pythonaibrain 1.1.9

pip install pythonaibrain

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Released: Jun 17, 2026

A versatile, plug-and-play Python AI toolkit for building offline intelligent assistants.

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👁 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, 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.

Requirements

Python 3.9 or later
pip 23+

Installation

Minimal install (core package only)

pipinstallpythonaibrain==1.1.9

Install with specific modules

Pick only what you need:

# Text-to-Speech
pipinstall"pythonaibrain[tts]==1.1.9"

# Speech-to-Text
pipinstall"pythonaibrain[stt]==1.1.9"

# Camera + QR/barcode
pipinstall"pythonaibrain[camera]==1.1.9"

# Image-to-Text (OCR)
pipinstall"pythonaibrain[itt]==1.1.9"

# AI Brain + NLP
pipinstall"pythonaibrain[core]==1.1.9"

# Named-Entity Recognition
pipinstall"pythonaibrain[ner]==1.1.9"

# Memory management
pipinstall"pythonaibrain[memory]==1.1.9"

# Mathematical AI + CAS
pipinstall"pythonaibrain[math]==1.1.9"

# Internet Search
pipinstall"pythonaibrain[search]==1.1.9"

# PowerPoint extraction
pipinstall"pythonaibrain[pptx]==1.1.9"

# PDF extraction
pipinstall"pythonaibrain[pdf]==1.1.9"

# Real-time object detection (YOLOv8)
pipinstall"pythonaibrain[eye]==1.1.9"

# Text summarisation
pipinstall"pythonaibrain[summarizer]==1.1.9"

# ZENTRAA encrypted chat server + clients
pipinstall"pythonaibrain[zentraa]==1.1.9"

# CLSE — image generation ⚠ AGPL-3.0-or-later
pipinstall"pythonaibrain[clse]==1.1.9"

Install multiple modules at once

pipinstall"pythonaibrain[core,tts,stt]==1.1.9"
pipinstall"pythonaibrain[core,ner,memory,summarizer]==1.1.9"

Install everything

pipinstall"pythonaibrain[all]==1.1.9"

Linux — System Dependencies

Some modules require native system libraries. Install them before running pip:

# STT (Speech-to-Text) — PortAudio
sudoaptinstallportaudio19-devpython3-pyaudio

# Camera / QR barcode — zbar
sudoaptinstalllibzbar0

Post-install Steps

NLTK data (required by Brain, Context, NER, SummarizerAI)

importpyaitk
pyaitk.InstallNLTKData()

Or manually:

python-mnltk.downloaderpunktpunkt_tabaveraged_perceptron_taggerstopwordswordnetwordsmaxent_ne_chunker

spaCy model (required by NER, optional for Core)

python-mspacydownloaden_core_web_sm

STT offline engine (optional — pocketsphinx)

pipinstallpocketsphinx

Verify Installation

importpyaitk

print(pyaitk.get_version()) # 1.1.9

info = pyaitk.get_info()
print(info)

availability = pyaitk.check_module_availability()
for module, ok in availability.items():
 status = "✔" if ok else "✘"
 print(f" {status}{module}")

Or from the terminal:

pythonaibrain--version
pythonaibrain--modules
pythonaibrain--info

Import Usage

Each module is imported directly by its submodule path:

importpyaitk.core
importpyaitk.TTS
importpyaitk.STT
importpyaitk.Camera
importpyaitk.NER
importpyaitk.Memory
importpyaitk.MathAI
importpyaitk.Search
importpyaitk.SummarizerAI
importpyaitk.CLSE
importpyaitk.eye
importpyaitk.ITT
importpyaitk.PPTExtract
importpyaitk.Grammar

License Notice

Component	License
Pythonaibrain (all modules)	LGPL-3.0-or-later
`pyaitk.CLSE`	AGPL-3.0-or-later

If you use pyaitk.CLSE in your application, the AGPL requires you to release your application's source code. See pyaitk/CLSE/LICENSE.txt for full terms.

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

importpyaitk

Core Component

The central orchestration layer of the Pythonaibrain (pyaitk) framework. Wires together intent classification, neural chatbot training, memory, NER, translation, frame analysis, weather, and an optional quantized LLM — all under two primary entry points: Brain and AdvanceBrain.

What Is This?

pyaitk.core is the heart of the pyaitk package, providing:

Brain — intent-based chatbot with memory, NER, translation, frame classification, grammar correction, and TTS
AdvanceBrain — routes through a local quantized LLM (pythonaibrain-llm) for open-ended generation, with the same interface as Brain
VectorizerMode — switchable feature extraction: Binary BoW (NumPy), TF-IDF (scikit-learn), or Gensim TF-IDF
IntentsManager — load, save, and extend intents.json files dynamically
Frame classification — lightweight neural sentence-type classifier (Statement / Question / Command / Name / …)
NER integration — entity extraction via the NER subsystem, with optional in-call training
Translation — GRU seq-to-seq translator (multilingual → English), trained once per process
Language detection — keyword-based classifier for English / Hindi / French / Spanish
Weather API — city-level weather lookup via OpenWeatherMap
configure() — load a .pbcfg file to override all settings before constructing any Brain
Structured logging — all subsystems use Python's logging module, config-driven level and format

Installation

# Optional: AdvanceBrain LLM support
pipinstallpythonaibrain-llm

Set your OpenWeatherMap API key in a .env file:

weather_api_key=YOUR_KEY_HERE

Quick Start

frompyaitk.coreimport Brain

with Brain() as brain:
 brain.load()
 print(brain.process_messages("Hello"))

Configuration

Load a .pbcfg file before constructing any Brain to override all settings:

importpyaitk.coreascore
core.configure("project.pbcfg")
brain = core.Brain()

configure() updates the global config singleton, applies logging settings, and returns the loaded AppConfig.

`VectorizerMode` — Feature Extraction

Controls how text is converted to feature vectors for the neural intent classifier.

Mode	Backend	Notes
`BOW` (default)	Pure NumPy	Binary Bag-of-Words; zero extra deps
`TFIDF`	scikit-learn	TF-IDF weighting via `TfidfVectorizer`
`GENSIM`	Gensim	Requires `pip install gensim`

frompyaitk.coreimport Brain, VectorizerMode

brain = Brain(vectorizer_mode=VectorizerMode.TFIDF)
brain = Brain(vectorizer_mode=VectorizerMode.GENSIM)
brain = Brain() # default BOW

`Brain` — Intent-Based Chatbot

Basic usage

frompyaitk.coreimport Brain

# Train from scratch
with Brain() as brain:
 brain.train()
 brain.save()
 print(brain.ask("What is the weather?"))

# Load a saved model
with Brain() as brain:
 brain.load()
 response = brain.process_messages("Tell me a joke", grammar=True)
 print(response)

Constructor parameters

Parameter	Type	Default	Description
`intents_path`	`str` or `None`	config	Path to `intents.json`
`condition`	`bool` or `None`	config	Enable dynamic intent learning from search results
`download`	`bool` or `None`	config	Auto-download NLTK data on init
`memory_path`	`str` or `None`	config	Path for memory persistence file
`smart_memory`	`bool` or `None`	config	Use `SmartMemory` (semantic search + clustering)
`memory_fit_interval`	`int` or `None`	config	Auto-fit SmartMemory every N memories
`config`	`AppConfig` or `None`	global	Override global config for this instance
`vectorizer_mode`	`VectorizerMode`	`BOW`	Feature extraction strategy
`**function_mapping`	`Any`	—	Map intent tags to Python callables

Methods

Method	Returns	Description
`train()`	`None`	Parse intents, build features, train neural model
`load()`	`None`	Load saved model from paths in config
`save()`	`None`	Save model weights and dimension file
`process_messages(message, grammar, TTS)`	`str`	Classify intent, respond, remember, optionally speak
`talk(message, grammar, TTS)`	`str`	Attempt web search first, fall back to `process_messages`
`ask(query, TTS)`	`str`	`talk()` + typewriter-style `write()` output
`write(message, set_timer, TTS)`	`None`	Print message character by character with optional TTS
`translator(message)`	`str`	Translate input to English via GRU seq-to-seq model
`classify_language(message)`	`str`	Detect language: `english`, `hindi`, `french`, `spanish`
`predict_message_type(message)`	`str`	Classify sentence type: Statement / Question / Command / …
`predict_entitie(message, train)`	`list`	Extract named entities via NER pipeline
`memorize_user_name(message)`	`None`	Detect and store user name from message
`recall_user_name()`	`str`	Retrieve stored user name from memory
`search_memory(query, top_k)`	`list`	Semantic or substring search over conversation history
`memory_intent(query)`	`str`	Predict intent of query from stored patterns
`memory_report()`	`Any`	Return SmartMemory cluster/intent analysis report
`export_memory_report(path)`	`bool`	Export memory report as JSON
`fit_memory()`	`bool`	Manually retrain SmartMemory summarizer
`is_loaded()` / `is_saved()` / `is_trained()`	`bool`	State flags
`count_query()`	`int`	Total number of queries processed
`pyai_say(*msg)`	`None`	Print with `PYAI :` prefix

Function mapping

Map intent tags to Python callables — called automatically when that intent is predicted:

defopen_calculator():
 importsubprocess; subprocess.Popen("calc.exe")

with Brain(calculator=open_calculator) as brain:
 brain.load()
 brain.process_messages("open calculator")

Memory

Brain uses SmartMemory by default (falls back to plain Memory if the summarizer module is absent). Every process_messages() call automatically stores the exchange.

# Semantic search over history
results = brain.search_memory("weather", top_k=3)

# Export memory analytics
brain.export_memory_report("memory_report.json")

# Manually trigger SmartMemory refit
brain.fit_memory()

`AdvanceBrain` — LLM-powered Brain

Routes responses through a local quantized LLM from pythonaibrain-llm. Falls back to the intent classifier when advance=False.

frompyaitk.coreimport AdvanceBrain

with AdvanceBrain() as brain:
 brain.load()
 print(brain.process_messages("What is Python?"))

# Skip LLM, use intent classifier only
print(brain.process_messages("Hello", advance=False))

AdvanceBrain has the same train(), load(), save(), translator(), classify_language(), predict_message_type(), and predict_entitie() methods as Brain.

Requires: pip install pythonaibrain-llm The LLM is lazy-loaded on first process_messages(advance=True) call.

`IntentsManager` — Dynamic Intent Management

frompyaitk.coreimport IntentsManager

im = IntentsManager("intents.json")

# Add or extend an intent
im.add_intent(
 tag="greeting",
 patterns=["Hi", "Hey there"],
 responses=["Hello!", "Hey! How can I help?"]
)

# Add a search-derived intent
im.add_search_intent("best Python books", ["Python Crash Course", "Fluent Python"])

Utility Functions

Weather

frompyaitk.coreimport get_weather

weather = get_weather("London") # e.g. "Clouds"

Requires weather_api_key in .env. Also available: longitude(city), latitude(city), humidity(city), temperature(city).

Frame classification

frompyaitk.coreimport predict_frame, VectorizerMode

frame = predict_frame("What time is it?") # → "Question"
frame = predict_frame("Open the door") # → "Command"
frame = predict_frame("The sky is blue") # → "Statement"
frame = predict_frame("My name is Divyanshu") # → "Name"

Supported frame types: Statement, Question, Command, Answer, Name, Know, Shutdown, Make Dir, Start.

Translation

frompyaitk.coreimport translate_to_en

text = translate_to_en("tum kaise ho") # → "how are you"
text = translate_to_en("hola como estas")

Model trains once per process on a built-in multilingual corpus (Hindi, French, Spanish, English).

Language detection

frompyaitk.coreimport language_classifier

lang = language_classifier("tum kaise ho") # → "hindi"
lang = language_classifier("je m'appelle") # → "french"
lang = language_classifier("hola como estas") # → "spanish"
lang = language_classifier("Hello there") # → "english"

NER

frompyaitk.coreimport Brain

with Brain() as brain:
 brain.load()
 entities = brain.predict_entitie("Apple was founded by Steve Jobs.")
 print(entities) # list of Entity objects

Or directly:

frompyaitk.coreimport predictNER

entities = predictNER("NASA launched Voyager 1.")
entities = predictNER("LeBron James plays for the Lakers.", train=True) # retrain first

Full Example

frompyaitk.coreimport Brain, VectorizerMode, configure

# Optional: load a custom config before anything else
configure("myproject.pbcfg")

# Train and save
with Brain(vectorizer_mode=VectorizerMode.TFIDF) as brain:
 brain.train()
 brain.save()

# Load and interact
with Brain() as brain:
 brain.load()

 # Chat
 print(brain.ask("Tell me a joke"))
 print(brain.process_messages("What is the weather in Delhi?"))

 # Language tools
 print(brain.classifier_language("tum kaise ho")) # hindi
 print(brain.translator("mera naam ravi hai")) # my name is ravi
 print(brain.predict_message_type("Shutdown /s")) # Shutdown

 # NER
 entities = brain.predict_entitie("Elon Musk founded SpaceX.")
 print(entities)

 # Memory
 brain.memorize_user_name("My name is Divyanshu")
 print(brain.recall_user_name())
 results = brain.search_memory("joke", top_k=3)
 brain.export_memory_report("report.json")

 # Stats
 print(brain.count_query())
 print(brain.is_loaded(), brain.is_saved())

Architecture Overview

Brain / AdvanceBrain
├── ChatbotAssistant ← intent parse → feature matrix → PyTorch neural classifier
│ ├── VectorizerMode ← BOW (NumPy) | TF-IDF (sklearn) | Gensim TF-IDF
│ └── IntentsManager ← load/save/extend intents.json
├── Memory / SmartMemory ← conversation persistence + semantic search
├── NERPipeline ← named entity recognition (spaCy-based)
├── GrammarCorrector ← optional post-processing on responses
├── TTS (speak) ← optional audio output
├── Search ← fallback web search for unknown intents
├── FrameClassifier ← sentence-type classifier (singleton, trains once)
├── GRU Translator ← multilingual → English (singleton, trains once)
├── _PyAILLM ← lazy-loaded quantized LLM (AdvanceBrain only)
└── WeatherAPI ← OpenWeatherMap lookup

TTS - Text To Speech

A fully offline, cross-platform Text-to-Speech module built on the pyttsx3 backend. Works on Windows (SAPI5), macOS (NSSpeechSynthesizer), and Linux (eSpeak) without any network calls.

How to Use

1. One-shot (simplest)

frompyaitk.TTSimport TTS

TTS().say("Hello, world!")

2. Context manager (recommended)

The engine is initialised once and shut down cleanly on exit — even if an exception is raised.

frompyaitk.TTSimport TTS, TTSConfig

with TTS(TTSConfig(voice="zira", rate=160)) as tts:
 tts.say("Hello!")
 tts.say("How are you?")

3. Module-level convenience function

frompyaitk.TTSimport speak

speak("Hello world!")
speak("Bonjour", voice="fiona", rate=140)

4. Save speech to a WAV file

frompyaitk.TTSimport TTS, TTSConfig

cfg = TTSConfig(output_path="greeting.wav")
with TTS(cfg) as tts:
 saved_path = tts.save("Hello, this will be saved to a file.")
 print(f"Saved to: {saved_path}")

You can also override the path at call time:

with TTS() as tts:
 tts.save("Custom path example", path="custom_output.wav")

5. List available voices

frompyaitk.TTSimport TTS

with TTS() as tts:
 for name in tts.available_voices():
 print(name)

6. Get platform-appropriate voice hints

frompyaitk.TTSimport TTS

with TTS() as tts:
 print(tts.platform_voice_hints())
# e.g. ['david', 'zira', 'mark'] on Windows

Configuration (`TTSConfig`)

All parameters are optional and fall back to values from your project config (config.tts.*).

Parameter	Type	Description
`rate`	`int`	Speech rate in words per minute
`volume`	`float`	Volume level from `0.0` (silent) to `1.0` (full)
`voice`	`str`	Voice name fragment (fuzzy matched)
`default_text`	`str`	Fallback text when `say()` is called with no argument
`output_path`	`str` or `None`	Default WAV output path for `save()`

cfg = TTSConfig(rate=180, volume=0.8, voice="samantha")

Voice Matching

Voices are resolved from a short fragment string using a ranked strategy:

Direct substring match — "zira" matches "Microsoft Zira Desktop"
Platform-hint-assisted match — uses known fragments for the current OS
First installed voice — fallback with a warning logged

Platform voice hints:

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	—	Default eSpeak	Linux
English-US	—	American English	Linux
English-UK	—	British English	Linux
MB-EN1	—	MBROLA English 1	Linux
MB-FR1	—	MBROLA French 1	Linux

Examples Summary

# Quickest usage
frompyaitk.TTSimport speak
speak("Hello!")

# Full control with context manager
frompyaitk.TTSimport TTS, TTSConfig
with TTS(TTSConfig(voice="david", rate=175, volume=0.9)) as tts:
 tts.say("Line one.")
 tts.say("Line two.")

# Save to file
with TTS(TTSConfig(voice="samantha")) as tts:
 tts.save("Saved audio example.", path="demo.wav")

# List voices
with TTS() as tts:
 voices = tts.available_voices()
 print(voices)

STT — Speech-to-Text Module

A fully cross-platform Speech-to-Text library supporting both online (Google Speech Recognition) and offline (PocketSphinx) backends, with automatic engine selection based on network availability.

What Is This?

