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FAQs

Can Python be used for a chatbot?

Yes, because of its simplicity, extensive library and ability to process languages, Python has become the preferred language for building chatbots.

Which language is best for a chatbot?

Python is one of the best languages for building chatbots because of its ease of use, large libraries and high community support.

Is it easy to learn chatbots?

You’ll need the ability to interpret natural language and some fundamental programming knowledge to learn how to create chatbots. But with the correct tools and commitment, chatbots can be taught and developed effectively.

What is the smartest chatbot?

Some of the best chatbots available include Microsoft XiaoIce, Google Meena, and OpenAI’s GPT 3. These chatbots employ cutting-edge artificial intelligence techniques that mimic human responses.

Which algorithms are used for chatbots?

Depending on their application and intended usage, chatbots rely on various algorithms, including the rule-based system, TFIDF, cosine similarity, sequence-to-sequence model, and transformers.

What is a chatbot in Python summary?

A Python chatbot is an artificial intelligence-based program that mimics human speech. Python is an effective and simple programming language for building chatbots and frameworks like ChatterBot.

How To Build A Chat Bot That Learns From The User In Python Tutorial

Chatbots can provide real-time customer support and are therefore a valuable asset in many industries. When you understand the basics of the ChatterBot library, you can build and train a self-learning chatbot with just a few lines of Python code.

You’ll get the basic chatbot up and running right away in step one, but the most interesting part is the learning phase, when you get to train your chatbot. The quality and preparation of your training data will make a big difference in your chatbot’s performance.

To simulate a real-world process that you might go through to create an industry-relevant chatbot, you’ll learn how to customize the chatbot’s responses. You’ll do this by preparing WhatsApp chat data to train the chatbot. You can apply a similar process to train your bot from different conversational data in any domain-specific topic.

In this tutorial, you’ll learn how to:

Build a command-line chatbot with ChatterBot
Train the chatbot to customize its responses
Export your WhatsApp chat history
Perform data cleaning on the chat export using regular expressions
Retrain the chatbot with industry-specific data

You’ll also learn how ChatterBot stores your training data, and you’ll find suggestions and pointers for next steps, so you can start collecting real user data and let the chatbot learn from it.

Overall, in this tutorial, you’ll quickly run through the basics of creating a chatbot with ChatterBot and learn how Python allows you to get fun and useful results without needing to write a lot of code.

Step 2: Begin Training Your Chatbot

In the previous step, you built a chatbot that you could interact with from your command line. The chatbot started from a clean slate and wasn’t very interesting to talk to.

In this step, you’ll train your chatbot using
ListTrainer
to make it a little smarter from the start. You’ll also learn about built-in trainers that come with ChatterBot, including their limitations.

Your chatbot doesn’t have to start from scratch, and ChatterBot provides you with a quick way to train your bot. You’ll use ChatterBot’s
ListTrainer
to provide some conversation samples that’ll give your chatbot more room to grow:

1# bot.py 2 3from chatterbot import ChatBot 4from chatterbot.trainers import ListTrainer 5 6chatbot = ChatBot("Chatpot") 7 8trainer = ListTrainer(chatbot) 9trainer.train([ 10 "Hi", 11 "Welcome, friend 🤗", 12]) 13trainer.train([ 14 "Are you a plant?", 15 "No, I'm the pot below the plant!", 16]) 17 18exit_conditions = (":q", "quit", "exit") 19while True: 20 query = input("> ") 21 if query in exit_conditions: 22 break 23 else: 24 print(f"🪴 {chatbot.get_response(query)}")

In line 4, you import
ListTrainer
, to which you pass your
chatbot
on line 8 to create
trainer
.

In lines 9 to 12, you set up the first training round, where you pass a list of two strings to
trainer.train()
. Using
.train()
injects entries into your database to build upon the graph structure that ChatterBot uses to choose possible replies.

Note: If you pass an iterable with exactly two items to
ListTrainer.train()
, then ChatterBot considers the first item a statement and the second item an acceptable response.

You can run more than one training session, so in lines 13 to 16, you add another statement and another reply to your chatbot’s database.

If you now run the interactive chatbot once again using
python bot.py
, you can elicit somewhat different responses from it than before:

> hi 🪴 Welcome, friend 🤗 > hello 🪴 are you a plant? > me? 🪴 are you a plant? > yes 🪴 hi > are you a plant? 🪴 No, I'm the pot below the plant! > cool 🪴 Welcome, friend 🤗

The conversation isn’t yet fluent enough that you’d like to go on a second date, but there’s additional context that you didn’t have before! When you train your chatbot with more data, it’ll get better at responding to user inputs.

The ChatterBot library comes with some corpora that you can use to train your chatbot. However, at the time of writing, there are some issues if you try to use these resources straight out of the box.

Note: The issues come from mismatches between versions of the dependencies, as well as the Python version that you use. You can work around them, but it’ll require some fiddling on your end.

Alternatively, you could parse the corpus files yourself using
pyYAML
because they’re stored as YAML files.

While the provided corpora might be enough for you, in this tutorial you’ll skip them entirely and instead learn how to adapt your own conversational input data for training with ChatterBot’s
ListTrainer
.

To train your chatbot to respond to industry-relevant questions, you’ll probably need to work with custom data, for example from existing support requests or chat logs from your company.

Moving forward, you’ll work through the steps of converting chat data from a WhatsApp conversation into a format that you can use to train your chatbot. If your own resource is WhatsApp conversation data, then you can use these steps directly. If your data comes from elsewhere, then you can adapt the steps to fit your specific text format.

To start off, you’ll learn how to export data from a WhatsApp chat conversation.

Dos and don’ts of building a chatbot

Now you know what the workflow of building chatbots looks like. But before you open the bot builder, have a look at these handy tips.

Do:

Add a little bit of human touch. Personality counts! Our research study about chatbot personality revealed that 53% of consumers build positive associations around brands whose bots use quick-witted comebacks. A unique tone of voice that fits your target audience will make you stand out from the crowd. Don’t be afraid to give your chatbot a name as well—it can work wonders for your brand voice and image. Our chatbot experts agree:

It’s all about engagement. Quality bots regularly see 80-90% response rates. Tailor your chatbot experience with graphic materials (e.g. GIFs, photos, illustrations), human touch (personalization, language), and targeting (e.g based on geography or timeframe).

Route complex conversations to human agents. As many as 69% of consumers admit that they prefer chatbots to resolve small issues and get quick responses. However, you need to remember that there are people who will always prefer to talk to a human agent—and it’s in your interest to make it possible. Make sure that you include this option in your conversation flow, especially if your business conversations revolve around complex issues.
Mimic a real human conversation. This means that your chatbot shouldn’t send long blocks of text at the speed of light. Break your long answers into a batch of quick replies and set a delay between each message, so the flow is more natural.

Don’t:

Design dead-end conversations. Picture this: you go to a shop and ask an assistant to help you with choosing a variant of a specific product. The assistant gives you 10% off a completely different product and just leaves without a word. To avoid such situations, make sure that the answers you provide are actionable. Even after the conversation is over, the customer should always have an option to restart it or get help from the FAQ chatbot.
Overcomplicate chatbot flows. Once you discover how easy it is to create a chatbot, you might be tempted to create complex conversation flows branching into many additional flows. It’s understandable! But bear in mind that the more interactive your chatbot becomes, the more difficult it is to manage it. After all, the number of messages grows exponentially with each additional scenario, so it’s more difficult to analyze them, too. Instead, try to keep it simple.
Allow for (too) open-intent conversation. Yes, we do think that mimicking a human conversation is the best option, but a chatbot’s main job is to guide the users in a specific direction. This means that the user should never end up figuring out what they are supposed to do. Your intelligent chatbot can be witty, and the conversation can take a few directions, but the outcome has to be specific, too.

Suggested read: Discover more useful tips for chatbot design.

How to create an accurate Chat Bot Response System in Python Tutorial (2021)

Step 5: Train Your Chatbot on Custom Data and Start Chatting

In this step, you’ll train your chatbot with the WhatsApp conversation data that you cleaned in the previous step. You’ll end up with a chatbot that you’ve trained on industry-specific conversational data, and you’ll be able to chat with the bot—about houseplants!

Open up
bot.py
and include calls to your cleaning functions in the code:

1# bot.py 2 3from chatterbot import ChatBot 4from chatterbot.trainers import ListTrainer 5from cleaner import clean_corpus 6 7CORPUS_FILE = "chat.txt" 8 9chatbot = ChatBot("Chatpot") 10 11trainer = ListTrainer(chatbot) 12cleaned_corpus = clean_corpus(CORPUS_FILE) 13trainer.train(cleaned_corpus) 14 15exit_conditions = (":q", "quit", "exit") 16while True: 17 query = input("> ") 18 if query in exit_conditions: 19 break 20 else: 21 print(f"🪴 {chatbot.get_response(query)}")

You don’t need to do a lot of extra work in this file, because you’ve prepared your cleaning functions well:

Line 5 imports

clean_corpus()
from
cleaner
.
Line 7 defines the filename of your chat export file. In this example, you assume that it’s called

"chat.txt"
, and it’s located in the same directory as
bot.py
. If you need more advanced path handling, then take a look at Python’s
pathlib
module.
Line 12 applies your cleaning code to the chat history file and returns a tuple of cleaned messages, which you call

cleaned_corpus
.
Line 13 finally uses that data as input to

.train()
, effectively training your chatbot with the WhatsApp conversation data.

It’s important to know that ChatterBot will treat an iterable with more than two items slightly differently than when there are only two items, like you had in your previous training runs:

If you provide longer lists of training conversations, then this will establish each item in the list as a possible response to its predecessor in the list. (Source)

Depending on your input data, this may or may not be exactly what you want. For the provided WhatsApp chat export data, this isn’t ideal because not every line represents a question followed by an answer.

To deal with this, you could apply additional preprocessing on your data, where you might want to group all messages sent by the same person into one line, or chunk the chat export by time and date. That way, messages sent within a certain time period could be considered a single conversation.

There’s always more to clean to improve your input data! For this tutorial, however, it’s time to give the conversational houseplant chatbot another go. Run
bot.py
and chat with your chatty pot to test how its responses have changed due to training:

> hi 🪴 Welcome, friend 🤗 > thanks for the green welcome 🪴 I let you > you let me be here? 🪴 It's a monsters! > did you mean monstera? 🪴 The leafs are getting dryer and dryer. But she's growing new ones > who? 🪴 Do raindrops touch their leaves? > very philosophical! 🪴 Lol > ;) 🪴 I don't grow any crop at home > no crops in pots 🪴 Ah, gotcha! > ah, I just thought you meant that 🪴 But water makes sense > yes, water in pots 🪴 Do you have any plant care pro tips? > water. 🪴 I always thought that love and music was more than enough > haha 🪴 Well, no. Just the balcony. This worked best > ok, balcony is notes 🪴 I let you

Your chatbot has increased its range of responses based on the training data that you fed to it. As you might notice when you interact with your chatbot, the responses don’t always make a lot of sense.

ChatterBot uses complete sentences as replies, and you trained it on just a small sample of conversational data. The small size of the training data means that it doesn’t have a big pool of replies to pick from. And because the data is conversational, not all of the sentences are very useful as replies. But
Chatpot
is doing all it can to find the best matching reply to any new message that you type!

To select a response to your input, ChatterBot uses the
BestMatch
logic adapter by default. This logic adapter uses the Levenshtein distance to compare the input string to all statements in the database. It then picks a reply to the statement that’s closest to the input string.

If you use well-structured input data, then the default settings of ChatterBot give you decent results out of the box. And if you’re ready to do some extra work to get just what you want, then you’re in luck! ChatterBot allows for a lot of customization and provides some instructions to guide you in the right direction:

Topic	Approach	Instructions
Training	Inherit from	Create a new training class
Input preprocessing	Write a function that takes and returns a	Create a new preprocessor
Selecting responses	Inherit from	Create a new logic adapter
Storing data	Inherit from	Create a new storage adapter
Comparing statements	Write a function that takes two statements and returns a number between 0 and 1	Create a new comparison function

ChatterBot provides you with reasonable defaults. But if you want to customize any part of the process, then it gives you all the freedom to do so.

In this section, you put everything back together and trained your chatbot with the cleaned corpus from your WhatsApp conversation chat export. At this point, you can already have fun conversations with your chatbot, even though they may be somewhat nonsensical. Depending on the amount and quality of your training data, your chatbot might already be more or less useful.

Key Takeaway

Building an AI chatbot, or even a simple conversational bot, may seem like a complex process. But if you believe that your users will benefit from it, you should definitely give it a try.

You can create a prototype all by yourself with a bot builder and add it to your business website.

To create your own chatbot:

Identify your business goals and customer needs
Choose a chatbot builder that you can use on your desired channels
Design your bot conversation flow by using the right nodes
Test your chatbot and collect messages to get more insights
Use data and feedback from customers to train your bot
Check which conversation routes are the most popular and improve them for a better user experience

Collect more data and monitor messages to see what are the most common questions. If your customers will be using it on a regular basis, you may think about additional automations.

Apart from being the most popular editor among visual chatbot builders, Tidio also offers a live chat widget and email marketing tools. You can seamlessly integrate your bots with customer support chats and digital newsletters.

