Pythonaibrain

Pythonaibrain

Pythonaibrain is a versatile, plug-and-play Python package designed to help you build offline intelligent AI assistants and applications effortlessly. With modules covering speech recognition, text-to-speech, natural language understanding, LLM support, and more, Pythonaibrain lets you create powerful AI solutions without deep expertise or complex setup. Whether you’re a beginner or an experienced developer, get ready to bring your AI ideas to life quickly and efficiently.

Installation

Install the Pythonaibrain package:

pip install pythonaibrain==1.1.9

About Pythonaibrain Package

Pythonaibrain package consists of pyaitk (which means Python AI Toolkit) module and PyAgent modules, pyaitk provides you methods to create your AI models/chatbots where as PyAgent provides best GUI and Web supports for intrection with models/chatbots. It also provide best contorl on your device. In this package you got default pre-trained models.

pyaitk (`Python AI ToolKit`)

pyaitk provides various type of methods and functions to create an advance AI.

How to import pyaitk

After pythonaibrain installations run this code for importing pyaitk

import pyaitk

On importing the module it internally train built-in models to work more smootly and fast.

Note : It takes few minutes to train model.

Built-in methods, modules and function in pyaitk :

Methods & Functions

Camera
TTS (Text To Speech)
PTT (PDF To Text)
ITT (Image To Text)
STT (Speech To Text)
MathAI
Search
Memory
Contexts
Brain
Advance Brain

Camera Method

The pyaitk toolkit supports Camera to click photos and make videos. It can save photos/videos and send them to pyaitk Brain method for processing.

Example: Start your camera

import pyaitk
from pyaitk import Camera
import tkinter as tk

root = tk.Tk()          # Create GUI
Camera(root)            # Start camera and pass root as master
root.mainloop()         # Run the GUI

Or simply:

from pyaitk.Camera import Start
Start()

Or via Brain module:

from pyaitk import Brain

brain = Brain()
brain.load()
brain.process_messages('Click Photo')

TTS (Text To Speech) Method

Converts text to speech in male or female voice.

Example

from pyaitk import TTS

tts = TTS(text='Hello World')
tts.say(voice='david')    # Male voice in windows
tts.say(voice='zira')  # Female voice in windows
tts.say(voice= 'alex') # Male voice in macos
tts.say(voice= 'samantha') # Female voice in macos
tts.say(voice= 'english') # for linux

By default, tts.say() uses the male voice of windows so agrest with your device type, also see the TTS doc.

You should know the voice for different OS

Voice	Gender	style	OS
David	Male	en-US	Windows
Mark	Male	en-US	Windows
Zira	Female	en-US	Windows
Alex	Male	en-US	MacOS
Samantha	Female	en-US	MacOS
Victoria	Female	en-US	MacOS
Fred	Male	robotic	MacOS
Daniel	Male	en-GB	MacOS
Fiona	Female	en-GB	MacOS
English	None	Default espeak voice	Linux
English-US	None	American English	Linux
English-UK	None	British English	Linux
MB-EN1	None	MBROLA English 1	Linux
MB-FR1	None	MBROLA French 1	Linux

Other ways to use TTS.

from pyaitk import speak

text = "Hello World" # Taking a text
speak(text) # speak functions speak the given text without doing all the setup of TTS method.

Syntax for speak function

speak(text: str = 'Hi', voice: str = <accordind to device type>):

What speak function returns?

speak function returns None.

Note: You can also set voice according to your choice

PTT (PDF To Text) Function

PTT (PPT To Text) function extract text from the given .pdf file.

Example

import pyaitk
from pyaitk import PTT

ptt = PTT(path='example.pdf')  # Provide your file path
print(ptt)                     # Extracted text output

What PPT function returns?

The PPT function return the extracted text in the string formate.

STT (Speech To Text) Method.

STT (Speech To Text) method is use to convert speech to text.

Example

from pyaitk import STT # Import the method form pyaitk module

stt = STT()

text = stt.listen() # Listen the voice and convert into text and return
print(text)

What STT().listen() return?

STT().listen() retuens string.

Why do we use it?

We use it to convert speech to text.

Note: It works with Internet.

MathAI Function

It is a built-in function in pyaitk for solving complex, and symbolic math problems.

Way to use MathAI function

Steps

First import MathAI function from pyaitk module.

from pyaitk import MathAI

Create a variable for storing query in string formate.

query = "X + Y - 2X + 3Y" # let's take a query

Now get the answer from the MathAI function for the query.

answer = MathAI(query= query) # MathAI gives the answer in string formate.

