How to create a Python chatbot from scratch for beginners? step by step guide

Chatbots are little bots that are programmed with the intention of providing answers to user questions. Although it seems that its functionality is basic, it is not, since it can provide a great number of benefits for your brand in terms of customer service.

Normally small companies or brands use this technology to be able to provide more attention when there is a great flow of questions and increase your audience in the digital world. It is also used to expand your brand and increase its recognition, although specific strategies are required.

Create a chatbot it is not a very simple taskbut with the Python help and our guide you can make the most suitable bot for your company.

What are the benefits of using a Chatbot for my digital company?

The chatbots are very important for the business sectornot only because it strengthens sales, but also for other benefits that allow us to evolve in this digital age.

Next, we will show you the benefits you get when using this technology:

Access to a large audience

Thanks to chatbots, you can work together with different social networksthey offer great attention to the messaging of your digital market.

They are implemented on different platforms such as:

• Facebook
• Twitter
• Instagram
• Skype
• slack

Not only does it allow you to serve your audience faster, It also leaves advantages by facilitating reaching a greater number of customers. High engagement is another possibility with low-cost bots.

Monitoring of consumer data and obtaining statistics

The use of chatbots can contribute to the collection of data, because on many occasions consumers do not provide much information from them. As well as allowing you to find the reason for its withdrawal on your website. Even, can be used to analyze the flow of your consumers and obtain optimized statistics according to the conversion of the audience you want to reach. At this point, the analysis of the low traffic of your page is included.

the chatbots can redirect the visitor, whenever they want to unsubscribe from your digital company to collect information on why they decide to unsubscribe. This data that can be collected allows you to adapt more to the needs of your target audience. Although it is not limited to there, because helps organize the different sales that have been made from your site. Smart management allows for better results and more accurate decision-making.

personalized attention

If you wish increase the sales of your products, first you must increase the number of consumers and this is one of the features that chatbots fully offer. Chatbots can be programmed to offer personalized searches and experiences, benefit that fits perfectly with the needs of your customers. comply with this, It is key for engagement to increase.

Enhance business branding with minimal effort

Due to the great competition that exists today in the digital world, attracting more clients has become a somewhat complicated task and more if it is a new company. For this reason, bots are usually an effective solution to problemssince it allows us to offer people the necessary information for their comfort. Increase experience and remove obstacles for the search or purchase of our clients, it is essential for them to catalog your company as one of their favorites.

They adjust to the needs of the business

chatbots can adapt to the different current needs of your company, allowing you to manage sales, staging indicators or automatically answering questions, opening up opportunities for expansion. All this thanks to its settings and programming mode that allow you to get the most out of your digital business.

What tasks of my sales funnel can a chatbot perform?

In general, chatbots can help manage potential customers and monitor all stages of a funnel. These can develop tasks that allow you to guide customers within the digital company, so that you can get the products and prices you most suit your needs.

instant messaging service collect all the information what customers need. On the other hand, they fulfill their basic function, which are programmed responses, whose characteristic increase engagement. Whether digital or physical, customers always looking for a fair attentionand based on this point it can be ensured that the chatbots they specialize in it.

The main idea is to seek comfort in consumers, and the objective is fulfilled when:

• Products of interest are offered
• Personalized attention is generated
• There are suggested searches
• Consumer needs are met
• The most important your time is valued!

In the latter there should be a greater emphasis, because in the digital world, where everything goes very fast, offering quick sales to new and old customers is essential so that their next purchases are made with your brand. For that reason, chatbots are very importantsince they are the ones that provide that personalized attention that consumers so much need, even though their task may only last a few seconds.

Why use Python to create my chatbot? Main advantages

The great programming experts prefer use python for the development of your projects, for its varied benefits.

There are many advantages that you can obtain by using this language:

Simplified and fast

Python allows us adapt to a programming mode according to what we are looking forin this case programming a chatbot, so using its patterns, we can create our chat bot with a scripting language. For this reason, it is considered that part of the great advantages that Python offers usis its simplicity and speed in all the chat bot programming processes.

elegant and flexible

Both are essential features of Python. This language has a large number of tools so that programming meet the objectives of the customer service strategy. You shouldn’t worry so much about repeating encodings as this language suggest the ones that fit so that the algorithm is fulfilled perfectly, and with a minimum of error.

Healthy and productive programming

Programming based on Python turns out to be very productivenot only for the details mentioned above, but also for its great ease when making our programming comply with applicable rules for the proper function. In other words, learning in Python it is extremely simple because its intelligence allows programming of high quality in operations, in the shortest possible time.

Portable

This language is totally portable, allowing programming from your Mac, Linux or Windows. In any of the systems where it is used, its perfection and quality is the same, including the fact that it does not require the installation of add-ons or third parties to complete the projects or programming.