Care to give it a try?

Create free chatbots to increase sales and improve customer service

Step 1: Create a Chatbot Using Python ChatterBot

In this step, you’ll set up a virtual environment and install the necessary dependencies. You’ll also create a working command-line chatbot that can reply to you—but it won’t have very interesting replies for you yet.

To get started with your chatbot project, create and activate a virtual environment, then install
chatterbot
and
pytz
:

PS> python -m venv venv PS> venv\Scripts\activate (venv) PS> python -m pip install chatterbot==1.0.4 pytz

$ python -m venv venv $ source venv/bin/activate (venv) $ python -m pip install chatterbot==1.0.4 pytz

Running these commands in your terminal application installs ChatterBot and its dependencies into a new Python virtual environment.

Note: At the time of writing, the ChatterBot library hasn’t seen a lot of maintenance for a while. It’s therefore facing some issues that can get annoying quickly.

For this tutorial, you’ll use ChatterBot 1.0.4, which also works with newer Python versions on macOS and Linux. On Windows, you’ll have to stay on a Python version below 3.8. ChatterBot 1.0.4 comes with a couple of dependencies that you won’t need for this project. However, you’ll quickly run into more problems if you try to use a newer version of ChatterBot or remove some of the dependencies.

So just relax into this selected version and give it a spin. If you’re hooked and you need more, then you can switch to a newer version later on.

After the installation is complete, running
python -m pip freeze
should bring up list of installed dependencies that’s similar to what you can find in the provided sample code’s
requirements.txt
file:

With the installation out of the way, and ignoring some of the issues that the library currently has, you’re ready to get started! Create a new Python file, call it
bot.py
, and add the code that you need to get a basic chatbot up and running:

1# bot.py 2 3from chatterbot import ChatBot 4 5chatbot = ChatBot("Chatpot") 6 7exit_conditions = (":q", "quit", "exit") 8while True: 9 query = input("> ") 10 if query in exit_conditions: 11 break 12 else: 13 print(f"🪴 {chatbot.get_response(query)}")

After importing
ChatBot
in line 3, you create an instance of
ChatBot
in line 5. The only required argument is a name, and you call this one
"Chatpot"
. No, that’s not a typo—you’ll actually build a chatty flowerpot chatbot in this tutorial! You’ll soon notice that pots may not be the best conversation partners after all.

In line 8, you create a
while
loop that’ll keep looping unless you enter one of the exit conditions defined in line 7. Finally, in line 13, you call
.get_response()
on the
ChatBot
instance that you created earlier and pass it the user input that you collected in line 9 and assigned to
query
.

The call to
.get_response()
in the final line of the short script is the only interaction with your
chatbot
. And yet—you have a functioning command-line chatbot that you can take for a spin.

When you run
bot.py
, ChatterBot might download some data and language models associated with the NLTK project. It’ll print some information about that to your console. Python won’t download this data again during subsequent runs.

Note: The NLTK project installs the data that ChatterBot uses into a default location on your operating system:

Windows:
C:\nltk_data\
Linux:
/usr/share/nltk_data/
macOS:
/Users/ /nltk_data/

NLTK will automatically create the directory during the first run of your chatbot.

If you’re ready to communicate with your freshly homegrown
Chatpot
, then you can go ahead and run the Python file:

$ python bot.py

After the language models are set up, you’ll see the greater than sign () that you defined in
bot.py
as your input prompt. You can now start to interact with your chatty pot:

> hello 🪴 hello > are you a plant? 🪴 hello > can you chat, pot? 🪴 hello

Well … your chat-pot is responding, but it’s really struggling to branch out. Tough to expect more from a potted plant—after all, it’s never gotten to see the world!

Note: On Windows PowerShell, the potted plant emoji (🪴) might not render correctly. Feel free to replace it with any other prompt you like.

Even if your chat-pot doesn’t have much to say yet, it’s already learning and growing. To test this out, stop the current session. You can do this by typing one of the exit conditions—
":q"
,
"quit"
, or
"exit"
. Then start the chatbot another time. Enter a different message, and you’ll notice that the chatbot remembers what you typed during the previous run:

> hi 🪴 hello > what's up? 🪴 are you a plant?

During the first run, ChatterBot created a SQLite database file where it stored all your inputs and connected them with possible responses. There should be three new files that have popped up in your working directory:

./ ├── bot.py ├── db.sqlite3 ├── db.sqlite3-shm └── db.sqlite3-wal

ChatterBot uses the default
SQLStorageAdapter
and creates a SQLite file database unless you specify a different storage adapter.

Note: The main database file is
db.sqlite3
, while the other two, ending with
-wal
and
-shm
, are temporary support files.

Because you said both hello and hi at the beginning of the chat, your chat-pot learned that it can use these messages interchangeably. That means if you chat a lot with your new chatbot, it’ll gradually have better replies for you. But improving its responses manually sounds like a long process!

Now that you’ve created a working command-line chatbot, you’ll learn how to train it so you can have slightly more interesting conversations.

How Does the Chatbot Python Work?

The main approaches to the development of chatbots are as follows:

Rule-Based Approach

The Chatbot Python adheres to predefined guidelines when it comprehends user questions and provides an answer. The developers often define these rules and must manually program them.

Self-Learning Approach:

Chatbots that learn their use of machine learning to develop better conversational skills over time. There are two categories of self-learning chatbots:

RetrievalBased Models: Based on an input question, these models can obtain predefined responses from a knowledge base. They evaluate user input and compare it to the closest equivalent response in the knowledge base.
Generative Models: Generative models create responses from scratch based on the input query. They employ approaches like sequence-to-sequence models or transformers, for example, to produce human-like answers.

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How Does the Chatbot Python Work?

The main approaches to the development of chatbots are as follows:

Rule-Based Approach

The Chatbot Python adheres to predefined guidelines when it comprehends user questions and provides an answer. The developers often define these rules and must manually program them.

Self-Learning Approach:

Chatbots that learn their use of machine learning to develop better conversational skills over time. There are two categories of self-learning chatbots:

RetrievalBased Models: Based on an input question, these models can obtain predefined responses from a knowledge base. They evaluate user input and compare it to the closest equivalent response in the knowledge base.
Generative Models: Generative models create responses from scratch based on the input query. They employ approaches like sequence-to-sequence models or transformers, for example, to produce human-like answers.

Create a Python GPT Chatbot - In Under 4 Minutes — Create a Python GPT Chatbot – In Under 4 Minutes

What is ChatterBot Library?

A Chatbot Python library called The ChatterBot makes it simpler to create chatbots. It manages the challenges of natural language processing and provides a specific API. The following are some of Chatterbot’s primary features:

Language Independence

You can use Chatterbot to create chatbots in various languages based on your target demographic.

How Does ChatterBot Library Work?

Chatterbot combines a spoken language data database with an artificial intelligence system to generate a response. It uses TF-IDF (Term Frequency-Inverse Document Frequency) and cosine similarity to match user input to the proper answers.

How To Install ChatterBot In Python

You can launch a terminal or command prompt. Your machine needs to be configured to run Ai Chatbot Python. To check if this is the case, run the command “python version” or “python3 Version” to ensure it returns a valid Python version.
Installing Chatterbot using the Chatbot Python Package Manager that comes with your Python program. To do this, issue the command “Pip installing chatterbot.”
This command will download and install the ChatterBot library and its dependencies.
Once setup is complete, add the following code to your Chatbot using Python script or interactive environment to include Chatterbot: Imported from Chatterbot is ChatBot.
You may now use Chatterbot to begin building your chatbot. Using the ChatterBot guide or other resources, you can learn how to set up and train a chatbot.

Next Steps

ChatterBot provides a way to install the library as a Django app. As a next step, you could integrate ChatterBot in your Django project and deploy it as a web app.

You can also swap out the database back end by using a different storage adapter and connect your Django ChatterBot to a production-ready database.

After you’ve completed that setup, your deployed chatbot can keep improving based on submitted user responses from all over the world.

Even if you keep running your chatbot on the CLI for now, there are many ways that you can improve the project and continue to learn about the ChatterBot library:

Handle more edge cases: Your regex pattern might not catch all WhatsApp usernames. You can throw some edge cases at it and improve the stability of your parsing while building tests for your code.
Improve conversations: Group your input data as conversations so that your training input considers consecutive messages sent by the same user within an hour a single message.
Parse the ChatterBot corpus: Skip the dependency conflicts, install

PyYAML
directly, and parse some of the training corpora provided in chatterbot-corpus yourself. Use one or more of them to continue training your chatbot.
Build a custom preprocessor: ChatterBot can modify user input before sending it to a logic adapter. You can use built-in preprocessors, for example to remove whitespace. Build a custom preprocessor that can replace swear words in your user input.
Include additional logic adapters: ChatterBot comes with a few preinstalled logic adapters, such as ones for mathematical evaluations and time logic. Add these logic adapters to your chatbot so it can perform calculations and tell you the current time.
Write a custom logic adapter: Create a custom logic adapter that triggers on specific user inputs, for example when your users ask for a joke.
Incorporate an API call: Build a logic adapter that can interact with an API service, for example by repurposing your weather CLI project so that it works within your chatbot.

There’s a lot that you can do! Check out what your chatbot suggests:

> what should i do next? 🪴 Yeah! I want them to be strong and take care of themselves at some point

Great advice! Or … at least it seems like your chatbot is telling you that you should help it become more self-sufficient?

A great next step for your chatbot to become better at handling inputs is to include more and better training data. If you do that, and utilize all the features for customization that ChatterBot offers, then you can create a chatbot that responds a little more on point than
🪴 Chatpot
here.

If you’re not interested in houseplants, then pick your own chatbot idea with unique data to use for training. Repeat the process that you learned in this tutorial, but clean and use your own data for training.

Did you decide to adapt your chatbot for a specific use case? Did you manage to have a philosophical conversation with it? Or did your chatbot keep switching topics in a funny way? Share your experience in the comments below!

Are you fed up with waiting in long queues to speak with a customer support representative? Can you recall the last time you interacted with customer service? There’s a chance you were contacted by a bot rather than a human customer support professional. In our blog post-ChatBot Building Using Python, we will discuss how to build a simple Chatbot in Python programming and its benefits.

Read on!

This article was published as a part of the Data Science Blogathon.

Bots are specially built software that interacts with internet users automatically. Bots are made up of deep learning and machine learning algorithms that assist them in completing jobs. By auto-designed, we mean they run independently, follow instructions, and begin the conservation process without human intervention. With their ability to mimic human languages seamlessly and engage in natural conversations, these smart bots have the power of self-learning and gain widespread adoption among companies in diverse industries.

Bots are responsible for the majority of internet traffic. For e-commerce sites, traffic can be significantly higher, accounting for up to 90% of total traffic. They can communicate with people on social media accounts and websites.

Some were programmed and manufactured to transmit spam messages to wreak havoc.

Also read: Python Tutorial for Beginners Overview

Here are type of bots:

Rule-based chatbots operate on predefined rules and patterns, relying on instructions to respond to user inputs. These bots excel in structured and specific tasks, offering predictable interactions based on established rules.

Building libraries should be avoided if you want to understand how a chatbot operates in Python thoroughly. In 1994, Michael Mauldin was the first to coin the term “chatterbot” as Julia.

Also read: A Comprehensive Guide to Python Automation.

A ChatBot is essentially software that facilitates interaction between humans. When you train your chatbot with Python 3, extensive training data becomes crucial for enhancing its ability to respond effectively to user inputs.

ChatBot examples include Apple’s Siri, Amazon’s Alexa, Google Assistant, and Microsoft’s Cortana. Following is the step-by-step guide with regular expressions for building ChatBot using Python:

First, we will ask Username

print("BOT: What is your name?") user_name = input()

Output

What is your name?Testing

To start, we assign questions and answers that the ChatBot must ask. For example, we assign three variables with different answers. It’s crucial to note that these variables can be used in code and automatically updated by simply changing their values. The format facilitates the easy passing of values.

name = “Bot Number 286” monsoon = “rainy” mood = “Smiley” resp = { “what’s your name?”: [ “They call me {0}”.format(name), “I usually go by {0}”.format(name), “My name is the {0}”.format(name) ], “what’s today’s weather?”: [ “The weather is {0}”.format(monsoon), “It’s {0} today”.format(monsoon)], “how are you?”: [ “I am feeling {0}”.format(mood), “{0}! How about you?”.format(mood), “I am {0}! How about yourself?”.format(mood), ], “”: [ “Hey! Are you there?”, “What do you mean by these?”, ], “default”: [ “This is a default message”] }

Library random imported to choose the response. It will select the answer by bot randomly instead of the same act.

import random def res(message): if message in resp: bot286_message = random.choice(resp[message]) else: bot286_message = random.choice(resp[“default”]) return bot286_message

Sometimes, the questions added are not related to available questions, and sometimes, some letters are forgotten to write in the chat. The bot will not answer any questions then, but another function is forward.

def real(xtext): if “name” in xtext: ytext = “what’s your name?” elif “monsoon” in xtext: ytext = “what’s today’s weather?” elif “how are” in xtext: ytext = “how are you?” else: ytext = “” return ytext

def send_message(message): print((message)) response = res(message) print((response))

while 1: my_input = input() my_input = my_input.lower() related_text = real(my_input) send_message(related_text) if my_input == “exit” or my_input == “stop”: break

Another example is installing a library chatterbot

Natural language Processing (NLP) is a necessary part of artificial intelligence that employs natural language to facilitate human-machine interaction.

