GaLoRA

GaLoRA is the definitive evolution of the previous solutions Lora Help and MagicALora. This solution represents the future development under the name GaLoRA, providing enhanced features and capabilities for managing and processing content for AI systems using Low RANK (LoRA) technology.

Features

Upload and Download: Effortlessly upload and download files to and from Google Drive, AWS S3, and Azure Blob Storage.
Transliteration: Convert text files into a standardized format, useful for pre-processing text data for NLP models.
JSON Creation: Automatically generate JSON files from text files by identifying and extracting content based on specified keywords.
Directory Management: Download entire directories from cloud storage to a local path for batch processing.
Media srt production: Create the subtitle files for big audio end video files so thay are more understandable both for AI and humans: this also overpass the limit of the need of small video and audio file for translitteration.

Components

1. Command Line Interface (CLI) - `galora.py`

The CLI component of Galora offers a powerful and flexible way to interact with the system through terminal commands.

Advantages of CLI:

Efficiency: Quickly perform batch operations and automate workflows.
Scriptable: Easily integrate with other tools and scripts for seamless automation.
Resource-Friendly: Lightweight and requires minimal system resources.

2. Graphical User Interface (GUI) - `gui.py`

The GUI component provides a user-friendly interface that simplifies interaction with Galora, especially for users who prefer visual interfaces over command-line operations.

Advantages of GUI:

Ease of Use: Intuitive interface that is easy to navigate, reducing the learning curve.
Visualization: Better visualization of processes and data, making it easier to manage complex tasks.
Accessibility: Accessible to users who are not comfortable with command-line operations.

What is a Low RANK (LoRA) Manager?

LoRA, or Low-Rank Adaptation, is a technique used in machine learning to efficiently fine-tune pre-trained language models. It involves adapting only a low-rank subset of the model's parameters, reducing the computational cost and time required for fine-tuning. This approach makes it possible to adapt models to new tasks with fewer resources and less data.

Additionally, GaLoRA allows you to create and optimize datasets for training AI models. It provides functionalities to handle various file types, process data, and manage cloud storage operations.

Seriously, can galora.py be useful for this purpose? Yes, GaLoRA can be quite useful for this purpose. It includes functionalities for handling various file types (such as text, PDF, Word, Excel, CSV, audio, and video files), managing cloud storage operations (with support for AWS S3, Google Drive, Azure Blob Storage, and Aruba Cloud), and processing data for creating and optimizing datasets. These features can streamline the preparation and management of datasets for training AI models.

Use Cases

1. Building Knowledge Bases

Organizations can use GaLoRA to upload and manage internal documentation, research papers, and reports to cloud storage. The AI models can then process this data to create a comprehensive knowledge base that employees can query to find relevant information quickly.

2. Training Chatbots

Companies can use Galora to collect and manage customer service interactions, emails, and support tickets. This data is uploaded to cloud storage and used to train AI chatbots, enabling them to understand and respond to customer inquiries more effectively.

3. Document Management

Businesses can use GaLoRA to automate the process of uploading, categorizing, and managing documents such as contracts, invoices, and HR files. AI models can then analyze these documents to extract key information, detect anomalies, and ensure compliance.

4. Research and Development

Research institutions can use Galora to gather and manage scientific data, experimental results, and publications. This data can be processed by AI models to identify trends, generate insights, and accelerate the R&D process.

5. Content Management

Media companies can use Galora to manage large volumes of video, audio, and text content. The AI models can transcode media files, generate subtitles, and create metadata to improve content discovery and user experience.

Prerequisites

To run GaLoRA, you need to have the following dependencies installed:

Python 3.x
boto3
azure-storage-blob
google-auth
google-auth-oauthlib
google-auth-httplib2
google-api-python-client
PyMuPDF
python-pptx
moviepy
speechrecognition
pydub
pandas
ebooklib
BeautifulSoup4
docx
vlc
requests

Installation

Clone the repository:

git clone https://github.com/yourusername/galora.git
cd galora

Install the required packages:
```
pip install -r requirements.txt
```

Usage

Uploading Files

To upload a file to Google Drive:
```
./upload_to_gdrive.bat
```
To upload a file to AWS S3:
```
./upload_to_aws.bat
```
To upload a file to Azure Blob Storage:
```
./upload_to_azure.bat
```

Downloading Files

To download a file from Google Drive:
```
./download_from_gdrive.bat
```
To download a file from AWS S3:
```
./download_from_aws.bat
```
To download a file from Azure Blob Storage:
```
./download_from_azure.bat
```

Transliterating Text Files

To transliterate a text file:
```
./transliterate_text.bat
```

Creating JSON Files

To create JSON from a single text file:
```
./create_json_single.bat
```
To create JSON from multiple text files:
```
./create_json_multiple.bat
```

Downloading Entire Directory

To download an entire directory from cloud storage:
```
./download_directory.bat
```

Producing the SRT from Media Files

To Generatr srt from media files:
```
./generate_srt_file.bat
```

Some hints and help

I provided you with some batch files to test the Galora functionalities

License

This project is licensed under the GPL 3.0 License - see the LICENSE file for details.

piretro999 / galora Goto Github PK

galora's Introduction