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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 text-to-speech, natural language understanding, 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:

bash
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

python
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)
  • —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

python
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:

python
from pyaitk.Camera import Start
Start()

Or via Brain module:

python
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

python
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

VoiceGenderstyleOS
DavidMaleen-USWindows
MarkMaleen-USWindows
ZiraFemaleen-USWindows
AlexMaleen-USMacOS
SamanthaFemaleen-USMacOS
VictoriaFemaleen-USMacOS
FredMaleroboticMacOS
DanielMaleen-GBMacOS
FionaFemaleen-GBMacOS
EnglishNoneDefault espeak voiceLinux
English-USNoneAmerican EnglishLinux
English-UKNoneBritish EnglishLinux
MB-EN1NoneMBROLA English 1Linux
MB-FR1NoneMBROLA French 1Linux

Other ways to use TTS.

python
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
text
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

python
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.

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.
python
from pyaitk import MathAI
  • —Create a variable for storing query in string formate.
python
query = "X + Y - 2X + 3Y" # let's take a query
  • —Now get the answer from the MathAI function for the query.
python
answer = MathAI(query= query) # MathAI gives the answer in string formate.
  • —After that print the answer.
python
print(answer)

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

Full Code
python
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, ``python query = "Solve X + Y - 2X + 3Y" `` Try to ask direct questions only.

Prompts for MathAI

Solve normal numeric problems.
c
1 + 2 + 3 + 4 - 5 (1 - 55) + 10
Solve symbolic methamatic.
c
X + 2Y - X + 10Z * (10 - 100)X
Matrix
c
Matrix([[1, 0], [0, 1]])

Matrix([[1,2,3], [2, 3, 10]])
Trigonometric Functions
sin
c
sin(0)
sin(30)
sin(45)
sin(60)
sin(90)
...
sin(180)
...
Syntax
c
sin(<value of theata>)
cos
c
cos(0)
cos(30)
cos(45)
cos(60)
cos(90)
...
cos(180)
...
Syntax
c
cos(<value of theata>)
tan
c
tan(0)
tan(30)
tan(45)
tan(60)
tan(90)
...
tan(180)
...
Syntax
c
tan(<value of theata>)
cosec
cpp
csc(0)
csc(30)
csc(45)
csc(60)
csc(90)
...
csc(180)
...
Syntax
cpp
csc(<value of theata>)
sec
cpp
sec(0)
sec(30)
sec(45)
sec(60)
sec(90)
...
sec(180)
...
Syntax
cpp
sec(<value of theata>)
cot
cpp
cot(0)
cot(30)
cot(45)
cot(60)
cot(90)
...
cot(180)
...
Syntax
cpp
cot(<value of theata>)
Limit
cpp
limit('2X')
limit('2X + 3Y')
Syntax
cpp
limit(<symbolic and trigonometric values in string formate>)
Determinants
cpp
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
cpp
log(10)
log(20)
log(0)
log(100)
...
Ln
cpp
ln(0)
ln(1)
ln(10)
ln(100)
ln(1000)
ln(102)
ln(3)
ln(90)
ln(893)
ln(9)
...
E
cpp
e()

↑ Get the value of e.

cpp
e(10)
e(3)
e(21)
e(38)
e(0)
...
⊼ (pi)
cpp
pi()

↑ Get the value of ⊼

Square

Get all square of the value.

cpp
sqrt(10)
sqrt(2)
sqrt(30)
sqrt(28039)
sqrt(19)
sqrt(289843190)
...
Differential
cpp
diff('2x')
diff('x')
diff('y')
...

Give all the values in string formate.

Integration (∫)

Give all the values in string formate.

cpp
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

cpp
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.
python
from pyaitk import Search
  • —Assigne a varible for query
python
query = 'pythonaibrain'
  • —Assigne a varible, and pass query in it.
python
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.
python
search.run()
  • —Get answer.
python
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.
python
answer = search.get_results_str()
  • —At last print all the fetch answers.
python
print(answer)
Full code
python
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.
python
from pyaitk import memory
  • —After that.
python
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.
python
memory.remember('user', 'Hi') # It takes key value pairs as a parameter.
  • —Now we recall the messages.
python
memory.recall('user') # It takes key as a peremeter.
  • —Now we save the memory for LTM.
python
memory.save_memory() # It saves the memory for long time.
  • —Now we load our LTM.
python
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.
python
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

python
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.
python
from pyaitk import Brain
  • —Let's create brain of our chatbot.
python
brain = Brain() # You can pass your intents path, and functions for function mapping.
  • —Let's load our default model.
python
brain.load() # It loads the default model
  • —If you give your dataset then you should train and save your model.
python
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).
python
while True:
    message = input('Message : ')
    answer = brain.process_messages(answer)
    brain.pyai_say(answer)
  • —Other ways to talk with our model (in advance style).
python
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
python
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
python
from pyaitk import Brain

brain = Brain()
  • —If you want to know the model is loaded or not, then use is_loaded function.
python
brain.is_loaded()

It returns True/ False.

  • —If you want to know the model is saved or not, then use is_saved function.
python
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.
python
brain.is_trained()

It also returns True/ False.

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

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

python
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,

python
brain.load() # it returns bool

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

python
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.

Usage

python
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 NameDescription
BrainBasic Smart AI brain using .json knowledge base
AdvanceBrainAdvanced AI brain with better understanding
TTSText to speech
MathAIUse to solve complex and symbolic math problems
PTTIt extract text from pdf
ITTImage to text extraction
CameraCapture photos and videos
ContextGet answers from text contexts
SearchIt 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

python
import PyAgent
PyAgent.App()

Web Server

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

Or

python
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
bash
pip install pythonaibrain-client

After that,

python
from pyaibrain.client import ClientServer

cServer = ClientServer()
cServer.serve()

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

Socket Server

python
from pyaitk import server

server()

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

Visit PyPI for installation and more details.

Visit GitHub for more detail about package.

Visit Pythonaibrain for Documentation.

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!