agenbits 0.1.2
A library to fetch, convert, and decode binary input data for ML models.
Requires Python: >=3.7

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:

---


# 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:

```bash
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:

```python
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:

```python
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:

```python
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:

```python
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:

```python
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:

```python
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](LICENSE) file for details.

---

## 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.

---

## Contact

**Author**: Enos
**Email**: [enosuppada2005@gmail.com](mailto:enosuppada2005@gmail.com)
**GitHub**: [https://github.com/dunkdumb/agenbits](https://github.com/dunkdumb/agenbits)

```

---

### 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**: 
   - Authorâ€™s contact information with GitHub and email.

---

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!
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