/DataSeter

Data handler

Primary LanguagePython

README - AI Toolbox This repository contains an AI Toolbox that provides a collection of useful functionalities for various tasks such as file system operations, machine learning modeling, API operations, natural language processing, content conversion, dataset management, document embedding, code generation, and more. The toolbox is designed to assist developers in implementing common operations and accelerate the development process.

Table of Contents Installation Usage Components Examples Contributing License Installation To use the AI Toolbox, simply clone this repository to your local machine:

git clone https://github.com/reconsumeralization/DataSeter The toolbox has the following dependencies:

requests sklearn pandas pydub textblob wordcloud matplotlib Pillow spacy beautifulsoup4 selenium You can install the dependencies using pip:

Copy code pip install -r requirements.txt Please ensure you have the required drivers for web scraping (e.g., ChromeDriver) installed and configured.

Usage The AI Toolbox provides various classes and functions for different tasks. Here's an overview of the main components:

FileSystem: Perform file system operations such as creating, deleting, renaming, moving, and copying files.

ModelZoo: Manage machine learning models, train models, and make predictions.

APIOperations: Perform HTTP GET and POST requests to interact with APIs.

NonMLAPIOperations: Perform non-machine learning related API calls.

NLPOperations: Perform natural language processing tasks such as sentiment analysis and text tokenization.

ContentConverter: Convert between different content formats such as text to image, audio to text, and text to audio.

DatasetManager: Load, save, preprocess, combine, split, and synthesize datasets.

DocumentVectorizer: Embed documents and find similar documents using vectorization techniques.

CodeGenerator: Generate code snippets in different programming languages.

ToolLibrary: Perform various string, numerical, and file operations.

You can use the provided classes and functions by creating instances of the respective classes and invoking the relevant methods.

Components The AI Toolbox consists of the following components:

FileSystem: This class provides methods for creating, deleting, renaming, moving, and copying files. You can also update file permissions using this class.

ModelZoo: The ModelZoo class allows you to manage machine learning models. You can train models and make predictions using the supported algorithms.

APIOperations: This class enables you to perform HTTP GET and POST requests to interact with APIs. It handles the request and response processing.

NonMLAPIOperations: This class provides a generic API call method for non-machine learning related operations.

NLPOperations: The NLPOperations class offers methods for performing natural language processing tasks. It includes sentiment analysis, text tokenization, content modification, and content generation.

ContentConverter: This class allows you to convert between different content formats. It supports conversion between text and image, audio and text, and text and audio.

DatasetManager: The DatasetManager class provides functionalities for loading, saving, preprocessing, combining, splitting, and synthesizing datasets.

DocumentVectorizer: This class enables document embedding and vectorization. You can vectorize documents and find similar documents using this class.

CodeGenerator: The CodeGenerator class generates code snippets in different programming languages based on predefined templates.

ToolLibrary: This class provides various utility methods for string operations, numerical operations, and file operations.

Examples The main.py file provides examples of how to use the AI Toolbox for different tasks. It demonstrates how to perform file operations, train machine learning models, make API requests, perform natural language processing tasks, convert content formats, manage datasets, perform document embedding, generate code snippets, and use utility methods.

Feel free to explore the examples and adapt them to your specific use cases.

Contributing Contributions to the AI Toolbox are welcome! If you find any issues or have suggestions for improvements, please submit an issue or pull request on the GitHub repository.

When contributing, please ensure that you follow the existing code style and provide clear documentation and test cases when applicable.

License The AI Toolbox is released under the MIT License. Feel free to use, modify, and distribute the code for personal or commercial projects. However, please include the original license file in any redistribution.

Class: CombinedClass

Description: This class combines various tools and functionalities from different domains into a single class. It provides a unified interface to perform file system operations, machine learning model training and prediction, API operations, NLP operations, content conversion, dataset management, document embedding and vectorization, code generation, tool library functions, math and scientific operations, statistical analysis, medical research tooling, charting and graphing capabilities, and more.

Usage:

  • Create an instance of the CombinedClass to access its functionalities.

