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🧠 Python Office Generator

Automatically generate PowerPoint, Word, and PDF presentations from structured data and research content.

License: MIT


📘 Overview

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.


🚀 Features

✅ 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


🧩 Technologies Used

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

📂 Output Files

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

⚙️ Installation

  1. Clone the repository:

    git clone https://github.com/BaseMax/python-office-generator
    cd python-office-generator
  2. Install the dependencies:

    pip install -r requirements.txt
  3. Add your source PDF file (e.g. pdf.pdf) to the project root.

  4. Run the generator:

    python app.py

🧠 Example Topic

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

🧩 Future Improvements

  • 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

📜 License

This project is licensed under the MIT License.

See the LICENSE file for details.

Copyright (c) 2025 Seyyed Ali Mohammadiyeh (Max Base)

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