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.
- ✅ 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
docker run -it --name presenton -p 5000:80 -v "./user_data:/app/user_data" ghcr.io/presenton/presenton:v0.3.0-betadocker run -it --name presenton -p 5000:80 -v "${PWD}\user_data:/app/user_data" ghcr.io/presenton/presenton:v0.3.0-betaOpen 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.
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
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-betadocker 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-betaTo 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-betaNote: GPU acceleration significantly improves the performance of Ollama models, especially for larger models. Make sure you have sufficient GPU memory for your chosen model.
| 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 |
| Model | Size | Graph Support |
|---|---|---|
gemma3:1b |
815MB | ❌ No |
gemma3:4b |
3.3GB | ❌ No |
gemma3:12b |
8.1GB | ❌ No |
gemma3:27b |
17GB | ✅ Yes |
| 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 |
| 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.
Endpoint: /api/v1/ppt/generate/presentation
Method: POST
Content-Type: multipart/form-data
| 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") |
{
"presentation_id": "string",
"path": "string",
"edit_path": "string"
}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"{
"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"
}Apache 2.0