Steps to create a chatbot for my company from scratch with Python

Next, we show you a series of steps and necessary requirements so that you can create a chatbot from scratch useful for your company.

It is advisable to pay close attention, because if your experience in this area is very low, you will have to repeat the process on some occasions:

Previous requirements

The most important requirement before being able to program with Python is to know the key concepts of NLTK, Scikit and NLP. If you do not have the slightest idea of ​​what we are mentioning, we invite you to read the concepts that correspond to each of them.

NLP

The Natural Language Processing, or also known as NLP, refers to the interaction that exists between the human language processor with that of computers. In other words, it is the intermediary or process translator.

NLTK

The Natural Language Toolkit platform is one of the most recognized when programming with Python. It is suitable for writing in a human language, providing flexible and easy-to-use interfaces. In her different libraries can be found ideal processing systems for classification, tokenization, derivation, labeling, analysis and semantic reasoning, working in conjunction with NLP.

scikit-learn

This is the machine learning library and it is characterized by offering a complete guide to programming algorithms in pythonso that different details that are highly valuable during the development of the chatbot can be perfectly mastered.

Downloading and installing NLTK

There are hundreds of websites that allow you to do a quick download of this program. Once you have the installer, the file is executed after downloading it, with the following code: pip install nltk.

Go for it:

• Afterwards, a test installation is performed by running Pythonyou must write: import nltk.
• Note: if you want more information about NLYK, visit their website https://www.nltk.org/install.html

Installing NLTK packages

Now we import and execute NLTK with the command nltk.download(). The previous code will open the software that is responsible for downloading the platform, allowing you to choose the corpus or models that you want to attach.

Text reprocessing with NLTK

Reprocessing text with NLTK It is very important before doing any NLP project.because the main problem with these texts is that they are presented in the form of strings.

Basically, reprocessing includes the following within its tasks:

• transform and convert the texts that are in uppercase or lowercase, as appropriate so that the algorithm can be correctly interpreted in the different steps.
• Convert text strings in token list, the process of which is known as tokenization. Among the different utilities that can be given to this process, is searching for words in strings.
• Dissipate the noise, it comes to delete the letters or numbers that are in standard.
• To exist within the programming common words, but that causes a lot of relevance, they are eliminated, categorizing them as Stop Wordscompletely excluding them from the documents being written.
• leads are processes where the number of words with inflection is reduced, from your root or root. In other words, they eliminate those synonyms that lead to the same act or fact.
• Unlike the previous one, the lemmatization is the existence in the programming of real words, which are not derived and are denoted as slogans.

Bag of Words

After reprocessing, words or array of numbers need to be converted or transform into a representation of texts. This process is known as bag of wordswhich its objective is to describe the spectrum of words within a document.

TF-IDF Approach

Part of the problems that arise when encoding, it’s the bag of words approach. This consists of grouping words that are very frequent with each other, but with little value in programming, so it will make our documents have more weight, for shorter functions. Focus consists in re-scaling the frequency of those words, so that the weight is decreased, but the value is increased. Such frequent words can be articles, for example: “the, oh an.”

cosine similarity

It consists of the transformation or interpretation that apply to texts so that they can be identified within a real vector in space. Within the NPL process you can take that similarity and divide it by the product of the norms, producing an angle between both vectors. More exactly and clearly, the cosine similarity is the measure between two or more vectors, whose value is different from zero.

There is a formula that can be used to find out when there is a similarity between two documents (d1 and d2):

• Cosine Similarity (d1, d2) = Dot product(d1, d2) / ||d1|| * ||d2||

Let’s start with the programming of the bot

now that Do you know what the NPL process consists of?and some essential terms, we proceed to the main idea: “create a chatbot from scratch”. First of all, you must define a name to your chatbotyou have total freedom in doing it, in our case we will use “STEAL”.

Importing the necessary libraries

After start and run the old platformyou should import all libraries what you will need

To do this, enter the following code in the command terminal:

import nltk
import numpy as np
import random
import string # to process standard Python strings

Body

You should look for a bodyand for this you go to any Internet page and copy all the corpusand save a file with the name of “chatbot.txt”.

reading in the data

we continue with a reading of the “chatbot.txt” file in a complete list of sentences and phrases for the processing of the reading of our bot.