After that print the answer.

print(answer)

From this way we can ask any query of math with MathAI.

Full Code

from pyaitk import MathAI

query = "X + Y - 2X + 3Y" # let's take a query
answer = MathAI(query= query) # MathAI gives the answer in string formate.

print(answer)

Note: MathAI have fixed pattern of prompts, means you can ask question in direct form not question with string like,
query = "Solve X + Y - 2X + 3Y"
Try to ask direct questions only.

Prompts for MathAI

Solve normal numeric problems.

1 + 2 + 3 + 4 - 5 (1 - 55) + 10

Solve symbolic methamatic.

X + 2Y - X + 10Z * (10 - 100)X

Matrix

Matrix([[1, 0], [0, 1]])

Matrix([[1,2,3], [2, 3, 10]])

Trigonometric Functions

sin

sin(0)
sin(30)
sin(45)
sin(60)
sin(90)
...
sin(180)
...

Syntax

sin(<value of theata>)

cos

cos(0)
cos(30)
cos(45)
cos(60)
cos(90)
...
cos(180)
...

Syntax

cos(<value of theata>)

tan

tan(0)
tan(30)
tan(45)
tan(60)
tan(90)
...
tan(180)
...

Syntax

tan(<value of theata>)

cosec

csc(0)
csc(30)
csc(45)
csc(60)
csc(90)
...
csc(180)
...

Syntax

csc(<value of theata>)

sec

sec(0)
sec(30)
sec(45)
sec(60)
sec(90)
...
sec(180)
...

Syntax

sec(<value of theata>)

cot

cot(0)
cot(30)
cot(45)
cot(60)
cot(90)
...
cot(180)
...

Syntax

cot(<value of theata>)

Limit

limit('2X')
limit('2X + 3Y')

Syntax

limit(<symbolic and trigonometric values in string formate>)

Determinants

det(Matrix([[10]]))
det(Matrix([[10], [20]]))
det(Matrix([[10], [30], [40]]))
det(Matrix([[10], [100], [0], [50]]))
det(Matrix([[10], [97], [95], [1], [99]]))
det(Matrix([[10], [11], [100], [3], [2], [150]]))
det(Matrix([[10, 20]]))
det(Matrix([[10, 20], [20, 30]]))
det(Matrix([[10, 20], [60, 70], [80, 100]]))
det(Matrix([[10, 20, 30]]))
det(Matrix([[10, 20, 30], [40, 50, 60]]))
det(Matrix([[10, 20, 30], [40, 50, 60], [90, 100, 102]]))
det(Matrix([[10, 20, 30], [40, 50, 60], [90, 100, 102], [95, 101, 1000]]))
...

Log

log(10)
log(20)
log(0)
log(100)
...

Ln

ln(0)
ln(1)
ln(10)
ln(100)
ln(1000)
ln(102)
ln(3)
ln(90)
ln(893)
ln(9)
...

E

e()

↑ Get the value of e.

e(10)
e(3)
e(21)
e(38)
e(0)
...

⊼ (`pi`)

pi()

↑ Get the value of ⊼

Square

Get all square of the value.

sqrt(10)
sqrt(2)
sqrt(30)
sqrt(28039)
sqrt(19)
sqrt(289843190)
...

Differential

diff('2x')
diff('x')
diff('y')
...

Give all the values in string formate.

Integration (`∫`)

Give all the values in string formate.

integrate('dx')
integrate('x dx')
integrate('2x dx')
integrate('2x dy')
integrate('x dy')
integrate('2x + 3y - 3y dx')
...

Factor

Get all the factors of the numbers

factor(10)
factor(213)
factor(389)
factor(1983)
factor(0)
factor(12)
...

Search Method

Search method is for searching queries from internate, and gives top five answers, links, summaries.