Dual engine support — Google Speech Recognition (online) and PocketSphinx (offline), selected automatically
Auto engine detection — checks network connectivity at runtime and picks the best available engine
Context manager interface — opens the microphone once and releases it cleanly on exit, even on exception
Ambient noise calibration — samples the noise floor before each listen to improve accuracy
Configurable capture parameters — energy threshold, pause detection, phrase time limits, and timeouts
Retry logic — automatically retries on transient service errors with configurable delay
Structured logging — all internal events use Python's logging module; no bare print statements in library code
Custom exception hierarchy — fine-grained errors (STTAudioError, STTRecognitionError, STTServiceError, STTEngineError) for clean error handling

Installation

On Linux, you may also need:
sudoaptinstallportaudio19-devpython3-pyaudio

How to Use

1. One-shot (simplest)

frompyaitk.sttimport STT

stt = STT()
text = stt.listen()
print(text)

2. Context manager (recommended)

The microphone is opened once and released cleanly on exit — even if an exception is raised.

frompyaitk.sttimport STT

with STT() as stt:
 text = stt.listen()
 print(text)

3. Force a specific engine

frompyaitk.sttimport STT, STTConfig, Engine

# Force offline mode
cfg = STTConfig(preferred_engine=Engine.POCKETSPHINX)
with STT(config=cfg) as stt:
 text = stt.listen()
 print(text)

# Force online Google mode
cfg = STTConfig(preferred_engine=Engine.GOOGLE)
with STT(config=cfg) as stt:
 text = stt.listen()
 print(text)

4. Check which engine is active

frompyaitk.sttimport STT

with STT() as stt:
 print(stt.active_engine) # Engine.GOOGLE or Engine.POCKETSPHINX
 text = stt.listen()

5. Custom configuration

frompyaitk.sttimport STT, STTConfig

cfg = STTConfig(
 timeout=8.0, # wait up to 8 seconds for speech to start
 phrase_time_limit=15.0, # cap each utterance at 15 seconds
 pause_threshold=1.0, # 1 second of silence = end of phrase
 ambient_noise_duration=2.0, # spend 2 seconds calibrating noise floor
 google_language="en-GB", # use British English
 max_retries=5, # retry up to 5 times on service errors
)

with STT(config=cfg) as stt:
 text = stt.listen()
 print(text)

6. Multi-turn listening loop

frompyaitk.sttimport STT, STTAudioError, STTRecognitionError

with STT() as stt:
 while True:
 try:
 text = stt.listen()
 print(f"You said: {text}")
 if "stop" in text.lower():
 break
 except STTAudioError:
 print("No speech detected, trying again…")
 except STTRecognitionError:
 print("Couldn't understand that, please repeat.")

Configuration (`STTConfig`)

All parameters are optional and fall back to values from your project config (config.stt.*).

Parameter	Type	Description
`energy_threshold`	`float` or `None`	Mic sensitivity; `None` = auto-calibrate
`dynamic_energy_threshold`	`bool`	Continuously adjust threshold for ambient noise
`pause_threshold`	`float`	Seconds of silence that mark end of phrase
`phrase_time_limit`	`float` or `None`	Hard cap per utterance in seconds
`timeout`	`float` or `None`	Seconds to wait for speech to begin
`ambient_noise_duration`	`float`	Seconds spent sampling the noise floor before listening
`preferred_engine`	`Engine` or `None`	Force a specific engine; `None` = auto-detect
`google_language`	`str`	BCP-47 language tag for Google (e.g. `"en-US"`, `"fr-FR"`)
`google_api_key`	`str` or `None`	Google API key; `None` = free tier
`sphinx_language`	`str`	Language/model name for PocketSphinx
`max_retries`	`int`	Max retry attempts on transient service errors
`retry_delay`	`float`	Seconds to wait between retries
`connectivity_host`	`str`	Host used for the network connectivity check
`connectivity_port`	`int`	Port used for the connectivity check
`connectivity_timeout`	`float`	Timeout for the connectivity probe

Engine Selection

The engine is resolved in this order:

config.preferred_engine — if explicitly set, always used
Network probe — a TCP connection to connectivity_host:connectivity_port is attempted
- Success → Engine.GOOGLE (online)
- Failure → Engine.POCKETSPHINX (offline)

Engine	Mode	Requires	Best for
`GOOGLE`	Online	Internet access	High accuracy, broad language support
`POCKETSPHINX`	Offline	`pocketsphinx`	Air-gapped / low-latency use

Exception Hierarchy

STTError
├── STTAudioError — mic could not be opened, or no speech detected in time
├── STTRecognitionError — audio captured but speech was unintelligible
├── STTServiceError — online service was unreachable or returned an error
└── STTEngineError — PocketSphinx failed to init or missing language data

Example error handling:

frompyaitk.STTimport STT, STTAudioError, STTRecognitionError, STTServiceError, STTEngineError

try:
 with STT() as stt:
 text = stt.listen()
 print(text)
except STTAudioError as e:
 print(f"Mic issue: {e}")
except STTRecognitionError as e:
 print(f"Could not understand: {e}")
except STTServiceError as e:
 print(f"Service unreachable: {e}")
except STTEngineError as e:
 print(f"Offline engine error: {e}")

Examples Summary

# Quickest usage
frompyaitk.STTimport STT
text = STT().listen()

# Context manager with config
frompyaitk.STTimport STT, STTConfig, Engine
cfg = STTConfig(preferred_engine=Engine.GOOGLE, google_language="fr-FR", timeout=6.0)
with STT(config=cfg) as stt:
 text = stt.listen()
 print(text)

# Resilient loop with error recovery
frompyaitk.STTimport STT, STTAudioError, STTRecognitionError
with STT() as stt:
 while True:
 try:
 print(stt.listen())
 except STTAudioError:
 pass # timed out, try again
 except STTRecognitionError:
 print("Please repeat.")

PTT (PDF To Text) Function

What Is This?

Full document extraction — reads all pages and joins them into a single string
Per-page fault tolerance — if a single page fails, extraction continues for the rest
Configurable page separator — control how pages are joined in the output string
Structured logging — all warnings and errors go through Python's logging module
Custom exception — PDFExtractionError wraps PyMuPDF errors for clean upstream handling
Input validation — checks for empty paths, missing files, and non-file paths before opening

How to Use

1. Basic extraction

frompyaitk.PTTimport extract_text_from_pdf

text = extract_text_from_pdf("document.pdf")
print(text)

2. Custom page separator

text = extract_text_from_pdf("report.pdf", page_separator="\n---\n")
print(text)

3. Custom encoding

text = extract_text_from_pdf("document.pdf", encoding="latin-1")

4. With error handling

frompyaitk.PTTimport extract_text_from_pdf, PDFExtractionError

try:
 text = extract_text_from_pdf("document.pdf")
 print(f"Extracted {len(text)} characters.")
except FileNotFoundError:
 print("File not found.")
except ValueError as e:
 print(f"Invalid input: {e}")
except PDFExtractionError as e:
 print(f"Could not read PDF: {e}")

5. Processing multiple files

frompathlibimport Path
frompyaitk.PTTimport extract_text_from_pdf, PDFExtractionError

results = {}
for pdf_path in Path("./docs").glob("*.pdf"):
 try:
 results[pdf_path.name] = extract_text_from_pdf(str(pdf_path))
 except PDFExtractionError as e:
 print(f"Skipping {pdf_path.name}: {e}")

for name, text in results.items():
 print(f"{name}: {len(text)} characters")

API Reference

`extract_text_from_pdf(pdf_path, encoding, page_separator)`

Extracts all text from a PDF and returns it as a single string.

Parameter	Type	Default	Description
`pdf_path`	`str`	(required)	Path to the PDF file
`encoding`	`str`	`"utf-8"`	Text encoding for extraction
`page_separator`	`str`	`"\n\n"`	String inserted between pages in the output

Returns: str — full extracted text from all pages.

Raises:

Exception	When
`ValueError`	`pdf_path` is `None`, empty, or not a file path
`FileNotFoundError`	The specified file does not exist on disk
`PDFExtractionError`	PDF is corrupted, invalid, or unreadable

Exception Reference

PDFExtractionError
└── Wraps fitz.FileDataError and other PyMuPDF runtime errors

PDFExtractionError is the single catch-all for PDF-level failures. FileNotFoundError and ValueError are raised directly for path/input issues and do not need to be caught as PDFExtractionError.

Behaviour Notes

Empty PDF — if the document has zero pages, an empty string "" is returned and a warning is logged.
Page-level errors — if one page fails to extract, that page contributes an empty string and extraction continues for remaining pages. The error is logged.
No partial results lost — all successfully extracted pages are still returned even if some pages failed.

Examples Summary

# Minimal usage
frompyaitk.PTTimport extract_text_from_pdf
text = extract_text_from_pdf("file.pdf")

# Custom separator between pages
text = extract_text_from_pdf("file.pdf", page_separator="\n--- PAGE BREAK ---\n")

# Full error handling
frompyaitk.PTTimport extract_text_from_pdf, PDFExtractionError
try:
 text = extract_text_from_pdf("file.pdf")
except FileNotFoundError:
 print("File not found.")
except PDFExtractionError as e:
 print(f"Extraction failed: {e}")

# Batch processing a folder
frompathlibimport Path
frompyaitk.PTTimport extract_text_from_pdf, PDFExtractionError
for f in Path("./docs").glob("*.pdf"):
 try:
 print(f"{f.name}: {len(extract_text_from_pdf(str(f)))} chars")
 except PDFExtractionError:
 print(f"{f.name}: failed")

PPTXExtractor — PowerPoint Content Extraction Utility

A straightforward utility for extracting text, images, and tables from .pptx PowerPoint files using python-pptx. Processes all slides and organises extracted content by slide number.

What Is This?

PPTXExtractor is a class-based PPTX extraction tool that provides:

Text extraction — pulls all non-empty text from every shape across all slides
Image extraction — saves embedded images to disk, preserving their original format (PNG, JPEG, etc.)
Table extraction — reads all table shapes and returns row/cell data as nested lists
All-in-one extraction — a single extract_all() call returns text, images, and tables together
Auto output directory — creates the image output folder automatically if it doesn't exist
Slide-indexed results — all output is keyed by slide number (1-based) for easy lookup

How to Use

1. Extract everything at once

frompyaitk.PPTExtractimport PPTXExtractor

extractor = PPTXExtractor("presentation.pptx")
data = extractor.extract_all()

# data["texts"] → {slide_num: [str, ...]}
# data["images"] → {slide_num: [image_path, ...]}
# data["tables"] → {slide_num: [[[cell, ...], ...], ...]}

2. Extract text only

extractor = PPTXExtractor("presentation.pptx")
texts = extractor.extract_text()

for slide_num, lines in texts.items():
 print(f"Slide {slide_num}:")
 for line in lines:
 print(f" {line}")

3. Extract and save images

extractor = PPTXExtractor("presentation.pptx", image_output_dir="my_images")
images = extractor.extract_images()

for slide_num, paths in images.items():
 for path in paths:
 print(f"Slide {slide_num}: saved → {path}")

Images are saved as slide{N}_image{M}.{ext} inside the output directory.

4. Extract tables

extractor = PPTXExtractor("presentation.pptx")
tables = extractor.extract_tables()

for slide_num, slide_tables in tables.items():
 for table in slide_tables:
 for row in table:
 print("\t".join(row))

5. Iterate all content by slide

extractor = PPTXExtractor("presentation.pptx")
data = extractor.extract_all()

for slide_num in data["texts"]:
 print(f"\n--- Slide {slide_num} ---")

 for text in data["texts"][slide_num]:
 print(f" Text: {text}")

 for img_path in data["images"].get(slide_num, []):
 print(f" Image: {img_path}")

 for table in data["tables"].get(slide_num, []):
 for row in table:
 print(" Row:", "\t".join(row))

API Reference

`PPTXExtractor(pptx_path, image_output_dir)`

Parameter	Type	Default	Description
`pptx_path`	`str`	(required)	Path to the `.pptx` file
`image_output_dir`	`str`	`"extracted_images"`	Directory where extracted images will be saved

The image output directory is created automatically if it does not exist.

`extract_text()` → `dict[int, list[str]]`

Returns all non-empty text from every shape across all slides.

{
 1: ["Title of Slide One", "Bullet point A", "Bullet point B"],
 2: ["Slide Two heading", "Some body text"],
}

`extract_images()` → `dict[int, list[str]]`

Saves all embedded images to image_output_dir and returns their file paths.

{
 1: ["extracted_images/slide1_image1.png"],
 3: ["extracted_images/slide3_image1.jpeg", "extracted_images/slide3_image2.png"],
}

Filenames follow the pattern: slide{slide_num}_image{shape_num}.{ext}

`extract_tables()` → `dict[int, list[list[list[str]]]]`

Returns table data as nested lists: slide → list of tables → list of rows → list of cell strings.

{
 2: [
 [["Header A", "Header B"], ["Row 1A", "Row 1B"], ["Row 2A", "Row 2B"]],
 ]
}

`extract_all()` → `dict`

Runs all three extractors and returns a combined dictionary:

{
 "texts": { 1: [...], 2: [...] },
 "images": { 1: [...], 3: [...] },
 "tables": { 2: [...] },
}

Examples Summary

frompyaitk.PPTExtractimport PPTXExtractor

# Extract everything
data = PPTXExtractor("deck.pptx").extract_all()

# Text only
texts = PPTXExtractor("deck.pptx").extract_text()

# Images saved to a custom folder
images = PPTXExtractor("deck.pptx", image_output_dir="assets").extract_images()

# Tables only
tables = PPTXExtractor("deck.pptx").extract_tables()

# Iterate slide by slide
extractor = PPTXExtractor("deck.pptx")
data = extractor.extract_all()
for slide_num in data["texts"]:
 print(f"Slide {slide_num}:", data["texts"][slide_num])

NER System — Named Entity Recognition

A complete, modular Named Entity Recognition system built on spaCy. Covers the full ML lifecycle: text preprocessing, inference, postprocessing, training with early stopping, evaluation with precision/recall/F1, and persistent entity storage.

What Is This?

This package is a NER system with six cooperating modules:

Module	Class / Function	Responsibility
`pipeline.py`	`NERPipeline`	Core inference — single and batch prediction
`trainer.py`	`NERTrainer`	Training loop with early stopping and model saving
`evaluator.py`	`NEREvaluator`	Precision / Recall / F1 metrics (exact + partial match)
`preprocessor.py`	`TextPreprocessor`	Cleans and normalises raw text before inference
`postprocessor.py`	`EntityPostprocessor`	Filters, deduplicates, and enriches entity outputs
`entity_store.py`	`EntityStore`	In-memory store with JSONL persistence and analytics
`logging_config.py`	`setup_logging()`	Configures root logger (console + optional file)

Quick Start

frompyaitk.NERimport NERPipeline, EntityStore

# Load a trained model
pipeline = NERPipeline.from_model_path("models/my_ner")

# Predict
result = pipeline.predict("Apple was founded by Steve Jobs in California.")
for entity in result.entities:
 print(entity.label, entity.text)
# ORG Apple
# PERSON Steve Jobs
# GPE California

# Store results
store = EntityStore(persist_path="entities.jsonl")
store.add(result)
print(store.top_entities("ORG", n=5))

Module Guide

NERPipeline — Inference

frompyaitk.NERimport NERPipeline

# From a saved model on disk
pipeline = NERPipeline.from_model_path("models/my_ner")

# From a blank model (for training or rule-based use)
pipeline = NERPipeline.from_blank(lang="en", labels=["ORG", "PERSON", "GPE"])

# Single prediction
result = pipeline.predict("Google acquired DeepMind in 2014.")
print(result.entities) # list of Entity objects
print(result.entity_types) # ['DATE', 'ORG']
print(result.filter_by_label("ORG")) # [Entity(text='Google', ...)]
print(result.processing_time_ms) # e.g. 12.4

# Batch prediction (lazy generator, memory-efficient)
texts = ["Text one.", "Text two.", "Text three."]
for result in pipeline.predict_batch(texts):
 print(result.entities)

# Save model to disk
pipeline.save("models/my_ner_v2")

# Inspect labels
print(pipeline.labels) # ['DATE', 'GPE', 'ORG', 'PERSON']

Constructor options:

Parameter	Type	Default	Description
`nlp`	`spacy.Language`	(required)	Loaded spaCy model
`preprocessor`	`TextPreprocessor`	`None`	Custom preprocessor; defaults to standard config
`postprocessor`	`EntityPostprocessor`	`None`	Custom postprocessor; defaults to standard config
`return_doc`	`bool`	`False`	Include raw spaCy `Doc` in results
`batch_size`	`int`	`64`	Chunk size for `nlp.pipe` in batch mode

NERTrainer — Training

frompyaitk.NERimport NERTrainer
frompyaitk.NER.trainerimport TrainerConfig

TRAIN_DATA = [
 ("Apple was founded by Steve Jobs.", {"entities": [(0, 5, "ORG"), (21, 31, "PERSON")]}),
 ("Google is based in Mountain View.", {"entities": [(0, 6, "ORG"), (19, 32, "GPE")]}),
]
DEV_DATA = [...]