Python uses many libraries such as NLTK, spacy, etc. A chatbot is a computer program that can communicate with humans in a natural language. They frequently rely on machine learning, particularly natural language processing (NLP).

Use the following commands in Terminal in Anaconda prompt.

pip install chatterbot pip install chatterbot_corpus

You can also install chatterbot from its source: https://github.com/gunthercox/ChatterBot/archive/master.zip

After installing, unzip, the file and open Terminal, and type in:

cd chatter_bot_master_directory.

Finally, type python setup.py install.

pip install -U spacy

For downloading model “en_core_web_sm”

import spacy from spacy.cli.download import download download(model=”en_core_web_sm”)

pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz

import spacy nlp = spacy.blank(“en”) doc = nlp(“This is a sentence.”)

You will need two further classes ChatBot and ListTrainers

from chatterbot import ChatBot from chatterbot.trainers import ListTrainer

1st Example

from chatterbot import ChatBot from chatterbot.trainers import ListTrainer # Create a new chatbot named Charlie chatbot = ChatBot(‘name’) trainer = ListTrainer(chatbot) trainer.train([ “Hi, can I help you?”, “Sure, I’d like to book a flight to Iceland.”, “Your flight has been booked.” ]) # Get a response to the input text ‘I would like to book a flight.’ response = chatbot.get_response(‘I would like to book a flight.’) print(response)

2nd Example by ChatBot

Bot286 = ChatBot(name=’PyBot’) talk = [‘hi buddy!’, ‘hi!’, ‘how do you do?’, ‘how are you?’, ‘i’m fine.’, ‘fine, you?’, ‘always smiling.’] list_trainer = ListTrainer(Bot286)for item in (talk): list_trainer.train(item) >>>print(my_bot.get_response(“hi”)) how do you do? from chatterbot.trainers import ChatterBotCorpusTrainercorpus_trainer = ChatterBotCorpusTrainer(Bot286) corpus_trainer.train(‘chatterbot.corpus.english’)

Here are the benefits of Bots:

Also read: Top 30+ Python Projects: Beginner to Advanced 2024

This article has delved into the fundamental definition of chatbots and underscored their pivotal role in business operations. By exploring the process of building your own chatbot using the Python programming language and the Chatterbot library, we’ve highlighted the critical aspects of handling text data, emphasizing the significance of preprocessing, such as tokenizing, for effective communication.

Moreover, including a practical use case with relevant parameters showcases the real-world application of chatbots, emphasizing their relevance and impact on enhancing user experiences. As businesses navigate the landscape of conversational AI, understanding the nuances of text data, mastering preprocessing techniques, and defining tailored use cases with specific parameters emerge as key considerations for successful chatbot integration.

Ans. Yes, Python is commonly used for chatbots. Its versatility, extensive libraries like NLTK and spaCy for natural language processing, and frameworks like ChatterBot make it an excellent choice. Python’s simplicity, readability, and strong community support contribute to its popularity in developing effective and interactive chatbot applications.

Ans. Popular Python libraries for chatbot development include NLTK, spaCy for natural language processing, TensorFlow, PyTorch for machine learning, and ChatterBot for simple implementations. Flask or Django can handle web integration. Rasa offers a comprehensive framework. Choose based on your project’s complexity, requirements, and library familiarity.

Ans. To create a chatbot in Python using the ChatterBot module, install ChatterBot, create a ChatBot instance, train it with a dataset or pre-existing data, and interact using the chatbot’s logic. Implement conversation flow, handle user input, and integrate with your application. Deploy the chatbot for real-time interactions.

Ans. To craft a generative chatbot in Python, leverage a natural language processing library like NLTK or spaCy for text analysis. Utilize chatgpt or OpenAI GPT-3, a powerful language model, to implement a recurrent neural network (RNN) or transformer-based model using frameworks such as TensorFlow or PyTorch. Train the model on a dataset and integrate it into a chat interface for interactive responses.

Ans. The time to create a chatbot in Python varies based on complexity and features. A simple one might take a few hours, while a sophisticated one could take weeks or months. It depends on the developer’s experience, the chosen framework, and the desired functionality and integration with other systems.

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I want to made a chatbot can u plzz help me..

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Chatbot in Python

In the past few years, chatbots in the Python programming language have become enthusiastically admired in the sectors of technology and business. These intelligent bots are so adept at imitating natural human languages and chatting with humans that companies across different industrial sectors are accepting them. From e-commerce industries to healthcare institutions, everyone appears to be leveraging this nifty utility to drive business advantages. In the following tutorial, we will understand the chatbot with the help of the Python programming language and discuss the steps to create a chatbot in Python.

Understanding the Chatbot

A Chatbot is an Artificial Intelligence-based software developed to interact with humans in their natural languages. These chatbots are generally converse through auditory or textual methods, and they can effortlessly mimic human languages to communicate with human beings in a human-like way. A chatbot is considered one of the best applications of natural languages processing.

We can categorize the Chatbots into two primary variants: Rule-Based Chatbots and Self-Learning Chatbots.

The first chatbot named ELIZA was designed and developed by Joseph Weizenbaum in 1966 that could imitate the language of a psychotherapist in only 200 lines of code. But as the technology gets more advance, we have come a long way from scripted chatbots to chatbots in Python today.

Chatbot in present Generation

Today, we have smart Chatbots powered by Artificial Intelligence that utilize natural language processing (NLP) in order to understand the commands from humans (text and voice) and learn from experience. Chatbots have become a staple customer interaction utility for companies and brands that have an active online existence (website and social network platforms).

With the help of Python, Chatbots are considered a nifty utility as they facilitate rapid messaging between the brand and the customer. Let us think about Microsoft’s Cortana, Amazon’s Alexa, and Apple’s Siri. Aren’t these chatbots wonderful? It becomes quite interesting to learn how to create a chatbot using the Python programming language.

Fundamentally, the chatbot utilizing Python is designed and programmed to take in the data we provide and then analyze it using the complex algorithms for Artificial Intelligence. It then delivers us either a written response or a verbal one. Since these bots can learn from experiences and behavior, they can respond to a large variety of queries and commands.

Although chatbot in Python has already started to rule the tech scenario at present, chatbots had handled approximately 85% of the customer-brand interactions by 2020 as per the prediction of Gartner.

In light of the increasing popularity and adoption of chatbots in the industry, we can increase the market value by learning how to create a chatbot in Python – among the most extensively utilized programming languages globally.

So, let’s get begun!

Understanding the ChatterBot Library

ChatterBot is a Python library that is developed to provide automated responses to user inputs. It makes utilization of a combination of Machine Learning algorithms in order to generate multiple types of responses. This feature enables developers to construct chatbots using Python that can communicate with humans and provide relevant and appropriate responses. Moreover, the ML algorithms support the bot to improve its performance with experience.

Another amazing feature of the ChatterBot library is its language independence. The library is developed in such a manner that makes it possible to train the bot in more than one programming language.

Understanding the working of the ChatterBot library

When a user inserts a particular input in the chatbot (designed on ChatterBot), the bot saves the input and the response for any future usage. This information (of gathered experiences) allows the chatbot to generate automated responses every time a new input is fed into it.

The program picks the most appropriate response from the nearest statement that matches the input and then delivers a response from the already known choice of statements and responses. Over time, as the chatbot indulges in more communications, the precision of reply progresses.

Creating a Chatbot using Python

We will follow a step-by-step approach and break down the procedure of creating a Python chat.

We will begin building a Python chatbot by importing all the required packages and modules necessary for the project. We will also initialize different variables that we want to use in it. Moreover, we will also be dealing with text data, so we have to perform data preprocessing on the dataset before designing an ML model.

This is where tokenizing supports text data – it converts the large text dataset into smaller, readable chunks (such as words). Once this process is complete, we can go for lemmatization to transform a word into its lemma form. Then it generates a pickle file in order to store the objects of Python that are utilized to predict the responses of the bot.

Another major section of the chatbot development procedure is developing the training and testing datasets.

Now that we have understood the fundamental concepts of chatbot development we need in Python, let us start with the actual process!

Preparing the Dependencies

The initial step to create a chatbot in Python using the ChatterBot library is to install the library in the system. We can also use a new Python virtual environment for the library installation as a good practice. We can install the library using the pip installer with the help of the following command in a Command prompt or Python terminal:

Syntax:

We can also install the latest development version of the ChatterBot library directly from GitHub. For this, we will have to use the following command:

Syntax:

If some of us would like to upgrade the library, we can use the following command

Syntax:

Now that the setup is ready, we can move on to the next step in order to create a chatbot using the Python programming language.

Importing the Classes

The second step in the Python chatbot development procedure is to import the required classes.

Let us consider the following snippet of code to understand the same.

File: my_chatbot.py

Explanation:

In the above snippet of code, we have imported two classes – ChatBot from chatterbot and ListTrainer from chatterbot.trainers.

Creating and Training the Chatbot

The next step is to create a chatbot using an instance of the class “ChatBot” and train the bot in order to improve its performance. Training the bot ensures that it has enough knowledge, to begin with, particular replies to particular input statements.

Let us consider the following snippet of code for the same.

File: my_chatbot.py

Explanation:

In the above snippet of code, we have defined a variable that is an instance of the class “ChatBot”. We have included various parameters within the class. The first parameter, ‘name’, represents the name of the Python chatbot. Another parameter called ‘read_only’ accepts a Boolean value that disables (TRUE) or enables (FALSE) the ability of the bot to learn after the training. We have also included another parameter named ‘logic_adapters’ that specifies the adapters utilized to train the chatbot.

While the ‘chatterbot.logic.MathematicalEvaluation’ helps the chatbot solve mathematics problems, the ` helps it select the perfect match from the list of responses already provided.

Since we have to provide a list of responses, we can perform it by specifying the lists of strings that we can use to train the Python chatbot and find the perfect match for a certain query. Let us consider the following example of responses we can train the chatbot using Python to learn.

File: my_chatbot.py

Explanation:

In the above snippet of code, we have defined some list of responses in order to train the chatbot. We can also create and train the chatbot by simple typing an instance of “ListTrainer” and providing it with a list of strings as shown below:

File: my_chatbot.py

Explanation:

In the above snippet of code, we have created an instance of the ListTrainer class and used the for-loop to iterate through each item present in the lists of responses.

Now, the Python chatbot is ready to communicate.

Communicating with the Python chatbot

We can use the get_response() function in order to interact with the Python chatbot. Let us consider the following execution of the program to understand it.

Output:

# starting a conversation >>> print(myBot.get_response(“Hi, there!”)) Hi >>> print(myBot.get_response(“What’s your name?”)) I’m Sakura. Ask me a math question, please. >>> print(myBot.get_response(“Do you know Pythagorean theorem”)) a squared plus b squared equals c squared. >>> print(myBot.get_response(“Tell me the formula of law of cosines”)) c**2 = a**2 + b**2 – 2*a*b*cos(gamma)

Explanation:

The above execution of the program tells us that we have successfully created a chatbot in Python using the chatterbot library. However, it is also necessary to understand that the chatbot using Python might not know how to answer all the queries. Since its knowledge and training are still very limited, we have to provide it time and give more training data to train it further.

Training the Python Chatbot using a Corpus of Data

As we move to the final step of creating a chatbot in Python, we can utilize a present corpus of data to train the Python chatbot even further.

Let us consider the following example of training the Python chatbot with a corpus of data given by the bot itself.

File: my_chatbot.py

Explanation:

In the above snippet of code, we have imported the ChatterBotCorpusTrainer class from the chatterbot.trainers module. We created an instance of the class for the chatbot and set the training language to English.

Moreover, from the last statement, we can observe that the ChatterBot library provides this functionality in multiple languages. Thus, we can also specify a subset of a corpus in a language we would prefer. Hence, our chatbot in Python has been created successfully.

A complete code for the Python chatbot project is shown below.

Complete Project Code

File: my_chatbot.py

Next TopicHow to Convert float to int in Python

Chatbot Python has gained widespread attention from both technology and business sectors in the last few years. These smart robots are so capable of imitating natural human languages and talking to humans that companies in the various industrial sectors accept them. They have all harnessed this fun utility to drive business advantages, from, e.g., the digital commerce sector to healthcare institutions.

Đây Là Cơ Hội Làm Giàu Cho Thế Hệ Mới: AI Agency

Why should you make a chatbot for your website?

Conversational bots are more than a fad, and chatbot makers develop them with specific purposes in mind. For example, chatbots can:

Make customers happy by answering their questions faster
Conduct multiple real-time conversations at once
Increase your sales by 67%
Make your website more interactive, engaging, and credible
Offer better user experiences on mobile devices
Can collect feedback with up to 90% of the response rate, or recover abandoned shopping carts
Help us explore the possibilities of conversational interfaces

They are essential for businesses such as ecommerce stores. A chatbot can single-handedly resolve 69% of customer queries from start to finish. This can translate to a 30% reduction in your customer service costs.

