AI PDF to Slide Generator

A scalable Python backend service that automatically generates slides from PDF documents or structured JSON data using AI.

🚀 Features

• FastAPI built with FastAPI for presentation generation
• PDF Text Extraction to analyze uploaded documents
• AI-Powered Content Generation using OpenAI to create slides from PDFs
• Asynchronous Processing with Celery for improved scalability
• Multiple Slide Types including title slides, content, bullet points, two-column, and image layouts
• Task Tracking to monitor the status of your presentation generation
• Containerized with Docker and Docker Compose for easy deployment

📋 Requirements

• Python 3.12+
• Redis (for Celery message broker)
• Docker and Docker Compose (for containerized deployment)
• OpenAI API key (for AI-powered content generation)

🛠️ Installation

Using Docker (Recommended)

1. Clone the repository:

   git clone https://github.com/yourusername/ai-presentation-generator.git
   cd ai-presentation-generator

2. Create a .env file with your OpenAI API key:

   APP_NAME=Presentation Generator
   REDIS_URL=redis://redis:6379/0
   RESULT_BACKEND=redis://redis:6379/0
   STORAGE_PATH=/app/storage
   OPENAI_API_KEY=your_openai_api_key_here
   

3. Build and run the containers:

   docker-compose up --build

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

Manual Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ai-presentation-generator.git
   cd ai-presentation-generator

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Set up your environment variables (create a .env file):

   APP_NAME=Presentation Generator
   REDIS_URL=redis://localhost:6379/0
   RESULT_BACKEND=redis://localhost:6379/0
   STORAGE_PATH=./storage
   OPENAI_API_KEY=your_openai_api_key_here
   

4. Make sure Redis is running locally or update the configuration in app/config.py.

5. Run the FastAPI application:

   uvicorn app.main:app --reload

6. In a separate terminal, start the Celery worker:

   celery -A celery_app worker --loglevel=info

📊 API Usage

Generate a Presentation from Structured Data

curl -X POST http://localhost:8000/api/presentations \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Quarterly Report",
    "author": "Your Team",
    "slides": [
      {
        "type": "title",
        "title": "Q2 Performance Review",
        "content": "Finance Department"
      },
      {
        "type": "bullet_points",
        "title": "Key Achievements",
        "bullet_points": [
          "Revenue increased by 15%",
          "New client acquisition up 22%",
          "Operational costs reduced by 8%"
        ]
      }
    ]
  }'

Response:

{
  "task_id": "b8f5e56a-915b-4c44-a784-2f86c1f0e3e9",
  "status": "pending"
}

Generate a Presentation from PDF

curl -X POST http://localhost:8000/api/presentations/from-pdf \
  -F "pdf_file=@/path/to/your/document.pdf" \
  -F "title=AI Generated Presentation" \
  -F "author=Your Name" \
  -F "num_slides=7"

Response:

{
  "task_id": "c7e4f45b-826a-4b33-a991-4f75d2f7e4a8",
  "status": "pending"
}

Check Presentation Status

curl http://localhost:8000/api/presentations/{task_id}

Response:

{
  "task_id": "c7e4f45b-826a-4b33-a991-4f75d2f7e4a8",
  "status": "completed",
  "file_url": "/download/f6e8d971-3841-4b2a-9e39-3e5fc3c8340b.pptx",
  "message": "Presentation generated successfully"
}

Download the Presentation

curl -O http://localhost:8000/api/download/{file_id}

🧩 Supported Slide Types

Type	Description
title	Title slide with subtitle
content	Standard content slide with title and body text
bullet_points	Slide with title and bulleted points
two_column	Two-column layout for comparing content
image	Image slide with a title

📂 Project Structure

presentation_generator/
├── app/
│   ├── __init__.py
│   ├── main.py              # FastAPI application
│   ├── models.py            # Pydantic models
│   ├── config.py            # Configuration
│   ├── ppt_generator.py     # PowerPoint generation logic
│   └── pdf_processor.py     # PDF processing and OpenAI integration
├── celery_app/
│   ├── __init__.py
│   ├── tasks.py             # Celery tasks
│   └── celery_config.py     # Celery configuration
├── requirements.txt
└── docker-compose.yml

🔧 Configuration

Configuration is managed through environment variables and defaults:

Variable	Default	Description
APP_NAME	"Presentation Generator"	Application name
REDIS_URL	"redis://redis:6379/0"	Redis URL for Celery broker
RESULT_BACKEND	"redis://redis:6379/0"	Result backend for Celery
STORAGE_PATH	"/app/storage"	Path for storing generated presentations
OPENAI_API_KEY	""	Your OpenAI API key

How It Works

PDF Processing Workflow

1. Upload PDF: User uploads a PDF file via the API
2. Text Extraction: System extracts text content using PyPDF2
3. AI Analysis: OpenAI processes the text and identifies key points
4. Content Generation: AI generates slide content based on the analysis
5. Presentation Creation: System creates PowerPoint slides using python-pptx
6. Notification: User can check task status and download the presentation

🛣️ Roadmap

• Add authentication for API endpoints
• Support for custom PowerPoint templates
• Chart generation from data in PDFs
• Image extraction and inclusion from PDFs
• Web interface for easier usage
• Cloud storage integration (S3, GCS)
• Support for more file formats (Word, HTML, Markdown)

📄 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📚 Documentation

For more detailed documentation, see the API Documentation when the service is running.