# Train from scratch
trainer = NERTrainer(config=TrainerConfig(n_iter=30, dropout=0.35))
trainer.prepare(TRAIN_DATA)
results = trainer.train(TRAIN_DATA, dev_data=DEV_DATA, output_dir="models/v1")

print(results["best_dev_f1"]) # e.g. 0.8712
print(results["history"]) # list of per-epoch loss + dev scores

# Fine-tune from an existing spaCy model
trainer = NERTrainer(base_model="en_core_web_sm")
trainer.train(TRAIN_DATA, output_dir="models/v1_finetuned")

# Export training data to spaCy v3 DocBin format
importspacy
nlp = spacy.blank("en")
NERTrainer.data_to_docbin(TRAIN_DATA, nlp, "train.spacy")

TrainerConfig fields:

Field	Default	Description
`lang`	`"en"`	spaCy language code for blank model
`n_iter`	`30`	Maximum training epochs
`dropout`	`0.35`	Dropout rate during training
`batch_start`	`4`	Starting batch size for compounding scheduler
`batch_compound`	`1.001`	Compounding factor for batch size growth
`eval_every`	`5`	Evaluate on dev set every N epochs
`patience`	`5`	Early stopping patience (epochs without improvement)
`min_delta`	`0.001`	Minimum F1 improvement to reset patience counter
`seed`	`42`	Random seed for reproducibility

NEREvaluator — Metrics

frompyaitk.NERimport NEREvaluator, NERPipeline

pipeline = NERPipeline.from_model_path("models/my_ner")
evaluator = NEREvaluator() # exact match (default)
evaluator_partial = NEREvaluator(partial=True) # any span overlap counts

gold_data = [
 ("Apple was founded by Steve Jobs.", {"entities": [(0, 5, "ORG"), (21, 31, "PERSON")]}),
]

report = evaluator.evaluate(gold_data, pipeline)
print(report)
# ============================================================
# Micro P=0.9200 R=0.8750 F1=0.8970
# Macro F1 = 0.8800
# ------------------------------------------------------------
# Label P R F1 TP FP FN
# ------------------------------------------------------------
# ORG 0.9500 0.9000 0.9245 ...
# PERSON 0.8100 0.8500 0.8295 ...
# ============================================================

print(report.to_dict()) # structured dict for logging / serialisation

# Evaluate pre-computed predictions (no pipeline needed)
gold_spans_list = [[(0, 5, "ORG"), (21, 31, "PERSON")]]
pred_spans_list = [[(0, 5, "ORG"), (21, 31, "PERSON")]]
report = evaluator.evaluate_from_predictions(gold_spans_list, pred_spans_list)

EvaluationReport properties:

Property	Description
`micro_precision`	TP / (TP + FP) across all labels
`micro_recall`	TP / (TP + FN) across all labels
`micro_f1`	Harmonic mean of micro precision and recall
`macro_f1`	Unweighted average F1 across all labels
`per_label`	Dict of `LabelScore` objects keyed by label

TextPreprocessor — Input Cleaning

frompyaitk.NERimport TextPreprocessor
frompyaitk.NER.preprocessorimport PreprocessorConfig

# Default config
preprocessor = TextPreprocessor()
clean = preprocessor.process("<b>Visit https://example.com for info.</b>")
# → "Visit for info."

# Custom config
config = PreprocessorConfig(
 lowercase=True,
 remove_urls=True,
 remove_emails=True,
 remove_html_tags=True,
 normalize_whitespace=True,
 normalize_unicode=True,
 max_length=512,
 custom_patterns=[r"\d{4}-\d{2}-\d{2}"], # strip ISO dates
)
preprocessor = TextPreprocessor(config)

# Batch
cleaned_texts = preprocessor.process_batch(["Text one.", "Text two."])

PreprocessorConfig fields:

Field	Default	Description
`lowercase`	`False`	Lowercase the entire text
`remove_urls`	`True`	Remove `http://`, `https://`, and `www.` URLs
`remove_emails`	`False`	Remove email addresses (kept by default as NER signal)
`remove_html_tags`	`True`	Strip HTML tags
`normalize_whitespace`	`True`	Collapse all whitespace to single spaces
`normalize_unicode`	`True`	NFC-normalise Unicode
`max_length`	`None`	Truncate text to this many characters
`custom_patterns`	`[]`	List of regex strings to remove

EntityPostprocessor — Output Cleaning

frompyaitk.NERimport EntityPostprocessor
frompyaitk.NER.postprocessorimport PostprocessorConfig

config = PostprocessorConfig(
 min_length=2,
 max_length=100,
 allowed_labels={"ORG", "PERSON", "GPE"},
 blocked_labels={"MISC"},
 deduplicate=True,
 merge_adjacent=True,
 strip_punct=True,
 custom_label_map={"PERSON": "PER"}, # rename labels
)
postprocessor = EntityPostprocessor(config)
entities = postprocessor.process(entities)

Processing chain (in order):

Filter by text length (min_length / max_length)
Filter by label (allowed_labels / blocked_labels)
Strip leading/trailing punctuation from entity text (strip_punct)
Remap label names (custom_label_map)
Lowercase labels (lowercase_labels)
Deduplicate identical spans (deduplicate)
Merge back-to-back same-label spans (merge_adjacent, gap ≤ 1 char)

EntityStore — Persistence & Analytics

frompyaitk.NERimport EntityStore

# In-memory only
store = EntityStore()

# With JSONL persistence (appends on each add; loads on init if file exists)
store = EntityStore(persist_path="entities.jsonl")

store.add(result) # add a single NERResult
store.add_batch([result1, result2]) # add multiple results

# Query
store.query(label="ORG") # all ORG records
store.query(text_contains="apple", min_score=0.8) # filtered
store.unique_entities(label="PERSON") # sorted unique names
store.top_entities("ORG", n=10) # [(entity, count), ...]
store.label_distribution() # {"ORG": 42, "PERSON": 31}

# Export
store.to_json("all_entities.json")

# Iterate
for record in store.iter_records():
 print(record["text"], record["label"])

print(len(store)) # total entity count
print(store) # EntityStore(total=132, labels={'ORG': 42, 'PERSON': 31})

Each stored record contains:

Field	Description
`text`	Entity surface form
`label`	Entity type (e.g. `ORG`, `PERSON`)
`start_char`	Start character offset in source text
`end_char`	End character offset in source text
`score`	Detection confidence (0.0–1.0)
`source_text`	The original text the entity came from

Logging Setup

frompyaitk.NER.logging_configimport setup_logging

setup_logging(level="DEBUG", log_file="ner.log")

Parameter	Default	Description
`level`	`"INFO"`	Logging level
`log_file`	`None`	Optional path for file output
`fmt`	`"%(asctime)s [%(levelname)s] %(name)s — …"`	Log record format string

Full End-to-End Example

frompyaitk.NERimport NERPipeline, NERTrainer, NEREvaluator, EntityStore
frompyaitk.NER.trainerimport TrainerConfig
frompyaitk.NER.logging_configimport setup_logging

setup_logging(level="INFO", log_file="ner.log")

# 1. Prepare data
TRAIN_DATA = [
 ("Apple was founded by Steve Jobs in California.", {
 "entities": [(0, 5, "ORG"), (21, 31, "PERSON"), (35, 45, "GPE")]
 }),
]
DEV_DATA = TRAIN_DATA # use your actual dev split

# 2. Train
trainer = NERTrainer(config=TrainerConfig(n_iter=20, eval_every=5))
results = trainer.train(TRAIN_DATA, dev_data=DEV_DATA, output_dir="models/v1")
print("Best dev F1:", results["best_dev_f1"])

# 3. Load and predict
pipeline = NERPipeline.from_model_path("models/v1")
result = pipeline.predict("Microsoft acquired GitHub in 2018.")
print(result.entities)

# 4. Evaluate
evaluator = NEREvaluator()
report = evaluator.evaluate(DEV_DATA, pipeline)
print(report)

# 5. Store
store = EntityStore(persist_path="entities.jsonl")
store.add(result)
print(store.top_entities("ORG"))
print(store.label_distribution())

Data Format

Training and evaluation data uses spaCy's standard annotation format:

[
 ("Text to annotate.", {"entities": [(start, end, "LABEL"), ...]}),
 ("Google is in Mountain View.", {"entities": [(0, 6, "ORG"), (13, 26, "GPE")]}),
]

start / end are character offsets (not token indices)
Spans must not overlap
Labels are arbitrary strings registered via NERTrainer.prepare() or NERPipeline.from_blank()

MathAI — Production-grade Mathematical Expression Solver

A robust symbolic mathematics solver built on SymPy, supporting simplification, equation solving, differentiation, integration, matrix analysis, and Taylor series expansion — with automatic operation detection, input validation, and structured result objects.

What Is This?

MathAI is a symbolic math engine that provides:

Auto-detection — process() and MathAI() infer the operation type from the query automatically
Symbolic computation — exact symbolic results powered by SymPy (no floating-point approximation unless requested)
Numeric evaluation — automatically computes a decimal approximation when the result is a pure number
Equation solving — single equations and systems of equations (via x + y = 5 and x - y = 1 syntax)
Calculus — differentiation (any order) and definite/indefinite integration
Matrix analysis — determinant, trace, rank, inverse, and eigenvalues in one call
Taylor/Laurent series — configurable expansion point and order
Input validation — rejects empty input, oversized expressions, and dangerous code patterns
Structured results — every operation returns a MathResult dataclass with success, result, simplified, numeric, and metadata fields
Structured logging — all internal events go through Python's logging module
Convenience API — single MathAI(query, operation) function for quick one-liner use

How to Use

1. One-liner convenience function (simplest)

frompyaitk.MathAIimport MathAI

print(MathAI("x^2 + 2*x + 1"))
print(MathAI("x^2 - 4 = 0", operation="solve"))
print(MathAI("sin(x)", operation="differentiate"))

2. Auto-detect operation

The MathAI() function (and MathSolver.process()) detect the operation from the query:

Contains = → solve
Starts with Matrix → matrix operations
Starts with diff or derivative(...) → differentiate
Starts with int or integrate(...) → integrate
Anything else → simplify

frompyaitk.MathAIimport MathAI

print(MathAI("x^2 - 9 = 0")) # → solve
print(MathAI("Matrix([[2, 1], [5, 3]])")) # → matrix
print(MathAI("sin(x)^2 + cos(x)^2")) # → simplify

3. Using `MathSolver` directly

frompyaitk.mathaiimport MathSolver

solver = MathSolver()

result = solver.simplify("sin(x)^2 + cos(x)^2")
print(result)

4. Simplification

result = solver.simplify("x^3 + 3*x^2 + 3*x + 1")
print(result.simplified) # (x + 1)**3
print(result.numeric) # set if result is a pure number

5. Solving equations

# Single equation
result = solver.solve_equation("x^2 - 4 = 0")
print(result.result) # [{x: -2}, {x: 2}]

# System of equations (separate with "and")
result = solver.solve_equation("x + y = 10 and x - y = 2")
print(result.result) # [{x: 6, y: 4}]

6. Differentiation

# First derivative (default)
result = solver.differentiate("x^3 + sin(x)")
print(result.result) # 3*x**2 + cos(x)
print(result.simplified) # simplified form

# Higher-order derivative
result = solver.differentiate("x^5", var="x", order=3)
print(result.result) # 60*x**2

7. Integration

# Indefinite integral
result = solver.integrate("x^2 + 3*x")
print(result.result) # x**3/3 + 3*x**2/2

# Definite integral
result = solver.integrate("x^2", var="x", limits=(0, 1))
print(result.result) # 1/3
print(result.numeric) # 0.333333333333333

8. Matrix operations

result = solver.matrix_operations("Matrix([[1, 2], [3, 4]])")
print(result.metadata["determinant"]) # -2
print(result.metadata["inverse"]) # Matrix([[-2, 1], [3/2, -1/2]])
print(result.metadata["eigenvalues"]) # {-sqrt(33)/2 + 5/2: 1, sqrt(33)/2 + 5/2: 1}
print(result.metadata["rank"]) # 2
print(result.metadata["trace"]) # 5

9. Taylor series expansion

# Default: expand around x=0, 6 terms
result = solver.series_expansion("sin(x)")
print(result.result) # x - x**3/6 + x**5/120 + O(x**6)

# Custom point and order
result = solver.series_expansion("exp(x)", var="x", point=0, order=4)
print(result.result) # 1 + x + x**2/2 + x**3/6 + O(x**4)

API Reference

`MathAI(query, operation)` → `str`

Top-level convenience function. Returns a formatted string of the result.

Parameter	Type	Default	Description
`query`	`str`	`'1x + 2x - 3*x'`	Mathematical expression or equation
`operation`	`str`	`'auto'`	One of: `auto`, `simplify`, `solve`, `differentiate`, `integrate`, `matrix`, `series`

`MathSolver` methods

Method	Description
`process(query)`	Auto-detect and dispatch to the right operation
`simplify(expr)`	Simplify a symbolic expression
`solve_equation(expr)`	Solve one or more equations
`differentiate(expr, var, order)`	Differentiate to any order
`integrate(expr, var, limits)`	Indefinite or definite integral
`matrix_operations(expr)`	Det, trace, rank, inverse, eigenvalues
`series_expansion(expr, var, point, order)`	Taylor/Laurent series around a point

`MathResult` fields

Every method returns a MathResult dataclass:

Field	Type	Description
`success`	`bool`	`True` if the operation completed without error
`operation`	`str`	Name of the operation performed
`input_expr`	`str`	The original input string
`result`	`str` or `None`	Primary result of the operation
`simplified`	`str` or `None`	Simplified form (where applicable)
`numeric`	`str` or `None`	Decimal evaluation (when result is a pure number)
`error`	`str` or `None`	Error message if `success` is `False`
`metadata`	`dict` or `None`	Extra details (variable, limits, matrix properties, etc.)

Calling str(result) produces a human-readable summary of all populated fields.

Supported Symbols and Functions

The solver pre-loads a wide set of SymPy functions accessible directly in expressions:

Category	Available
Trigonometric	`sin`, `cos`, `tan`, `cot`, `sec`, `csc`, `asin`, `acos`, `atan`, `sinh`, `cosh`, `tanh`
Exponential / Log	`exp`, `log`, `ln`
Constants	`e`, `pi`, `I` (imaginary unit), `oo` (infinity)
Algebra	`sqrt`, `abs`, `floor`, `ceil`, `factorial`, `binomial`
Calculus	`diff`, `integrate`, `limit`, `Sum`, `Product`
Linear Algebra	`Matrix`, `det`, `transpose`
Simplification	`factor`, `expand`, `simplify`, `cancel`, `apart`, `together`
Special Functions	`gamma`, `erf`
Variables	`x y z a b c … theta phi alpha beta gamma delta epsilon`

Input Validation

Before any operation, expressions are checked for:

Empty or whitespace-only input
Expressions exceeding 10,000 characters
Forbidden patterns: __, import, eval, exec, compile, open, file

Rejected inputs return a MathResult with success=False and a descriptive error message — no exceptions are raised to the caller.

Examples Summary

frompyaitk.mathaiimport MathAI, MathSolver

# One-liner API
print(MathAI("x^2 + 2*x + 1"))
print(MathAI("x^2 - 4 = 0", operation="solve"))
print(MathAI("sin(x)", operation="differentiate"))
print(MathAI("x^2", operation="integrate"))
print(MathAI("Matrix([[1, 2], [3, 4]])", operation="matrix"))
print(MathAI("cos(x)", operation="series"))

# Using MathSolver directly
solver = MathSolver()
r = solver.solve_equation("x + y = 10 and x - y = 2")
print(r.result)

r = solver.differentiate("x^5", order=3)
print(r.simplified)

r = solver.integrate("x^2", limits=(0, 1))
print(r.numeric)

r = solver.matrix_operations("Matrix([[1, 2], [3, 4]])")
print(r.metadata)

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(<valueoftheata>)

cos

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

Syntax

cos(<valueoftheata>)

tan

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

Syntax

tan(<valueoftheata>)

cosec

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

Syntax

csc(<valueoftheata>)

sec

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

Syntax

sec(<valueoftheata>)

cot

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

Syntax

cot(<valueoftheata>)

Limit

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

Syntax

limit(<symbolicandtrigonometricvaluesinstringformate>)

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('xdx')
integrate('2xdx')
integrate('2xdy')
integrate('xdy')
integrate('2x+3y-3ydx')
...

Factor

Get all the factors of the numbers

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

ITT — Image-to-Text (OCR) Utility

A minimal, dependency-light OCR utility built on EasyOCR. Extracts text from images in a single function call, with the recognition model loaded once at startup for efficient repeated use.

What Is This?

ITT is a lightweight image-to-text module that provides:

Single function API — ITT(image_path) returns all recognised text as a plain string
EasyOCR backend — deep learning-based OCR supporting 80+ languages out of the box
Model loaded once — the reader is initialised at module level so repeated calls pay no reload cost
Multi-language support — pass any EasyOCR-supported language codes at call time
Zero boilerplate — no configuration classes, no context managers; just import and call

Note

EasyOCR downloads model weights on first use (~100 MB). An internet connection is required for the initial download; subsequent runs work fully offline.

How to Use

1. Basic usage

frompyaitk.ITTimport ITT

text = ITT("screenshot.png")
print(text)

2. Extract text from any image format

EasyOCR supports JPEG, PNG, BMP, TIFF, and more.

text = ITT("photo.jpg")
text = ITT("scan.bmp")
text = ITT("document.tiff")

3. Multi-language recognition

Pass a list of BCP-47 / EasyOCR language codes as the second argument.

# English + French
text = ITT("menu.jpg", languages=["en", "fr"])

# English + Hindi
text = ITT("sign.png", languages=["en", "hi"])

Note: The module-level reader is initialised with ['en'] only. To use other languages reliably, re-initialise easyocr.Reader with the desired codes before calling readtext.

