AI Presentation Generator

A next-generation AI-powered presentation creation platform that transforms your ideas into stunning, professional presentations with the power of artificial intelligence.

✨ Features

• 🤖 AI-Powered Content Generation: Create complete presentations from simple prompts
• 🎨 Customizable Themes: Choose from a variety of professional themes or create your own
• 🖼️ Rich Media Support: Embed videos, charts, interactive elements, and forms
• 📐 Smart Layout Engine: Automatically organize content for optimal visual impact
• 👥 Collaborative Editing: Work together with your team in real-time
• 📤 Export Options: Download as PDF, export to PowerPoint, or present directly from the web

📖 Table of Contents

• Demo
• Getting Started
  • Prerequisites
  • Installation
  • Development
• Self-Hosting & Deployment
• Environment Configuration
• Project Structure
• Architecture
• API
• CI/CD & Testing
• Contributing
• License

🎮 Demo

Visit our live demo (coming soon) to experience the platform.

🚀 Getting Started

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (v18 or later)
• npm (v8 or later)
• Docker (optional, for containerized deployment)
• Python (v3.9 or later, for AI service)

Installation

1. Clone the repository

git clone https://github.com/marvelai-org/PresentationGenerator.git
cd PresentationGenerator

2. Install dependencies

npm ci

3. Create a .env file based on .env.example

cp .env.example .env

4. Update the environment variables with your own values

Development

Start the development server:

npm run dev

This will start the application at http://localhost:3000.

Run tests:

npm test

Run linting:

npm run lint

Format code:

npm run format

AI Service

This repository includes a Python-based AI service for presentation generation:

1. Navigate to the AI service directory:

cd ai-services

2. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the service:

python app.py

The API will be available at http://localhost:8000.

🏗️ Self-Hosting & Deployment

Docker Deployment

The easiest way to deploy the application is using Docker Compose:

1. Clone this repository
2. Configure your environment variables in a .env file (see Environment Configuration)
3. Run docker-compose up -d

The application will be available at http://localhost:3000.

For detailed deployment instructions, see docs/deployment.md.

CI/CD Pipeline

The project includes GitHub Actions workflows for continuous integration and deployment:

• Automated linting and testing
• Build process for the Next.js application
• Docker image creation for easy deployment

All workflows are vendor-agnostic and focused on building, testing, and packaging the application for self-hosting.

🔧 Environment Configuration

Create a .env file in the root directory with the following variables (see .env.example for a template):

# Database
DATABASE_URL="postgresql://username:password@host:port/database"

# Authentication (Supabase)
NEXT_PUBLIC_SUPABASE_URL="https://your-project.supabase.co"
NEXT_PUBLIC_SUPABASE_ANON_KEY="your-supabase-anon-key"
SUPABASE_SERVICE_ROLE_KEY="your-service-role-key"

# AI APIs
OPENAI_API_KEY="your-openai-api-key"
COHERE_API_KEY="your-cohere-api-key"

# Vector DB (Optional)
QDRANT_API_KEY="your-qdrant-api-key"
QDRANT_URL="your-qdrant-url"

For local development without Supabase credentials, you can enable mock authentication:

# Set CI_ENVIRONMENT to true in your .env file
echo "CI_ENVIRONMENT=true" >> .env

This enables the mock Supabase client which allows the application to function without real credentials.

📁 Project Structure

.
├── ai-services/         # Python-based AI service
├── docs/                # Documentation files
├── prisma/              # Database schema and migrations
├── public/              # Static assets
├── src/                 # Source code
│   ├── app/             # Next.js App Router routes
│   ├── components/      # Reusable React components
│   ├── lib/             # Utility functions and services
│   ├── providers/       # React context providers
│   ├── styles/          # Global styles
│   └── types/           # TypeScript type definitions
├── .env.example         # Example environment variables
├── docker-compose.yml   # Docker Compose configuration
├── Dockerfile           # Docker configuration
└── README.md            # Project documentation

🏛️ Architecture

The project is built with a modern tech stack and architecture:

• Frontend: Next.js, React, Tailwind CSS
• Backend: Next.js API routes, Python FastAPI
• Database: PostgreSQL with Prisma ORM
• Authentication: Supabase Auth
• AI: Integration with various AI models via AI service
• Vector Storage: Qdrant for embeddings (optional)

🔌 API

AI Service

The AI service provides endpoints for generating and editing presentations:

• GET / - Health check endpoint
• POST /generate-presentation - Generates a presentation from a prompt
• POST /edit-slide - Edits a specific slide
• POST /preview-slide - Generates a preview image for a slide

Full API documentation is available when running the service at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

🧪 CI/CD & Testing

CI/CD Pipeline

The project includes GitHub Actions workflows for continuous integration and deployment:

• Automated linting and testing
• Build process for the Next.js application
• Docker image creation for easy deployment

All workflows are vendor-agnostic and focused on building, testing, and packaging the application for self-hosting.

Authentication in CI Environment

For CI/CD environments, the project uses a mock authentication strategy that:

• Automatically detects CI environments
• Uses mock Supabase clients and dummy credentials
• Allows builds and tests to run without real authentication keys

For more information, see docs/ci-cd-auth-mocking.md.

👥 Contributing

We welcome contributions! Please check out our contribution guidelines first.

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests and linting
5. Commit your changes (git commit -m 'Add some amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

For major changes, please open an issue first to discuss what you would like to change.

📄 License

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

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_{Built with ❤️ by the AI Presentation Generator team}