Automatically generate PowerPoint, Word, and PDF presentations from structured data and research content.
Python Office Generator is a script that automates the creation of academic and research presentations in multiple formats PPTX, DOCX, and PDF. This tool demonstrates how Python can integrate document-generation libraries to produce consistent, professional-quality materials for reports, lectures, and conferences.
The example included focuses on:
Machine Learning Applications in Environmental Sustainability 🌱
It extracts figures from a PDF (using PyMuPDF), creates presentation slides, generates summary PDFs, and writes accompanying lecture notes in Word.
✅ Extracts images directly from a PDF file ✅ Automatically builds PowerPoint slides from structured content ✅ Generates corresponding DOCX and PDF summary documents ✅ Adds visual figures into presentation slides ✅ Uses clean and readable formatting (fonts, colors, spacing) ✅ Ideal for academic and research-oriented automation
| Library | Purpose |
|---|---|
python-pptx | Generate PowerPoint slides |
python-docx | Create Word documents |
reportlab | Build formatted PDF summaries |
PyMuPDF (fitz) | Extract images from PDFs |
os | File system management |
When executed, the script produces the following files automatically:
| File | Description |
|---|---|
Presentation_ML_Sustainability.pptx | Generated PowerPoint presentation |
Presentation_ML_Sustainability.pdf | Summary document in PDF format |
Presentation_ML_Sustainability.docx | Presentation notes in Word format |
figure_*.png | Extracted images from source PDF |
Clone the repository:
git clone https://github.com/BaseMax/python-office-generator cd python-office-generatorInstall the dependencies:
pip install -r requirements.txt
Add your source PDF file (e.g.
pdf.pdf) to the project root.Run the generator:
python app.py
Machine Learning Applications in Environmental Sustainability
This example presentation includes sections like:
- Introduction to ML and sustainability
- Supervised & Unsupervised learning approaches
- Deep Learning in climate science
- Renewable energy forecasting case study
- Future research directions
- Add CLI interface for dynamic content generation
- Support for LaTeX → PPTX and Markdown → DOCX conversion
- Automatic design themes and layouts
- Integration with AI summarization tools
This project is licensed under the MIT License.
See the LICENSE file for details.
Copyright (c) 2025 Seyyed Ali Mohammadiyeh (Max Base)