4. Batch processing multiple images

frompyaitk.ITTimport ITT
frompathlibimport Path

results = {}
for img in Path("./images").glob("*.png"):
 results[img.name] = ITT(str(img))

for name, text in results.items():
 print(f"{name}: {text[:80]}")

5. Using the result downstream

text = ITT("invoice.png")

# Search for keywords
if "TOTAL" in text.upper():
 print("Invoice contains a total amount.")

# Write extracted text to file
with open("output.txt", "w", encoding="utf-8") as f:
 f.write(text)

API Reference

`ITT(image_path, languages)` → `str`

Runs OCR on the given image and returns all detected text joined into a single space-separated string.

Parameter	Type	Default	Description
`image_path`	`str`	(required)	Path to the image file
`languages`	`list[str]`	`['en']`	EasyOCR language codes to use for recognition

Returns: str — all detected text regions joined by spaces, in detection order.

Module-level `reader`

reader = easyocr.Reader(['en'])

The reader is created once when the module is imported. This means:

Model files are downloaded on the first import only
All subsequent ITT() calls reuse the same loaded model — no per-call overhead
If you need languages beyond English, create your own easyocr.Reader instance with the required codes

Supported Languages (selected)

EasyOCR supports 80+ languages. Common codes:

Code	Language	Code	Language
`en`	English	`fr`	French
`hi`	Hindi	`de`	German
`zh`	Chinese	`ja`	Japanese
`ko`	Korean	`ar`	Arabic
`es`	Spanish	`pt`	Portuguese

Full list: https://www.jaided.ai/easyocr

Notes

Detection order — text regions are returned in the order EasyOCR detects them, which follows a rough top-to-bottom, left-to-right reading order but may vary for complex layouts.
Low-quality images — blurry, low-contrast, or heavily compressed images will reduce accuracy. Pre-processing with a library like Pillow (resize, sharpen, greyscale) can improve results.
GPU acceleration — EasyOCR uses the GPU automatically if a CUDA-capable device is available. On CPU, recognition is slower but fully functional.

Examples Summary

frompyaitk.ITTimport ITT

# Basic
text = ITT("image.png")

# Multi-language
text = ITT("document.jpg", languages=["en", "fr"])

# Batch
frompathlibimport Path
for img in Path("./scans").glob("*.jpg"):
 print(img.name, ITT(str(img)))

# Save to file
with open("extracted.txt", "w") as f:
 f.write(ITT("receipt.png"))

GrammarCorrector — Grammar Correction Pipeline

A three-tier grammar correction system combining SpaCy rule-based morphology, a scikit-learn statistical token corrector, and a PyTorch LSTM Seq2Seq neural model — all unified behind a single GrammarCorrector facade with a JSON intents system for training on real-world error pairs.

What Is This?

Grammar provides a complete grammar correction pipeline with three escalating tiers:

Tier	Engine	Requires fitting?	Description
1	SpaCy + regex rules	No	Morphological rules, subject-verb agreement, capitalisation, confused-word pairs
2	Scikit-learn (Logistic Regression + DictVectorizer)	Yes	Statistical token-level correction trained on (corrupted, correct) pairs
3	PyTorch LSTM Seq2Seq	Yes	Neural fallback for out-of-vocabulary and complex patterns

Tier 1 always runs. Tiers 2 and 3 activate after fit() and each falls back gracefully if unavailable.

Additional features:

JSON intents system — load structured {incorrect, correct, tag} pairs from a file; two supported layouts (flat and grouped)
Auto-corruption engine — 100+ deterministic corruption rules (homophones, slang, typos, contractions) to generate training data from plain correct sentences
BPE tokenizer — trained from scratch on the training corpus via HuggingFace tokenizers
Context manager interface — loads SpaCy on enter, releases all GPU/CPU resources on exit
quick_correct() — one-liner convenience function for rule-only or full pipeline correction

How to Use

1. Rule-only correction (no fitting needed)

Tier 1 (SpaCy + rules) always runs — no training data required.

frompyaitk.Grammarimport GrammarCorrector

with GrammarCorrector() as gc:
 print(gc.correct("i wants to here more about this"))
 # → "I wants to hear more about this."

 print(gc.correct("she go to the market"))
 # → "She goes to the market."

2. Full three-tier pipeline from a sentence list

frompyaitk.Grammarimport GrammarCorrector

sentences = [
 "I am going to the market.",
 "She has a beautiful garden.",
 "They were playing football yesterday.",
]

with GrammarCorrector() as gc:
 gc.fit(sentences)
 print(gc.correct("i wants to here more"))
 print(gc.correct("she go to market"))

3. Train from a JSON intents file (recommended)

frompyaitk.Grammarimport GrammarCorrector

with GrammarCorrector() as gc:
 gc.fit_from_intents_file("intents.json")
 print(gc.correct("i wants to here you"))

4. Train from intents with a tag filter

Only use specific error categories for training:

frompyaitk.Grammarimport GrammarCorrector, load_intents

examples = load_intents("intents.json", tags=["verb_agreement", "capitalisation"])

with GrammarCorrector() as gc:
 gc.fit_from_intents(examples)
 print(gc.correct("he go home"))

5. Merge intents + plain sentences

frompyaitk.Grammarimport GrammarCorrector, load_intents

sentences = ["The cat sat on the mat.", "I enjoy reading books."]
examples = load_intents("intents.json")

with GrammarCorrector() as gc:
 gc.fit(sentences, intents=examples)
 print(gc.correct("u r gr8"))

6. Save and load a trained model

frompyaitk.Grammarimport GrammarCorrector

# Train and save
with GrammarCorrector() as gc:
 gc.fit_from_intents_file("intents.json")
 gc.save("models/grammar_v1")

# Load and use
with GrammarCorrector() as gc:
 gc.load("models/grammar_v1")
 print(gc.correct("she go to the market"))

7. One-liner convenience function

frompyaitk.Grammarimport quick_correct

# Rule-only (no training data)
print(quick_correct("i go to school"))

# With training
sentences = ["She goes to school.", "I am happy."]
print(quick_correct("i go to school", sentences=sentences))

8. Custom configuration

frompyaitk.Grammarimport GrammarCorrector, CorrectorConfig

cfg = CorrectorConfig(
 epochs=20,
 hidden_dim=512,
 num_layers=3,
 dropout=0.4,
 learning_rate=5e-4,
 vocab_size=3000,
 device="cuda",
)

with GrammarCorrector(cfg) as gc:
 gc.fit_from_intents_file("intents.json")
 print(gc.correct("they was at home"))

JSON Intents File Format

Two supported layouts — freely mixable in the same file.

Layout A — flat pair

{
"intents":[
{
"tag":"subject_verb_agreement",
"incorrect":"she go to the market",
"correct":"she goes to the market"
},
{
"tag":"pronoun_capitalisation",
"incorrect":"i am here",
"correct":"I am here"
}
]
}

Layout B — grouped examples

{
"intents":[
{
"tag":"capitalisation",
"examples":[
{"incorrect":"i am here","correct":"I am here"},
{"incorrect":"hi my name","correct":"Hi my name"}
]
}
]
}

Rules:

"tag" is required; defaults to "general" if omitted
Pairs where incorrect == correct are silently skipped
Duplicate pairs (case-insensitive) are deduplicated automatically
Both layouts may be mixed freely in the same file

Loading and inspecting intents

frompyaitk.Grammarimport load_intents, intents_summary, intents_to_pairs

# Load all
examples = load_intents("intents.json")

# Load with tag filter
examples = load_intents("intents.json", tags=["verb_agreement"])

# Load from a dict (useful for testing)
examples = load_intents({
 "intents": [{"tag": "test", "incorrect": "i go", "correct": "I go"}]
})

# Summary: {tag: count}
summary = intents_summary(examples)
print(summary) # {"capitalisation": 5, "verb_agreement": 8}

# Convert to raw pairs
pairs = intents_to_pairs(examples) # [("i go", "I go"), ...]

API Reference

`GrammarCorrector(config?)`

Parameter	Type	Default	Description
`config`	`CorrectorConfig` or `None`	`None`	Uses `CorrectorConfig()` defaults if `None`

Methods

Method	Returns	Description
`fit(sentences, intents?)`	`self`	Train all three tiers; auto-corrupts `sentences`; merges `intents` pairs
`correct(text)`	`str`	Apply all available tiers in order
`fit_from_intents(intents, sentences?, tags?)`	`self`	Train primarily from `IntentExample` list
`fit_from_intents_file(path, sentences?, tags?)`	`self`	Load JSON file and train in one call
`save(path)`	`None`	Save Seq2Seq weights + BPE tokenizer to a directory
`load(path)`	`self`	Restore weights + tokenizer from a directory

All fit* methods return self for chaining:

gc.fit_from_intents_file("intents.json").correct("she go to market")

`CorrectorConfig` fields

Field	Type	Default	Description
`vocab_size`	`int`	`2000`	BPE tokenizer vocabulary size
`special_tokens`	`list`	`["<s>", "</s>", "<unk>", "<pad>"]`	Special tokens
`emb_dim`	`int`	`128`	Token embedding dimension
`hidden_dim`	`int`	`256`	LSTM hidden layer size
`num_layers`	`int`	`2`	Number of LSTM layers
`dropout`	`float`	`0.3`	Dropout rate
`epochs`	`int`	`10`	Seq2Seq training epochs
`learning_rate`	`float`	`1e-3`	Adam optimizer learning rate
`teacher_forcing_ratio`	`float`	`0.5`	Probability of using teacher forcing per step
`max_decode_len`	`int`	`120`	Max tokens to generate during inference
`spacy_model`	`str`	`en_core_web_sm`	SpaCy model name
`device`	`str`	`"cuda"` if available else `"cpu"`	PyTorch device

Exception Hierarchy

GrammarCorrectorError
├── NotFittedError — correct() called before fit()
├── TokenizerError — BPE tokenizer missing special tokens
└── IntentsValidationError — JSON intents file fails schema validation

Data Helpers

frompyaitk.Grammarimport corrupt_sentence, build_dataset

# Apply deterministic corruption rules to one sentence
noisy = corrupt_sentence("She goes to the market.")
# → "She go 2 the market."

# Build (corrupted, correct) pairs from a list of correct sentences
dataset = build_dataset(["I am happy.", "She goes to school."])
# → [("i'm happy.", "I am happy."), ...]

The corruption engine covers 100+ rules across five categories: homophones (their/there/they're), internet slang (u/r/gr8/lol), informal contractions (wanna/gonna/gotta), common typos (teh/freind/recieve), and punctuation corruption.

Low-level API

These are available for advanced use when you want direct access to individual tiers or the training loop:

frompyaitk.Grammarimport (
 rule_based_corrector, # Tier 1 standalone
 train_tokenizer, # BPE tokenizer training
 Seq2Seq, Encoder, Decoder, # Tier 3 PyTorch models
 train_model, # Tier 3 training loop
 correct_sentence_neural, # Tier 3 greedy inference
)
importspacy

nlp = spacy.load("en_core_web_sm")
corrected = rule_based_corrector("she go to the market", nlp)
print(corrected) # "She goes to the market."

Architecture Overview

GrammarCorrector.correct(text)
 │
 ▼
┌───────────────────────────────┐
│ Tier 1 — SpaCy + Regex │ Always runs
│ • Lowercase "i" → "I" │
│ • Sentence capitalisation │
│ • Subject-verb agreement │
│ • Confused-word pairs │
└───────────────┬───────────────┘
 │
 ▼ (if fitted)
┌───────────────────────────────┐
│ Tier 2 — Sklearn Pipeline │ Runs after fit()
│ • DictVectorizer features │
│ • OneVsRest LogisticReg. │
│ • Token-level sequence fix │
└───────────────┬───────────────┘
 │
 ▼ (if fitted)
┌───────────────────────────────┐
│ Tier 3 — LSTM Seq2Seq │ Runs after fit()
│ • BPE tokenizer │
│ • Encoder (embedding + LSTM) │
│ • Decoder (teacher forcing) │
│ • Greedy inference │
└───────────────────────────────┘

Examples Summary

frompyaitk.Grammarimport GrammarCorrector, load_intents, quick_correct

# Rule-only (no fitting)
with GrammarCorrector() as gc:
 print(gc.correct("i go to school"))

# Full pipeline from sentences
with GrammarCorrector() as gc:
 gc.fit(["She goes to school.", "I am happy."])
 print(gc.correct("she go to school"))

# From intents file
with GrammarCorrector() as gc:
 gc.fit_from_intents_file("intents.json", tags=["verb_agreement"])
 print(gc.correct("he dont know"))

# Save and reload
with GrammarCorrector() as gc:
 gc.fit_from_intents_file("intents.json")
 gc.save("models/v1")

with GrammarCorrector() as gc:
 gc.load("models/v1")
 print(gc.correct("they was playing"))

# One-liner
print(quick_correct("i go home"))

EYE — Real-Time Object Detection

A production-ready webcam object detection application built on YOLOv8 (Ultralytics) and CustomTkinter. Supports a full GUI with live controls, headless CLI detection, a unified EyeSession facade, and full context-manager support on every public class — with extensive threading fixes and feature additions over a naive implementation.

What Is This?

EYE is a fully-featured real-time object detection module that provides:

Full context-manager protocol — every public class (CameraManager, ObjectDetector, DetectionApp, EyeSession) supports with blocks with guaranteed resource cleanup
EyeSession facade — unified entry point with .gui() and .headless() factory context managers covering the complete lifecycle
Live webcam detection — reads frames from any connected camera and runs YOLOv8 inference at a capped FPS
Full GUI application — dark-mode CustomTkinter UI with video feed, control panel, and detection log
Headless CLI mode — simple_detect() uses context managers internally for safe camera/detector teardown
Hot-swappable model variants — switch between yolov8n/s/m/l/x.pt at runtime without restarting
Class filter — show bounding boxes only for specific classes (e.g. person, car)
Confidence threshold — adjustable slider (0.1–0.95), persisted to ~/.yolov8_app.json
Detection heat-map — alpha-blended overlay that accumulates and decays detection positions
Video recording — save annotated footage to .mp4 at camera resolution
Screenshot — save the current annotated frame as a timestamped JPG
Live FPS counter — exponential moving average over the last 30 frames
Detection log — scrollable timestamped list of the last 200 detection events
Keyboard shortcuts — Space (pause), S (screenshot), R (record), Q (quit)
Serialisable config — DetectionConfig saves/loads all settings as JSON
Thread-safe design — all widget mutations dispatched via self.after(), frame buffer protected with threading.Lock
Legacy aliases — EYE() and OpenEYE() for backwards compatibility with core.py / pyaitk

Note

YOLOv8 model weights are downloaded automatically on first use (~6 MB for yolov8n.pt). An internet connection is required for the initial download; subsequent runs work offline.

Context-Manager Patterns

All four public classes now support the full context-manager protocol. Resources are always released — even when exceptions are raised.

`EyeSession` — recommended top-level API

frompyaitk.eyeimport EyeSession

# GUI mode (blocks until window closed)
with EyeSession.gui() as session:
 session.run()
 print(session.last_detections)

# Headless mode (blocks until target seen or 'q' pressed)
with EyeSession.headless(target_class="person") as session:
 detected = session.run()
 print(detected)

# GUI with custom config
frompyaitk.eyeimport DetectionConfig
cfg = DetectionConfig(model_name="yolov8s.pt", show_heatmap=True, confidence_threshold=0.6)
with EyeSession.gui(config=cfg) as session:
 session.run()

`CameraManager` — camera resource

frompyaitk.eyeimport CameraManager

# __enter__ / __exit__
with CameraManager(camera_index=0, width=640, height=480) as cam:
 ret, frame = cam.read()

# Factory context manager
with CameraManager.open_device(0, width=1280, height=720) as cam:
 ret, frame = cam.read()
 print(cam.is_opened) # True

`ObjectDetector` — inference resource

frompyaitk.eyeimport ObjectDetector, DetectionConfig

cfg = DetectionConfig()

# __enter__ / __exit__ (provide your own model)
frompyaitk.eyeimport ModelLoader
model = ModelLoader.load_model("yolov8n.pt")
with ObjectDetector(model, cfg) as det:
 classes, annotated, count, confs = det.detect(frame)

# Factory context manager (loads model internally)
with ObjectDetector.from_model("yolov8n.pt", cfg) as det:
 classes, annotated, count, confs = det.detect(frame)

`DetectionApp` — GUI application

frompyaitk.eyeimport DetectionApp, DetectionConfig
importcustomtkinterasctk

# __enter__ / __exit__
ctk.set_appearance_mode("dark")
ctk.set_default_color_theme("blue")
with DetectionApp(DetectionConfig()) as app:
 app.mainloop()

# Factory context manager (sets up CTk theme automatically)
with DetectionApp.run_app(DetectionConfig()) as app:
 app.mainloop()

Composing context managers (low-level pipeline)

frompyaitk.eyeimport CameraManager, ObjectDetector
importcv2

with CameraManager(0) as cam:
 with ObjectDetector.from_model("yolov8n.pt") as det:
 while True:
 ret, frame = cam.read()
 if not ret:
 break
 classes, annotated, count, confs = det.detect(frame)
 cv2.imshow("Detection", annotated)
 if cv2.waitKey(1) & 0xFF == ord("q"):
 break
cv2.destroyAllWindows()
# Camera and detector released automatically on exit

How to Use

1. Launch the GUI (simplest)

frompyaitk.eyeimport launch_gui

launch_gui()

2. GUI with custom config

frompyaitk.eyeimport launch_gui, DetectionConfig

config = DetectionConfig(
 model_name="yolov8s.pt",
 confidence_threshold=0.6,
 target_fps=25,
 show_heatmap=True,
 filter_classes=["person", "car"],
)
launch_gui(config)

3. Headless detection (no GUI)

simple_detect() opens an OpenCV window and runs until the target class is seen or q is pressed. Uses CameraManager and ObjectDetector context managers internally — camera is always released.

frompyaitk.eyeimport simple_detect

detected = simple_detect(target_class="person")
print("Detected:", detected)

macOS note: cv2.imshow must be called from the main thread. Only call simple_detect() from the main thread.

