agenbits 0.1.1

A library to fetch, convert, and decode binary input data for ML models.

Here is the README.md with a complete set of examples for each supported file type and its usage, including a demo for all the supported files:

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agenbits

A Python library to fetch, convert, and decode binary input data for machine learning models.

agenbits simplifies the process of fetching data from URLs, converting it into a temporary binary file, and decoding it into usable formats such as images, audio, text, videos, and more for ML models.

Features

• Fetch data: Fetch files from URLs (HTTP/HTTPS).
• Convert to binary: Converts files from the URL into binary format and stores them temporarily.
• Decode files: Supports decoding of various data formats, including Excel, CSV, JSON, images, audio, videos, and more.
• Automatic format detection: Automatically detects the file type and handles it accordingly.

Installation

To install agenbits from PyPI, run the following command:

pip install agenbits

Usage

agenbits provides multiple format-specific functions to fetch, convert, and decode files. Here's how you can use it:

Example 1: Fetch and Use Excel Data (CSV, XLSX)

You can fetch and convert Excel or CSV files into usable data for machine learning models:

from agenbits import Excel

# Fetch Excel data from a URL
data = Excel("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv")
print(data.head())  # Show first few rows (for pandas DataFrame)

Example 2: Fetch and Display Image

Fetch an image from a URL, convert it to binary format, and display it:

from agenbits import ImageFile

# Fetch and display image
img = ImageFile(
    "https://images.pexels.com/photos/17647410/pexels-photo-17647410/free-photo-of-airplanes-flying-at-an-air-show.jpeg")
img.show()  # This will open the image using the default viewer

Example 3: Fetch and Use Audio Data (WAV, FLAC)

You can also fetch and decode audio files such as WAV or FLAC:

from agenbits import Audio

# Fetch and process audio data
audio = Audio("https://example.com/audio.wav")
audio.play()  # Play audio using an appropriate library like soundfile

Example 4: Fetch and Use Video Data (MP4)

Fetch and decode a video file (e.g., MP4) and play it or perform any video-related operation:

from agenbits import Video

# Fetch video data
video = Video("https://www.example.com/video.mp4")
video.play()  # Implement using appropriate video playing library, like OpenCV or ffmpeg

Example 5: Fetch and Use Text Data (JSON, TXT, XML)

You can fetch and decode text-based files such as JSON, TXT, or XML:

from agenbits import Text

# Fetch JSON data from URL
json_data = Text("https://example.com/data.json")
print(json_data)  # Print decoded JSON content

Example 6: Fetch and Handle Binary Files

For any other type of binary file (e.g., TensorFlow Record or PyTorch Model), agenbits can help fetch and convert those as well:

from agenbits import Binary

# Fetch and convert binary file
binary_data = Binary("https://example.com/model.pt")
# Handle this binary file as needed, e.g., load into PyTorch or TensorFlow

Supported Formats

agenbits supports a wide variety of formats, including:

• CSV (.csv): Comma-separated values files, typically used for tabular data.
• JSON (.json): JavaScript Object Notation, often used for APIs and data exchange.
• TXT (.txt): Plain text files.
• XLSX (Excel): Excel files, including .xlsx and .xls.
• JPEG (.jpg): JPEG image files.
• PNG (.png): Portable Network Graphics image files.
• MP4 (.mp4): Video files, typically for multimedia data.
• WAV (.wav): Uncompressed audio files, typically for audio data processing.
• FLAC (.flac): Lossless audio files.
• TSV (.tsv): Tab-separated values files, similar to CSV but using tabs for separation.
• XML (.xml): Extensible Markup Language files, used for storing and transporting data.
• Binary Files: Any raw binary data, such as TensorFlow Records or other custom formats.
• Edge List (Graph): Data in the form of an edge list, often used for graph data structures.
• SGM (.sgm): Standard Generalized Markup files, typically for text data like articles or web pages.
• TensorFlow Record (.tfrecord): A binary storage format for TensorFlow data.
• PyTorch Model (.pt, .bin): Binary files containing PyTorch models.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contributing

If you want to contribute to agenbits, feel free to fork the repository and create a pull request. Please ensure that your code follows the existing style and includes tests for new features.

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Contact

Author: Enos Email: [e-mail] GitHub: https://github.com/dunkdumb/agenbits

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### Explanation of Updates:

1. **General Introduction**: 
   - Describes the main purpose of the library.
   
2. **Features**: 
   - Lists key features, such as fetching data from URLs, converting to binary format, and automatically decoding files for machine learning models.
   
3. **Installation**: 
   - Instructions to install the package from PyPI.

4. **Usage Examples**: 
   - Added various examples of using the library with different file types (Excel, CSV, Image, Audio, Video, Text, Binary, etc.).
   - Each example demonstrates how you can pass a URL to the library and fetch/convert/decode the file into a usable format.

5. **Supported Formats**: 
   - An extended list of supported file formats, including details about each file type.

6. **License**: 
   - Information on the MIT license.

7. **Contributing**: 
   - Encourages others to contribute to the library, with instructions for forking and submitting pull requests.

8. **Contact Information**: 
   - Author’s contact information with GitHub and email.

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This `README.md` file should now be comprehensive and well-structured for users to understand and use your library effectively. Let me know if you need further adjustments!