Your own custom chatbot gives you:

Advanced automations based on any action on your website
Deeper integration with your technological stack
Personalized customer experiences for your audience
More control over conversation flows

It’s not surprising that recently there’s been a 160% increase in client interest around implementing bots. But how to make an AI chatbot?

Coding a chatbot that utilizes machine learning technology can be a challenge. Especially if you are doing it in-house and start from scratch. Natural language processing (NLP) and artificial intelligence algorithms are the hardest part of advanced chatbot development. And they increase the development cost exponentially.

That’s why it is easier to use an AI chatbot solution powered by a third-party platform. Companies such as Tidio can leverage the power of millions of real-life conversations to train their intent recognition systems. And with a dataset based on typical interactions between customers and businesses, it is much easier to create virtual assistants in minutes.

Read more: Discover all you need to know about customer service chatbots, including their benefits, best tools, and inspiring use cases.

Now—

Let’s start our chatbot tutorial and learn how to create one with a chatbot building platform.

Conclusion

Chatbot Python development may be rewarding and exciting. Using the ChatterBot library and the right strategy, you can create chatbots for consumers that are natural and relevant. By mastering the power of Python’s chatbot-building capabilities, it is possible to realize the full potential of this artificial intelligence technology and enhance user experiences across a variety of domains. Simplilearn’s postgraduate program in Machine Learning and AI, in collaboration with Purdue University and IBM will help you learn in-demand skills such as deep learning, reinforcement learning, NLP, computer vision, generative AI, explainable AI, and many more.

Build Talking AI ChatBot with Text-to-Speech using Python!

FAQs

Can Python be used for a chatbot?

Yes, because of its simplicity, extensive library and ability to process languages, Python has become the preferred language for building chatbots.

Which language is best for a chatbot?

Python is one of the best languages for building chatbots because of its ease of use, large libraries and high community support.

Is it easy to learn chatbots?

What is the smartest chatbot?

Some of the best chatbots available include Microsoft XiaoIce, Google Meena, and OpenAI’s GPT 3. These chatbots employ cutting-edge artificial intelligence techniques that mimic human responses.

Which algorithms are used for chatbots?

Depending on their application and intended usage, chatbots rely on various algorithms, including the rule-based system, TFIDF, cosine similarity, sequence-to-sequence model, and transformers.

What is a chatbot in Python summary?

A Python chatbot is an artificial intelligence-based program that mimics human speech. Python is an effective and simple programming language for building chatbots and frameworks like ChatterBot.

Prerequisites

Before you get started, make sure that you have a Python version available that works for this ChatterBot project. What version of Python you need depends on your operating system:

You need to use a Python version below 3.8 to successfully work with the recommended version of ChatterBot in this tutorial. You can install Python 3.7.9 using
pyenv-win
.

You should be able to run the project on Ubuntu Linux with a variety of Python versions. However, if you bump into any issues, then you can try to install Python 3.7.9, for example using
pyenv
.

You can run the project with a variety of Python versions. The chatbot was built and tested with Python 3.10.7 but should also run with older Python versions.

If you’ve installed the right Python version for your operating system, then you’re ready to get started. You’ll touch on a handful of Python concepts while working through the tutorial:

Conditional statements
while
loops for iteration
Lists and tuples
Python functions
Substring checks and substring replacement
File input/output
Python comprehensions and generator expressions
Regular expressions (regex) using
re

If you’re comfortable with these concepts, then you’ll probably be comfortable writing the code for this tutorial. If you don’t have all of the prerequisite knowledge before starting this tutorial, that’s okay! In fact, you might learn more by going ahead and getting started. You can always stop and review the resources linked here if you get stuck.

Hướng dẫn cách làm 1 con ChatGPT của riêng bạn, lấy kiến thức từ file bạn upload lên

Limitations With A Chatbot

While chatbots have come a long way, there are still some limitations to be aware of:

Lack of semantic understanding: Chatbots may require assistance comprehending the discourse, which could result in misinterpretation or incorrect responses.
Dependency on training data: The caliber and volume of training data greatly impact chatterbotpython performance. There may be a need for more accurate or biased training data, which can result in incorrect responses.
Handling complicated queries: Chatbots could encounter questions beyond simple pattern matching and call for greater comprehension or deductive reasoning.

Demo

At the end of this tutorial, you’ll have a command-line chatbot that can respond to your inputs with semi-meaningful replies:

You’ll achieve that by preparing WhatsApp chat data and using it to train the chatbot. Beyond learning from your automated training, the chatbot will improve over time as it gets more exposure to questions and replies from user interactions.

How To Connect OpenAI To WhatsApp (Python Tutorial)

Step 3: Export a WhatsApp Chat

At the end of this step, you’ll have downloaded a TXT file that contains the chat history of a WhatsApp conversation. If you don’t have a WhatsApp account or don’t want to work with your own conversational data, then you can download a sample chat export below:

If you’re going to work with the provided chat history sample, you can skip to the next section, where you’ll clean your chat export.

To export the history of a conversation that you’ve had on WhatsApp, you need to open the conversation on your phone. Once you’re on the conversation screen, you can access the export menu:

Click on the three dots (⋮) in the top right corner to open the main menu.
Choose More to bring up additional menu options.
Select Export chat to create a TXT export of your conversation.

In the stitched-together screenshots below, you can see the three consecutive steps numbered and outlined in red:

Once you’ve clicked on Export chat, you need to decide whether or not to include media, such as photos or audio messages. Because your chatbot is only dealing with text, select WITHOUT MEDIA. Then, you can declare where you’d like to send the file.

Again, you can see an example of these next steps in two stitched-together WhatsApp screenshots with red numbers and outlines below:

In this example, you saved the chat export file to a Google Drive folder named Chat exports. You’ll have to set up that folder in your Google Drive before you can select it as an option. Of course, you don’t need to use Google Drive. As long as you save or send your chat export file so that you can access to it on your computer, you’re good to go.

Once that’s done, switch back to your computer. Find the file that you saved, and download it to your machine.

Specifically, you should save the file to the folder that also contains
bot.py
and rename it
chat.txt
. Then, open it with your favorite text editor to inspect the data that you received:

9/15/22, 14:50 - Messages and calls are end-to-end encrypted. ⮑ No one outside of this chat, not even WhatsApp, can read ⮑ or listen to them. Tap to learn more. 9/15/22, 14:49 - Philipp: Hi Martin, Philipp here! 9/15/22, 14:50 - Philipp: I'm ready to talk about plants! 9/15/22, 14:51 - Martin: Oh that's great! 9/15/22, 14:52 - Martin: I've been waiting for a good convo about ⮑ plants for a long time 9/15/22, 14:52 - Philipp: We all have. 9/15/22, 14:52 - Martin: Did you know they need water to grow? ...

If you remember how ChatterBot handles training data, then you’ll see that the format isn’t ideal to use for training.

ChatterBot uses complete lines as messages when a chatbot replies to a user message. In the case of this chat export, it would therefore include all the message metadata. That means your friendly pot would be studying the dates, times, and usernames! Not exactly great conversation fertilizer.

To avoid this problem, you’ll clean the chat export data before using it to train your chatbot.

Limitations With A Chatbot

While chatbots have come a long way, there are still some limitations to be aware of:

Lack of semantic understanding: Chatbots may require assistance comprehending the discourse, which could result in misinterpretation or incorrect responses.
Dependency on training data: The caliber and volume of training data greatly impact chatterbotpython performance. There may be a need for more accurate or biased training data, which can result in incorrect responses.
Handling complicated queries: Chatbots could encounter questions beyond simple pattern matching and call for greater comprehension or deductive reasoning.

ChatGPT hoạt động như thế nào? Tự viết ChatGPT chạy local SIÊU DỄ

Conclusion

Read on!

This article was published as a part of the Data Science Blogathon.

Some were programmed and manufactured to transmit spam messages to wreak havoc.

Also read: Python Tutorial for Beginners Overview

Here are type of bots:

Building libraries should be avoided if you want to understand how a chatbot operates in Python thoroughly. In 1994, Michael Mauldin was the first to coin the term “chatterbot” as Julia.

Also read: A Comprehensive Guide to Python Automation.

ChatBot examples include Apple’s Siri, Amazon’s Alexa, Google Assistant, and Microsoft’s Cortana. Following is the step-by-step guide with regular expressions for building ChatBot using Python:

First, we will ask Username

print("BOT: What is your name?") user_name = input()

Output

What is your name?Testing

name = “Bot Number 286” monsoon = “rainy” mood = “Smiley” resp = { “what’s your name?”: [ “They call me {0}”.format(name), “I usually go by {0}”.format(name), “My name is the {0}”.format(name) ], “what’s today’s weather?”: [ “The weather is {0}”.format(monsoon), “It’s {0} today”.format(monsoon)], “how are you?”: [ “I am feeling {0}”.format(mood), “{0}! How about you?”.format(mood), “I am {0}! How about yourself?”.format(mood), ], “”: [ “Hey! Are you there?”, “What do you mean by these?”, ], “default”: [ “This is a default message”] }

Library random imported to choose the response. It will select the answer by bot randomly instead of the same act.

import random def res(message): if message in resp: bot286_message = random.choice(resp[message]) else: bot286_message = random.choice(resp[“default”]) return bot286_message

def real(xtext): if “name” in xtext: ytext = “what’s your name?” elif “monsoon” in xtext: ytext = “what’s today’s weather?” elif “how are” in xtext: ytext = “how are you?” else: ytext = “” return ytext

def send_message(message): print((message)) response = res(message) print((response))

while 1: my_input = input() my_input = my_input.lower() related_text = real(my_input) send_message(related_text) if my_input == “exit” or my_input == “stop”: break

Another example is installing a library chatterbot

Natural language Processing (NLP) is a necessary part of artificial intelligence that employs natural language to facilitate human-machine interaction.

Use the following commands in Terminal in Anaconda prompt.

pip install chatterbot pip install chatterbot_corpus

You can also install chatterbot from its source: https://github.com/gunthercox/ChatterBot/archive/master.zip

After installing, unzip, the file and open Terminal, and type in:

cd chatter_bot_master_directory.

Finally, type python setup.py install.

pip install -U spacy

For downloading model “en_core_web_sm”

import spacy from spacy.cli.download import download download(model=”en_core_web_sm”)

pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz

import spacy nlp = spacy.blank(“en”) doc = nlp(“This is a sentence.”)

You will need two further classes ChatBot and ListTrainers

from chatterbot import ChatBot from chatterbot.trainers import ListTrainer

1st Example

from chatterbot import ChatBot from chatterbot.trainers import ListTrainer # Create a new chatbot named Charlie chatbot = ChatBot(‘name’) trainer = ListTrainer(chatbot) trainer.train([ “Hi, can I help you?”, “Sure, I’d like to book a flight to Iceland.”, “Your flight has been booked.” ]) # Get a response to the input text ‘I would like to book a flight.’ response = chatbot.get_response(‘I would like to book a flight.’) print(response)

2nd Example by ChatBot

Bot286 = ChatBot(name=’PyBot’) talk = [‘hi buddy!’, ‘hi!’, ‘how do you do?’, ‘how are you?’, ‘i’m fine.’, ‘fine, you?’, ‘always smiling.’] list_trainer = ListTrainer(Bot286)for item in (talk): list_trainer.train(item) >>>print(my_bot.get_response(“hi”)) how do you do? from chatterbot.trainers import ChatterBotCorpusTrainercorpus_trainer = ChatterBotCorpusTrainer(Bot286) corpus_trainer.train(‘chatterbot.corpus.english’)

Here are the benefits of Bots:

Also read: Top 30+ Python Projects: Beginner to Advanced 2024

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I want to made a chatbot can u plzz help me..

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It looks like you want to create your own chatbot. That’s an excellent idea. Today, everyone can build chatbots with visual drag and drop bot editors. You don’t need coding skills or any other superpowers.

And yet…

Most people feel intimidated by the process. It looks like a complex task, and it is unclear how to make a chatbot or where to start.

Well, you can start right here and now.

We will show you that making your own chatbot is actually very fast and easy. And did we mention that it is going to be fun too?

In this article, we will show you the no-code chatbot creation process explained step by step:

Step 1: Identify the purpose of your chatbot
Step 2: Decide where you want it to appear
Step 3: Choose the chatbot platform
Step 4: Design the chatbot conversation in a chatbot editor
Step 5: Test your chatbot
Step 6: Train your chatbot
Step 7: Collect feedback from users
Step 8: Monitor chatbot analytics to improve it

Plus, the advantages of developing chatbots on your own, tips and rules for making chatbots from scratch, and chatbot-building FAQ

Can’t keep with customer queries? Accelerate your support with Chatbots

To learn more about Tidio’s chatbot features and benefits, visit our page dedicated to chatbots.

We also have more in-depth articles if you are interested in making chatbots for specific channels:

How to Make a Facebook Messenger Chatbot [Facebook Bot Examples]
Chatbot Design: Best Practices & 12 Inspiring Tips
WhatsApp Chatbot for Businesses [A Quick Guide]
Chatbot Pricing: How Much Does a Chatbot Cost? [2023]

Project Overview

The ChatterBot library combines language corpora, text processing, machine learning algorithms, and data storage and retrieval to allow you to build flexible chatbots.