4. Headless with custom config

frompyaitk.eyeimport simple_detect, DetectionConfig

config = DetectionConfig(
 model_name="yolov8m.pt",
 confidence_threshold=0.45,
 camera_index=1,
 target_fps=20,
)
detected = simple_detect(target_class="car", config=config)
print(detected)

5. Legacy aliases (pyaitk / core.py compatibility)

frompyaitk.eyeimport EYE, OpenEYE

detected = EYE() # → simple_detect()
OpenEYE() # → launch_gui()

6. Low-level components with context managers

frompyaitk.eyeimport CameraManager, ObjectDetector, DetectionConfig
importcv2

config = DetectionConfig()

with CameraManager(camera_index=0, width=640, height=480) as cam:
 with ObjectDetector.from_model("yolov8n.pt", config) as det:
 ret, frame = cam.read()
 if ret:
 detected_classes, annotated, count, confidences = det.detect(frame)
 print(f"Detected {count} objects: {detected_classes}")
 cv2.imshow("Frame", annotated)
 cv2.waitKey(0)
cv2.destroyAllWindows()
# No explicit release needed — context managers handle it

GUI Controls Reference

Control	Description
Model selector	Switch between `yolov8n/s/m/l/x.pt` (hot-swap, no restart needed)
Confidence slider	Detection threshold from 0.10 to 0.95
Class filter field	Type a class name and press Enter or "Add Filter"
Clear Filter button	Remove all filters (show all classes)
Heatmap toggle	Enable/disable the alpha-blended detection heat-map
Pause / Resume button	Freeze/unfreeze the video loop
Screenshot button	Save annotated frame as `screenshot_YYYYMMDD_HHMMSS.jpg`
Record button	Start/stop saving annotated video as `recording_*.mp4`
Detection log	Scrollable list of last 200 timestamped events
Clear Log button	Empty the detection log
Status bar	Camera info, resolution, active model name

Keyboard Shortcuts

Key	Action
`Space`	Pause / Resume
`S`	Screenshot
`R`	Start / Stop recording
`Q`	Quit

API Reference

`EyeSession`

Unified high-level facade with two factory context managers.

`EyeSession.gui(config?)` → context manager

Launches the full CustomTkinter GUI. Blocks on session.run() until the window is closed. Tears down the app on exit.

with EyeSession.gui(config=DetectionConfig(model_name="yolov8s.pt")) as session:
 session.run()
 print(session.last_detections)

`EyeSession.headless(target_class, config?)` → context manager

Headless detection. Blocks on session.run() until target is detected or q is pressed. Calls cv2.destroyAllWindows() on exit.

with EyeSession.headless("car") as session:
 detected = session.run()

Property	Type	Description
`last_detections`	`list[str]`	Class names detected at the time of exit

`launch_gui(config)` → `None`

Functional launcher. Uses DetectionApp.run_app() context manager internally.

Parameter	Type	Default	Description
`config`	`DetectionConfig` or `None`	`None`	Load from `~/.yolov8_app.json` if `None`

`simple_detect(target_class, config)` → `list[str]`

Headless OpenCV detection loop. Uses CameraManager and ObjectDetector context managers internally.

Parameter	Type	Default	Description
`target_class`	`str`	`"person"`	Class name to trigger exit on
`config`	`DetectionConfig` or `None`	`None`	Uses defaults if `None`

Returns: sorted list of all class names detected at the time of exit.

`CameraManager(camera_index, width, height)`

Parameter	Type	Default	Description
`camera_index`	`int`	`0`	OpenCV device index
`width`	`int`	`640`	Requested frame width
`height`	`int`	`480`	Requested frame height

Method / Property	Returns	Description
`open()`	`bool`	Open the device; returns `True` on success
`read()`	`tuple[bool, ndarray\|None]`	Read one frame
`release()`	`None`	Release the capture device
`is_opened`	`bool`	`True` if device is currently open
`__enter__` / `__exit__`	—	Opens on enter, releases on exit
`CameraManager.open_device(idx, w, h)`	context manager	Factory `@contextmanager`

`ObjectDetector(model, config)`

Method	Returns	Description
`detect(frame)`	`tuple`	Run inference; see return table below
`__enter__` / `__exit__`	—	Clears heatmap accumulator on exit
`ObjectDetector.from_model(name, config?)`	context manager	Loads model + yields detector

detect() return values:

Field	Type	Description
`detected_classes`	`set[str]`	All class names above confidence threshold
`annotated_frame`	`np.ndarray`	Original-resolution BGR frame with bounding boxes
`object_count`	`int`	Total number of detections
`class_confidences`	`dict[str, float]`	Highest confidence per class name

`DetectionApp(config)`

Method	Description
`mainloop()`	Start the Tk event loop (blocks)
`__enter__` / `__exit__`	Calls `_close_app()` on exit
`DetectionApp.run_app(config?)`	Factory context manager; sets CTk theme

`DetectionConfig` fields

Field	Type	Default	Description
`model_name`	`str`	`"yolov8n.pt"`	YOLOv8 weight file name
`target_class`	`str`	`"person"`	Class to watch for in headless mode
`confidence_threshold`	`float`	`0.5`	Minimum detection confidence (0.0–1.0)
`frame_width`	`int`	`640`	Requested camera frame width
`frame_height`	`int`	`480`	Requested camera frame height
`detection_size`	`tuple[int,int]`	`(416, 416)`	Resize resolution for YOLO inference
`camera_index`	`int`	`0`	OpenCV camera index
`target_fps`	`int`	`30`	FPS cap for the detection loop
`show_heatmap`	`bool`	`False`	Enable detection heat-map overlay
`filter_classes`	`list[str]`	`[]`	Restrict boxes to these classes; empty = all

Persistence:

config = DetectionConfig(confidence_threshold=0.7)
config.save() # → ~/.yolov8_app.json

config = DetectionConfig.load() # restore from file

`ModelLoader.load_model(model_name)` → `YOLO | None`

Tries four strategies in order:

Package resource (if running inside a package)
Script directory and adjacent eye/ subfolder
Current working directory
Auto-download from Ultralytics

Architecture Overview

EyeSession
├── EyeSession.gui() → DetectionApp.run_app() → DetectionApp.__enter__/__exit__
└── EyeSession.headless() → simple_detect()
 ├── CameraManager.__enter__/__exit__
 └── ObjectDetector.from_model().__enter__/__exit__

DetectionApp (GUI)
├── Main thread ──→ CTk event loop + all widget mutations (via self.after())
│ ↑
│ polls frame queue at ~60 Hz
│ ↑
└── Video thread ──→ camera.read() → detector.detect() → FramePacket → Queue(maxsize=2)

Resource ownership
──────────────────
CameraManager.__exit__ → cap.release()
ObjectDetector.__exit__ → _heatmap = None
DetectionApp.__exit__ → thread.join(2s) → writer.release() → camera.release() → destroy()
EyeSession.__exit__ → app._close_app() (if GUI) / cv2.destroyAllWindows() (if headless)

Examples Summary

# ── EyeSession (recommended) ─────────────────────────────────────────────────

frompyaitk.eyeimport EyeSession, DetectionConfig

# GUI
with EyeSession.gui() as session:
 session.run()
 print(session.last_detections)

# GUI with custom config
cfg = DetectionConfig(model_name="yolov8s.pt", show_heatmap=True)
with EyeSession.gui(config=cfg) as session:
 session.run()

# Headless
with EyeSession.headless("car") as session:
 detected = session.run()

# ── Functional API ───────────────────────────────────────────────────────────

frompyaitk.eyeimport launch_gui, simple_detect
launch_gui()
detected = simple_detect("person")

# ── Low-level context managers ───────────────────────────────────────────────

frompyaitk.eyeimport CameraManager, ObjectDetector
importcv2

with CameraManager(0) as cam:
 with ObjectDetector.from_model("yolov8n.pt") as det:
 ret, frame = cam.read()
 classes, annotated, count, confs = det.detect(frame)
cv2.destroyAllWindows()

# ── DetectionApp directly ────────────────────────────────────────────────────

frompyaitk.eyeimport DetectionApp, DetectionConfig
with DetectionApp.run_app(DetectionConfig()) as app:
 app.mainloop()

# ── Legacy aliases ───────────────────────────────────────────────────────────

frompyaitk.eyeimport EYE, OpenEYE
EYE() # headless detect
OpenEYE() # launch GUI

CLSE - Compositional Latent Synthesis Engine

A complete, self-contained Compositional Latent Synthesis Engine pipeline built entirely on NumPy, NLTK, scikit-learn, and PyTorch — no Stable Diffusion or external model weights required. Converts natural-language prompts into images through a multi-stage NLP → neural model → renderer architecture, with full support for procedural art, visual effects, animation, streaming, and a rich CLI.

What Is This?

The CLSE system is a nine-module package covering every layer of the Compositional Latent Synthesis Engine stack:

Module	Class / Entry Point	Responsibility
`TTI_config.py`	`TTIConfig`, `get_config()`	Master config — all tuneable parameters
`TTI_core.py`	`TTIImage`, `ImageCanvas`, `ColorUtils`, `ImageIO`	Pixel-level image engine (BMP/PNG/JPEG I/O, drawing)
`TTI_art.py`	`ProceduralArt`, `VisualEffects`, `StreamingWriter`, `AnimationEngine`	Procedural art, effects, animation, large-image streaming
`TTI_ai.py`	`TTIGenerator`, `NLPAnalyser`, `PromptAnalysis`	NLP pipeline + numpy VAE renderer + colour predictor
`TTI_model.py`	`TTIModel`, `TTITrainer`, `TTILoss`	4-layer transformer VAE (3.8M params), training loop
`TTI_dataset.py`	`TTIDataset`, `Vocabulary`	Synthetic 50k-sample dataset generator with caching
`TTI_pipeline.py`	`TTIPipeline`	Full end-to-end pipeline connecting all modules
`TTI_train.py`	CLI training script	Production training with checkpointing and early stopping
`TTI_main.py`	`TTIPipeline` façade + `main()`	Unified entry point + full CLI

Note

These *.py files are inner file of CLSE (Compositional Latent Synthesis Engine).

Quick Start

frompyaitk.CLSEimport TTIPipeline

pipe = TTIPipeline()

# Generate from a text prompt
img = pipe.generate("a calm blue ocean at sunset", output="ocean.png")

# Procedural art (no model needed)
img = pipe.art("mandelbrot", output="fractal.png")

# Apply an effect to an existing image
img = pipe.effect("sepia", "photo.png", output="photo_sepia.png")

# Analyse a prompt
analysis = pipe.analyse("dark gothic castle at midnight")
print(analysis.scene_type, analysis.colour_matches)

Module Guide

Configuration

frompyaitk.CLSEimport TTIConfig, get_config, update_config, reset_config

# Global singleton (auto-discovers tti_config.json next to the module)
cfg = get_config()

# Read settings
print(cfg.image.default_width) # 512
print(cfg.ai.model_type) # "vae_numpy"
print(cfg.art.fractal_max_iter) # 256
print(cfg.paths.output_dir) # "tti_output"

# Bulk update
update_config(
 image={"default_width": 1024, "default_height": 1024},
 ai={"seed": 42, "num_inference_steps": 100},
)

# Save / load JSON snapshot
cfg.save("tti_config.json")
cfg2 = TTIConfig.load("tti_config.json")

# Create all output directories
cfg.ensure_dirs()

# Reset to factory defaults
reset_config()

Config sections:

Section	Dataclass	Key fields
`cfg.image`	`ImageConfig`	`default_width`, `default_height`, `default_format`, `background_color`, `jpeg_quality`
`cfg.ai`	`AIConfig`	`model_type`, `latent_dim`, `vocab_size`, `num_inference_steps`, `guidance_scale`, `seed`
`cfg.art`	`ArtConfig`	`fractal_max_iter`, `blur_default_radius`, `noise_default_intensity`, `animation_fps`
`cfg.paths`	`PathConfig`	`output_dir`, `model_dir`, `cache_dir`, `log_dir`
`cfg.log`	`LogConfig`	`level`, `log_to_file`, `log_filename`, `show_progress`

Image Engine

frompyaitk.CLSEimport TTIImage, ImageCanvas, ColorUtils, ImageIO, ImageValidator

# Create a blank image
img = TTIImage(width=512, height=512, bpp=24, background=(20, 30, 60))

# Pixel operations
img.set_pixel(100, 100, (255, 0, 0))
color = img.get_pixel(100, 100) # (255, 0, 0)

# NumPy array interop
arr = img.to_array() # shape (H, W, 3), dtype=uint8
img.from_array(arr)

# Save and load
img.save("output.png")
img.save("output.jpg", fmt="jpeg")
img.save("output.bmp", fmt="bmp")
img2 = TTIImage.load("output.png")

# Drawing (ImageCanvas)
canvas = ImageCanvas(img)
canvas.line(0, 0, 511, 511, (255, 255, 0))
canvas.rectangle(50, 50, 200, 200, (255, 100, 0), filled=True)
canvas.circle(256, 256, 100, (0, 200, 255), filled=False)
canvas.fill_background((10, 10, 30))

# Colour utilities
rgb = ColorUtils.hsv_to_rgb(0.6, 0.8, 0.9)
blended = ColorUtils.lerp((255, 0, 0), (0, 0, 255), t=0.5)
rgba = ColorUtils.to_rgba((100, 150, 200)) # adds alpha=255
clamped = ColorUtils.clamp(300) # → 255

# Multi-format I/O (static)
img = ImageIO.load("photo.png")
ImageIO.save(img, "photo.jpeg", quality=85)

# Integrity check
ImageValidator.validate(img) # raises TTIImageError if corrupt

Exception hierarchy:

TTIError
├── TTIImageError — pixel-level or dimension errors
└── TTIIOError — file read/write failures

Procedural Art & Effects

ProceduralArt

All methods are static and return TTIImage objects.

frompyaitk.CLSEimport ProceduralArt, VisualEffects, StreamingWriter, AnimationEngine

# Fractals
img = ProceduralArt.mandelbrot_set(width=800, height=600)
img = ProceduralArt.julia_set(800, 600, c_real=-0.7, c_imag=0.27015)
img = ProceduralArt.sierpinski_triangle(512, 512, depth=7)

# Patterns
img = ProceduralArt.plasma(512, 512)
img = ProceduralArt.voronoi(512, 512, n_cells=25, seed=42)
img = ProceduralArt.perlin_noise_image(512, 512, octaves=4)

# Gradients
img = VisualEffects.create_linear_gradient(512, 512, (70, 130, 200), (200, 80, 120))
img = VisualEffects.create_radial_gradient(512, 512, (255, 220, 50), (30, 30, 120))

VisualEffects

Apply filters to existing TTIImage objects (all return the modified image):

img = VisualEffects.blur(img, radius=3)
img = VisualEffects.gaussian_blur(img, sigma=2.0)
img = VisualEffects.sharpen(img, factor=1.5)
img = VisualEffects.edge_detect(img)
img = VisualEffects.emboss(img)
img = VisualEffects.grayscale(img)
img = VisualEffects.sepia(img, strength=0.8)
img = VisualEffects.invert(img)
img = VisualEffects.add_noise(img, intensity=0.15)
img = VisualEffects.pixelate(img, block_size=10)
img = VisualEffects.vignette(img, strength=0.6)
img = VisualEffects.adjust_brightness(img, factor=1.2)
img = VisualEffects.adjust_contrast(img, factor=1.3)
img = VisualEffects.blend(img1, img2, alpha=0.5)

StreamingWriter — large images without full RAM

frompyaitk.CLSEimport StreamingWriter

# Write a 4000×4000 image row by row
with StreamingWriter("large.png", width=4000, height=4000, bpp=24) as sw:
 for y in range(4000):
 row = [(y % 255, 100, 200)] * 4000 # list of RGB tuples
 sw.write_row(row)

AnimationEngine — frame sequences

frompyaitk.CLSEimport AnimationEngine

engine = AnimationEngine(fps=24)
frames = engine.generate_frames(base_img, n_frames=48, mode="zoom")
engine.save_frames(frames, output_dir="frames/", fmt="png")

CustomBitDepth — non-standard pixel formats

frompyaitk.CLSEimport CustomBitDepth

# 16-bit per channel, 3 channels
cbd = CustomBitDepth(width=256, height=256, bits_per_channel=16, n_channels=3)
cbd.set_pixel(0, 0, [65535, 0, 32768])
cbd.save("high_depth.custimg")
cbd.save_preview("preview.png") # downsample to 8-bit PNG for viewing

