Block 2 """  This block will import csv file and save it to a variable   """  db_main = pd.read_csv(r'D:\Python\QnA_using_NLTK\qa_database.csv', sep = ',',                        names=["Question", "Answer", "user_response"])   # Block 3 """ Data Cleaning and preprocessing  """ #nltk.download('stopwords')    corpus = []             # corpus list is created to append output of initial cleaning of data  wordnet=WordNetLemmatizer() for i in range(0, len(db_main)):     review = re.sub('[^a-zA-Z0-9]', ' ', db_main['Question'][i])     review = review.lower()     review = review.split()     review = [wordnet.lemmatize(word) for word in review if not word in stopwords.words('english')]     review = ' '.join(review)     corpus.append(review)   #sent_tokens = nltk.sent_tokenize(db_main.Question)# converts to list of sentences  #word_tokens = nltk.word_tokenize(db_main.Question)# converts to list of words  #Block 4  """ This block will create tfidf vector and will create bag of words  """  # Creating the Bag of Words model cv = TfidfVectorizer() X = cv.fit_transform(corpus).toarray()  #Block 5 """ This block will define 2 functions"""  lemmer = WordNetLemmatizer() def LemTokens(tokens):     return [lemmer.lemmatize(token) for token in tokens] remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation) def LemNormalize(text):     return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))  #Block 6 """ this block will listdown user inputs and probable outputs  """  GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey",) GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"] 

#Block 7

"""Checking for greetings """  def greeting(sentence):     """If user's input is a greeting, return a greeting response"""     for word in sentence.split():         if word.lower() in GREETING_INPUTS:             return random.choice(GREETING_RESPONSES)     # Block 8 """ Generating response """ def response(user_response):     robo_response=''     corpus.append(user_response)     TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')     tfidf = TfidfVec.fit_transform(corpus)     vals = cosine_similarity(tfidf[-1], tfidf)     idx=vals.argsort()[0][-2]     flat = vals.flatten()     flat.sort()     req_tfidf = flat[-2]     if(req_tfidf==0):         robo_response=robo_response+"I am sorry! I don't understand you"         return robo_response     else:         robo_response = robo_response+corpus[idx]         return robo_response   flag=True print("ROBO: My name is Robo. I will answer your queries about Chatbots. If you want to exit, type Bye!")  while(flag==True):     user_response = input()     user_response=user_response.lower()     if(user_response!='bye'):         if(user_response=='thanks' or user_response=='thank you' ):             flag=False             print("ROBO: You are welcome..")         else:             if(greeting(user_response)!=None):                 print("ROBO: "+greeting(user_response))             else:                 print("ROBO: ",end="")                 print(response(user_response))                 corpus.remove(user_response)     else:         flag=False         print("ROBO: Bye! take care..")     

Implementing multiple session support in a chatbot

I am currently implementing a Chatbot purely in python.

In my current implementation, each time the user starts a new chat from a session, another Chatbot instance is launched and hence the Chatbot starts from the initial state.

I wish to change that behaviour and make it similar to let’s say chat on Facebook/Messenger, in which you can seamlessly move between sessions while having a chat without inconsistencies. Namely, I want these attributes:

  1. If the user enters anything from let’s say session A it should be immediately visible in all ongoing sessions. Similarly, the Chatbot reply should be visible in all the devices immediately.
  2. Have all sessions show the same chat history

To implement the first point, I used this example from the django-channels docs and modified it by creating a single group/chatroom for each user. All the sessions from the same user get connected to the same group/chatroom and hence receive all the messages in the group/chatroom regardless of where they were sent from.

However, this implementation currently has a bug. Each time that a user is connected, it initializes a Chatbot instance which starts from the initial state again while the older connections have Chatbot instances that are currently at a different state.

This leads to inconsistent replies which are different based on which window the user typed something in.

Basically instead of having two sessions talk to the same Chatbot instance, we have two sessions talking to two different Chatbot instances and messages from all these four sources are getting added to the same chatroom.

Moreover, we are wasting resources by keeping multiple Chatbot instances per user which increases with the number of currently active sessions.

I want all of the user windows to interact with the same Chatbot instance. What would be the best way to implement that?