You can build an industry-specific chatbot by training it with relevant data. Additionally, the chatbot will remember user responses and continue building its internal graph structure to improve the responses that it can give.

Attention: While ChatterBot is still a popular open source solution for building a chatbot in Python, it hasn’t been actively maintained for a while and has therefore accumulated a significant number of issues.

There are multiple forks of the project that implement fixes and updates to the existing codebase, but you’ll have to personally pick the fork that implements the solution you’re looking for and then install it directly from GitHub. A fork might also come with additional installation instructions.

To get started, however, you won’t use a fork. Instead, you’ll use a specific pinned version of the library, as distributed on PyPI. You’ll find more information about installing ChatterBot in step one.

In this tutorial, you’ll start with an untrained chatbot that’ll showcase how quickly you can create an interactive chatbot using Python’s ChatterBot. You’ll also notice how small the vocabulary of an untrained chatbot is.

Next, you’ll learn how you can train such a chatbot and check on the slightly improved results. The more plentiful and high-quality your training data is, the better your chatbot’s responses will be.

Therefore, you’ll either fetch the conversation history of one of your WhatsApp chats or use the provided
chat.txt
file that you can download here:

It’s rare that input data comes exactly in the form that you need it, so you’ll clean the chat export data to get it into a useful input format. This process will show you some tools you can use for data cleaning, which may help you prepare other input data to feed to your chatbot.

After data cleaning, you’ll retrain your chatbot and give it another spin to experience the improved performance.

When you work through this process from start to finish, you’ll get a good idea of how you can build and train a Python chatbot with the ChatterBot library so that it can provide an interactive experience with relevant replies.

ChatGPT / BARD API - Build a FREE Chatbot in Python w/ Google Colab — ChatGPT / BARD API – Build a FREE Chatbot in Python w/ Google Colab

Step 4: Clean Your Chat Export

In this step, you’ll clean the WhatsApp chat export data so that you can use it as input to train your chatbot on an industry-specific topic. In this example, the topic will be … houseplants!

Most data that you’ll use to train your chatbot will require some kind of cleaning before it can produce useful results. It’s just like the old saying goes:

Garbage in, garbage out (Source)

Take some time to explore the data that you’re working with and to identify potential issues:

9/15/22, 14:50 - Messages and calls are end-to-end encrypted. ⮑ No one outside of this chat, not even WhatsApp, can read ⮑ or listen to them. Tap to learn more. ... 9/15/22, 14:50 - Philipp: I'm ready to talk about plants! ... 9/16/22, 06:34 - Martin: ...

For example, you may notice that the first line of the provided chat export isn’t part of the conversation. Also, each actual message starts with metadata that includes a date, a time, and the username of the message sender.

If you scroll further down the conversation file, you’ll find lines that aren’t real messages. Because you didn’t include media files in the chat export, WhatsApp replaced these files with the text .

All of this data would interfere with the output of your chatbot and would certainly make it sound much less conversational. Therefore, it’s a good idea to remove this data.

Open up a new Python file to preprocess your data before handing it to ChatterBot for training. Start by reading in the file content and removing the chat metadata:

1# cleaner.py 2 3import re 4 5def remove_chat_metadata(chat_export_file): 6 date_time = r"(\d+\/\d+\/\d+,\s\d+:\d+)" # e.g. "9/16/22, 06:34" 7 dash_whitespace = r"\s-\s" # " - " 8 username = r"([\w\s]+)" # e.g. "Martin" 9 metadata_end = r":\s" # ": " 10 pattern = date_time + dash_whitespace + username + metadata_end 11 12 with open(chat_export_file, "r") as corpus_file: 13 content = corpus_file.read() 14 cleaned_corpus = re.sub(pattern, "", content) 15 return tuple(cleaned_corpus.split("\n")) 16 17if __name__ == "__main__": 18 print(remove_chat_metadata("chat.txt"))

This function removes conversation-irrelevant message metadata from the chat export file using the built-in
re
module, which allows you to work with regular expressions:

Line 3 imports

re
.
Lines 6 to 9 define multiple regex patterns. Constructing multiple patterns helps you keep track of what you’re matching and gives you the flexibility to use the separate capturing groups to apply further preprocessing later on. For example, with access to

username
, you could chunk conversations by merging messages sent consecutively by the same user.
Line 10 concatenates the regex patterns that you defined in lines 6 to 9 into a single pattern. The complete pattern matches all the metadata that you want to remove.
Lines 12 and 13 open the chat export file and read the data into memory.
Line 14 uses

re.sub()
to replace each occurrence of the pattern that you defined in
pattern
with an empty string (
""
), effectively deleting it from the string.
Line 15 first splits the file content string into list items using

.split("\n")
. This breaks up
cleaned_corpus
into a list where each line represents a separate item. Then, you convert this list into a tuple and return it from
remove_chat_metadata()
.
Lines 17 and 18 use Python’s name-main idiom to call

remove_chat_metadata()
with
"chat.txt"
as its argument, so that you can inspect the output when you run the script.

Eventually, you’ll use
cleaner
as a module and import the functionality directly into
bot.py
. But while you’re developing the script, it’s helpful to inspect intermediate outputs, for example with a
print()
call, as shown in line 18.

Note: It’s a good idea to run your script often while you’re developing the code. As an alternative to printing the output, you could use
breakpoint()
to inspect your code with
pdb
. If you use a debugger such as
pdb
, then you can interact with the code objects rather than just printing a static representation.

After removing the message metadata from each line, you also want to remove a few complete lines that aren’t relevant for the conversation. To do this, create a second function in your data cleaning script:

1# cleaner.py 2 3# ... 4 5def remove_non_message_text(export_text_lines): 6 messages = export_text_lines[1:-1] 7 8 filter_out_msgs = (" ",) 9 return tuple((msg for msg in messages if msg not in filter_out_msgs)) 10 11if __name__ == "__main__": 12 message_corpus = remove_chat_metadata("chat.txt") 13 cleaned_corpus = remove_non_message_text(message_corpus) 14 print(cleaned_corpus)

In
remove_non_message_text()
, you’ve written code that allows you to remove irrelevant lines from the conversation corpus:

Line 6 removes the first introduction line, which every WhatsApp chat export comes with, as well as the empty line at the end of the file.
Line 8 creates a tuple where you can define what strings you want to exclude from the data that’ll make it to training. For now, it only contains one string, but if you wanted to remove other content as well, you could quickly add more strings to this tuple as items.
Line 9 filters

messages
for the strings defined in
filter_out_msgs
using a generator expression that you convert to a tuple before returning it.

Finally, you’ve also changed lines 12 to 14. You now collect the return value of the first function call in the variable
message_corpus
, then use it as an argument to
remove_non_message_text()
. You save the result of that function call to
cleaned_corpus
and print that value to your console on line 14.

Because you want to treat
cleaner
as a module and run the cleaning code in
bot.py
, it’s best to now refactor the code in the name-main idiom into a main function that you can then import and call in
bot.py
:

1# cleaner.py 2 3import re 4 5def clean_corpus(chat_export_file): 6 message_corpus = remove_chat_metadata(chat_export_file) 7 cleaned_corpus = remove_non_message_text(message_corpus) 8 return cleaned_corpus 9 10# ... 11 12# Deleted: if __name__ == "__main__":

You refactor your code by moving the function calls from the name-main idiom into a dedicated function,
clean_corpus()
, that you define toward the top of the file. In line 6, you replace
"chat.txt"
with the parameter
chat_export_file
to make it more general. You’ll provide the filename when calling the function. The
clean_corpus()
function returns the cleaned corpus, which you can use to train your chatbot.

After creating your cleaning module, you can now head back over to
bot.py
and integrate the code into your pipeline.

Conclusion

Congratulations, you’ve built a Python chatbot using the ChatterBot library! Your chatbot isn’t a smarty plant just yet, but everyone has to start somewhere. You already helped it grow by training the chatbot with preprocessed conversation data from a WhatsApp chat export.

In this tutorial, you learned how to:

Build a command-line chatbot with ChatterBot
Train a chatbot to customize its responses
Export your WhatsApp chat history
Perform data cleaning on the chat export using regular expressions
Retrain the chatbot with industry-specific data

Because the industry-specific chat data in the provided WhatsApp chat export focused on houseplants,
Chatpot
now has some opinions on houseplant care. It’ll readily share them with you if you ask about it—or really, when you ask about anything.

With big data comes big results! You can imagine that training your chatbot with more input data, particularly more relevant data, will produce better results.

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Nowadays almost 30 percent of the tasks are fulfilled by chatbots. Companies use the chatbots to provide services like customer support, generating information, etc. With examples like Siri, Alexa it becomes clear how a chatbot can make a difference in our daily lives. In this article, we will learn how to make a chatbot in python using the ChatterBot library which implements various machine learning algorithms to generate responses. Following are the topics discussed in this blog:

A chatbot also known as a chatterbot, bot, artificial agent, etc is basically software program driven by artificial intelligence which serves the purpose of making a conversation with the user by texts or by speech. Famous examples include Siri, Alexa, etc.

These chatbots are inclined towards performing a specific task for the user. Chatbots often perform tasks like making a transaction, booking a hotel, form submissions, etc. The possibilities with a chatbot are endless with the technological advancements in the domain of artificial intelligence.

Almost 30 percent of the tasks are performed by the chatbots in any company. Companies employ these chatbots for services like customer support, to deliver information, etc. Although the chatbots have come so far down the line, the journey started from a very basic performance. Let’s take a look at the evolution of chatbots over the last few decades.

It started in 1966 when Joseph Weizenbaum made a natural language conversational program that featured a dialog between a user and a computer program. With this great breakthrough came the new age chatbot technology that has taken an enormous leap throughout the decades.

Traditional Bots	Current Bots	Future Bots
System Driven	Driven by back-and-forth communication	Communication at multiple-levels
Automation based	The automation is at the task level	Automation at the service level
Minimal Functionality	Maintains system context	Ability to maintain task, system and people context
Maintained only system context	Maintains task context as well	Introduction to master bots and eventually a bot OS as well.

With increasing advancements, there also comes a point where it becomes fairly difficult to work with the chatbots. Following are a few limitations we face with the chatbots.

Domain Knowledge – Since true artificial intelligence is still out of reach, it becomes difficult for any chatbot to completely fathom the conversational boundaries when it comes to conversing with a human.

Personality – Not being able to respond correctly and fairly poor comprehension skills has been more than frequent errors of any chatbot, adding a personality to a chatbot is still a benchmark that seems far far away. But we are more than hopeful with the existing innovations and progress-driven approaches.

We can define the chatbots into two categories, following are the two categories of chatbots:

Rule-Based Approach – In this approach, a bot is trained according to rules. Based on this a bot can answer simple queries but sometimes fails to answer complex queries.

Self-Learning Approach – These bots follow the machine learning approach which is rather more efficient and is further divided into two more categories.

Retrieval-Based Models – In this approach, the bot retrieves the best response from a list of responses according to the user input.

Generative Models – These models often come up with answers than searching from a set of answers which makes them intelligent bots as well.

Let us try to make a chatbot from scratch using the chatterbot library in python.

ChatterBot is a library in python which generates responses to user input. It uses a number of machine learning algorithms to produce a variety of responses. It becomes easier for the users to make chatbots using the ChatterBot library with more accurate responses.

Language Independence

The design of ChatterBot is such that it allows the bot to be trained in multiple languages. On top of this, the machine learning algorithms make it easier for the bot to improve on its own using the user’s input.

ChatterBot makes it easy to create software that engages in conversation. Every time a chatbot gets the input from the user, it saves the input and the response which helps the chatbot with no initial knowledge to evolve using the collected responses.

With increased responses, the accuracy of the chatbot also increases. The program selects the closest matching response from the closest matching statement that matches the input, it then chooses the response from the known selection of statements for that response.

How To Install ChatterBot In Python?

Run the following command in the terminal or in the command prompt to install ChatterBot in python.

pip install chatterbot

Chatterbot comes with a data utility module that can be used to train the chatbots. At the moment there is training data for more than a dozen languages in this module. Take a look at the data files here.

Following is a simple example to get started with ChatterBot in python.

from chatterbot import chatbot from chatterbot.trainers import ListTrainer #creating a new chatbot chatbot = Chatbot(‘Edureka’) trainer = ListTrainer(chatbot) trainer.train([ ‘hi, can I help you find a course’, ‘sure I’d love to find you a course’, ‘your course have been selected’]) #getting a response from the chatbot response = chatbot.get_response(“I want a course”) print(response)

In this example, we get a response from the chatbot according to the input that we have given. Let us try to build a rather complex flask-chatbot using the chatterbot-corpus to generate a response in a flask application.

After we are done setting up the flask app, we need to add two more directories static and templates for HTML and CSS files. Following is the code for the flask ChatterBot app.