AI Engine

frompyaitk.CLSEimport TTIGenerator, NLPAnalyser

# NLP analysis
nlp = NLPAnalyser()
analysis = nlp.analyse("a mysterious purple galaxy with glowing stars")

print(analysis.scene_type) # "starfield"
print(analysis.nouns) # ["galaxy", "stars"]
print(analysis.adjectives) # ["mysterious", "purple", "glowing"]
print(analysis.colour_matches) # [("purple", (138,43,226)), ("gold", (255,215,0)), ...]
print(analysis.modifiers) # {"mysterious": 0.8, "glowing": 0.6}
print(analysis.filtered_tokens) # ["mysterious", "purple", "galaxy", "glowing", "stars"]
print(analysis.complexity()) # 0.72 (float 0–1)

# Full generation
frompyaitk.CLSEimport get_config
gen = TTIGenerator(get_config())
img = gen.generate("stormy sea at dusk", width=512, height=512, seed=99)
img.save("storm.png")

# Variations
imgs = gen.generate_variations("neon city at night", n_variations=4)
for i, img in enumerate(imgs):
 img.save(f"variation_{i}.png")

# Interpolation between two prompts
imgs = gen.interpolate("sunrise", "midnight", steps=6)
for i, img in enumerate(imgs):
 img.save(f"interp_{i:02d}.png")

AI pipeline (internal stages):

NLPAnalyser → 256-d SemanticVector (TF-IDF + PCA, NLTK tokeniser)
ColourPredictor → PaletteSpec (sklearn KNN on 170+ colour keywords)
SceneComposer → 128-d LatentCode (numpy VAE encoder)
ImageDecoder → TTIImage (14 scene-type renderers)

Neural Architecture

frompyaitk.CLSEimport TTIModel, TTIModelLarge, ModelConfig, TTITrainer, TTILoss

# Default model (4-layer transformer, 3.8M params)
cfg = ModelConfig(vocab_size=8192, embed_dim=256, n_layers=4, n_heads=8, latent_dim=128)
model = TTIModel(cfg)

# Large variant (6-layer, 512-d, ~14M params)
model_large = TTIModelLarge()

# Forward pass
importtorch
tokens = torch.randint(0, 8192, (4, 32)) # batch=4, seq_len=32
out = model(tokens)
# out.scene_logits : (4, 15) — 15-class scene prediction
# out.colour_pred : (4, 18) — 6 colours × RGB
# out.param_pred : (4, 64) — scene renderer parameters
# out.mu, out.logvar: (4, 128) — VAE latent distribution

# Multi-task loss
criterion = TTILoss(scene_weight=1.0, colour_weight=0.5, param_weight=0.3, kl_weight=0.01)
loss = criterion(out, scene_labels, colour_targets, param_targets)

# Training loop
trainer = TTITrainer(model, dataset, val_dataset, output_dir="tti_models/")
history = trainer.train(epochs=20, batch_size=64, lr=3e-4)
print(history["best_val_loss"])

Model architecture:

TokenEmbedding — learned token + sinusoidal positional embeddings
TransformerEncoder — 4× MultiHeadSelfAttention + FFN (BERT-style, pre-LN)
 ├── ColourHead — 3-layer MLP → 18-d colour palette
 ├── SceneClassifier — 2-layer MLP → 15-class scene logit
 └── ParamDecoder — VAE: μ/σ → z → 64-d parameter vector

Dataset Generator

frompyaitk.CLSEimport TTIDataset, Vocabulary, build_dataset

# Build a 50,000-sample dataset (cached to disk with SHA-256 integrity check)
dataset = build_dataset(
 n_samples=50_000,
 cache_dir="tti_models/",
 force_rebuild=False, # use cache if available
)

# Train / val / test splits
train_ds, val_ds, test_ds = dataset.splits()
print(len(train_ds), len(val_ds), len(test_ds)) # 40000, 5000, 5000

# DataLoader-compatible access
sample = train_ds[0]
# sample["token_ids"] : torch.LongTensor (32,)
# sample["scene_label"] : int (0–14)
# sample["colour_vec"] : torch.FloatTensor (18,)
# sample["param_vec"] : torch.FloatTensor (64,)
# sample["prompt"] : str

# Vocabulary
vocab = Vocabulary.load("tti_models/vocab.json")
ids = vocab.encode("a stormy sea at dusk") # list of int
text = vocab.decode(ids) # str
print(vocab.size) # 8192

Dataset statistics (default build):

Split	Samples
Train	40,000
Validation	5,000
Test	5,000
Vocab size	8,192
Scene classes	15
Colour dims	18 (6 colours × RGB)
Parameter dims	64

Unified Pipeline

frompyaitk.CLSEimport TTIPipeline

pipe = TTIPipeline()

# Generate
img = pipe.generate("a bright rainbow over a misty waterfall", output="rainbow.png")

# Variations
imgs = pipe.variations("neon city at night", n=4, output_dir="variations/")

# Interpolation
imgs = pipe.interpolate("sunrise", "midnight", steps=6, output_dir="interp/")

# Procedural art
img = pipe.art("julia", output="julia.png", c_real=-0.4, c_imag=0.6)

# Effects
img = pipe.effect("sepia", input_path="photo.png", output="photo_sepia.png")

# NLP analysis only
info = pipe.analyse("dark gothic castle at midnight")

# Animation
frames = pipe.animate("sunset over the ocean", n_frames=24, output_dir="frames/")

# Stream a very large image (memory-safe)
pipe.stream_large("huge.png", width=4000, height=4000, pattern="gradient")

# Model info
print(pipe.model_info())

# Config management
pipe.show_config()
pipe.save_config("snapshot.json")

Architecture Overview

TTI_main.py / TTI_pipeline.py ← unified façade
│
├── TTI_config.py ← all settings (reads from config.pbcfg / tti_config.json)
│
├── TTI_ai.py ← NLP + VAE renderer (no model weights needed)
│ ├── NLPAnalyser NLTK tokenise → TF-IDF → PCA → SemanticVector
│ ├── ColourPredictor KNN on 170+ colour keywords → PaletteSpec
│ ├── SceneComposer numpy VAE encoder → 128-d LatentCode
│ └── ImageDecoder 14 scene-type renderers → TTIImage
│
├── TTI_model.py ← PyTorch transformer VAE (optional, boosts quality)
│ ├── TokenEmbedding learnable + positional
│ ├── TransformerEncoder 4-layer BERT-style
│ ├── ColourHead → 18-d palette
│ ├── SceneClassifier → 15-class scene
│ └── ParamDecoder → 64-d renderer params (VAE)
│
├── TTI_dataset.py ← 50k-sample synthetic generator + Vocabulary + DataLoader
│
├── TTI_train.py ← training script (gradient ckpt, cosine LR, early stop)
│
├── TTI_core.py ← pixel engine (TTIImage, ImageCanvas, ColorUtils, I/O)
│
└── TTI_art.py ← procedural art, effects, streaming, animation

Examples Summary

frompyaitk.CLSEimport TTIPipeline
frompyaitk.CLSEimport ProceduralArt, VisualEffects
frompyaitk.CLSEimport NLPAnalyser
frompyaitk.CLSEimport get_config, update_config

pipe = TTIPipeline()

# Generate
img = pipe.generate("stormy sea at midnight", output="storm.png")

# Batch
pipe.generate_batch(["sunrise", "sunset", "noon"], output_dir="batch/")

# Variations + interpolation
pipe.generate_variations("neon city", n=4, output_dir="vars/")
pipe.interpolate("calm lake", "raging ocean", steps=6, output_dir="interp/")

# Procedural art
pipe.art("mandelbrot", output="mandelbrot.png", width=800, height=600)
pipe.art("julia", output="julia.png", c_real=-0.4, c_imag=0.6)

# Effects
pipe.effect("sepia", "input.png", output="sepia.png")
pipe.effect("vignette", "input.png", strength=0.7, output="vig.png")

# Analysis
info = pipe.analyse("mysterious purple galaxy")
print(info.scene_type, info.complexity())

# Config override
update_config(image={"default_width": 1024}, ai={"seed": 7})

# Full demo
pipe.demo(output_dir="tti_demo/")

Camera — Camera Module

A Tkinter-based camera viewer with live QR/barcode scanning, image capture, video recording, and full context-manager support. Wraps OpenCV and pyzbar behind a clean class-based API with thread-safe state management.

What Is This?

Camera provides a camera module that combines:

Live video preview — Tkinter GUI window displaying frames from any OpenCV-compatible device
QR / barcode scanning — auto-decodes QR codes and barcodes from every frame via pyzbar; accumulates all unique payloads thread-safely
Image capture — saves the current frame as a timestamped .png with one method call
Video recording — start/stop writing annotated frames to a timestamped .avi file using XVID codec
Context-manager protocol — Camera.open() creates the Tk root, opens the device, and releases everything on exit
Functional entry-point — Start() launches the GUI and returns all scanned data in one call
Auto-install of pyzbar — silently attempts pip install pyzbar if the library is missing; degrades gracefully if it still can't be imported
Thread-safe design — separate RLock guards for scanned data and video writer; frame loop never blocks on recording

Installation

On Linux, pyzbar also needs the native zbar library:
sudoaptinstalllibzbar0
On Windows, the pyzbar wheel bundles the DLL automatically.

How to Use

1. Simplest — functional one-liner

Launches the GUI; blocks until the window is closed; returns all scanned payloads.

frompyaitk.Cameraimport Start

data = Start()
for item in data:
 print(item)

2. Context manager (recommended)

frompyaitk.Cameraimport Camera

with Camera.open() as cam:
 cam.run() # blocks until the window is closed

print(cam.scanned_data) # set of all scanned QR/barcode strings

3. Custom device and output directory

frompyaitk.Cameraimport Camera

with Camera.open(device=1, output_dir="./recordings") as cam:
 cam.run()

4. Programmatic capture and recording

frompyaitk.Cameraimport Camera
importtkinterastk

root = tk.Tk()
cam = Camera(root, device=0, output_dir="./output")

# Capture a single frame immediately
path = cam.capture_image()
print(f"Saved: {path}")

# Start and stop recording
record_path = cam.start_recording()
print(f"Recording to: {record_path}")

# … run some frames …
cam.run() # blocks until window closed

cam.stop_recording()
cam.close()

5. Inspect scanned data before closing

frompyaitk.Cameraimport Camera
importthreading

with Camera.open() as cam:
 # Poll scanned_data from another thread
 defmonitor():
 importtime
 while not cam.is_closed:
 print("Scanned so far:", cam.scanned_data)
 time.sleep(2.0)

 t = threading.Thread(target=monitor, daemon=True)
 t.start()
 cam.run()

print("Final scanned data:", cam.scanned_data)

6. State checks

with Camera.open() as cam:
 print(cam.is_recording) # False
 cam.start_recording()
 print(cam.is_recording) # True
 cam.stop_recording()
 print(cam.is_closed) # False
 cam.run()

print(cam.is_closed) # True

GUI Reference

The camera window opens with three buttons:

Button	Action
📸 Capture Image	Saves current frame as `captured_YYYYMMDD_HHMMSS.png` in `output_dir`; shows a confirmation dialog
🎥 Start Recording / ⏹️ Stop Recording	Toggles video recording; saves to `output_YYYYMMDD_HHMMSS.avi` in `output_dir`
❌ Quit	Stops recording (if active), releases the camera, and destroys the window

Closing the window via the title bar × button also triggers a clean shutdown.

API Reference

`Start(device, output_dir)` → `set[str]`

Functional entry-point. Launches the GUI, blocks until the window is closed, and returns all scanned QR/barcode payloads.

Parameter	Type	Default	Description
`device`	`int`	`0`	OpenCV camera device index
`output_dir`	`Path` or `str`	`"."`	Directory for saved images and videos

`Camera.open(device, output_dir)` → context manager

Class-level @contextmanager factory. Creates a tk.Tk root, instantiates Camera, yields it, and calls close() on exit.

with Camera.open(device=0, output_dir="./out") as cam:
 cam.run()

`Camera(window, device, output_dir)`

Direct constructor for advanced use when you manage the Tk root yourself.

Parameter	Type	Default	Description
`window`	`tk.Tk`	—	Root Tkinter window
`device`	`int`	`0`	OpenCV capture device index
`output_dir`	`Path` or `str`	`"."`	Directory for saved images and videos

Raises: CameraError if the device cannot be opened.

Instance methods

Method	Returns	Description
`run()`	`None`	Enter the Tk main-loop (blocks until window closed)
`capture_image()`	`Path` or `None`	Capture current frame to `output_dir`; returns saved path
`start_recording()`	`Path` or `None`	Begin writing frames to `.avi`; returns output path
`stop_recording()`	`None`	Stop recording and flush the video file
`close()`	`None`	Release all resources; safe to call multiple times

Properties

Property	Type	Description
`scanned_data`	`set[str]`	Thread-safe snapshot of all decoded QR/barcode payloads
`is_recording`	`bool`	`True` while a video is being written
`is_closed`	`bool`	`True` after `close()` has been called

Output Files

All files are written to output_dir (default: current directory).

File pattern	Format	Created by
`captured_YYYYMMDD_HHMMSS.png`	PNG	`capture_image()` / 📸 button
`output_YYYYMMDD_HHMMSS.avi`	AVI (XVID, 20 fps)	`start_recording()` / 🎥 button

Exception Reference

CameraError(RuntimeError)
├── Raised when the camera device cannot be opened
└── Raised when run() is called after the camera is already closed

Architecture Notes

Frame loop — driven by window.after(_FRAME_DELAY_MS, _update_frame) (10 ms ≈ 100 fps cap); never blocks the Tk event loop
QR decoding — runs on every frame inside the frame loop; new unique payloads are added to _scanned_data under _data_lock
Video writer — guarded by _writer_lock so the recording flag and cv2.VideoWriter are always consistent across threads
pyzbar graceful degradation — if not installed, a pip install is attempted once at import time; if still unavailable, QR scanning is silently disabled and the rest of the module works normally
Clean shutdown — close() stops recording, releases the OpenCV capture device, and destroys the Tk window; idempotent (safe to call multiple times)

Examples Summary

# Simplest: launch and collect scanned QR data
frompyaitk.Cameraimport Start
data = Start()

# Context manager
frompyaitk.Cameraimport Camera
with Camera.open(device=0, output_dir="./out") as cam:
 cam.run()
print(cam.scanned_data)

# Custom device
with Camera.open(device=1) as cam:
 cam.run()

# Programmatic capture before running
importtkinterastk
frompyaitk.Cameraimport Camera
root = tk.Tk()
cam = Camera(root, output_dir="./shots")
cam.capture_image() # save a frame immediately
cam.start_recording() # start video
cam.run() # blocks
cam.stop_recording()
cam.close()

# State checks
with Camera.open() as cam:
 print(cam.is_recording) # False
 cam.start_recording()
 print(cam.is_recording) # True
 cam.run()

Memory — Memory Module for Pythonaibrain

A two-tier episodic memory system for Brain and AdvanceBrain. Memory provides a thread-safe, JSON-backed key/value store. SmartMemory extends it transparently with a full ML pipeline — TF-IDF → Autoencoder → Clustering → IntentClassifier — enabling semantic search over conversation history without changing any call sites.

What Is This?