Ways to use Search method

Steps

First import Search method from pyaitk module.

from pyaitk import Search

Assigne a varible for query

query = 'pythonaibrain'

Assigne a varible, and pass query in it.

search = Search(query = query)

If no query given it by default takes ‘pythonaibrain’ as a query.
Run the run funcition inside Search to get all top five answers, links, summaries.

search.run()

Get answer.

search.get_results_str()

Note: It return all the answer in string formate, so we will try to fetch all the answers in a variable.
Fetch answer.

answer = search.get_results_str()

At last print all the fetch answers.

print(answer)

Full code

from pyaitk import Search

query = 'pythonaibrain'

search = Search(query)
search.run()

# Get answer.

answer = search.get_results_str()

# print the fetched answer.

print(answer)

Memory Method

It is use for LTM (Long-Term-Memory) ans STM (Short-Term-Memory), or we can say LSTM (Long-Short-Term-Memory)

Way to use Memory Method

Steps

First we will import Memory method from pyaitk module.

from pyaitk import memory

After that.

memory = Memory() # you can pass name of your memory file otherwise it by default takes memory.json

Note : The memory file extention should be .json
Now we will remember function for remember.

memory.remember('user', 'Hi') # It takes key value pairs as a parameter.

Now we recall the messages.

memory.recall('user') # It takes key as a peremeter.

Now we save the memory for LTM.

memory.save_memory() # It saves the memory for long time.

Now we load our LTM.

memory.load_memory() # It load our saved memory, but if the memory is not saved it takes empty dict.

If we want to delete our existing memory we will use clear_memory function.

memory.clear_memory() # It cleare all the data to our memory.

Remember : It will not delete the file.

Contexts Method

Contexts method extract sutable answer asked by user from the given context.

Example

from pyaitk import Contexts

context = '''
Patanjali Ayurved is an Indian company. It was founded by Baba Ramdev and Acharya Balkrishna in 2006.
'''

question = 'Who founded Patanjali Ayurved?'
contexts = Contexts()
answer = contexts.ask(context=context, question=question)
print(answer)

What Contexts method returns?

The Contexts method itself rethurn nothing, but it’s ask attribute returns answers in string formate.

Brain Method

It is a powerfull method which allow you to create a smart AI models and chatbots. It gives you better flexibility to work on your model.

You can also give your dataset to train it. It also supports function mapping.

You can also download pre-trained models from it.

Note: Not all models, you can download models which supports pythonaibrain.

Ways to use Brain Method

Steps

First we will import Brain from pyaitk module.

from pyaitk import Brain

Let’s create brain of our chatbot.

brain = Brain() # You can pass your intents path, and functions for function mapping.

Let’s load our default model.

brain.load() # It loads the default model

If you give your dataset then you should train and save your model.

brain = Brain('intents.json')

brain.train() # It takes few minutes or may be hour, it depends you dataset size.
brain.save()

Let’s start chatting with default model (in basic style).

while True:
    message = input('Message : ')
    answer = brain.process_messages(answer)
    brain.pyai_say(answer)

Other ways to talk with our model (in advance style).

while True:
    message = input('Message : ')
    answer = brain.talk(answer, grammer= False, TTS= True) # You can also close TTS by passing TTS= False.
    brain.write(answer)

Full Code

from pyaitk import Brain

brain = Brain()

# def Hello():
#     return "Hello"
# brain = Brain(intents_path= 'intents.json', hello= Hello)
# brain.train()
# brain.save()

brain.load()

while True:
    message = input("Message : ")
    # answer = brain.process_messages(message)
    answer = brain.talk(message, grammer= False, TTS= True)
    # brain.pyai_say(answer)
    brain.write(answer)

# for more detail about functions run:
# print(brain.__all__)

Functions of `Brain` method

from pyaitk import Brain

brain = Brain()

If you want to know the model is loaded or not, then use is_loaded function.

brain.is_loaded()

It returns True/ False.

If you want to know the model is saved or not, then use is_saved function.

brain.is_saved()

It also returns True/ False.

Or, if you want to konw the model is trained or not, then use is_trained function.

brain.is_trained()

It also returns True/ False.

If you want to know type of inputed message then use predict_message_type function.

brain.predict_message_type('What is your name?') # Returns -> Question.

What `predict_message_type` function can returns for the message

Question
Answer
Command
Shutdown
Make Directory
Statement
Name
Know
Start

Notes:

Shutdown and Start commands require terminal support and do not work on Android or iOS.
Make Directory creates folders on your device.
Statement is any plain text that is not a command/question.
Name detects if a message contains a person’s name.

How to create `intents.json`

{
  "intents": [
    {
      "tag": "greeting",
      "patterns": ["Hi", "Hello", "Hey", "What's up?", "Howdy"],
      "responses": ["Hello! How can I help you today?", "Hey there!", "Hi! What can I do for you?"]
    },
    {
      "tag": "bye",
      "patterns": ["Bye", "See you soon", "Take care"],
      "responses": ["Bye! Have a great day", "See you"]
    }
  ]
}

Save this as a .json file and provide it to the Brain module.

