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AGI Pipeline

Overview

The AGI (Artificial General Intelligence) Pipeline is a comprehensive and modular software framework designed to integrate various AI capabilities, including Natural Language Processing (NLP), Computer Vision (CV), Multi-Modal Processing, Reinforcement Learning (RL), and Real-Time Video Processing. This pipeline leverages state-of-the-art models and techniques to provide a robust and scalable solution for diverse AI tasks.

Features

  • Natural Language Processing (NLP): Utilizes the BART model for text summarization and other NLP tasks.
  • Computer Vision (CV): Employs the ResNet50 model for image classification and advanced data augmentation techniques.
  • Multi-Modal Processing: Integrates the CLIP model to process and understand text and image inputs simultaneously.
  • Reinforcement Learning (RL): Implements the PPO algorithm for training RL agents with a custom environment.
  • Real-Time Video Processing: Supports real-time video processing using OpenCV.
  • Voice and Speech Integration: Incorporates speech-to-text and text-to-speech capabilities.
  • Interactive Visualization: Utilizes Plotly for dynamic and interactive data visualization.
  • Deployment and Scalability: Designed for easy deployment to cloud platforms such as AWS, GCP, and Heroku.
  • Comprehensive Testing and Validation: Implements unit tests and integration tests using PyTest.
  • User Interface: Provides a web-based user interface using Flask and React.

Installation

Prerequisites

  • Python 3.8 or higher
  • Docker (for containerization)
  • Git (for version control)

Clone the Repository

git clone https://github.com/your-username/AGI-Pipeline.git
cd AGI-Pipeline

Install Dependencies

pip install -r requirements.txt

Running the Application

Using Docker

  1. Build the Docker Image:
docker build -t agi-pipeline .
  1. Run the Docker Image:
docker run -p 8000:8000 agi-pipeline

Without Docker

uvicorn app:app --host 0.0.0.0 --port 8000

Usage

API Endpoints

  • /process/: Processes text and video inputs for multi-modal integration.
  • /nlp/: Processes text for summarization.
  • /cv/: Processes images for classification.
  • /real-time-video/: Starts real-time video processing.
  • /speech-to-text/: Converts speech to text.
  • /text-to-speech/: Converts text to speech.
  • /secure-endpoint/: A secure endpoint demonstrating token-based authentication.

Example Requests

Text Summarization

curl -X POST "http://localhost:8000/nlp/" -H "Content-Type: application/json" -d '{"text": "Summarize this text."}'

Image Classification

curl -X POST "http://localhost:8000/cv/" -F "image=@path_to_image.jpg"

Multi-Modal Processing

curl -X POST "http://localhost:8000/process/" -F "text=Describe this image" -F "video=@path_to_video.mp4"

Contributing

We welcome contributions to the AGI Pipeline project! Please follow these steps to contribute:

  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. Open a pull request.

License

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

Acknowledgements

We would like to thank the developers and contributors of the libraries and frameworks used in this project, including Hugging Face, PyTorch, OpenCV, FastAPI, and many others.