App.py

from flask import Flask, render_template, request from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer app = Flask(__name__) english_bot = ChatBot(“Chatterbot”, storage_adapter=”chatterbot.storage.SQLStorageAdapter”) trainer = ChatterBotCorpusTrainer(english_bot) trainer.train(“chatterbot.corpus.english”) @app.route(“/”) def home(): return render_template(“index.html”) @app.route(“/get”) def get_bot_response(): userText = request.args.get(‘msg’) return str(english_bot.get_response(userText)) if __name__ == “__main__”: app.run()

index.html

Flask Chatterbot Example

Hi! I’m Chatterbot.

index.html file will have the template of the app and style.css will contain the style sheet with the CSS code. After we execute the above program we will get the output like the image shown below.

Style.css

body { font-family: Garamond; background-color: black; } h1 { color: black; margin-bottom: 0; margin-top: 0; text-align: center; font-size: 40px; } h3 { color: black; font-size: 20px; margin-top: 3px; text-align: center; } #chatbox { background-color: black; margin-left: auto; margin-right: auto; width: 40%; margin-top: 60px; } #userInput { margin-left: auto; margin-right: auto; width: 40%; margin-top: 60px; } #textInput { width: 87%; border: none; border-bottom: 3px solid #009688; font-family: monospace; font-size: 17px; } #buttonInput { padding: 3px; font-family: monospace; font-size: 17px; } .userText { color: white; font-family: monospace; font-size: 17px; text-align: right; line-height: 30px; } .userText span { background-color: #009688; padding: 10px; border-radius: 2px; } .botText { color: white; font-family: monospace; font-size: 17px; text-align: left; line-height: 30px; } .botText span { background-color: #EF5350; padding: 10px; border-radius: 2px; } #tidbit { position:absolute; bottom:0; right:0; width: 300px; }

Output:

Go to the address shown in the output, and you will get the app with the chatbot in the browser.

The chatbot will look something like this, which will have a textbox where we can give the user input, and the bot will generate a response for that statement.

In this article, we have learned how to make a chatbot in python using the ChatterBot library using the flask framework. With new-age technological advancements in the artificial intelligence and machine learning domain, we are only so far away from creating the best version of the chatbot available to mankind. Don’t be in the sidelines when that happens, to master your skills enroll in Edureka’s Python certification program and become a leader.

Also, If you wish to learn more about ChatGPT, Edureka is offering a great and informative ChatGPT Certification Training Course which will help to upskill your knowledge in the IT sector.

Have any questions? Mention them in the comments. We will get back to you as soon as possible.

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Bài viết được sự cho phép của tác giả Nguyễn Việt Hưng

What is ChatterBot Library?

Language Independence

You can use Chatterbot to create chatbots in various languages based on your target demographic.

How Does ChatterBot Library Work?

How To Install ChatterBot In Python

You can launch a terminal or command prompt. Your machine needs to be configured to run Ai Chatbot Python. To check if this is the case, run the command “python version” or “python3 Version” to ensure it returns a valid Python version.
Installing Chatterbot using the Chatbot Python Package Manager that comes with your Python program. To do this, issue the command “Pip installing chatterbot.”
This command will download and install the ChatterBot library and its dependencies.
Once setup is complete, add the following code to your Chatbot using Python script or interactive environment to include Chatterbot: Imported from Chatterbot is ChatBot.
You may now use Chatterbot to begin building your chatbot. Using the ChatterBot guide or other resources, you can learn how to set up and train a chatbot.

How to make a chatbot from scratch in 8 steps

Follow this eight-step tutorial that will guide you through the process of selecting the right chatbot provider and designing a conversational flow.

Step 1: Give your chatbot a purpose

Identify what you want your chatbot to do. The more specific you are, the better.

You can start by asking yourself a few questions:

What are you building the chatbot for? Customer support automation, improving customer experience, or lead generation? Or maybe, all of the above?
What are your most popular customer use cases? Look through your queries and outline a few examples.
What is the main feature of a chatbot that you would find helpful? Answering questions on autopilot? Routing the questions to the support team? Saving abandoned carts, or qualifying leads?

Once you have the answers, it will be much easier to identify the features and types of chatbots you’ll need.

You want to create a chatbot that will collect customers’ emails, so you can send them updates about sales and new products.

Step 2: Decide where you want it to appear

What is your main communication channel? Do your customers contact you mainly on social media or via a live chat widget on your website?

Either way, check whether the chatbot platform of your choice integrates with the tools you already use, so you can serve your customers where they are at:

Your website. The majority of chatbot building platforms offer integrations with popular website providers such as WordPress, Magento, or Shopify.
Your social media channels such as WhatsApp, Facebook Messenger, Instagram, or Telegram.
Other messaging platforms and tools you have in your stack (such as Slack).
Alternatively, check if you can configure the integration yourself via code snippet or an open API.

Many chatbot development service providers and platforms offer multiple integrations, so you can use chatbots across many channels.

Read more: Discover how to install Tidio on your website and how easy it is to launch Instagram chatbots. Also, learn more about WordPress chatbots, their benefits, and how to add them to your website.

Step 3: Choose the chatbot platform

Now that you know what chatbot variants you want to create and which channels you want to cover, it’s time to choose the provider.

You have two options: the framework or the platform.

AI frameworks. Chatbot frameworks (such as Google’s Dialogflow, IBM Watson, or Microsoft Bot) act as libraries for software developers who then build the chatbots by coding.
Chatbot platforms. They provide easy-to-use chatbot builders that enable you to create a chatbot with building blocks. They are growing in popularity because building the bots with their help is much easier and less time-consuming while providing comparable results. Not to mention that some platforms, like Tidio, provide forever-free plans!

You would like to avoid coding and hiring developers, so you go for a chatbot platform instead of an AI framework. Additionally, chatbots of this type are made for ecommerce. They can resolve typical customer service scenarios out of the box.

Once you pick your provider, it’s time to register, log in, and get to work.

Step 4: Design the chatbot conversation in a chatbot editor

You can build the conversation flow by dragging and dropping building blocks, so they create a sequence. Let’s assume you want to give a product discount to users who enter a specific landing page of your yoga accessories shop and collect their emails in return.

To start, log in and go to the bot builder. Begin with the trigger—a condition that makes the chatbot send a welcome message. If you want the chatbot to appear on a specific landing page, start with a Visitor opens a specific page node.

Then, type in the message you want to send and add a decision node with quick replies. Set messages for those who want a discount for your product and those who don’t.

Congratulations! You just designed your first chatbot.

Read more: Always adjust the tone of voice to your customers. Learn how to write chatbot scripts like a pro.

Step 5: Test your chatbot

Now it’s time to test if everything works as it should. To do that, click the Test it out button. A window will appear that will show you what the chatbot would look like for the end-user. Thanks to the preview, you can always come back to the editor and correct the flow.

Step 6: Train your chatbots

If you want to use simple chatbots based on decision tree flows, you can skip this step. If you want your bot to understand the user’s intent, you need to add an NLP trigger to your chatbot.

At Tidio, we have a Visitor says node that uses predefined data sets such as words, phrases, and questions to recognize the query and act upon it.

To train the bot, analyze your customer conversations, and find the most popular queries and frequent issues. You can do it manually, or use a word cloud generator like Free Word Generator. Then, add the words, phrases, and questions related to a chosen subject (like shipping) to the Visitor says node.

This way, you will “feed” the NLP engine, so the chatbot will recognize similar queries that can appear in future conversations. The more phrases you add, the better!

Once you “feed” the NLP with these phrases, you can train the chatbot to act upon these three intents and give better product recommendations to your customers.

Step 7: Collect feedback from users

No one will rate the effectiveness of your chatbot efforts better than your visitors and customers. The best thing you can do? Let the chatbots send an automatic customer satisfaction survey, asking the users whether they are satisfied with the chatbot interaction. Based on the results, you can see what works and where the areas for improvement are.

Step 8: Monitor chatbot analytics to improve it

Last, but not least, commit to monitoring your chatbot activity. This will give you the chance to spot types of chatbots that don’t provide the best customer experience and don’t work for your visitors. To make it easier, Tidio lets you monitor the drop-off rate at specific message nodes:

Read more: Discover inspiring chatbot examples.

Chatbot là gì?

Trước khi thò tay vào hì hục code, ta cần hiểu chatbot là gì đã?

Chatbot là một chương trình thực hiện cuộc hội thoại qua phương pháp gửi nhận văn bản hoặc các object như hình ảnh, file, … Chú ý Chatbot không nhất thiết là phải thông minh, là phải dùng trí tuệ nhân tạo, etc …

Có bao giờ sắp đến giao thừa hay một dịp mà bạn muốn nhắn tin cho nhiều người vào 12h đêm mà bạn không thể dậy được, hoặc bạn quá lười để làm một việc lặp đi lặp lại? Câu trả lời là viết một chatbot và hẹn giờ cho nó.

Ultimate Guide to Building Chatbots to Chat with Your Data | AI Chatbot Tutorial

Viết chatbot

Trong bài viết này mình sử dụng 2 thư viện có sẵn trên mạng là fbchat, schedule do đó bạn cần tạo virtualenv trước tiên, sau đó dùng pip để cài 2 lib trên rồi tạo một file code python tùy ý, ở đây mình dùng
chatbot.py
.

Đầu tiên, import những lib mình cần 🎉

import logging import os import time from threading import Thread from fbchat import Client from fbchat.models import Message, ThreadType import schedule

Sau đó tạo một class
Bot
kế thừa
Client
:

class Bot(Client):

Tạo 1 function trong class
Bot
để thực hiện gửi tin nhắn, dưới đây là code của mình:

class Bot(Client): def do_something(self): #Đổi tên function cho phù hợp logging.basicConfig(level=logging.INFO) lst_id = […] # List chứa fb id của những người bạn muốn gửi for user_id in lst_id: self.send(Message(text=”Chúc mừng năm mới”), thread_id=user_id, thread_type=ThreadType.USER) self.sendLocalImage(‘/home/dosontung007/Pictures/wallpaper.png’, message=Message(text=’Chúc mừng năm mới’), thread_id=user_id, thread_type=ThreadType.USER) logging.info(‘Sent success to %s’ % str(user_id))

Và để nhận được tin nhắn từ những người gửi cho mình cho mình , ta viết function
onMessage
trong class
Bot
và xử lí các tin nhắn đó:

def onMessage(self, message_object, author_id, thread_id, thread_type, **kwargs): lst_msg = list(‘Chúc mừng năm mới’) if author_id != self.uid and message_object.text in lst_msg: self.send(Message(text=’Năm mới chúc …..’), thread_id=author_id, thread_type=thread_type)

Tham khảo thêm tại https://fbchat.readthedocs.io/en/master/

Job thực hiện việc gửi tin nhắn trong này đó là:

Bot(os.environ['USERNAME_'], os.environ['PASSWORD']).do_something()

Class
Bot
kế thừa
Client
, khi tạo một Bot object, ta cần truyền 2 tham số là username và password của Facebook của bạn. Do đó bạn cần set value cho 2 var
USERNAME_
và
PASSWORD
bằng câu lệnh
export var=value
trong bash trước khi chạy chương trình (vì ta không muốn ghi trực tiếp password vào file code – lộ mật khẩu nếu up lên GitHub). Lưu ý
USERNAME_
chứ không phải
USERNAME
.

Bây giờ còn một công việc duy nhất là hẹn giờ cho job làm việc thôi!

def job(): Bot(os.environ[‘USERNAME_’], os.environ[‘PASSWORD’]).do_something() def send_msg(): schedule.every().day.at(’00:00′).do(job_that_executes_once)) while True: schedule.run_pending() time.sleep(1)

Thay đổi
00:00
bằng thời gian mà bạn muốn hẹn giờ.

Để nhận được message, ta sử dụng function
listen
từ
Client
, về cơ bản
listen
khi chạy sẽ truyền các arguments vào
onMessage
mỗi lần Facebook bạn có event mới (VD: có người nhắn cho bạn, bạn nhắn cho người khác hoặc tin nhắn trong nhóm, …):

def reply_msg(): Bot(os.environ[‘USERNAME_’], os.environ[‘PASSWORD’]).listen()

Ở function main, mình sử dụng lib threading để chạy song song 2 job là reply_msg và send_msg :

def main(): Thread(target=send_msg).start() Thread(target=reply_msg).start()

Cuối cùng cũng xong 🎉.Sau tất cả, đây là một con chatbot hoàn chỉnh :

import logging import os import time from threading import Thread from fbchat import Client from fbchat.models import Message, ThreadType import schedule class Bot(Client): def onMessage(self, message_object, author_id, thread_id, thread_type, **kwargs): lst_msg = list(‘Chúc mừng năm mới’) if author_id != self.uid and message_object.text in lst_msg: self.send(Message(text=’Năm mới chúc …..’), thread_id=author_id, thread_type=ThreadType.USER) def do_something(self): logging.basicConfig(level=logging.INFO) user_ids = [‘100012610305665’] for user_id in user_ids: self.send(Message(text=”Chúc mừng năm mới”), thread_id=user_id, thread_type=ThreadType.USER) self.sendLocalImage(‘/home/dosontung007/Pictures/wallpaper.png’, message=Message(text=’Chúc mừng năm mới’), thread_id=user_id, thread_type=ThreadType.USER) logging.info(‘Sent success to %s’ % “100012610305665”) def job_that_executes_once(): Bot(os.environ[‘USERNAME_’], os.environ[‘PASSWORD’]).something() return schedule.CancelJob def reply_msg(): Bot(os.environ[‘USERNAME_’], os.environ[‘PASSWORD’]).listen() def send_msg(): schedule.every().day.at(’00:00′).do(job_that_executes_once)) while True: schedule.run_pending() time.sleep(1) def main(): thread1 = Thread(target=send_msg) thread2 = Thread(target=reply_msg) thread1.start() thread2.start() thread1.join() thread2.join() if __name__ == ‘__main__’: main()

Bây giờ export username, password rồi chạy thôi. Và đây là thành quả:

Nếu bạn muốn chạy luôn mà không cần hẹn giờ thì chỉ cần xoá function
job_that_executes_once
và thay function
send_msg
bằng:

def send_msg(): Bot(os.environ[‘USERNAME_’],os.environ[‘PASSWORD’]).do_something()

HẾT.

