Time to Create a BSC Token:

BEP-20 tokens are the standard framework for launching BSC tokens on Binance Smart Chain.

Time to Create a BSC Token!

Sending off your own bsc token generator on Binance Smart Chain (BSC) is amazingly like sending off an ERC-20 token on Ethereum. On a basic level, cryptographic money tokens are simply bits of code. Besides, with the pre-set symbolic principles (for example BEP-20, BEP-2, ERC-20, or ERC-721 for non-fungible tokens (assuming that you are searching for a NFT)) most of the code is as of now spread out all set. There are a couple of boundaries that need changing. We should find exactly that making a BSC token is so natural.

1) OpenZeppelin

One of the advantages of decentralization and open-source programming is the capacity to reorder code, then change and redo in like manner. The pre-set code token standard expected to send off a BSC token is accessible through OpenZeppelin, an open-source association that orders different symbolic norms for engineers to utilize. As create bep20 token is for all intents and purposes equivalent to Ethereum's ERC-20 symbolic norm, with a couple of changed boundaries, you can utilize the ERC-20 symbolic standard code while sending off a BSC token on Binance Smart Chain (BSC) and change as needs be.

2) Create a New Contract

Perhaps the most well known climate for sending shrewd agreements is Remix, so this is the place where you will programme your own BSC token. Go to remix.ethereum.org and go to 'Agreements' on the left-hand-side menu. Then, you'll have to make another record by choosing the upper left image of an archive. Preferably, you need to name this something applicable to your token/project name. Presently, you're prepared to glue across the code!

3) Programming Your Own BSC Token

While replicating across the code from OpenZeppelin, there will be sure boundaries that will require changing and components to be aware of while programming your BSC token. These are as per the following:

The main line of code ought to generally be the adaptation of Solidity being utilized.

The following line ought to be to import the symbolic format. This will incorporate a URL connect to a Github interface that contains the full symbolic design.

Then, you should duplicate across the symbolic brilliant agreement. It is at this stage you can tweak the boundaries of the token.

The second line of the savvy contract entered alludes to running the constructor when you make your very own BSC badge. It is here you can determine your symbolic's name and ticker image. The following line affirms the stamping of the token, and receipt of said printed tokens into the wallet of the individual who sent the savvy contract. Here, you can affirm how much tokens you might want to be stamped.

4) Compile the Contract

Whenever you are finished composition out your agreement, the following stage is to put it through the compiler to guarantee there are no bugs or issues with the agreement. To do this, you should go to the 'Strength compiler' symbol, second down on the left-hand menu under 'Record pilgrims'.

You should initially choose the compiler variant from the top drop-down. This is the adaptation of Solidity you are utilizing, which is as of now determined in your agreement. Then, click 'Gather'! Assuming there are no issues (ideally not on the off chance that you're adhering to these directions cautiously!) you can continue on to the following stage!

5) Deploy and Launch Your BSC Token

In this last advance, you should choose the 'Send and run exchanges' image underneath the 'Robustness compiler' symbol on the left. The main drop-down menu prompts you to pick the climate. Ensure you select Injected Web3. Then, ensure your record address is equivalent to your record address in MetaMask. Under the record field, you will see fields for the 'Gas Limit' and 'Worth' with a selection of measurements. Neither of these need changing and, for the reason to make your very own create bsc token badge, you can simply disregard these. At last, ensure that your agreement chose matches the name of the new record you made. Presently, click convey!

Step by step instructions to View Your BSC Token

Congrats! Soon after tapping the send button in Remix, MetaMask will give a spring up naturally affirming the exchange. Here, you have the choice to "view on BSCscan". Click on this connect to open up the Binance Smart Chain block traveler.

You will see a breakdown of the exchange hash, status of the exchange, the square tallness, time stamp, and the wallet address the tokens were made from. Also, and all the more critically, the 'To' contract address displayed on BSCscan is the recently shaped agreement address for your BSC token.

To see your new BSC tokens in your MetaMask wallet, look down to the lower part of the Remix organization tab to track down a rundown of 'Sent Contracts' with token locations. The latest (or maybe just) token location shown will be your recently sent off BSC token location. Click the 'duplicate to clipboard' image close to the symbolic location. A similar location will likewise show up on BSCscan and will work assuming that you duplicate this location across all things considered.

Later, open up your MetaMask wallet. Look down to the button where it says 'Add Token'. Then, at that point, select the right-hand tab; 'Custom Token', and glue in your bep20 token generator token location in the provoked 'Token Contract Address' container. Now, the symbolic image and decimals of accuracy ought to naturally fill in. At the point when you return into your fundamental MetaMask wallet now, you will see your new BSC token.