The process is carried out using the following formula:

f=open('chatbot.txt','r',errors="ignore")raw=f.read()raw=raw.lower()# converts to lowercasenltk.download('punkt') # first-time use only
nltk.download('wordnet') # first-time use onlysent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences
word_tokens = nltk.word_tokenize(raw)# converts to list of words

Once the above is done, you can read or see the structure of the “sent tokens” and “Word tokens”, through the following:

sent_tokens[:2]
['a chatbot (also known as a talkbot, chatterbot, bot, im bot, interactive agent, or artificial conversational entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.',
'such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the turing test.']
word_tokens[:2]
['a', 'chatbot', '(', 'also', 'known']

Raw text pre-processing

At this level, you will have to define the function so that it calls the lem tokenswhich will take the tokens in the form of inputs, but will not alter any data, since it will later return them normalized:

lemmer = nltk.stem.WordNetLemmatizer()
#WordNet is a semantically-oriented dictionary of English included in NLTK.def LemTokens(tokens):
return [lemmer.lemmatize(token) for token in tokens]
remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)
def LemNormalize(text):
return
LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))

keyword matching

Now you will have to define a function that will allow activate by greeting. Greetings are often used a lot by bots, so you should program a greeting where the bot provides a fair response.

This can be a simple “Hello”, but it depends on what you are interested in adding:

GREETING_NPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey",)GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"]def greeting(sentence):
for word in sentence.split():
if word.lower() in GREETING_INPUTS:
return random.choice(GREETING_RESPONSES)

Generating response

Like any conversation responses need to be generated. For this reason, entry questions and answers must be added so that the conversation with the audience or client begins. For it starts with the import of certain necessary modules.

From the library, directly import the TFidf vectorizer, which will be used to convert a collection of documents into an easy to interpret array:

• from sklearn.feature_extraction.text import TfidfVectorizer

The import process must also be done with the cosine similarity module, from the Scikit learning library:

• from sklearn.metrics.pairwise import cosine_similarity

The goal of this is that the algorithm find similarities between the different words entered by the user, and can find a solution or answer to it. This is one of the implementations easier than a chatbot.

Answer not found

For define the answers the appropriate keywords should be used when the user asks questions and give the correct answers. Among the answers can also be integrated one that tells the client that ask the question again.

Normally the bot is programmed in such a way that if it does not understand the questions asked, it will show the user a message indicating to ask the questions again: “Sorry! I do not understand you”:

def response(user_response):
robo_response=""
sent_tokens.append(user_response)TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words="english")
tfidf = TfidfVec.fit_transform(sent_tokens)
vals = cosine_similarity(tfidf[-1], tfidf)
idx=vals.argsort()[0][-2]
flat = vals.flatten()
flat.sort()
req_tfidf = flat[-2]if(req_tfidf==0):
robo_response=robo_response+"I am sorry! I don't understand you"
return robo_response
else:
robo_response = robo_response+sent_tokens[idx]
return robo_response

To close with the programming, you must include some lines that our robot must say when starting or ending the conversation, the code for this is as follows:

flag=True
print("ROBO: My name is Robo. I will answer your queries about Chatbots. If you want to exit, type Bye!")while(flag==True):
user_response = input()
user_response=user_response.lower()
if(user_response!='bye'):
if(user_response=='thanks' or user_response=='thank you' ):
flag=False
print("ROBO: You are welcome..")
else:
if(greeting(user_response)!=None):
print("ROBO: "+greeting(user_response))
else:
print("ROBO: ",end="")
print(response(user_response))
sent_tokens.remove(user_response)
else:
flag=False
print("ROBO: Bye! take care..")

Yes you have completed the steps as we mentioned them, then your programming has been totally successful, so now you can use it and test it so you can see how it interacts with users.

Examples of chats from well-known brands that have been very successful

The chatbots are the tools essential for the growth of a brand.

Many of the brands that are widely recognized have boosted their success, thanks to the usefulness of chatbots:

National Geographic

Everybody knows the great science channel National Geographicpart of their current success is due to the use of chatbots with the intention of strengthen the connection with your audience. In its latest innovation, developed a chatbot for Facebook messagingso that they provide answers to their audience regarding the ideas of Albert Einstein.

The objective of the development of this bot according to the authors, was to share with the public all the knowledge that was housed inside the mind of the very complex physique, which turned out to be very effective and prompted greater recognition of this brand.

Marvel

Like Disney, Marvel used a bot for the promotion of Secret Empirewhere the algorithm was to allow fans to talk with one of the characters. All this process was carried out by means of FacebookMessenger. The result of this chatbot was a resounding success because the brand grew and the promotion of Secret Empire was cheaper than using other means.

starbucks

Although this has always been one of the brands with the most interaction with its customers, it is always open to new innovations. This is reinforced using a chatbotbut with audio mode where it allows your Amazon users place orders using only your voice. The name of this bot is “On-Command Ordering” and like all the results with the bots, it was a complete success.

KLM

To finish you must meet this dutch airline that allows its customers to use an application with artificial intelligence through Facebook messaging, to receive flight updates and notifications depending on the questions they ask the bot.