Other way

from pyaitk import Brain

brain = Brain(intents_path='intents.json')  # or Brain() with default
brain.train()
brain.save()
message = input('Message: ')
message_type = brain.predict_message_type(message=message)

if message_type in ['Question', 'Answer']:
    print(f'Answer: {brain.process_messages(message=message)}')

Note : train and save the file when use first time or change the default intents.json with your intents.json otherwise use load function by writting,

brain.load() # it returns bool

And also don’t forgot to load otherwise it through an error Or,

from pyaitk import Brain

brain = Brain()
brain.load()
message = input('Message: ')
message_type = brain.predict_message_type(message=message)

if message_type in ['Question', 'Answer']:
    print(f'Answer: {brain.process_messages(message=message)}')

Advance Brain Module

An advanced version of the Brain module with smarter classification and better entity recognition. It also suport LLMs for better responce.

Usage

from pyaitk import AdvanceBrain

advance_brain = AdvanceBrain(intents_path='intents.json')  # or AdvanceBrain()

message = input('Message: ')
message_type = advance_brain.predict_message_type(message=message)

if message_type in ['Question', 'Answer']:
    print(f'Answer: {advance_brain.process_messages(message=message)}')

Same limitations apply as Brain module for commands.

Pythonaibrain Summary.

Module Name	Description
Brain	Basic Smart AI brain using `.json` knowledge base
AdvanceBrain	Advanced AI brain with better understanding and LLM supports
TTS	Text to speech
MathAI	Use to solve complex and symbolic math problems
PTT	It extract text from pdf
ITT	Image to text extraction
Camera	Capture photos and videos
Context	Get answers from text contexts
Search	It search top five result from internet

Built-in AI Assistant - PyAgent

If you prefer not to code your own AI, use the built-in PyAgent GUI or web assistant.

GUI

import PyAgent
PyAgent.App()

Web Server

from PyAgent import PYAS
PYAS.app.run(debug=False)

Or

from PyAgent import Server
server = Server()
server.run()

Socket Client-AI Server

Make your own client-AI server for handling multiples client togeather, for client side we use pythonaibrain-client module.

Install pythonaibrain-client

pip install pythonaibrain-client

After that,

from pyaibrain.client import ClientServer

cServer = ClientServer()
cServer.serve()

This will create client side in cli (Command Line Interface).

Also,

client-server

Socket Server

from pyaitk import server

server()

From this way we can make main server for which handle clients.

Console Scripts

download-nlu       # Downloads NLTK Data
tiger-server       # Starts AI Agent Chat Server
tiger-web-server   # Starts Web-based AI Server

Visit PyPI for installation and more details.

Visit GitHub for more detail about package.

Visit Pythonaibrain Issues for any issues.

Start building your AI assistant today with Pythonaibrain! Try to ask your doubt with AI releated to this package!

Pythonaibrain

Installation

About Pythonaibrain Package

pyaitk (Python AI ToolKit)

How to import pyaitk

Methods & Functions

Camera Method

Example: Start your camera

TTS (Text To Speech) Method

Example

Syntax for speak function

What speak function returns?

PTT (PDF To Text) Function

Example

What PPT function returns?

STT (Speech To Text) Method.

Example

What STT().listen() return?

Why do we use it?

MathAI Function

Way to use MathAI function

Steps

Full Code

Prompts for MathAI

Solve normal numeric problems.

Solve symbolic methamatic.

Matrix

Trigonometric Functions

sin

Syntax

cos

Syntax

tan

Syntax

cosec

Syntax

sec

Syntax

cot

Syntax

Limit

Syntax

Determinants

Log

Ln

E

⊼ (pi)

Square

Differential

Integration (∫)

Factor

Search Method

Ways to use Search method

Steps

Full code

Memory Method

Way to use Memory Method

Steps

Contexts Method

Example

What Contexts method returns?

Brain Method

Ways to use Brain Method

Steps

Full Code

Functions of Brain method

What predict_message_type function can returns for the message

Notes:

How to create intents.json

Other way

Advance Brain Module

Usage

Same limitations apply as Brain module for commands.

Pythonaibrain Summary.

Built-in AI Assistant - PyAgent

GUI

Web Server

Socket Client-AI Server

Install pythonaibrain-client

Socket Server

pyaitk (`Python AI ToolKit`)

⊼ (`pi`)

Integration (`∫`)

Functions of `Brain` method

What `predict_message_type` function can returns for the message

How to create `intents.json`