AuraNexus - AI Content Orchestration Platform

Features

• AI-Powered Content Generation: Generate text, images, and multimedia content using open-source models
• Multi-Platform Distribution: Publish to Twitter, Instagram, LinkedIn, YouTube, and more
• Smart Optimization: Automatically optimize content for different platforms and audiences
• Analytics Dashboard: Track performance and get AI-powered insights
• Privacy-First: Self-hosted solution with complete control over your data

Architecture

1. AI Gateway: Unified interface for LLMs and diffusion models
2. Content Studio: Context-aware editor with auto-generation capabilities
3. Multimedia Factory: Pipeline for text-to-image, text-to-video generation
4. Omni-Channel Distributor: Adapters for various social platforms
5. Insight Dashboard: Analytics and recommendation engine

Quick Start

Prerequisites

• Python 3.8+
• Docker and Docker Compose (optional, for containerized deployment)

Running the Platform

1. Clone the repository:
   bash

   1git clone https://huggingface.co/[YOUR_USERNAME]/auranexus 2cd auranexus
2. Start the platform:
   ./startup.sh
3. Access the platform:
   • Frontend: http://localhost:3000
   • API: http://localhost:8000
   • API Documentation: http://localhost:8000/docs
4. Create an account using the registration form
5. Start generating content using the content creation interface

Using Docker (Alternative Method)

1. Build and start services:
   docker-compose up --build
2. Access the platform:
   • Frontend: http://localhost:3000
   • API: http://localhost:8000

API Endpoints

• POST /api/v1/auth/register - Register a new user
• POST /api/v1/auth/token - Get authentication token
• POST /api/v1/content/generate - Generate new content
• POST /api/v1/content/optimize - Optimize existing content
• GET /api/v1/content/my - Get your content items
• GET /api/v1/models - List available AI models

Development

Project Structure

auranexus/
├── api/                 # FastAPI application
├── database/            # Database models and session management
├── core/               # Core utilities (AI gateway, security)
├── services/           # Business logic services
├── utils/              # Utility functions
├── frontend/           # Frontend files
├── main.py             # Main application entry point
├── requirements.txt    # Python dependencies
└── docker-compose.yml  # Docker configuration

Extending the Platform

1. Add new AI models: Extend the AIGateway class in core/ai_gateway.py
2. Add platform adapters: Create new classes in services/ for additional social platforms
3. Enhance content types: Modify the content studio service to support new content types

Production Deployment

1. Set environment variables:
   bash

   1export SECRET_KEY="your-super-secret-key-change-in-production" 2export DATABASE_URL="postgresql://user:password@host:port/dbname"
2. Use the provided Docker Compose configuration
3. Set up SSL certificates for HTTPS
4. Configure a reverse proxy (nginx recommended)

Contributing

License

Support