Example: combined = CombinedClass()

Methods:

  • File System Operations:

    • create_file(filename)
    • delete_file(filename)
    • rename_file(old_filename, new_filename)
    • move_file(old_path, new_path)
    • copy_file(old_path, new_path)
    • update_file_permissions(filename, permissions)

  • Machine Learning Model Zoo:

    • train_model(model_name, X, y)
    • predict_model(model_name, X)

  • API Operations:

    • get_request(url)
    • post_request(url, data)

  • Non-ML API Operations:

    • call_api(api_endpoint, request_type='GET', data=None, headers=None)

  • NLP Operations:

    • sentiment_analysis(text)
    • tokenize_text(text)
    • content_modification(text)
    • content_generation(input_text)

  • Content Conversion:

    • text_to_image(text, filename)
    • audio_to_text(audio_file, filename)
    • text_to_audio(text, filename)

  • Dataset Manager:

    • load_dataset(dataset_names, file_format='csv', delimiter=',')
    • save_dataset(datasets, file_format='csv', delimiter=',')
    • preprocess_dataset(datasets, preprocess_steps=None)
    • combine_datasets(datasets)
    • split_datasets(dataset, num_splits)
    • synthesize_dataset(num_samples, feature_columns, label_column=None)

  • Document Embedding and Vectorization:

    • vectorize_documents(documents)
    • find_similar_documents(query_document, documents, top_n=5)

  • Code Generator:

    • generate_code(language)

  • Tool Library:

    • string_operations(text)
    • numerical_operations(numbers)
    • file_operations(filepath)

  • Math Library:

    • fibonacci(n)

  • Charting:

    • plot_line_chart(x, y, title, xlabel, ylabel)
    • plot_bar_chart(x, y, title, xlabel, ylabel)

  • Statistics:

    • mean(data)
    • median(data)
    • mode(data)

  • Analytics:

    • perform_data_analysis(data)

  • DarkStrings:

    • encrypt_string(text, key)
    • decrypt_string(encrypted_text, key)

  • Advanced Statistical Analysis:

    • hypothesis_testing(data)

  • Medical Research Tooling:

    • collect_data(patient_id)
    • analyze_data(data)

  • Charting and Graphing:

    • plot_pie_chart(labels, sizes, title)
    • plot_histogram(data, bins, title, xlabel, ylabel)

Note: Refer to the individual method documentation for detailed information on each method's functionality and usage.

CombinedClass - A Comprehensive Library for Various Functionalities

This class is a comprehensive library that combines multiple functionalities into a single entity. It incorporates various operations and tools from different domains, providing a wide range of capabilities to handle tasks efficiently. The class encompasses file system operations, machine learning model zoo, API operations, natural language processing (NLP) operations, content conversion, dataset management, document embedding and vectorization, code generation, tool library, mathematical operations, charting and graphing, statistics, analytics, dark strings, advanced statistical analysis, medical research tooling, and charting and graphing capabilities.

Usage: Instantiate the CombinedClass by providing the directory path for datasets: combined = CombinedClass("datasets/")

File System Operations:

  • create_file(filename): Create a new file.
  • delete_file(filename): Delete an existing file.
  • rename_file(old_filename, new_filename): Rename a file.
  • move_file(old_path, new_path): Move a file from one location to another.
  • copy_file(old_path, new_path): Copy a file to a new location.
  • update_file_permissions(filename, permissions): Update the permissions of a file.

Machine Learning Model Zoo:

  • train_model(model_name, X, y): Train a machine learning model with the specified name.
  • predict_model(model_name, X): Make predictions using a trained machine learning model.

API Operations:

  • get_request(url): Perform a GET request to the specified URL.
  • post_request(url, data): Perform a POST request to the specified URL with the provided data.

Non-ML API Operations:

  • call_api(api_endpoint, request_type='GET', data=None, headers=None): Call an API endpoint with the specified request type, data, and headers.

NLP Operations:

  • sentiment_analysis(text): Perform sentiment analysis on the given text.
  • tokenize_text(text): Tokenize the given text.
  • content_modification(text): Perform content modification on the given text.
  • content_generation(input_text): Generate content based on the input text.

Content Conversion:

  • text_to_image(text, filename): Convert text into an image using a word cloud.
  • audio_to_text(audio_file, filename): Convert audio into text.
  • text_to_audio(text, filename): Convert text into audio.

Dataset Manager:

  • load_dataset(dataset_names, file_format='csv', delimiter=','): Load datasets from files with the specified names, format, and delimiter.
  • save_dataset(datasets, file_format='csv', delimiter=','): Save datasets to files with the specified format and delimiter.
  • preprocess_dataset(datasets, preprocess_steps=None): Preprocess datasets using the provided preprocess steps.
  • combine_datasets(datasets): Combine multiple datasets into a single dataset.
  • split_datasets(dataset, num_splits): Split a dataset into multiple smaller datasets.
  • synthesize_dataset(num_samples, feature_columns, label_column=None): Synthesize a dataset with the specified number of samples, feature columns, and an optional label column.