#create bsc token     #bsc token generator      #bep20 token generator



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Buddha Community

Time to Create a BSC Token:
Easter  Deckow

Easter Deckow


PyTumblr: A Python Tumblr API v2 Client



Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install


Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

  1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
  2. The Tumblr API console at https://api.tumblr.com/console
  3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

  • state - a string, the state of the post. Supported types are published, draft, queue, private
  • tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
  • tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
  • date - a string, the customized GMT that you want
  • format - a string, the format that your post is in. Support types are html or markdown
  • slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

  • title - a string, the title of post that you want. Supports HTML entities.
  • url - a string, the url that you want to create a link post for.
  • description - a string, the desciption of the link that you have
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Tamale  Moses

Tamale Moses


Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python. 

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

  • Lists
  • Sets
  • Dictionaries
  • User-Defined Classes (It purely depends upon the user to define the characteristics) 

Objects of built-in type that are immutable are:

  • Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
  • Strings
  • Tuples
  • Frozen Sets
  • User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

  • Identity – This refers to the address that the object refers to in the computer’s memory.
  • Type – This refers to the kind of object that is created. For example- integer, list, string etc. 
  • Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities  

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format


Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities


#Printing the elements from the list cities, separated by a comma & space 

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format


Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0. 

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
TypeError: 'tuple' object does not support item assignment 

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays


Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format


Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays


Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format


Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it.  Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs:  Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender 

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple


Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format


Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple


Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format


Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list


Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format


Output [2]: 0x1691d5b13c8	

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format


Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’ 

x = 10

#printing the value of ‘x’


Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format


Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’


Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format


Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick checkPython Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.


1. Difference between mutable vs immutable in Python?

Mutable ObjectImmutable Object
State of the object can be modified after it is created.State of the object can’t be modified once it is created.
They are not thread safe.They are thread safe
Mutable classes are not final.It is important to make the class final before creating an immutable object.

2. What are the mutable and immutable data types in Python?

  • Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

  • Some immutable data types are: 

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

Join Great Learning Academy’s free online courses and upgrade your skills today.

Original article source at: https://www.mygreatlearning.com


aaron silva

aaron silva


SafeMoon Clone | Create A DeFi Token Like SafeMoon | DeFi token like SafeMoon

SafeMoon is a decentralized finance (DeFi) token. This token consists of RFI tokenomics and auto-liquidity generating protocol. A DeFi token like SafeMoon has reached the mainstream standards under the Binance Smart Chain. Its success and popularity have been immense, thus, making the majority of the business firms adopt this style of cryptocurrency as an alternative.

A DeFi token like SafeMoon is almost similar to the other crypto-token, but the only difference being that it charges a 10% transaction fee from the users who sell their tokens, in which 5% of the fee is distributed to the remaining SafeMoon owners. This feature rewards the owners for holding onto their tokens.

Read More @ https://bit.ly/3oFbJoJ

#create a defi token like safemoon #defi token like safemoon #safemoon token #safemoon token clone #defi token

Token Center

Token Center


BEP20 Token Generator | Create BEP20 Token for FREE

How to Create BEP20 Token? BEP20 Token Create Tool
If you would like to create BEP20 token under Binance Smart Chain without any coding skills, you are invited to use the BEP20 Token Generator which is free and easy to use with its user friendly interface.

BEP20 Token Generator: https://tokencenter.github.io/bep20-generator/

BEP20 Token Generator is a free DApp which allows you to create your own BEP20 token in less than a minute.

How to use the BEP20 Token Generator
It is super easy to use the tool

Install Metamask and login.
Enter your token details such as name, symbol, decimals and supply.
Create your token.

#bsc #bep20 #token #bep20 token create #solidity #metamask

Chatbot en Python From Scratch avec code source

Un chatbot est un logiciel basé sur l'IA conçu pour interagir avec les humains dans leur langue naturelle. Ces chatbots sont généralement conversés via des méthodes auditives ou textuelles, et ils peuvent imiter sans effort les langues humaines pour communiquer avec les êtres humains d'une manière humaine. Un chatbot est sans doute l'une des meilleures applications de traitement du langage naturel.

Au cours des dernières années, les chatbots en Python sont devenus très populaires dans les secteurs de la technologie et des affaires. Ces robots intelligents sont si aptes à imiter les langages humains naturels et à converser avec les humains que des entreprises de divers secteurs industriels les adoptent. Des entreprises de commerce électronique aux établissements de santé, tout le monde semble tirer parti de cet outil astucieux pour générer des avantages commerciaux. Dans cet article, nous allons découvrir le chatbot utilisant Python et comment créer un chatbot en python . 

