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https://github.com/user-attachments/assets/938889e8-d7d8-4f4f-b2a1-07ee3ef3991a
The main contributor of this repo is a Master's student graduating in 2026, currently on the job market. Feel free to reach out for collaboration or job opportunities.
本仓库的主要贡献者是一名 2026 届硕士毕业生,正在求职中,欢迎联系。

[!IMPORTANT] 1. All these API keys, configurations, and services are required. 2. Gemini 2.5/3 are the only LLMs for the Design Agent that achieve consistently good results. Other LLMs (including GPT-5/Sonnet) will lead to severe performance degradation. 3. We do not support offline serving for now.
bash
cd docker
docker build -t desktop-commander-deeppresenter .
bash
npm install -g playwright
npx playwright install chromium
From the project root directory, run:
pip install -e deeppresenter
Also from the project root directory, run:
python webui.py
[!TIP] 🚀 All configurable variables can be found in constants.py.































If you find this project helpful, please use the following to cite it:
@inproceedings{zheng-etal-2025-pptagent,
title = "{PPTA}gent: Generating and Evaluating Presentations Beyond Text-to-Slides",
author = "Zheng, Hao and
Guan, Xinyan and
Kong, Hao and
Zhang, Wenkai and
Zheng, Jia and
Zhou, Weixiang and
Lin, Hongyu and
Lu, Yaojie and
Han, Xianpei and
Sun, Le",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.728/",
doi = "10.18653/v1/2025.emnlp-main.728",
pages = "14413--14429",
ISBN = "979-8-89176-332-6",
abstract = "Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, overlooking visual appeal and structural coherence, which limits their practical applicability. To address these limitations, we propose PPTAgent, which comprehensively improves presentation generation through a two-stage, edit-based approach inspired by human workflows. PPTAgent first analyzes reference presentations to extract slide-level functional types and content schemas, then drafts an outline and iteratively generates editing actions based on selected reference slides to create new slides. To comprehensively evaluate the quality of generated presentations, we further introduce PPTEval, an evaluation framework that assesses presentations across three dimensions: Content, Design, and Coherence. Results demonstrate that PPTAgent significantly outperforms existing automatic presentation generation methods across all three dimensions."
}
$ claude mcp add PPTAgent \
-- python -m otcore.mcp_server <graph>