Bài viết gốc được đăng tải tại pp.pymi.vn

How to Make a Chatbot in Python?

Preparing the Dependencies

The right dependencies need to be established before we can create a chatbot. Python and a ChatterBot library must be installed on our machine. With Pip, the Chatbot Python package manager, we can install ChatterBot.

Creating and Training the Chatbot

Once the dependence has been established, we can build and train our chatbot. We will import the ChatterBot module and start a new Chatbot Python instance. If so, we might incorporate the dataset into our chatbot’s design or provide it with unique chat data.

Communicating with the Python chatbot

We can send a message and get a response once the chatbot Python has been trained. Creating a function that analyses user input and uses the chatbot’s knowledge store to produce appropriate responses will be necessary.

Complete Project Code

We will give you a full project code outlining every step and enabling you to start. This code can be modified to suit your unique requirements and used as the foundation for a chatbot.

3 Ways to Make a Custom AI Assistant | RAG, Tools, & Fine-tuning

How to Make a Chatbot in Python?

Preparing the Dependencies

Creating and Training the Chatbot

Communicating with the Python chatbot

Complete Project Code

We will give you a full project code outlining every step and enabling you to start. This code can be modified to suit your unique requirements and used as the foundation for a chatbot.

FAQ: How to build a chatbot

Are you still afraid that designing your own conversational bot is too much? Just take a deep breath and read on. Here are some of the most frequently asked questions about creating chatbots. It should give you some more insights into the chatbot creation process.

To create an AI chatbot you need a conversation database to train your conversational AI model. But you can also try using one of the chatbot development platforms powered by AI technology. Tidio is one of the most popular solutions that offers tools for building chatbots that recognize user intent for free. It also allows you to train your chatbots by uploading a list of conversations and text messages.If you don’t want to use a no-code chatbot development platform, there are many other options available. Professional developers interested in machine learning should consider using Dialogflow API (owned by Google) as their primary framework.

Creating chatbots is extremely easy and within everyone’s reach. There are tons of online bot development tools that you can use for free. However, creating a chatbot for a website may be a bit easier for beginners than making social media bots.

The best and easiest way to create your first chatbot is to use a ready-made chatbot template. Simply select the bot you are interested in and open it in the editor. You will be able to see how it is designed and change the messages or alter conversation flow logic as you wish. Solutions such as Tidio, Botsify, or Chatfuel allow you to tinker with chatbot templates or create chatbots from scratch.

Creating a sophisticated chatbot can take years for an entire team of developers. On the other hand, if you want a simple chatbot for your website or your school assignment, it can take half an hour. Just use a chatbot platform of your choice. Its users may not even notice the difference. A well-thought-out chatbot conversation can feel more interactive and interesting than the experiences offered by many high-tech solutions.

The easiest way to add a chatbot to your site is to install a WordPress chatbot plugin. If you don’t have a site powered by WordPress, many chatbot solutions can be integrated with sites on platforms like Shopify, Wix, Magento, or BigCommerce. Chatbots can also be integrated into your website by pasting a JavaScript snippet. But you may want some help from your programmers for that.

To make a free chatbot, create a Tidio account. You will get unlimited access to the chatbot editor. The free plan is available to all users. You will be able to test the chatbot to your heart’s content and have unlimited chats as long as the bot is used by less than 100 people per month.

To create a Facebook chatbot, create a Tidio account. During configuration, you will have the possibility to integrate the panel with your Facebook page and your Messenger. You can then use the Bots Launcher to specify which chatbots should be triggered on the website and which ones should appear in Facebook Messenger.

Python hands on tutorial with 50+ Python Application (10 lines of code) @xiaowuc2

Updated

Feb 15, 2024
Python

Python hands on tutorial with 50+ Python Application (10 lines of code) @xiaowuc2

AI ChatBot using Python Tensorflow and Natural Language Processing (NLP) along side TFLearn

A custom, 100% Python Twitch Chatbot that stores chat/viewership data in a PostgreSQL database.

decentralising the Ai Industry, free gpt-4/3.5 scripts through several reverse engineered api’s ( poe.com, phind.com, chat.openai.com, phind.com, writesonic.com, sqlchat.ai, t3nsor.com, you.com etc…)

Artificial Intelligent ChatBot using Tensorflow and NLP that understand the Context and Intent of Human Language.

Chat with GPT LLMs over voice, UI & terminal, with access to internet. Uses the OpenAI API.

It is a simple python socket-based chat application where communication established between a single server and client.

An Omegle Chatbot for promotion of Social media content or use it to increase views on YouTube. With the help of Chatterbot AI, this chatbot can be customized with new QnAs and will deal in a humanly way.

Chatbot made using Chatterbot and Chatterbot Corpus packages.

Vecna is a Python chatbot which recommends songs and movies depending upon your feelings

Python Chatbot is a bot designed by Kapilesh Pennichetty and Sanjay Balasubramanian that performs actions with user interaction.

A chatbot framework and chatbot example implemented with RL3 and Python

This project showcases engaging interactions between two AI chatbots.

Python Contextual Chatbot with Voice Recognition.

This is a python chat bot. It will replay based on predefined Questions and Answers.

Web based Online Voting and Campaigning Portal with an AI Chatbot

This is a simple Python chat BOT. It will replay questions based on predetermined questions and answers.

Add a description, image, and links to the python-chatbot topic page so that developers can more easily learn about it.

To associate your repository with the python-chatbot topic, visit your repo’s landing page and select “manage topics.”

In this article, we will create an AI chatbot using Natural Language Processing (NLP) in Python. Our goal is to help you build a smart chatbot. First, we’ll explain NLP, which helps computers understand human language. Then, we’ll show you how to use AI to make a chatbot to have real conversations with people. Finally, we’ll talk about the tools you need to create a chatbot like ALEXA or Siri.

This guide is hands-on. We’ll give you step-by-step instructions, and you can follow our examples or change them to fit your needs. So, let’s begin this journey into the world of NLP and AI chatbots!

This article was published as a part of the Data Science Blogathon.

Quiz Time

Get ready for the Ultimate Trivia Challenge! Put your knowledge to the test and see how many questions you can answer correctly.

Question

Your answer:

Correct answer:

Were you ever curious as to how to build a talking ChatBot with Python and also have a conversation with your own personal AI?

As the topic suggests we are here to help you have a conversation with your AI today. To have a conversation with your AI, you need a few pre-trained tools which can help you build an AI chatbot system. In this article, we will guide you to combine speech recognition processes with an artificial intelligence algorithm.

Natural Language Processing or NLP is a prerequisite for our project. NLP allows computers and algorithms to understand human interactions via various languages. In order to process a large amount of natural language data, an AI will definitely need NLP or Natural Language Processing. Currently, we have a number of NLP research ongoing in order to improve the AI chatbots and help them understand the complicated nuances and undertones of human conversations.

AI Chatbots are applications that businesses and other entities employ to facilitate automated conversations between AI and humans. These conversations can occur through text or speech. Chatbots must comprehend and imitate human conversation when engaging with people worldwide. Chatbots have made significant progress from ELIZA, the first-ever chatbot, to today’s Amazon ALEXA. This tutorial will cover all the fundamental concepts necessary for creating a basic chatbot that can comprehend and respond to human interactions. We will utilize speech recognition APIs and pre-trained Transformer models.

NLP, or Natural Language Processing, stands for teaching machines to understand human speech and spoken words. NLP combines computational linguistics, which involves rule-based modeling of human language, with intelligent algorithms like statistical, machine, and deep learning algorithms. Together, these technologies create the smart voice assistants and chatbots we use daily.

In human speech, there are various errors, differences, and unique intonations. NLP technology, including AI chatbots, empowers machines to rapidly understand, process, and respond to large volumes of text in real-time. You’ve likely encountered NLP in voice-guided GPS apps, virtual assistants, speech-to-text note creation apps, and other chatbots that offer app support in your everyday life. In the business world, NLP, particularly in the context of AI chatbots, is instrumental in streamlining processes, monitoring employee productivity, and enhancing sales and after-sales efficiency.

Interpreting and responding to human speech presents numerous challenges, as discussed in this article. Humans take years to conquer these challenges when learning a new language from scratch. Programmers, with the help of AI chatbot technology, have integrated various functions into NLP technology to tackle these hurdles and create practical tools for understanding human speech, processing it, and generating suitable responses.

NLP tasks involve breaking down human text and audio signals from voice data in ways that computers can analyze and convert into comprehensible data. Some of the tasks in NLP data ingestion include:

AI Chatbots are a relatively recent concept and despite having a huge number of programs and NLP tools, we basically have just two different categories of chatbots based on the NLP technology that they utilize. These two types of chatbots are as follows:

Scripted ai chatbots are chatbots that operate based on pre-determined scripts stored in their library. When a user inputs a query, or in the case of chatbots with speech-to-text conversion modules, speaks a query, the chatbot replies according to the predefined script within its library. One drawback of this type of chatbot is that users must structure their queries very precisely, using comma-separated commands or other regular expressions, to facilitate string analysis and understanding. This makes it challenging to integrate these chatbots with NLP-supported speech-to-text conversion modules, and they are rarely suitable for conversion into intelligent virtual assistants.

Artificially intelligent ai chatbots, as the name suggests, are designed to mimic human-like traits and responses. NLP (Natural Language Processing) plays a significant role in enabling these chatbots to understand the nuances and subtleties of human conversation. When NLP is combined with artificial intelligence, it results in truly intelligent chatbots capable of responding to nuanced questions and learning from each interaction to provide improved responses in the future. AI chatbots find applications in various platforms, including automated chat support and virtual assistants designed to assist with tasks like recommending songs or restaurants.

In the current world, computers are not just machines celebrated for their calculation powers. Today, the need of the hour is interactive and intelligent machines that can be used by all human beings alike. For this, computers need to be able to understand human speech and its differences.

NLP technologies have made it possible for machines to intelligently decipher human text and actually respond to it as well. However, communication amongst humans is not a simple affair. There are a lot of undertones dialects and complicated wording that makes it difficult to create a perfect chatbot or virtual assistant that can understand and respond to every human.

To overcome the problem of chaotic speech, developers have had to face a few key hurdles which need to be explored in order to keep improving these chatbots. To understand these hurdles or problems we need to under how NLP works to convert human speech into something an algorithm or AI understands. Here’s a list of snags that a chatbot hits whenever users are trying to interact with it:

To a human brain, all of this seems really simple as we have grown and developed in the presence of all of these speech modulations and rules. However, the process of training an AI chatbot is similar to a human trying to learn an entirely new language from scratch. The different meanings tagged with intonation, context, voice modulation, etc are difficult for a machine or algorithm to process and then respond to. NLP technologies are constantly evolving to create the best tech to help machines understand these differences and nuances better.

We will begin by installing a few libraries which are as follows :

Code:

# To be able to convert text to Speech ! pip install SpeechRecognition #(3.8.1) #To convey the Speech to text and also speak it out !pip install gTTS #(2.2.3) # To install our language model !pip install transformers #(4.11.3) !pip install tensorflow #(2.6.0, or pytorch)

We will start by importing some basic functions:

import numpy as np

We will begin by creating an empty class which we will build step by step. To build the chatbot, we would need to execute the full script. The name of the bot will be “ Dev”

# Beginning of the AI class ChatBot(): def __init__(self, name): print("----- starting up", name, "-----") self.name = name # Execute the AI if __name__ == "__main__": ai = ChatBot(name="Dev")

Output :

NLP or Natural Language Processing has a number of subfields as conversation and speech are tough for computers to interpret and respond to. One such subfield of NLP is Speech Recognition. Speech Recognition works with methods and technologies to enable recognition and translation of human spoken languages into something that the computer or AI chatbot can understand and respond to.

For computers, understanding numbers is easier than understanding words and speech. When the first few speech recognition systems were being created, IBM Shoebox was the first to get decent success with understanding and responding to a select few English words. Today, we have a number of successful examples which understand myriad languages and respond in the correct dialect and language as the human interacting with it. Most of this success is through the SpeechRecognition library.