Pour créer un Chatbot en Python à partir de zéro, nous suivons ces étapes.

  • Étape 1 : Importer et charger le fichier de données
  • Étape 2 : prétraiter les données
  • Étape 3 : Créer des données d'entraînement et de test
  • Étape 4 : Construire le modèle
  • Étape 5 : prédire la réponse

Étape 1 : Importer et charger le fichier de données

Tout d'abord, vous devez créer un fichier appelé train_chatbot.py. Nous apportons les packages dont notre chatbot a besoin et configurons les variables que nous utiliserons dans notre projet Python.

Le fichier de données est au JSONformat , nous avons donc utilisé le json packagepour lire le JSONfichier en Python.

import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import json
import pickle
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import SGD
from tensorflow.keras.optimizers import SGD
import random
classes = []
documents = []
ignore_words = ['?', '!']
data_file = open('intents.json').read()
intents = json.loads(data_file)

Étape 2 : prétraiter les données

Avant de pouvoir créer un modèle d'apprentissage automatique ou d'apprentissage en profondeur à partir de données textuelles, nous devons traiter les données de différentes manières. Selon les besoins, nous devons utiliser différentes opérations pour prétraiter les données.

La tokenisation des données textuelles est la première et la plus élémentaire chose que vous puissiez faire avec. La tokenisation est le processus qui consiste à diviser un texte en petits morceaux, comme des mots.

Ici, nous passons en revue les modèles, utilisons la nltk.word_tokenize()fonction pour diviser la phrase en mots et ajoutons chaque mot à la liste de mots. Nous dressons également une liste des classes auxquelles appartiennent nos balises.

for intent in intents['intents']:
    for pattern in intent['patterns']:
        #tokenize each word
        w = nltk.word_tokenize(pattern)
        #add documents in the corpus
        documents.append((w, intent['tag']))
        # add to our classes list
        if intent['tag'] not in classes:

Maintenant, nous allons comprendre ce que signifie chaque mot et nous débarrasser de tous les mots qui sont déjà sur la liste. La lemmatisation est le processus consistant à transformer un mot en sa forme lemmaire, puis à créer un fichier pickle pour stocker les objets Python que nous utiliserons lors de la prédiction.

# lemmatize, lower each word and remove duplicates
words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
words = sorted(list(set(words)))
# sort classes
classes = sorted(list(set(classes)))
# documents = combination between patterns and intents
print (len(documents), "documents")
# classes = intents
print (len(classes), "classes", classes)
# words = all words, vocabulary
print (len(words), "unique lemmatized words", words)

Étape 3 : Créer des données d'entraînement et de test

Maintenant, nous allons créer les données d'apprentissage, qui incluront à la fois les entrées et les sorties. Le motif sera notre entrée et la classe à laquelle appartient le motif sera notre sortie. Mais l'ordinateur ne peut pas lire les mots, nous allons donc transformer les mots en nombres.

# create our training data
training = []
# create an empty array for our output
output_empty = [0] * len(classes)
# training set, bag of words for each sentence
for doc in documents:
    # initialize our bag of words
    bag = []
    # list of tokenized words for the pattern
    pattern_words = doc[0]
    # lemmatize each word - create base word, in attempt to represent related words
    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
    # create our bag of words array with 1, if word match found in current pattern
    for w in words:
        bag.append(1) if w in pattern_words else bag.append(0)
    # output is a '0' for each tag and '1' for current tag (for each pattern)
    output_row = list(output_empty)
    output_row[classes.index(doc[1])] = 1
    training.append([bag, output_row])
# shuffle our features and turn into np.array
training = np.array(training)
# create train and test lists. X - patterns, Y - intents
train_x = list(training[:,0])
train_y = list(training[:,1])
print("Training data created")

Étape 4 : Construire le modèle

Maintenant que nos données d'entraînement sont prêtes, nous allons construire un réseau neuronal profond à 3 couches. Nous le faisons avec l' KerasAPI séquentielle. Après avoir entraîné le modèle pendant 200 itérations, il était précis à 100 %. Nommons le fichier « chatbot model.h5» et sauvegardons-le.

# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons
# equal to number of intents to predict output intent with softmax
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(len(train_y[0]), activation='softmax'))
# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
#fitting and saving the model
hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
model.save('chatbot_model.h5', hist)
print("model created")

Étape 5 : prédire la réponse

Pour prédire les phrases et obtenir une réponse de l'utilisateur, laissez-nous créer un nouveau fichier nommé " chatapp.py."

Nous allons charger le modèle formé, puis utiliser une interface utilisateur graphique pour prédire la réponse du bot. Le modèle ne nous dira qu'à quelle classe il appartient, nous allons donc créer des fonctions qui détermineront la classe, puis choisirons une réponse aléatoire dans la liste des réponses.

Encore une fois, nous chargeons les fichiers pickle ' words.pkl' et ' classes.pkl' que nous avons créés lorsque nous avons entraîné notre modèle :

import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np
from keras.models import load_model
model = load_model('chatbot_model.h5')
import json
import random
intents = json.loads(open('intents.json').read())
words = pickle.load(open('words.pkl','rb'))
classes = pickle.load(open('classes.pkl','rb'))

Pour prédire la classe, nous devrons donner des informations de la même manière que nous l'avons fait lors de la formation. Nous allons donc créer des fonctions qui effectueront un prétraitement sur le texte, puis devinerons la classe.

def clean_up_sentence(sentence):
    # tokenize the pattern - split words into array
    sentence_words = nltk.word_tokenize(sentence)
    # stem each word - create short form for word
    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
    return sentence_words
# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=True):
    # tokenize the pattern
    sentence_words = clean_up_sentence(sentence)
    # bag of words - matrix of N words, vocabulary matrix
    bag = [0]*len(words)
    for s in sentence_words:
        for i,w in enumerate(words):
            if w == s:
                # assign 1 if current word is in the vocabulary position
                bag[i] = 1
                if show_details:
                    print ("found in bag: %s" % w)
def predict_class(sentence, model):
    # filter out predictions below a threshold
    p = bow(sentence, words,show_details=False)
    res = model.predict(np.array([p]))[0]
    results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]
    # sort by strength of probability
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
    return return_list

Après avoir prédit la classe, nous obtiendrons une réponse aléatoire à partir de la liste des intentions.

def getResponse(ints, intents_json):
    tag = ints[0]['intent']
    list_of_intents = intents_json['intents']
    for i in list_of_intents:
        if(i['tag']== tag):
            result = random.choice(i['responses'])
    return result
def chatbot_response(text):
    ints = predict_class(text, model)
    res = getResponse(ints, intents)
    return res

Maintenant, nous allons créer une interface utilisateur graphique (GUI). Utilisons la bibliothèque Tkinter, qui est fournie avec de nombreuses autres bibliothèques d'interface graphique utiles.

Nous prendrons le message de l'utilisateur et utiliserons les fonctions d'assistance que nous avons créées pour obtenir la réponse du bot et l'afficher sur l'interface graphique. Voici le code source complet de l'interface graphique.

#Creating GUI with tkinter
import tkinter
from tkinter import *
def send():
    msg = EntryBox.get("1.0",'end-1c').strip()
    if msg != '':
        ChatLog.insert(END, "You: " + msg + '\n\n')
        ChatLog.config(foreground="#442265", font=("Verdana", 12 ))
        res = chatbot_response(msg)
        ChatLog.insert(END, "Bot: " + res + '\n\n')
base = Tk()
base.resizable(width=FALSE, height=FALSE)
#Create Chat window
ChatLog = Text(base, bd=0, bg="white", height="8", width="50", font="Arial",)
#Bind scrollbar to Chat window
scrollbar = Scrollbar(base, command=ChatLog.yview, cursor="heart")
ChatLog['yscrollcommand'] = scrollbar.set
#Create Button to send message
SendButton = Button(base, font=("Verdana",12,'bold'), text="Send", width="12", height=5,
                    bd=0, bg="#32de97", activebackground="#3c9d9b",fg='#ffffff',
                    command= send )
#Create the box to enter message
EntryBox = Text(base, bd=0, bg="white",width="29", height="5", font="Arial")
#EntryBox.bind("<Return>", send)
#Place all components on the screen
scrollbar.place(x=376,y=6, height=386)
ChatLog.place(x=6,y=6, height=386, width=370)
EntryBox.place(x=128, y=401, height=90, width=265)
SendButton.place(x=6, y=401, height=90)

Exécutez le chatbot Python

Pour exécuter le chatbot, nous avons deux fichiers principaux ; train_chatbot.py et chatapp.py.

Tout d'abord, nous formons le modèle à l'aide de la commande dans le terminal :

python train_chatbot.py