Memory provides two public classes and a factory function:

Class / Function	Description
`Memory`	Thread-safe, JSON-backed key/value episodic store with LRU eviction
`SmartMemory`	Drop-in superset of `Memory` with semantic search, intent prediction, and cluster analytics
`build_memory()`	Factory that returns `SmartMemory` when available, `Memory` otherwise
`MemoryEntry`	Dataclass holding a single episodic record with timestamps and access metadata

Key properties shared by both classes:

Thread-safe — all public methods acquire an RLock; the background ML fit thread takes only a snapshot so reads/writes are never blocked
Atomic writes — save_memory() writes to a .tmp file then renames it, preventing corruption on crash
LRU eviction — when the store reaches max_entries, the least-recently-used key is evicted
Backward-compatible format — transparently migrates legacy flat {key: value} JSON files to the v2 rich format
Graceful degradation — if SummarizerAI is absent, SmartMemory silently falls back to plain Memory behaviour

How to Use

1. Plain `Memory` (classic key/value)

frompyaitk.Memoryimport Memory

mem = Memory(path="memory.json")

mem.remember("user_name", "Divyanshu")
mem.remember("last_topic", "Python programming")

print(mem.recall("user_name")) # "Divyanshu"
print(mem.recall("missing_key", default="N/A")) # "N/A"

mem.save_memory()
mem.load_memory() # reload from disk

2. `SmartMemory` — drop-in with ML extras

frompyaitk.Memoryimport SmartMemory

mem = SmartMemory(
 path="memory.json",
 auto_fit=True, # refit every fit_interval calls to remember()
 fit_interval=20, # default: 20
)

mem.remember("Hello! How are you?", "I'm doing great, thanks!")
mem.remember("Tell me a joke", "Why did the chicken cross the road?")
# … add more entries …

# Semantic search
results = mem.semantic_search("a funny story", top_k=3)
for r in results:
 print(r["score"], r["key"], r["value"])

# Intent prediction
intent = mem.predict_intent("What's the weather today?")
print(intent) # e.g. "weather_query"

# Export cluster/intent analytics report
mem.export_report("memory_report.json")
mem.print_report()

mem.save_memory()

3. `build_memory()` factory (recommended for `Brain`)

frompyaitk.Memoryimport build_memory

# Returns SmartMemory if SummarizerAI is available, Memory otherwise
mem = build_memory("memory.json", smart=True, fit_interval=50)
mem.remember("greeting", "Hello!")
mem.save_memory()

4. Checking and controlling the summarizer

frompyaitk.Memoryimport SmartMemory

mem = SmartMemory(path="memory.json", auto_fit=False)

# Manually trigger a (blocking) fit
success = mem.fit_summarizer()
print(success) # True / False

# Check fit state
print(mem.is_summarizer_fitted()) # True after fit

# Get the report object
report = mem.get_report() # MemorySummaryReport dataclass or None

5. Extended `Memory` API

frompyaitk.Memoryimport Memory

mem = Memory()

mem.remember("a", "alpha")
mem.remember("b", "beta")

# Delete one entry
mem.forget("a")

# Check membership
print("b" in mem) # True
print("a" in mem) # False

# Iterate
for key in mem.keys():
 print(key)

for key, value in mem.items():
 print(key, "→", value)

# Snapshot as plain dict
snapshot = mem.to_dict() # {"b": "beta"}

print(mem.size) # 1
print(len(mem)) # 1

# Clear everything (in-memory only; disk unchanged until save_memory)
mem.wipe()

6. Inspecting `MemoryEntry` metadata

frompyaitk.Memoryimport Memory

mem = Memory()
mem.remember("topic", "AI and robotics")

# Access internal entry (advanced)
entry = mem._entries["topic"]
print(entry.key) # "topic"
print(entry.value) # "AI and robotics"
print(entry.timestamp) # Unix timestamp of creation
print(entry.access_count) # Number of recalls
print(entry.last_accessed) # Unix timestamp of last recall
print(entry.to_dict()) # Full dict for serialization

API Reference

`Memory(path, max_entries, auto_load)`

Parameter	Type	Default	Description
`path`	`str`	`memory.json`	Path to the JSON persistence file
`max_entries`	`int`	`10_000`	Max entries before LRU eviction
`auto_load`	`bool`	`True`	Load from disk automatically if file exists on init

Core contract methods (used by Brain / AdvanceBrain)

Method	Returns	Description
`load_memory()`	`None`	Load (or reload) from disk; silent no-op if file absent
`remember(key, value)`	`None`	Store or update; evicts LRU entry when at `max_entries`
`recall(key, default="")`	`str`	Retrieve value for key; returns `default` if not found
`save_memory()`	`None`	Atomically persist current state to disk

Extended methods

Method	Returns	Description
`forget(key)`	`bool`	Delete one entry; returns `True` if key existed
`wipe()`	`None`	Clear all in-memory entries (disk unchanged)
`keys()`	`Iterator[str]`	Iterate over all stored keys
`items()`	`Iterator[tuple[str,str]]`	Iterate over `(key, value)` pairs
`to_dict()`	`dict[str, str]`	Plain `{key: value}` snapshot without metadata

Properties

Property	Type	Description
`path`	`Path`	Resolved path to the backing JSON file
`size`	`int`	Number of currently stored entries

`SmartMemory(path, max_entries, auto_load, auto_fit, fit_interval, summarizer_config)`

Inherits all Memory methods. Additional constructor parameters:

Parameter	Type	Default	Description
`auto_fit`	`bool`	`True`	Auto-refit summarizer every `fit_interval` calls to `remember()`
`fit_interval`	`int`	`20`	Number of `remember()` calls between automatic refits
`summarizer_config`	`dict` or `None`	`None`	Pass custom config to `MemorySummarizer`; `None` = from `.pbcfg`

SmartMemory-only methods

Method	Returns	Description
`fit_summarizer(force=False)`	`bool`	(Re-)train ML pipeline; blocks caller; returns `True` on success
`semantic_search(text, top_k=3)`	`list[dict]`	Ranked semantic matches; falls back to substring search if unfit
`predict_intent(text)`	`str`	Predict intent of query from trained classifier
`get_report()`	`MemorySummaryReport` or `None`	Return cluster/intent analysis report
`export_report(path)`	`bool`	Write report as JSON; returns `True` on success
`print_report()`	`None`	Pretty-print cluster report to stdout
`is_summarizer_fitted()`	`bool`	`True` if summarizer is trained and not dirty

`semantic_search()` result format

Each item in the returned list:

Field	Type	Description
`score`	`float`	Cosine similarity score 0.0–1.0
`key`	`str`	Original input key stored in memory
`value`	`str`	Stored response value
`intent`	`str`	Predicted intent label
`cluster`	`int`	Cluster assignment index (`-1` if unassigned)

`build_memory(path, smart, **kwargs)` → `Memory`

Factory function. Returns SmartMemory when smart=True and SummarizerAI is importable; returns plain Memory otherwise.

Parameter	Type	Default	Description
`path`	`str`	`memory.json`	Path for JSON persistence
`smart`	`bool`	`True`	Attempt to use `SmartMemory`
`**kwargs`	—	—	Forwarded to `SmartMemory` or `Memory` constructor

`MemoryEntry` fields

Field	Type	Description
`key`	`str`	Entry key
`value`	`str`	Stored value
`timestamp`	`float`	Unix timestamp of creation
`access_count`	`int`	Number of times `recall()` accessed this key
`last_accessed`	`float`	Unix timestamp of most recent recall

Architecture

Memory
├── _entries : OrderedDict[str, MemoryEntry] ← LRU-ordered episodic store
├── _lock : threading.RLock ← protects all mutations
└── _path : Path ← JSON backing file

SmartMemory(Memory)
├── _summarizer : MemorySummarizer | None ← ML pipeline
├── _dirty : bool ← needs refit flag
├── _auto_fit : bool ← enable background refits
├── _fit_interval : int ← refit every N remembers
├── _fit_lock : threading.Lock ← serializes fit calls
└── _fit_thread : Thread | None ← background training thread

MemorySummarizer (optional external module)
└── TF-IDF → Autoencoder → KMeans/DBSCAN → IntentClassifier → PatternMatcher

Background fit behaviour:

Every fit_interval calls to remember(), _fit_background() spawns a daemon thread
The thread takes a snapshot of _store under the lock, then trains outside it — memory reads/writes are never blocked by training
If a fit is already running, additional triggers are skipped
semantic_search() triggers a blocking fit_summarizer() automatically if the summarizer is unfit (_dirty=True)

File Format

Memory is persisted as a JSON file with a version marker:

{
"__version__":2,
"saved_at":1718000000.0,
"entry_count":3,
"entries":[
{
"key":"user_name",
"value":"Divyanshu",
"timestamp":1718000000.0,
"access_count":4,
"last_accessed":1718001000.0
}
]
}

Legacy flat {key: value} files from v1 are automatically migrated on load.

Examples Summary

# Plain Memory
frompyaitk.Memoryimport Memory
mem = Memory("memory.json")
mem.remember("name", "Divyanshu")
print(mem.recall("name"))
mem.save_memory()

# SmartMemory
frompyaitk.Memoryimport SmartMemory
mem = SmartMemory("memory.json", auto_fit=True, fit_interval=10)
mem.remember("How are you?", "I'm great!")
results = mem.semantic_search("how are things", top_k=3)
intent = mem.predict_intent("What's the weather?")
mem.export_report("report.json")
mem.save_memory()

# Factory (recommended)
frompyaitk.Memoryimport build_memory
mem = build_memory("memory.json", smart=True, fit_interval=50)
mem.remember("greeting", "Hello!")
mem.save_memory()

# Extended API
mem.forget("greeting")
print("greeting" in mem) # False
print(mem.size)
print(mem.to_dict())
mem.wipe()

Pythonaibrain Config

The master configuration layer for the entire Pythonaibrain / pyaitk framework. All subsystems — Brain, STT, TTS, NER, TTI, Memory Summarizer, LLM — read their settings from a single .pbcfg file via typed dataclass section objects on one unified AppConfig instance.

What Is This?

Config provides:

.pbcfg file format — INI-style config file with ; # comment support and inline comments
AppConfig — unified config manager that reads/writes all sections and exposes them as typed dataclasses
22 section dataclasses — one per subsystem, each with typed fields and sensible defaults
Auto-discovery — searches upward from cwd to find config.pbcfg, falls back to built-in defaults
Typed get() / set() — generic read/write with optional type casting and fallback
generate_default_config() — writes a fully-populated default .pbcfg to disk
JSON export — serialize the entire config to JSON string or file
Module-level singleton — get_config() returns the same instance across the entire application

File Format (`.pbcfg`)

The config file uses standard INI format. Comments start with ; or #. Inline comments are supported.

; PythonAIBrain configuration file

[brain]
intents_path=./intents.json
condition=true
smart_memory=true
memory_path=memory.json
memory_fit_interval=20
username=user_name
download=false

[model]
model_path=model.pth
dimension_path=dimensions.json
batch_size=8
learning_rate=0.001
epochs=100

[llm]
n_ctx=2048
n_threads=0; 0 → use os.cpu_count()
max_tokens=512
verbose=false

[tts]
rate=150
volume=1.0
voice=david
default_text=Hello from PyAI
output_path=; if set, saves audio here instead of playing

[stt]
energy_threshold=; empty → auto-calibrate
dynamic_energy_threshold=true
pause_threshold=0.8
phrase_time_limit=; hard cap per utterance (seconds)
timeout=5.0
ambient_noise_duration=0.5
connectivity_host=8.8.8.8
connectivity_port=53
connectivity_timeout=2.0
preferred_engine=None; None | google | pocketsphinx
sphinx_language=en-US
google_language=en-US
google_api_key=; empty → free tier
max_retries=3
retry_delay=0.5

[logging]
level=INFO
format=%(asctime)s [%(levelname)s] %(name)s - %(message)s

[weather]
base_url=https://api.openweathermap.org/data/2.5/weather
units=metric

[memory]
auto_load=true
auto_fit=true

[search]
max_results=5

[webassistant]
intents_path=./Webintents.json
model_path=WebAssistantModel.pth
dimension_path=WebAssistantDimensions.json
batch_size=8
learning_rate=0.001
epochs=100

[embedding]
tfidf_max_features=5000
tfidf_ngram_range=1,3
tfidf_sublinear_tf=true
embed_dim=128
vocab_size=10000

[clustering]
n_clusters=8
kmeans_max_iter=300
kmeans_random_state=42
dbscan_eps=0.5
dbscan_min_samples=2
agglo_linkage=ward
agglo_distance_threshold=

[classifier]
lr_max_iter=500
lr_c=1.0
lr_solver=lbfgs
lr_multi_class=auto
similarity_threshold=0.65

[summarizer]
latent_dim=64
hidden_dim=256
ae_epochs=30
ae_lr=0.001
ae_batch_size=16
top_patterns_per_cluster=3
min_cluster_size=2

[tti_image]
default_width=512
default_height=512
default_bpp=24
default_format=png
background_color=255,255,255
jpeg_quality=92

[tti_ai]
nlp_backend=nltk
max_prompt_tokens=128
use_stopword_filter=true
model_type=vae_numpy
latent_dim=128
text_embed_dim=256
vocab_size=4096
hidden_dim=512
palette_clusters=8
num_inference_steps=50
guidance_scale=7.5
seed=

[tti_art]
fractal_max_iter=256
blur_default_radius=2
noise_default_intensity=0.15
animation_fps=24
streaming_chunk_mb=32

[tti_paths]
output_dir=tti_output
model_dir=tti_models
cache_dir=tti_cache
log_dir=tti_logs

[postprocessor]
min_length=1
max_length=
allowed_labels=
blocked_labels=
deduplicate=true
merge_adjacent=false
lowercase_labels=false
strip_punct=true
custom_label_map=

[preprocessor]
lowercase=false
remove_urls=true
remove_emails=false
remove_html_tags=true
normalize_whitespace=true
normalize_unicode=true
max_length=
custom_patterns=

Boolean values accept: true/false, yes/no, on/off, 1/0. Empty values (e.g. timeout =) resolve to None for optional fields. Tuple fields (e.g. tfidf_ngram_range, background_color) are stored as comma-separated integers.

How to Use

1. Module-level singleton (recommended)

frompyaitk.configimport get_config

cfg = get_config() # auto-discovers config.pbcfg from cwd upward
print(cfg.brain.smart_memory) # True
print(cfg.model.epochs) # 100
print(cfg.stt.pause_threshold) # 0.8

2. Load a specific file

frompyaitk.configimport AppConfig

cfg = AppConfig("myproject.pbcfg")
print(cfg.tts.voice) # "david"
print(cfg.llm.n_ctx) # 2048

3. Auto-discover

cfg = AppConfig.discover() # search cwd → home
cfg = AppConfig.discover("/my/dir") # search from a custom start path

4. Build from a dict

cfg = AppConfig.from_dict({
 "brain": {"smart_memory": True, "username": "divyanshu"},
 "model": {"epochs": 50, "learning_rate": 0.0005},
})

5. Read, mutate, and save

cfg = get_config()

# Read with typed access
print(cfg.brain.intents_path)
print(cfg.embedding.tfidf_max_features)

# Generic get with cast and fallback
val = cfg.get("tti_ai", "latent_dim", fallback=128, cast=int)

# Mutate in memory
cfg.set("tti_ai", "seed", 42)
cfg.set("tts", "rate", 180)

# Persist to disk
cfg.save() # saves to original path
cfg.save("backup.pbcfg") # save to alternate path

6. Reload from disk

cfg.load() # reload from current path
cfg.load("other.pbcfg") # reload from different file

7. Generate a default config file

frompyaitk.configimport generate_default_config

path = generate_default_config() # → ./config.pbcfg
path = generate_default_config("myapp.pbcfg")

8. Reset to factory defaults

frompyaitk.configimport reset_config

cfg = reset_config() # clears singleton, returns AppConfig with all defaults

9. Export to JSON

json_str = cfg.to_json(indent=2)
cfg.save_json("config.json")

10. Inspect all settings

print(cfg.dump()) # human-readable dump of all sections
d = cfg.as_dict() # nested dict {section: {key: value}}

Section Reference

`[brain]` → `cfg.brain` (`BrainConfig`)

Key	Type	Default	Description
`intents_path`	`str`	`./intents.json`	Path to intents JSON file
`condition`	`bool`	`True`	Enable dynamic intent learning from web search
`smart_memory`	`bool`	`True`	Use `SmartMemory` (semantic search + clustering)
`memory_path`	`str`	`memory.json`	Path for memory persistence
`memory_fit_interval`	`int`	`20`	Auto-fit SmartMemory every N stored memories
`username`	`str`	`user_name`	Key used for user name storage in memory
`download`	`bool`	`False`	Auto-download NLTK data on Brain init

`[model]` → `cfg.model` (`ModelConfig`)

Key	Type	Default	Description
`model_path`	`str`	`model.pth`	Path to save/load model weights
`dimension_path`	`str`	`dimensions.json`	Path to save/load vocab/intent map
`batch_size`	`int`	`8`	Training batch size
`learning_rate`	`float`	`0.001`	Adam optimizer learning rate
`epochs`	`int`	`100`	Training epochs

`[llm]` → `cfg.llm` (`LLMConfig`)

Key	Type	Default	Description
`n_ctx`	`int`	`2048`	LLM context window size
`n_threads`	`int`	`0`	CPU threads; `0` = `os.cpu_count()`
`max_tokens`	`int`	`512`	Max tokens to generate per response
`verbose`	`bool`	`False`	Enable llama.cpp verbose logging

`[tts]` → `cfg.tts` (`TTSConfig`)

Key	Type	Default	Description
`rate`	`int`	`150`	Words per minute
`volume`	`float`	`1.0`	Volume 0.0–1.0 (validated in `__post_init__`)
`voice`	`str`	`david`	Voice name fragment (fuzzy matched)
`default_text`	`str`	`Hello from PyAI`	Fallback text for empty `say()` calls
`output_path`	`str` or `None`	`None`	Save to WAV file instead of playing

`[stt]` → `cfg.stt` (`STTConfig`)

Key	Type	Default	Description
`energy_threshold`	`float` or `None`	`None`	Mic sensitivity; `None` = auto-calibrate
`dynamic_energy_threshold`	`bool`	`True`	Continuously adjust threshold
`pause_threshold`	`float`	`0.8`	Silence seconds marking end of phrase
`phrase_time_limit`	`float` or `None`	`None`	Hard cap per utterance in seconds
`timeout`	`float` or `None`	`5.0`	Seconds to wait for speech to start
`ambient_noise_duration`	`float`	`0.5`	Seconds to sample noise floor before listening
`connectivity_host`	`str`	`8.8.8.8`	Host used for the network connectivity probe
`connectivity_port`	`int`	`53`	Port for the connectivity probe
`connectivity_timeout`	`float`	`2.0`	Timeout for the connectivity probe
`preferred_engine`	`Engine` or `None`	`None`	Force `GOOGLE` or `POCKETSPHINX`; `None` = auto
`sphinx_language`	`str`	`en-US`	PocketSphinx language code
`google_language`	`str`	`en-US`	Google Speech API BCP-47 language tag
`google_api_key`	`str` or `None`	`None`	Google API key; `None` = free tier
`max_retries`	`int`	`3`	Max retry attempts on service errors
`retry_delay`	`float`	`0.5`	Seconds between retries

`[logging]` → `cfg.logging` (`LoggingConfig`)

Key	Type	Default	Description
`level`	`str`	`INFO`	Root logging level
`format`	`str`	`%(asctime)s [%(levelname)s] %(name)s - %(message)s`	Log record format

`[memory]` → `cfg.memory` (`MemoryConfig`)

Key	Type	Default	Description
`auto_load`	`bool`	`True`	Load memory from disk on init
`auto_fit`	`bool`	`True`	Auto-fit SmartMemory summarizer on load

`[embedding]` → `cfg.embedding` (`EmbeddingConfig`)

