Markdown to PowerPoint Converter

This project converts Markdown files to PowerPoint presentations. You can use this GPTs to generate the base of the Markdown file.

Installation

Follow these steps to set up a Python virtual environment and install the required packages:

1. Clone this repository:

   git clone https://github.com/treeleaves30760/Hackmd_PPT_Converter
   cd Hackmd_PPT_Converter

2. Install the required packages:

   conda create -n hackmd_ppt python==3.11.8 -y
   conda activate hackmd_ppt
   pip install -r requirements.txt

Usage

GUI

If you want to use the GUI, you can run the GUI script:

• Windows:

  Start_GUI.bat

• Linux/macOS:

  sh Start_GUI.sh

In the GUI, you can input the hackmd code into the textarea, then press the convert button to generate the PPT.

API

You can import the converter.py:

from src.converter.converter import MarkdownToPptConverter

converter = MarkdownToPptConverter('', 'example.pptx', mode=1)
converter.convert('example.md')

Build Instructions

To create standalone executables for Windows and macOS, follow these steps:

Prerequisites

1. Ensure you have PyInstaller installed:

   pip install pyinstaller

2. Make sure your project structure is correctly set up.

Building the Application

1. Create icon files for your application:

   • For Windows: Create a .ico file
   • For macOS: Create a .icns file

2. Update the build_release.py script in the project root with the correct paths to your icon files.

3. To build the application:

   • On Windows:

     python build_release.py

   • On macOS:

     python build_release.py

4. The executable will be created in the dist folder.

Distribution

• For Windows: Distribute the .exe file from the dist folder.
• For macOS: Distribute the .app file from the dist folder.

Notes

• Test the executables thoroughly on fresh systems to ensure all dependencies are correctly bundled.
• You may need to adjust the PyInstaller arguments in build_release.py based on your specific project structure and requirements.
• Consider using tools like Inno Setup (Windows) or Disk Utility (macOS) to create installers or DMG files for easier distribution.

Format

Below is an example of a markdown file:

# Introduction to Stable Diffusion

---

## Table of Contents

1. What is Stable Diffusion?
2. Core Features
3. Use Cases
4. Advantages
5. Limitations

---

## What is Stable Diffusion?

Stable Diffusion is a deep learning model used for generating high-quality images. It can create images based on textual descriptions or edit and enhance existing images.

---

## Core Features

- **Text-to-Image Conversion**: Ability to generate images based on natural language descriptions.
- **Image-to-Image Transformation**: Can transform input images into images of a different style.
- **High-Resolution Support**: Capable of generating high-quality, high-resolution images.
- **Wide Range of Applications**: Suitable for various fields such as art creation, game development, entertainment industry, etc.

---

## Use Cases

- **Art Creation**: Artists and designers use Stable Diffusion to create new artworks.
- **Content Generation**: Automatically generate visual content for social media, advertising, and other domains.
- **Game Development**: Generate game scenes, characters, or textures.

---

## Advantages

- **Fast and Efficient**: Stable Diffusion can generate high-quality images faster compared to traditional image generation techniques.
- **Flexibility**: Users can control the style and details of the generated images by adjusting parameters.

---

## Limitations

- **Creative Constraints**: Generated images may be limited by the training data and may not always fully meet the user's creative requirements.
- **Quality Fluctuations**: While it can produce high-quality images most of the time, there may be instances of unstable image quality.

Usage	Sign	Example
Page break	---	---
Title	#	# PPT to AI
Page Title	##	## What is AI
List Number	1.	1. **The usage of AI**
List Points	-	- **AI Development**