RunAsh AI

RunAsh AI is an efficient live streaming action recognition model designed to provide intelligent solutions for humanity. This repository contains the source code, documentation, and other resources related to the RunAsh AI.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Contributing
• License
• Contact

Introduction

RunAsh AI leverages state-of-the-art machine learning algorithms and artificial intelligence to solve complex problems. It is designed to be flexible, scalable, and easy to integrate into various systems.

Features

• High Performance: Optimized algorithms for fast and efficient processing.
• Scalability: Easily scale to handle large datasets and multiple users.
• Flexibility: Modular design allows for easy customization and extension.
• User-Friendly: Simple API and comprehensive documentation for easy integration.

Installation

To install RunAsh AI, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/RunAsh-AI.git

2. Navigate to the project directory:

   cd RunAsh-AI

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Here is a basic example of how to use RunAsh AI:

from runash_ai import RunAsh

# Initialize the RunAsh AI module
runash = RunAsh()

# Load data
data = runash.load_data('path/to/your/data.csv')

# Train the model
model = runash.train_model(data)

# Make predictions
predictions = runash.predict(model, new_data)
print(predictions)

Sentiment Analysis

from runash_ai import RunAsh

runash = RunAsh()
data = runash.load_data('path/to/sentiment_data.csv')
model = runash.train_sentiment_model(data)
predictions = runash.predict_sentiment(model, new_text_data)
print(predictions)

Image Classification

from runash_ai import RunAsh

runash = RunAsh()
images = runash.load_images('path/to/images')
model = runash.train_image_model(images)
predictions = runash.predict_image(model, new_images)
print(predictions)

For more examples and detailed usage,refer to the API Reference

Contributing

We welcome contributions from the community. To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Create a pull request.

Please ensure that your code adheres to the project's coding standards and includes appropriate tests.

License

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

Contact

For any questions or inquiries, please contact us at [email protected].

Feel free to customize this README file according to the specific details and requirements of your RunAsh AI project.