Currently I can think of three solutions:

  1. Creating another Django project the Chatbot and make requests to that HTTP server. The Chatbot state is maintained in that server and any request from the user will go to the same Chatbot instance.
    • This is straightforward to implement for me (simply spin up another server)
    • This naturally solves all the problems regarding state as all the instances will query the same Chatbot object
  2. Creating a Master channel thread which will hold the actual Chatbot instance(a python object) and any new channels will will defer to it for the reply from the Chatbot.
    • This will be complicated to implement
    • I will have to maintain which thread is master and which ones are slaves
    • In situations where a user closes the master thread connection I will somehow have to change one of the slave connections to a master connection and pass the entire object(?!) or atleast pass the state variable and re-create the chatbot instance.
  3. Spawn an independent thread/process in python for the chatbot instance and have all the channel connections talk to that thread/process .
    • This will be hard for me to implement as I don’t currently know how to do IPC in python

Are there any other solutions possible? What would be the ideal solution?

I am using the following technologies:

  1. Django as the main backend, with Django Channels for WebSockets
  2. RASA NLU for the NLU component of the chatbot and a finite state machine model implemented using pytransitions for the dialog management in python

Chatbot Marketing Mastery your own custom chatbot without any programming knowledge for $2

Discover the very best tools for creating your own custom chat bot without any programming knowledge! With sites like Facebook opening their platforms to automated messaging for companies, chat bots have really exploded in popularity. Facebook went from zero chat bots in February 2016 to 18,000 by July of the same year. had approximately 300,000,000 registered users, and those users exchanged 350,000,000 automated messages with the platform in the first seven months of its chat bot. You’ve probably seen chat bots in action. They are on all sorts of websites, from major retail chains to mobile phone service providers and many other types of sites and apps. At first, you might think you’re talking to a real person. Usually, a popup appears with a picture of an agent, along with a name. The “agent” asks something like, “May I help you with anything?” Or, “Do you have any questions?” Chat bots use artificial intelligence that is often quite advanced to answer many questions a user might have, and in the event that the bot is unable to help the user, it will usually as the user to call, email, or fill out a support form, or perhaps to check a F.A.Q. page. Chat bots are quite advanced, and many of them can almost manage to fool users into thinking they are speaking to a real person. This is beneficial, because it allows companies to lower their overhead by using chat bots to replace customer service agents in many circumstances, and only when the chat bot is unsuccessful in helping the customer must a real agent step in. Chat bots have a few drawbacks, but they also have many benefits. In this guide, you’re going to learn more about how chat bots can be used for marketing, and whether or not chat bots are a good fit for your business.

by: Omate
Created: —
Category: eBook
Viewed: 209


How to handle state for a Twilio SMS chatbot which registers new users?

We are building an API, and we want the functionality that users are able to register over SMS through a Twilio bot we are building. Currently, we have a message_handler with function HandleMessage which Twilio calls upon receiving a message. Unfortunately, Twilio doesn’t do any state management to tell us what the user has sent in the past (even within a specific timeframe).

I’m a University student, and I figure my current solution is probably not the best way to go about it. The messages we display are like

1. Register a new account 2. View account information 3. Option 3 ... 

If the user replies 1, we then show them something like Let's register a new account for you then. What's your full name? When they reply to that, we would like to show What is your email? and so forth.

My current implementation includes a map[string]int where each string represents every possible ‘flow’ the user can opt for, and the integer represents a unique code assigned to it based on which our API sends them the next message. This looks something like

optionsList = map[string]int{     "1":               1,     "1#1":             2,     "1#1#2":           3,     "1#1#2#3":         4,     "1#1#2#3#4":       5,     "1#1#2#3#4#5":     6,     "1#1#2#3#4#5#6":   7,     "1#1#2#3#4#5#6#7": 8, } 

Every # implies the user chose an option after it. For example, if the user chose to Register a new account hence replying 1, that is identified by the int 1 which would tell the API to ask them for their name, that then becomes 1#1 identified by int 2 which tells the API to ask for their email, and so forth.

Now, I have a couple of questions.

Q1. How do I better manage this state to let the API know what message to send next?

Q2. Every reply they sent, should slowly be populating a User struct (if they’re registering a new account), and in the end the API should save that in the DB. What is the best way I can store the User through multiple asynchronous calls? Is it needed that I write incomplete entries to the DB to manage it? In that case, should I be running a worker which periodically checks for “timed out” sessions and deletes those rows from the DB?

Thanks!