Metadata-Version: 2.4
Name: ai-mk-toolkit
Version: 8.8.2
Summary: Unified AI/ML package providing a consistent API for accessing 205 models and tasks from scikit-learn, PyTorch, TensorFlow, and other popular libraries.
Author-email: Manojna Karuparthi <manojnak03@gmail.com>
License-Expression: MIT
Keywords: machine-learning,deep-learning,nlp,computer-vision,reinforcement-learning,ai,unified-api
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Dynamic: license-file

# AI_MK_Toolkit

AI_MK_Toolkit is a production-ready Python package that provides a **unified, consistent interface** for accessing over **205 machine learning, deep learning, natural language processing (NLP), computer vision (CV), and reinforcement learning (RL) models and tasks** from leading open-source frameworks.

Rather than reimplementing algorithms, AI_MK_Toolkit acts as a lightweight abstraction layer that simplifies the use of multiple AI frameworks while preserving the functionality, performance, and flexibility of their official implementations.

**Python Requirement:** Python **3.10 or later**

**Supported Platforms:** Windows, Linux, and macOS

---

## 🚀 Why AI_MK_Toolkit?

Modern AI development often requires switching between multiple frameworks, each with its own APIs, import patterns, dependency management, and usage conventions. AI_MK_Toolkit removes this complexity by providing a **single, intuitive, and consistent interface** across popular AI ecosystems, allowing developers to focus on building solutions instead of framework-specific boilerplate.

---

## ✨ Key Features

- **Unified API** – Access models and tasks from multiple AI frameworks through a consistent interface.
- **205+ Supported Entries** – Includes 51 Machine Learning models, 67 Deep Learning models, 54 NLP models, 19 Reinforcement Learning algorithms, and 14 Computer Vision task APIs.
- **Lazy Loading** – Optional dependencies are imported only when required, reducing startup time and installation overhead.
- **Framework Agnostic** – Use models from libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face Transformers, Stable-Baselines3, and more through a common API.
- **Production Ready** – Built with type hints, comprehensive error handling, and PEP 561 (`py.typed`) support.
- **Modular Installation** – Install only the dependencies needed for your specific use case using optional extras.
- **Transparent Attribution** – Clearly documents the original projects and their licenses, ensuring proper acknowledgment of all underlying open-source libraries.
- **Developer Friendly** – Simplifies experimentation, prototyping, education, and production workflows by minimizing framework-specific code.

---

## 📖 About This Project

**AI-MK-Toolkit** is a production-ready Python package that provides a **unified, consistent interface** to over **205 machine learning, deep learning, natural language processing, computer vision, reinforcement learning, and large language model (LLM) implementations** from widely adopted open-source frameworks.

Rather than reimplementing algorithms, AI-MK-Toolkit serves as a lightweight abstraction layer that enables developers to access models from multiple ecosystems through a single, intuitive API while preserving the functionality and performance of the underlying libraries.

---

### Key Principles

- **Unified API** – Access models from multiple frameworks using a consistent interface.
- **No Algorithm Reimplementation** – Uses the official implementations provided by libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face Transformers, Stable-Baselines3, and others.
- **Modular Installation** – Install only the dependencies required for your specific use case through optional extras.
- **Transparent Attribution** – Properly acknowledges and respects the licenses of all underlying open-source projects.
- **Developer-Friendly** – Simplifies experimentation, prototyping, and production development by reducing framework-specific boilerplate.

> **Important:** AI-MK-Toolkit is an interface and integration library. It does **not** claim ownership of, modify, or replace the original machine learning algorithms or pretrained models provided by the respective open-source projects. All credit for the underlying implementations belongs to their original authors and maintainers.

---

# 📦 Installation

### Base Installation

```bash
pip install ai-mk-toolkit
```

### Deep Learning

```bash
pip install ai-mk-toolkit[dl]
```

### Natural Language Processing

```bash
pip install ai-mk-toolkit[nlp]
```

### Reinforcement Learning

```bash
pip install ai-mk-toolkit[rl]
```

### Complete Installation

```bash
pip install ai-mk-toolkit[full]
```

---

# 🚀 Quick Start

## Machine Learning

```python
from ai_mk_toolkit import ml

model = ml.RandomForest()
model.fit(X_train, y_train)

predictions = model.predict(X_test)
```

## Deep Learning

```python
from ai_mk_toolkit import dl

model = dl.ResNet50(pretrained=True)
```

## Natural Language Processing

```python
from ai_mk_toolkit import nlp

bert = nlp.BERT()
tokenizer = nlp.Tokenizer()
```

## Reinforcement Learning

```python
from ai_mk_toolkit import rl

agent = rl.DQN(policy="MlpPolicy", env=env)
```

## Computer Vision

```python
from ai_mk_toolkit import cv

detector = cv.ObjectDetection()
classifier = cv.ImageClassification()
```

---

# 📊 Package Overview

| Module | Description |
|---------|-------------|
| **ML** | 51 classical machine learning models |
| **DL** | 67 deep learning model wrappers |
| **NLP** | 54 NLP models and task APIs |
| **RL** | 19 reinforcement learning algorithms |
| **CV** | 14 computer vision task APIs |

Total: **205+ unified model wrappers and task interfaces**

---

# 🏗 Package Structure

```
ai_mk_toolkit/
├── ml/
├── dl/
├── nlp/
├── rl/
├── cv/
├── automl/
├── datasets/
├── preprocessing/
├── metrics/
├── explainability/
├── visualization/
├── deployment/
├── plugins/
├── backends/
└── core/
```

---

# ✅ Quality

- Production-ready package structure
- Type annotated (PEP 561)
- Comprehensive automated test suite
- Wheel and source distributions
- Lazy-loading architecture
- Clean wrapper design (no algorithm reimplementation)

---

# 🧪 Testing

```bash
pytest

pytest -v
```

---

# 📄 License

AI_MK_Toolkit is distributed under the **MIT License**.

The package wraps existing open-source libraries, each of which retains its own license.

> **Important:** Some optional dependencies have copyleft licenses (for example, **Ultralytics (AGPL-3.0)** and **Gensim (LGPL-2.1+)**). Users are responsible for complying with the licenses of any optional dependencies they choose to install.

---

# 🙏 Acknowledgments

AI_MK_Toolkit builds upon the excellent work of the open-source community.

Major frameworks include:

- scikit-learn
- NumPy
- PyTorch
- torchvision
- TensorFlow / Keras
- Hugging Face Transformers
- XGBoost
- LightGBM
- CatBoost
- Stable-Baselines3
- timm
- spaCy
- NLTK
- Gensim
- sentence-transformers
- Ultralytics

> **Important:** All trademarks, models, algorithms, and implementations remain the intellectual property of their respective authors and maintainers.

---

# 🤝 Contributing

Contributions, feature requests, and bug reports are welcome.

Before contributing:

- Follow the existing project structure.
- Document any new dependency and its license.
- Prefer permissive licenses (MIT, BSD, Apache-2.0).
- Keep optional dependencies modular whenever possible.

---

# ⚠ Disclaimer

AI_MK_Toolkit is a **wrapper library**, not a machine learning framework.

> **Important:** It provides a unified Python interface to official implementations from existing open-source projects and **does not claim ownership or authorship of the underlying algorithms or pretrained models**. All credit belongs to the original authors and maintainers of those projects.