Document Embedding and Vectorization:

  • vectorize_documents(documents): Vectorize a list of documents.
  • find_similar_documents(query_document, documents, top_n=5): Find similar documents to a query document within a collection of documents.

Code Generator:

  • generate_code(language): Generate code snippets in the specified programming language.

Tool Library:

  • string_operations(text): Perform string operations on the given text.
  • numerical_operations(numbers): Perform numerical operations on the given numbers.
  • file_operations(filepath): Perform file operations on the specified file.

Math Library:

  • fibonacci(n): Generate the Fibonacci sequence up to the specified number of terms.

Charting:

  • plot_line_chart(x, y, title, xlabel, ylabel): Plot a line chart with the given data and labels.
  • plot_bar_chart(x, y, title, xlabel, ylabel): Plot a bar chart with the given data and labels.

Statistics:

  • mean(data): Calculate the mean of the given data.
  • median(data): Calculate the median of the given data.
  • mode(data): Calculate the mode of the given data.

Analytics:

  • perform_data_analysis(data): Perform advanced data analysis on the given dataset.

DarkStrings:

  • encrypt_string(text, key): Encrypt the given text using the specified key.
  • decrypt_string(encrypted_text, key): Decrypt the given encrypted text using the specified key.

Advanced Statistical Analysis:

  • hypothesis_testing(data): Perform hypothesis testing on the given data.

Medical Research Tooling:

  • collect_data(patient_id): Collect data for the specified patient ID.
  • analyze_data(data): Analyze the collected data.

Charting and Graphing:

  • plot_pie_chart(labels, sizes, title): Plot a pie chart with the given labels, sizes, and title.
  • plot_histogram(data, bins, title, xlabel, ylabel): Plot a histogram with the given data, bins, title, xlabel, and ylabel.

Note: Make sure to refer to the individual method documentation for more details on their specific usage and parameters.

The provided code represents a comprehensive library, known as CombinedClass, that integrates functionalities from various domains into a single unified interface. This library serves as a versatile toolkit, offering a wide range of operations and tools that can be utilized across different applications.

The CombinedClass encapsulates functionalities from several domains, including File System Operations, Machine Learning Model Zoo, API Operations, NLP Operations, Content Conversion, Dataset Management, Document Embedding and Vectorization, Code Generation, Tool Library, Math and Scientific Operations, Statistical Analysis, Medical Research Tooling, and Charting and Graphing capabilities.

File System Operations encompass file creation, deletion, renaming, moving, copying, and updating permissions. The Machine Learning Model Zoo provides the ability to train and predict using various machine learning models. API Operations facilitate making GET and POST requests to APIs. NLP Operations cover sentiment analysis, text tokenization, content modification, and content generation. Content Conversion allows text-to-image and audio-to-text conversions, as well as text-to-audio conversion. Dataset Management includes loading, saving, preprocessing, combining, splitting, and synthesizing datasets. Document Embedding and Vectorization support converting documents into vectorized representations and finding similar documents. Code Generation assists in generating code snippets in different programming languages. The Tool Library includes string operations, numerical operations, and file operations. Math and Scientific Operations encompass the Fibonacci sequence generation. Statistical Analysis provides mean, median, mode, and advanced statistical hypothesis testing. Medical Research Tooling facilitates data collection and analysis for medical research. Charting and Graphing support visualizations such as line charts, bar charts, pie charts, and histograms.

By combining these diverse functionalities into a single library, the CombinedClass enables users to conveniently access and utilize different tools and operations without the need for separate libraries or modules. The code follows best practices, utilizing async and parallel processing where appropriate, chunking data for large downloads, and incorporating advanced statistical analysis techniques. It also emphasizes code modularity, applying design patterns and abstract templates where applicable. Furthermore, the code incorporates detailed documentation for each method, enabling users to understand and leverage the functionalities effectively.

With its extensive capabilities and user-friendly interface, the CombinedClass library empowers developers, researchers, and data scientists to perform a wide range of tasks spanning file operations, machine learning, API interactions, NLP, content conversion, dataset management, document analysis, code generation, mathematical computations, statistical analysis, medical research, and visualizations, all within a unified and cohesive framework.