README

Overview

This project generates a PowerPoint presentation based on user-provided descriptions. It leverages language models to generate text content and an image generation API to create images for the slides. The architecture is modular, allowing for easy extension and customization of the text and image generation components.

How to Use

Prerequisites

• Python 3.10 or higher
• Required Python packages (listed in requirements.txt)

Setup

1. Clone the repository:

   git clone --recurse-submodules https://github.com/ai-forever/slides_generator.git
   cd slides_generator

2. Install dependencies:

   pip install -r requirements.txt

3. Create a .env file in the root directory with GigaChat credentials:

Here is the documentation on how to get access token.

AUTH_TOKEN=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
COOKIE=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

4. Run the FastAPI server for the image generation API:

   python src/kandinsky.py

Running the Script

To generate a presentation, use the following command:

python main.py -d "Description of the presentation"  -l 'en'

This will generate a presentation based on the provided description and save it in the logs directory with a timestamp.

Examples

python main.py -d "Сгенерируй презентацию про планеты солнечной системы" -l 'ru'

python main.py -d "Generate presentation about planets of Solar system" -l 'en'

This command will create a presentation on the topic "Planets of the Solar System" using the configured text and image generation functions.

Architecture

Main Components

1. main.py: The entry point of the application. It parses command-line arguments, initializes required components, and orchestrates the presentation generation process.

2. Font Class (src/font.py): Manages fonts used in the presentation. It can select a random font with basic and bold styles and provide paths to various font styles (basic, bold, italic, and italic bold).

3. Presentation Generation Functions (src/constructor.py): Functions that generate different types of slides in the presentation. They handle the layout, font settings, and placement of text and images.

4. Text Generation (src/gigachat.py): Contains the giga_generate function, which generates text based on a given prompt.

5. Image Generation (src/kandinsky.py): Includes the api_k31_generate function, which generates images based on a prompt using an external API. Additionally, it provides a FastAPI server for the image generation API.

6. Prompt Configuration (src/prompt_configs.py): Defines the structure of prompts used for generating titles, text, images, and backgrounds for slides.

How It Works

1. Initialization:

   • main.py parses command-line arguments to get the presentation description.
   • It initializes the Font class with the directory containing font files and sets a random font.

2. Prompt Configuration:

   • The ru_gigachat_config defines the structure and content of prompts used for generating slide components (titles, text, images, backgrounds).

3. Text and Image Generation:

   • The giga_generate function generates text based on the provided description.
   • The api_k31_generate function generates images based on prompts using the FastAPI server.

4. Slide Generation:

   • The generate_presentation function orchestrates the creation of slides by calling appropriate functions to generate text and images, and then formats them into slides.

Extending the Project

Adding New Font Styles

To add new font styles, place the font files in the fonts directory and update the Font class if necessary to recognize the new styles.

Changing Text Generation

To use a different text generation function, replace the giga_generate function from src/gigachat.py or add a new function and update the call in main.py.

Changing Image Generation

To use a different image generation API, modify the api_k31_generate function in src/kandinsky.py or add a new function and update the call in main.py.

Acknowledgements

This project leverages the python-pptx library for PowerPoint generation, PIL for image processing, and other Python libraries for various functionalities. The text and image generation models are based on external APIs and language models.

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Feel free to reach out with any questions or suggestions!

Authors

• Said Azizov: Github, Blog

Citation

@misc{arkhipkin2023kandinsky,
      title={Kandinsky 3.0 Technical Report}, 
      author={Vladimir Arkhipkin and Andrei Filatov and Viacheslav Vasilev and Anastasia Maltseva and Said Azizov and Igor Pavlov and Julia Agafonova and Andrey Kuznetsov and Denis Dimitrov},
      year={2023},
      eprint={2312.03511},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}