Key	Type	Default	Description
`tfidf_max_features`	`int`	`5000`	Max vocabulary size for TF-IDF
`tfidf_ngram_range`	`tuple`	`(1, 3)`	N-gram range (stored as `1,3` in file)
`tfidf_sublinear_tf`	`bool`	`True`	Apply sublinear TF scaling
`embed_dim`	`int`	`128`	Learned embedding dimension
`vocab_size`	`int`	`10000`	Vocabulary size for learned embeddings

`[clustering]` → `cfg.clustering` (`ClusteringConfig`)

Key	Type	Default	Description
`n_clusters`	`int`	`8`	KMeans cluster count
`kmeans_max_iter`	`int`	`300`	KMeans max iterations
`kmeans_random_state`	`int`	`42`	KMeans random seed
`dbscan_eps`	`float`	`0.5`	DBSCAN epsilon radius
`dbscan_min_samples`	`int`	`2`	DBSCAN minimum samples per cluster
`agglo_linkage`	`str`	`ward`	Agglomerative linkage criterion
`agglo_distance_threshold`	`float` or `None`	`None`	Agglomerative distance threshold

`[classifier]` → `cfg.classifier` (`ClassifierConfig`)

Key	Type	Default	Description
`lr_max_iter`	`int`	`500`	Logistic Regression max iterations
`lr_c`	`float`	`1.0`	LR regularization strength (lower = stronger)
`lr_solver`	`str`	`lbfgs`	LR solver
`lr_multi_class`	`str`	`auto`	LR multi-class strategy
`similarity_threshold`	`float`	`0.65`	Min cosine similarity for pattern matching

`[summarizer]` → `cfg.summarizer` (`SummarizerConfig`)

Key	Type	Default	Description
`latent_dim`	`int`	`64`	Autoencoder latent space dimension
`hidden_dim`	`int`	`256`	Autoencoder hidden layer size
`ae_epochs`	`int`	`30`	Autoencoder training epochs
`ae_lr`	`float`	`0.001`	Autoencoder learning rate
`ae_batch_size`	`int`	`16`	Autoencoder training batch size
`top_patterns_per_cluster`	`int`	`3`	Top patterns to extract per cluster
`min_cluster_size`	`int`	`2`	Minimum cluster size for summarization

`[tti_image]` → `cfg.tti_image` (`TTIImageConfig`)

Key	Type	Default	Description
`default_width`	`int`	`512`	Output image width in pixels
`default_height`	`int`	`512`	Output image height in pixels
`default_bpp`	`int`	`24`	Bits per pixel
`default_format`	`str`	`png`	Output format: `png`, `bmp`, `jpeg`
`background_color`	`tuple`	`(255, 255, 255)`	RGB background color (stored as `255,255,255`)
`jpeg_quality`	`int`	`92`	JPEG compression quality (1–95)

`[tti_ai]` → `cfg.tti_ai` (`TTIAIConfig`)

Key	Type	Default	Description
`nlp_backend`	`str`	`nltk`	NLP tokenizer: `nltk` or `spacy`
`max_prompt_tokens`	`int`	`128`	Max tokens per prompt
`use_stopword_filter`	`bool`	`True`	Filter stopwords from prompts
`model_type`	`str`	`vae_numpy`	AI model type: `vae_numpy` or `torch_vae`
`latent_dim`	`int`	`128`	VAE latent space dimension
`text_embed_dim`	`int`	`256`	Text embedding dimension
`vocab_size`	`int`	`4096`	Vocabulary size for text encoding
`hidden_dim`	`int`	`512`	Hidden layer size
`palette_clusters`	`int`	`8`	KMeans clusters for palette extraction
`palette_model_path`	`str`	`tti_palette_model.pkl`	Path for saved palette model
`num_inference_steps`	`int`	`50`	Diffusion inference steps
`guidance_scale`	`float`	`7.5`	Classifier-free guidance scale
`seed`	`int` or `None`	`None`	Random seed; empty in file resolves to `None`

`[tti_art]` → `cfg.tti_art` (`TTIArtConfig`)

Key	Type	Default	Description
`fractal_max_iter`	`int`	`256`	Max iterations for fractal generation
`blur_default_radius`	`int`	`2`	Default Gaussian blur radius
`noise_default_intensity`	`float`	`0.15`	Default noise overlay intensity
`animation_fps`	`int`	`24`	Frames per second for animations
`streaming_chunk_mb`	`int`	`32`	Chunk size for streaming output (MB)

`[tti_paths]` → `cfg.tti_paths` (`TTIPathConfig`)

Key	Type	Default	Description
`output_dir`	`str`	`tti_output`	Directory for generated images
`model_dir`	`str`	`tti_models`	Directory for saved models
`cache_dir`	`str`	`tti_cache`	Directory for cached data
`log_dir`	`str`	`tti_logs`	Directory for TTI log files

Call cfg.ensure_tti_dirs() (or cfg.tti_paths.ensure_dirs()) to create all four directories.

`[postprocessor]` → `cfg.postprocessor` (`PostprocessorConfig`)

Key	Type	Default	Description
`min_length`	`int`	`1`	Minimum entity text length
`max_length`	`int` or `None`	`None`	Maximum entity text length
`allowed_labels`	`set[str]` or `None`	`None`	Allow only these labels; `None` = allow all
`blocked_labels`	`set[str]`	`{}`	Always exclude these labels
`deduplicate`	`bool`	`True`	Remove duplicate spans
`merge_adjacent`	`bool`	`False`	Merge consecutive same-label spans
`lowercase_labels`	`bool`	`False`	Lowercase all label names
`strip_punct`	`bool`	`True`	Strip leading/trailing punctuation from text
`custom_label_map`	`dict[str, str]`	`{}`	Rename labels e.g. `{"PERSON": "PER"}`

`[preprocessor]` → `cfg.preprocessor` (`PreprocessorConfig`)

Key	Type	Default	Description
`lowercase`	`bool`	`False`	Lowercase the text
`remove_urls`	`bool`	`True`	Strip `http://`, `https://`, `www.` URLs
`remove_emails`	`bool`	`False`	Strip email addresses
`remove_html_tags`	`bool`	`True`	Strip HTML tags
`normalize_whitespace`	`bool`	`True`	Collapse whitespace to single spaces
`normalize_unicode`	`bool`	`True`	NFC Unicode normalization
`max_length`	`int` or `None`	`None`	Truncate text to this many characters
`custom_patterns`	`list[str]`	`[]`	Regex patterns to strip

`AppConfig` API Reference

Method / Property	Returns	Description
`load(path?)`	`AppConfig`	Parse `.pbcfg` file and populate all sections
`save(path?)`	`AppConfig`	Write current config to `.pbcfg`
`get(section, key, fallback, cast)`	`Any`	Read a raw value with optional type casting
`set(section, key, value)`	`None`	Write a value into the in-memory config (call `save()` to persist)
`to_json(indent?)`	`str`	Serialize all sections to a JSON string
`save_json(filepath?)`	`None`	Write config to a JSON file
`as_dict()`	`dict`	Return entire config as a nested `{section: {key: value}}` dict
`dump()`	`str`	Human-readable summary of all settings
`ensure_tti_dirs()`	`None`	Create TTI output/model/cache/log directories if missing
`AppConfig.discover(start?)`	`AppConfig`	Search cwd → home for `config.pbcfg`; use defaults if not found
`AppConfig.from_dict(data, path?)`	`AppConfig`	Build config from a nested dict

Module-Level Helpers

frompyaitk.configimport get_config, reset_config, generate_default_config

# Get or create the global singleton
cfg = get_config()

# Force a specific file into the singleton
cfg = get_config("custom.pbcfg")

# Reset singleton to factory defaults
cfg = reset_config()

# Write a default config file to disk
path = generate_default_config() # → ./config.pbcfg
path = generate_default_config("myapp.pbcfg")

Examples Summary

frompyaitk.configimport AppConfig, get_config, generate_default_config

# Generate a default file
generate_default_config("myproject.pbcfg")

# Load and read
cfg = AppConfig("myproject.pbcfg")
print(cfg.brain.smart_memory) # True
print(cfg.model.epochs) # 100
print(cfg.stt.google_language) # "en-US"
print(cfg.tti_image.default_format) # "png"
print(cfg.embedding.tfidf_ngram_range) # (1, 3)

# Mutate and save
cfg.set("model", "epochs", 200)
cfg.set("tts", "voice", "zira")
cfg.set("tti_ai", "seed", 1337)
cfg.save()

# Generic typed get
val = cfg.get("clustering", "n_clusters", fallback=8, cast=int)

# JSON export
cfg.save_json("config_backup.json")

# Factory methods
cfg2 = AppConfig.discover()
cfg3 = AppConfig.from_dict({
 "brain": {"username": "divyanshu", "smart_memory": True},
 "llm": {"max_tokens": 256},
})

# Create TTI directories
cfg.ensure_tti_dirs()

# Inspect
print(cfg.dump())

PyAgent — ZENTRAA CLI Reference

ZENTRAA (Zone for Encrypted Networked Talks & Real-time AI Agent)
Powered by Pythonaibrain v1.1.9 · Author: Divyanshu Sinha
Encryption: RSA-2048-OAEP · AES-256-GCM · RSA-PSS · Curve25519

Installation

pipinstall"pythonaibrain[zentraa]"

Linux only — install PortAudio before pip if you plan to use the TIGER AI voice features:
sudoaptinstallportaudio19-devpython3-pyaudio

Quick Start

Every command is available through the pythonaibrain (or pyaitk) dispatcher:

pythonaibrain zentraa <command> [options]

Or as a standalone alias:

zentraa-server / zentraa-client / zentraa-tiger-ai / zentraa-web

Typical startup order:

1. zentraa server ← start first
2. zentraa web ← optional browser bridge
3. zentraa ai ← optional TIGER AI agent
4. zentraa client ← one per human user

Commands

`zentraa server` — TCP Chat Server

Starts the encrypted ZENTRAA chat server that all clients connect to.

pythonaibrainzentraaserver
pythonaibrainzentraaserver--host0.0.0.0--port9999
pythonaibrainzentraaserver--config/path/to/ZENTRAA.pbcfg

Option	Short	Default	Description
`--config`	`-c`	`ZENTRAA.pbcfg`	Path to config file
`--host`	`-H`	from config	Override bind host
`--port`	`-p`	from config	Override bind port
`--help`	Show help and exit

`zentraa client` — Chat Client (TUI)

Interactive terminal client for human users. Supports direct messages, broadcasts, and talking to TIGER AI.

pythonaibrainzentraaclient
pythonaibrainzentraaclient--host127.0.0.1--port9999--useridAlice
pythonaibrainzentraaclient--config/path/to/ZENTRAA.pbcfg

Option	Short	Default	Description
`--config`	`-c`	`ZENTRAA.pbcfg`	Path to config file
`--host`	`-H`	from config	Server host
`--port`	`-p`	from config	Server port
`--userid`	`-u`	prompted	Your user ID
`--help`	Show help and exit

In-chat commands

Command	Description
`<message>`	Broadcast to all users
`@<userid> <message>`	Direct message a user
`@uid1 @uid2 ... <message>`	Multi-user direct message
`@ai <message>`	Ask TIGER AI privately
`@ai @<uid> <message>`	Ask AI, share reply with `<uid>`
`/help`	Show help
`/clear` or `/cls`	Clear the screen
`/ai`	Show TIGER AI info
`/setting`	View current settings
`/users`	List online users
`/me <action>`	Send an action / emote message
`/whois <userid>`	Show info about a user
`/ping`	Ping the server manually
`/stats`	Show session statistics
`/notify <on\|off>`	Toggle bell notifications
`/timestamps <on\|off>`	Toggle message timestamps
`/quit` or `/exit`	Disconnect

Keyboard shortcuts

Key	Action
`↑` / `↓`	Browse command history
`Tab`	Autocomplete `@userid` or `/command`
`Ctrl+C` / `Ctrl+D`	Quit

`zentraa ai` — TIGER AI Agent

Connects an automated AI agent (TIGER AI) to the server. Other users can query it with @ai <message>.

pythonaibrainzentraaai
pythonaibrainzentraaai--smart
pythonaibrainzentraaai--basic--host127.0.0.1--port9999
pythonaibrainzentraaai--config/path/to/ZENTRAA.pbcfg

Option	Short	Default	Description
`--config`	`-c`	`ZENTRAA.pbcfg`	Path to config file
`--host`	`-H`	from config	Server host
`--port`	`-p`	from config	Server port
`--smart`	from config	Force AdvanceBrain (LLM mode)
`--basic`	from config	Force Brain (intent-matching mode)
`--help`	Show help and exit

--smart and --basic are mutually exclusive. If neither is passed, the value from ZENTRAA.pbcfg is used.

`zentraa web` — HTTP / WebSocket Bridge

Starts the HTTP and WebSocket bridge so browser clients can connect to the ZENTRAA TCP server. The bridge auto-selects a free port starting from 7080 unless --no-auto-port is set.

pythonaibrainzentraaweb
pythonaibrainzentraaweb--http-port7080--tcp-port9999
pythonaibrainzentraaweb--no-auto-port--max-upload-mb128--history1000

Option	Default	Description
`--host`	`0.0.0.0`	HTTP bind host
`--http-port`	`7080` (auto)	HTTP / WebSocket port
`--tcp-host`	`127.0.0.1`	ZENTRAA TCP server host
`--tcp-port`	`9999`	ZENTRAA TCP server port
`--no-auto-port`	off	Fail instead of scanning for a free port
`--max-upload-mb`	`64`	Maximum file upload size in MB
`--history`	`500`	Messages stored per conversation
`--ping-interval`	`20`	WebSocket ping interval in seconds
`--help`	Show help and exit

Once running, open your browser at:

http://localhost:7080

Bridge features

Typing indicators
Read receipts
Emoji reactions
Message delivery confirmations
Rich user list (online/offline status)
File upload via POST /api/upload (chunked, ≤128 KB per chunk)
REST GET /api/users — online users and metadata
REST GET /api/history/{conv_id} — last N messages

Global CLI Flags

pythonaibrain--version# Print version
pythonaibrain--info# Package metadata + module availability
pythonaibrain--modules# Per-module availability table
pythonaibrain--help# Full help

Configuration

All commands read ZENTRAA.pbcfg from the current directory by default. Pass --config to use a different path.

A minimal config example:

[network]
host=0.0.0.0
port=9999

[client]
default_host=127.0.0.1
default_port=9999

[ai]
smart_ai=true

[ui]
banner_style=full

.env files and RSA key files (.pem) in .zentraa_keys/ are generated locally and are never included in the package. Keep them out of version control.

Architecture

Browser ──WS/JSON──► HTTP Bridge (zentraa web)
 │
 TCP/Encrypted
 │
 ZENTRAA Server (zentraa server)
 │
 ┌────────────┴────────────┐
 Chat Clients TIGER AI Agent
 (zentraa client) (zentraa ai)

License

PyAgent / ZENTRAA: LGPL-3.0-or-later
pyaitk.CLSE: AGPL-3.0-or-later (see pyaitk/CLSE/LICENSE.txt)

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!

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Project description

Pythonaibrain

Requirements

Installation

Minimal install (core package only)

Install with specific modules

Install multiple modules at once

Install everything

Linux — System Dependencies

Post-install Steps

NLTK data (required by Brain, Context, NER, SummarizerAI)

spaCy model (required by NER, optional for Core)

STT offline engine (optional — pocketsphinx)

Verify Installation

Import Usage

License Notice

About Pythonaibrain Package

pyaitk (Python AI ToolKit)

How to import pyaitk

Core Component

What Is This?

Installation

Quick Start

Configuration

VectorizerMode — Feature Extraction

Brain — Intent-Based Chatbot

Basic usage

Constructor parameters

Methods

Function mapping

Memory

AdvanceBrain — LLM-powered Brain

IntentsManager — Dynamic Intent Management

Utility Functions

Weather

Frame classification

Translation

Language detection

NER

Full Example

Architecture Overview

TTS - Text To Speech

How to Use

1. One-shot (simplest)

2. Context manager (recommended)

3. Module-level convenience function

4. Save speech to a WAV file

5. List available voices

6. Get platform-appropriate voice hints

Configuration (TTSConfig)

Voice Matching

Examples Summary

STT — Speech-to-Text Module

What Is This?

Installation

How to Use

1. One-shot (simplest)

2. Context manager (recommended)

3. Force a specific engine

4. Check which engine is active

5. Custom configuration

6. Multi-turn listening loop

Configuration (STTConfig)

Engine Selection

Exception Hierarchy

Examples Summary

PTT (PDF To Text) Function

What Is This?

How to Use

1. Basic extraction

pyaitk (`Python AI ToolKit`)

`VectorizerMode` — Feature Extraction

`Brain` — Intent-Based Chatbot

`AdvanceBrain` — LLM-powered Brain

`IntentsManager` — Dynamic Intent Management

Configuration (`TTSConfig`)

Configuration (`STTConfig`)

`extract_text_from_pdf(pdf_path, encoding, page_separator)`

`PPTXExtractor(pptx_path, image_output_dir)`

`extract_text()` → `dict[int, list[str]]`

`extract_images()` → `dict[int, list[str]]`

`extract_tables()` → `dict[int, list[list[list[str]]]]`

`extract_all()` → `dict`

3. Using `MathSolver` directly

`MathAI(query, operation)` → `str`

`MathSolver` methods

`MathResult` fields