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EasiScriptX

☆_(coming soon)_☆

Introduction

EasiScriptX a high level programming language designed and devloped in C++ specifically for Artifical Intelligence (AI), Machine Learning (ML), Deep Learning and Large Language Models (LLMs). It supports both Object-Oriented and Functional Programming paradigms, making it adaptable and easy to use. While being powerful and flexible, ESX remains user-friendly for developers at all levels. Also, it is one of the First dedicated programming language for artifical Intelligence

Key Features:

  • AI and ML: Over 100 built-in commands/functions specifically tailored for building AI and ML projects, LLM training, and Deep Learning models.
  • IoT Integration (available shortly after ESX inital release): Special functions to work with IoT devices like Raspberry Pi and Arduino, making it a great choice for AI-IoT projects (e.g., creating smart assistants, autonomous robotics).
  • Complete Backward Compatibility: ESX inherits all 150 commands from EasiScript.
  • Planned Extensions: Audio output features are in the roadmap for future updates.
  • Advanced Networking: Commands to handle distributed computing and data across networks.
Feature/Capability Python Other Languages EasiScriptX (ESX)
General Purpose Programming ✔ ✔ ✔
Object-Oriented Programming ✔ ✔ ✔
Functional Programming ✔ ✔ ✔
Memory Management (Garbage Collection) ✔ ✔ (varies) ✔
Support for AI/ML Libraries ✔ ✔ (varies) ✔
Ease of Use ✔ ✔ (varies) ✔
Dynamic Typing ✔ ✔ (varies) ✔
Extensive Standard Library ✔ ✔ (varies) ✔
Integration with Python Libraries ✔ ✔ (varies) ✔
Built-in Support for Networking ✔ ✔ (varies) ✔
Support for IoT Devices (e.g., Raspberry Pi, Arduino) ✔ ✔ (varies) ✔
Advanced AI/ML and LLM Support Limited Varies ✔
Audio Output Capabilities ✔ ✔ (varies) Planned
Ease of Creating Custom Commands ✔ ✔ (varies) ✔
User-Friendly Syntax ✔ ✔ (varies) ✔
Web-Based Integration ✔ ✔ (varies) Planned
Creating with just basic knowledge in programming and AI Maybe? Maybe? ✔
Dedicated for AI No No Info, depends ✔

Specialties of EasiScriptX (ESX):

  • Advanced AI/ML and LLM Support: ESX is designed with a focus on AI, ML, and LLMs, providing specialized commands and functions for these tasks.
  • Integrated Python Library Support: ESX allows integration with Python libraries, combining ease of use with advanced capabilities.
  • IoT Device Integration: ESX includes features for working with IoT devices like Raspberry Pi and Arduino, making it suitable for IoT projects.
  • Dynamic and User-Friendly: ESX combines object-oriented and functional programming features in a user-friendly manner, making it accessible and versatile.

This table highlights the core capabilities and unique features of each language/platform.

Example Use Case: AI-IoT Integration

You could build a smart home system using Raspberry Pi, where you train an AI model to recognize objects using a camera module and control IoT devices like lights or locks based on real-time AI inferences.

Supported Commands

ESX has all 150 commands from EasiScript, along with additional commands specific to AI, ML, LLMs, and IoT. Below is the current list of commands with brief descriptions.

Core AI and ML Commands

  • TRAIN_MODEL: Trains a machine learning model using a specified dataset. 
    TRAIN_MODEL dataset.csv model.h5

'''

EVAL_MODEL: Evaluates the performance of a trained model using validation data.

EVAL_MODEL model.h5 val_data.csv

LOAD_MODEL: Loads a pre-trained model for inference or further training.

LOAD_MODEL model.h5

PREDICT: Makes a prediction based on input data using a pre-trained model.

PREDICT model.h5 input_data.csv

TUNE_MODEL: Fine-tunes a pre-trained model with new data.

TUNE_MODEL model.h5 new_data.csv

CREATE_LAYER: Creates a neural network layer for custom model architecture.

CREATE_LAYER type=Dense units=128 activation=relu

ADD_LAYER: Adds a layer to an existing model architecture.

ADD_LAYER model.h5 layer=Conv2D filters=32 kernel_size=3x3

SAVE_MODEL: Saves the current model state to a file.

SAVE_MODEL model.h5

Data Processing Commands

  • LOAD_DATA: Loads a dataset from a file for processing.
  LOAD_DATA dataset.csv
  • NORMALIZE_DATA : Normalizes the input data.
NORMALIZE_DATA dataset.csv
  • SPLIT_DATA: Splits the dataset into training and testing sets.
SPLIT_DATA dataset.csv test_size=0.2
  • ** TRANSFORM_DATA**: Transforms data using specified functions.
TRANSFORM_DATA dataset.csv function=log

IoT and Networking commands

  • CONNECT_IOT_DEVICE: Connects to an IoT device (e.g., Raspberry Pi, Arduino).
  CONNECT_IOT_DEVICE device_id="RaspberryPi4"
  • SEND_IOT_COMMAND: Sends a command to an IoT device.
  SEND_IOT_COMMAND device_id="ArduinoUno" command="LED_ON"
  • RECEIVE_IOT_DATA: Receives data from a connected IoT device.
RECEIVE_IOT_DATA device_id="RaspberryPi4"
  • CONNECT_TO_NETWORK: Connects to a network for distributed computing.
CONNECT_TO_NETWORK network_id="AI_Cluster"
  • SEND_DATA: Sends data to another machine or device on the network.
SEND_DATA network_id="AI_Cluster" data="sensor_readings.csv"
  • RECEIVE_DATA: Receives data from another machine or device.
RECEIVE_DATA network_id="AI_Cluster"

Advanced AI and ML Commands

  • CREATE_LLM: Creates a new Large Language Model architecture.
  CREATE_LLM type=Transformer layers=12 hidden
  • TRAIN_LLM : Trains a new Large Language Model with a dataset.
TRAIN_LLM llm_model.h5 dataset.txt epochs=10
  • GENERATE_TEXT: Generates text from a pre-trained LLM.
GENERATE_TEXT llm_model.h5 prompt="Hello, world!"
  • FINE_TUNE_LLM: Fine-tunes a pre-trained LLM with new text data.
FINE_TUNE_LLM llm_model.h5 new_data.txt

  • Data Visualization Commands

  • PLOT_DATA: Plots the dataset as a graph for visualization.

  PLOT_DATA dataset.csv x=feature1 y=feature2
  • SHOW_GRAPH: Displays the current model's accuracy/loss graph.
SHOW_GRAPH model.h5

Utility Commands

  • SAVE_LOG: Saves the session log to a file.
  SAVE_LOG file_name="session_log.txt"
  • LOAD_CONFIG: Loads a configuration file for the session.
LOAD_CONFIG config_file="config.json"
  • DEBUG: Enables or disables debug mode for detailed logs.
DEBUG mode=on

Planned Features:

  • Audio Output Support: Planned for future releases.

  • Enhanced IoT Commands: Additional commands for robotics and automation projects.

How to Use (after release)

    1. Clone the repository (in command prompt):
git clone https://github.com/PulsewaveSoftware/EasiScriptX.git
    1. Explore the examples/ folder for project examples.
    1. Start coding in ESX to build powerful AI and ML models, and integrate them with IoT devices.