To use popular Google APIs we will use the following code:

Code:

import speech_recognition as sr def speech_to_text(self): recognizer = sr.Recognizer() with sr.Microphone() as mic: print("listening...") audio = recognizer.listen(mic) try: self.text = recognizer.recognize_google(audio) print("me --> ", self.text) except: print("me --> ERROR")

Note: The first task that our chatbot must work for is the speech to text conversion. Basically, this involves converting the voice or audio signals into text data. In summary, the chatbot actually ‘listens’ to your speech and compiles a text file containing everything it could decipher from your speech. You can test the codes by running them and trying to say something aloud. It should optimally capture your audio signals and convert them into text.

# Execute the AI if __name__ == "__main__": ai = ChatBot(name="Dev") while True: ai.speech_to_text()

Output :

Note: Here I am speaking and not typing

Next, our AI needs to be able to respond to the audio signals that you gave to it. In simpler words, our ai chatbot has received the input. Now, it must process it and come up with suitable responses and be able to give output or response to the human speech interaction. To follow along, please add the following function as shown below. This method ensures that the chatbot will be activated by speaking its name. When you say “Hey Dev” or “Hello Dev” the bot will become active.

Code:

def wake_up(self, text): return True if self.name in text.lower() else False

As a cue, we give the chatbot the ability to recognize its name and use that as a marker to capture the following speech and respond to it accordingly. This is done to make sure that the chatbot doesn’t respond to everything that the humans are saying within its ‘hearing’ range. In simpler words, you wouldn’t want your chatbot to always listen in and partake in every single conversation. Hence, we create a function that allows the chatbot to recognize its name and respond to any speech that follows after its name is called.

After the ai chatbot hears its name, it will formulate a response accordingly and say something back. For this, the chatbot requires a text-to-speech module as well. Here, we will be using GTTS or Google Text to Speech library to save mp3 files on the file system which can be easily played back.

The following functionality needs to be added to our class so that the bot can respond back

Code:

from gtts import gTTS import os @staticmethod def text_to_speech(text): print("AI --> ", text) speaker = gTTS(text=text, lang="en", slow=False) speaker.save("res.mp3") os.system("start res.mp3") #if you have a macbook->afplay or for windows use->start os.remove("res.mp3")

Code :

#Those two functions can be used like this # Execute the AI if __name__ == “__main__”: ai = ChatBot(name=”Dev”) while True: ai.speech_to_text() ## wake up if ai.wake_up(ai.text) is True: res = “Hello I am Dev the AI, what can I do for you?” ai.text_to_speech(res)

Output :

Next, we can consider upgrading our chatbot to do simple commands like some o the virtual assistants help you to do. An example of such a task would be to equip the chatbot to be able to answer correctly whenever the user asks for the current time. To add this function to the chatbot class, follow along with the code given below:

Code:

import datetime @staticmethod def action_time(): return datetime.datetime.now().time().strftime('%H:%M') #and run the script after adding the above function to the AI class

# Run the AI if __name__ == "__main__": ai = ChatBot(name="Dev") while True: ai.speech_to_text() ## waking up if ai.wake_up(ai.text) is True: res = "Hello I am Dev the AI, what can I do for you?" ## do any action elif "time" in ai.text: res = ai.action_time() ## respond politely elif any(i in ai.text for i in ["thank","thanks"]): res = np.random.choice( ["you're welcome!","anytime!", "no problem!","cool!", "I'm here if you need me!","peace out!"]) ai.text_to_speech(res)

Output :

After all of the functions that we have added to our chatbot, it can now use speech recognition techniques to respond to speech cues and reply with predetermined responses. However, our chatbot is still not very intelligent in terms of responding to anything that is not predetermined or preset. It is now time to incorporate artificial intelligence into our chatbot to create intelligent responses to human speech interactions with the chatbot or the ML model trained using NLP or Natural Language Processing.

Here, we will use a Transformer Language Model for our AI chatbot. This model, presented by Google, replaced earlier traditional sequence-to-sequence models with attention mechanisms. The AI chatbot benefits from this language model as it dynamically understands speech and its undertones, allowing it to easily perform NLP tasks. Some of the most popularly used language models in the realm of AI chatbots are Google’s BERT and OpenAI’s GPT. These models, equipped with multidisciplinary functionalities and billions of parameters, contribute significantly to improving the chatbot and making it truly intelligent.

This is where the AI chatbot becomes intelligent and not just a scripted bot that will be ready to handle any test thrown at it. The main package we will be using in our code here is the Transformers package provided by HuggingFace, a widely acclaimed resource in AI chatbots. This tool is popular amongst developers, including those working on AI chatbot projects, as it allows for pre-trained models and tools ready to work with various NLP tasks. In the code below, we have specifically used the DialogGPT AI chatbot, trained and created by Microsoft based on millions of conversations and ongoing chats on the Reddit platform in a given time.

import transformers nlp = transformers.pipeline("conversational", model="microsoft/DialoGPT-medium") #Time to try it out input_text = "hello!" nlp(transformers.Conversation(input_text), pad_token_id=50256)

Reminder: Don’t forget to provide the pad_token_id as the current version of the library we are using in our code raises a warning when this is not specified. What you can do to avoid this warning is to add this as a parameter.

nlp(transformers.Conversation(input_text), pad_token_id=50256)

You will get a whole conversation as the pipeline output and hence you need to extract only the response of the chatbot here.

chat = nlp(transformers.Conversation(ai.text), pad_token_id=50256) res = str(chat) res = res[res.find(“bot >> “)+6:].strip()

Finally, we’re ready to run the Chatbot and have a fun conversation with our AI. Here’s the full code:

Great! The bot can both perform some specific tasks like a virtual assistant (i.e. saying the time when asked) and have casual conversations. And if you think that Artificial Intelligence is here to stay, she agrees:

# for speech-to-text import speech_recognition as sr # for text-to-speech from gtts import gTTS # for language model import transformers import os import time # for data import os import datetime import numpy as np # Building the AI class ChatBot(): def __init__(self, name): print("----- Starting up", name, "-----") self.name = name def speech_to_text(self): recognizer = sr.Recognizer() with sr.Microphone() as mic: print("Listening...") audio = recognizer.listen(mic) self.text="ERROR" try: self.text = recognizer.recognize_google(audio) print("Me --> ", self.text) except: print("Me --> ERROR") @staticmethod def text_to_speech(text): print("Dev --> ", text) speaker = gTTS(text=text, lang="en", slow=False) speaker.save("res.mp3") statbuf = os.stat("res.mp3") mbytes = statbuf.st_size / 1024 duration = mbytes / 200 os.system('start res.mp3') #if you are using mac->afplay or else for windows->start # os.system("close res.mp3") time.sleep(int(50*duration)) os.remove("res.mp3") def wake_up(self, text): return True if self.name in text.lower() else False @staticmethod def action_time(): return datetime.datetime.now().time().strftime('%H:%M') # Running the AI if __name__ == "__main__": ai = ChatBot(name="dev") nlp = transformers.pipeline("conversational", model="microsoft/DialoGPT-medium") os.environ["TOKENIZERS_PARALLELISM"] = "true" ex=True while ex: ai.speech_to_text() ## wake up if ai.wake_up(ai.text) is True: res = "Hello I am Dave the AI, what can I do for you?" ## action time elif "time" in ai.text: res = ai.action_time() ## respond politely elif any(i in ai.text for i in ["thank","thanks"]): res = np.random.choice(["you're welcome!","anytime!","no problem!","cool!","I'm here if you need me!","mention not"]) elif any(i in ai.text for i in ["exit","close"]): res = np.random.choice(["Tata","Have a good day","Bye","Goodbye","Hope to meet soon","peace out!"]) ex=False ## conversation else: if ai.text=="ERROR": res="Sorry, come again?" else: chat = nlp(transformers.Conversation(ai.text), pad_token_id=50256) res = str(chat) res = res[res.find("bot >> ")+6:].strip() ai.text_to_speech(res) print("----- Closing down Dev -----")

Output:

Note: I had later switched from google collab to my local machine due to some module issues which I faced during implementation and hence I am sharing my experience here so that if any of you also face the same issue can solve it. Obviously, Google is also there but the following lines will explain the issue. I used Python 3.9 as it had all the modules necessary and Python 3.6 and older versions will also work. Python 3.8 or the latest version might not have all the modules ported to match the version and hence I would suggest using Python 3.9 or older versions than 3.6.

To run a file and install the module, use the command “python3.9” and “pip3.9” respectively if you have more than one version of python for development purposes. “PyAudio” is another troublesome module and you need to manually google and find the correct “.whl” file for your version of Python and install it using pip.

The link to the full code can be found here.

Bonus tips: Feel free to drop a star if you liked this tutorial or bot and feel free to fork and create your own AI chatbot and call it whatever you want!

In this guide, we’ve provided a step-by-step tutorial for creating a conversational AI chatbot. You can use this chatbot as a foundation for developing one that communicates like a human. The code samples we’ve shared are versatile and can serve as building blocks for similar AI chatbot projects.

Consider enrolling in our AI and ML Blackbelt Plus Program to take your skills further. It’s a great way to enhance your data science expertise and broaden your capabilities. With the help of speech recognition tools and NLP technology, we’ve covered the processes of converting text to speech and vice versa. We’ve also demonstrated using pre-trained Transformers language models to make your chatbot intelligent rather than scripted.

A. An NLP chatbot is a conversational agent that uses natural language processing to understand and respond to human language inputs. It uses machine learning algorithms to analyze text or speech and generate responses in a way that mimics human conversation. NLP chatbots can be designed to perform a variety of tasks and are becoming popular in industries such as healthcare and finance.

A. To create an NLP chatbot, define its scope and capabilities, collect and preprocess a dataset, train an NLP model, integrate it with a messaging platform, develop a user interface, and test and refine the chatbot based on feedback. Tools such as Dialogflow, IBM Watson Assistant, and Microsoft Bot Framework offer pre-built models and integrations to facilitate development and deployment.

The media shown in this article on AI Chatbot is not owned by Analytics Vidhya and are used at the Author’s discretion.

Hey there, Thanks for sharing. I’m a newbie python user and I’ve tried your code, added some modifications and it kind of worked and not worked at the same time. The code runs perfectly with the installation of the pyaudio package but it doesn’t recognize my voice, it stays stuck in listening… is there any solutions to this? Thanks in advance.

This is a great blog post – So clear and easy to follow. All your hard work is so much appreciated.

Thanks for your blog, it helps a lot : )

Thanks, it helped me to gain insight for my project.

It’s informative, engaging, and provides valuable insights. Kudos to the creators for delivering such top-notch information. I’m impressed!

Thanks for your help

i am building a chatbot and this article really helped me to understand the importance of NLP in solving customer need and from dev view of point

Great tutorial, easy to read and follow instructions.

Looks great, but doesn’t seem to work in 2023. Would it be possible to get an updated version ?

Hello Arnab, First of all, great thanks for your blog, it very clear and profound to understand. I’m getting following error with this source code. AttributeError: module ‘transformers’ has no attribute ‘pipeline’ any suggestion on this. Thanks Amit

Nice blog, thanks you sharing such a wonderful blog. chat your favorite AI chatbot online with Kamoto.AI

I’d love to hear your thoughts on the article “How to Build Your AI Chatbot with NLP in Python?”. To provide the most relevant and insightful response,

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FAQs

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Step 2: Begin Training Your Chatbot

Dos and don’ts of building a chatbot

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Key Takeaway

Step 1: Create a Chatbot Using Python ChatterBot

How Does the Chatbot Python Work?

Rule-Based Approach

Self-Learning Approach:

Best Machine Learning and AI Courses Online

Post Graduate Program in ML & AI on Simplilearn

Stanford University’s “Artificial Intelligence” course on Coursera

How Does the Chatbot Python Work?

Rule-Based Approach

Self-Learning Approach:

What is ChatterBot Library?

Language Independence

How Does ChatterBot Library Work?

How To Install ChatterBot In Python

Next Steps

Why should you make a chatbot for your website?

Conclusion

FAQs

Can Python be used for a chatbot?

Which language is best for a chatbot?

Is it easy to learn chatbots?

What is the smartest chatbot?

Which algorithms are used for chatbots?

What is a chatbot in Python summary?

Prerequisites

Limitations With A Chatbot

Demo

Step 3: Export a WhatsApp Chat

Limitations With A Chatbot

Conclusion

Project Overview

Step 4: Clean Your Chat Export

Conclusion

Data Science and Machine Learning Internship …

Flask Chatterbot Example

What is ChatterBot Library?

Language Independence

How Does ChatterBot Library Work?

How To Install ChatterBot In Python

How to make a chatbot from scratch in 8 steps

Step 1: Give your chatbot a purpose

Step 2: Decide where you want it to appear

Step 3: Choose the chatbot platform

Step 4: Design the chatbot conversation in a chatbot editor

Step 5: Test your chatbot

Step 6: Train your chatbots

Step 7: Collect feedback from users

Step 8: Monitor chatbot analytics to improve it

Chatbot là gì?

Viết chatbot

How to Make a Chatbot in Python?

Preparing the Dependencies

Creating and Training the Chatbot

Communicating with the Python chatbot

Complete Project Code

How to Make a Chatbot in Python?

Preparing the Dependencies

Creating and Training the Chatbot

Communicating with the Python chatbot

Complete Project Code

FAQ: How to build a chatbot

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