Open-Source, Locally-Run AI Presentation Generator (Gamma Alternative)

Presenton is an open-source application for generating presentations with AI — all running locally on your device. Stay in control of your data and privacy while using models like OpenAI, Gemini, and others. Just plug in your own API keys and only pay for what you use.

✨ More Freedom with AI Presentations

• ✅ Bring Your Own Key — Only pay for what you use. OpenAI, Gemini (More coming soon...)
• ✅ Ollama Support — Run open-source models locally with Ollama integration
• ✅ Runs Locally — All code runs on your device
• ✅ Privacy-First — No tracking, no data stored by us
• ✅ Flexible — Generate presentations from prompts or outlines
• ✅ Export Ready — Save as PowerPoint (PPTX) and PDF
• ✅ Fully Open-Source — Apache 2.0 licensed

Running Presenton Docker

1. Start Presenton

Linux/MacOS (Bash/Zsh Shell):

docker run -it --name presenton -p 5000:80 -v "./user_data:/app/user_data" ghcr.io/presenton/presenton:v0.3.0-beta

Windows (PowerShell):

docker run -it --name presenton -p 5000:80 -v "${PWD}\user_data:/app/user_data" ghcr.io/presenton/presenton:v0.3.0-beta

2. Open Presenton

Open http://localhost:5000 on browser of your choice to use Presenton.

Note: You can replace 5000 with any other port number of your choice to run Presenton on a different port number.

Deployment Configurations

You may want to directly provide your API KEYS as environment variables and keep them hidden. You can set these environment variables to achieve it.

• CAN_CHANGE_KEYS=[true/false]: Set this to false if you want to keep API Keys hidden and make them unmodifiable.
• LLM=[openai/google/ollama]: Select LLM of your choice.
• OPENAI_API_KEY=[Your OpenAI API Key]: Provide this if LLM is set to openai
• GOOGLE_API_KEY=[Your Google API Key]: Provide this if LLM is set to google
• OLLAMA_MODEL=[Ollama Model Name]: Provide this if LLM is set to ollama
• PEXELS_API_KEY=[Your Pexels API Key]: Provide this if LLM is set to ollama

Using Openai

docker run -it --name presenton -p 5000:80 -e LLM="openai" -e OPENAI_API_KEY="******" -e CAN_CHANGE_KEYS="false" -v "./user_data:/app/user_data" ghcr.io/presenton/presenton:v0.3.0-beta

Using Ollama

docker run -it --name presenton -p 5000:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./user_data:/app/user_data" ghcr.io/presenton/presenton:v0.3.0-beta

Running Presenton with GPU Support

To use GPU acceleration with Ollama models, you need to install and configure the NVIDIA Container Toolkit. This allows Docker containers to access your NVIDIA GPU.

Once the NVIDIA Container Toolkit is installed and configured, you can run Presenton with GPU support by adding the --gpus=all flag:

docker run -it --name presenton --gpus=all -p 5000:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./user_data:/app/user_data" ghcr.io/presenton/presenton:v0.3.0-beta

Note: GPU acceleration significantly improves the performance of Ollama models, especially for larger models. Make sure you have sufficient GPU memory for your chosen model.

Supported Ollama Models:

Llama Models:

Model	Size	Graph Support
llama3:8b	4.7GB	❌ No
llama3:70b	40GB	✅ Yes
llama3.1:8b	4.9GB	❌ No
llama3.1:70b	43GB	✅ Yes
llama3.1:405b	243GB	✅ Yes
llama3.2:1b	1.3GB	❌ No
llama3.2:3b	2GB	❌ No
llama3.3:70b	43GB	✅ Yes
llama4:16x17b	67GB	✅ Yes
llama4:128x17b	245GB	✅ Yes

Gemma Models:

Model	Size	Graph Support
gemma3:1b	815MB	❌ No
gemma3:4b	3.3GB	❌ No
gemma3:12b	8.1GB	❌ No
gemma3:27b	17GB	✅ Yes

DeepSeek Models:

Model	Size	Graph Support
deepseek-r1:1.5b	1.1GB	❌ No
deepseek-r1:7b	4.7GB	❌ No
deepseek-r1:8b	5.2GB	❌ No
deepseek-r1:14b	9GB	❌ No
deepseek-r1:32b	20GB	✅ Yes
deepseek-r1:70b	43GB	✅ Yes
deepseek-r1:671b	404GB	✅ Yes

Qwen Models:

Model	Size	Graph Support
qwen3:0.6b	523MB	❌ No
qwen3:1.7b	1.4GB	❌ No
qwen3:4b	2.6GB	❌ No
qwen3:8b	5.2GB	❌ No
qwen3:14b	9.3GB	❌ No
qwen3:30b	19GB	✅ Yes
qwen3:32b	20GB	✅ Yes
qwen3:235b	142GB	✅ Yes

Note: Models with graph support can generate charts and diagrams in presentations. Larger models provide better quality but require more system resources.

Using Presenton API

Generate Presentation

Endpoint: /api/v1/ppt/generate/presentation

Method: POST

Content-Type: multipart/form-data

Request Body

Parameter	Type	Required	Description
prompt	string	Yes	The main topic or prompt for generating the presentation
n_slides	integer	No	Number of slides to generate (default: 8, min: 5, max: 15)
language	string	No	Language for the presentation (default: "English")
theme	string	No	Presentation theme (default: "light"). Available options: "light", "dark", "cream", "royal_blue", "faint_yellow", "light_red", "dark_pink"
documents	File[]	No	Optional list of document files to include in the presentation. Supported file types: PDF, TXT, PPTX, DOCX
export_as	string	No	Export format ("pptx" or "pdf", default: "pptx")

Response

{
    "presentation_id": "string",
    "path": "string",
    "edit_path": "string"
}

Example Request

curl -X POST http://localhost:5000/api/v1/ppt/generate/presentation \
  -F "prompt=Introduction to Machine Learning" \
  -F "n_slides=5" \
  -F "language=English" \
  -F "theme=light" \
  -F "export_as=pptx"

Example Response

{
  "presentation_id": "d3000f96-096c-4768-b67b-e99aed029b57",
  "path": "/static/user_data/d3000f96-096c-4768-b67b-e99aed029b57/Introduction_to_Machine_Learning.pptx",
  "edit_path": "/presentation?id=d3000f96-096c-4768-b67b-e99aed029b57"
}

Features

1. Add prompt, select number of slides and language

2. Select theme

3. Review and edit outline

4. Select theme

5. Present on app

6. Change theme

7. Export presentation as PDF and PPTX

Community

Discord

License

Apache 2.0