## Running Presenton
You can run Presenton in two ways: **Docker** for a one-command setup without installing a local dev stack, or the **Electron desktop app** for a native app experience (ideal for development or offline use).
### Option 1: Docker
#### 1. Start Presenton
##### Linux/MacOS (Bash/Zsh Shell):
```bash
docker run -it --name presenton -p 5000:80 -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
##### Windows (PowerShell):
```bash
docker run -it --name presenton -p 5000:80 -v "${PWD}\app_data:/app_data" ghcr.io/presenton/presenton:latest
```
#### 2. Open Presenton
Open http://localhost:5000 on browser of your choice to use Presenton.
> **Note: You can replace 5000 with any other port number of your choice to run Presenton on a different port number.**
#### (Optional) Enable Codex Auth — Sign in with ChatGPT
If you want to sign in with your free or paid ChatGPT account, also expose port `1455` for Codex auth support:
##### Linux/MacOS:
```bash
docker run -it --name presenton -p 5000:80 -p 1455:1455 -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
##### Windows (PowerShell):
```bash
docker run -it --name presenton -p 5000:80 -p 1455:1455 -v "${PWD}\app_data:/app_data" ghcr.io/presenton/presenton:latest
```
### Option 2: Electron (Desktop App)
Run Presenton as a native desktop application. LLM and image provider (API keys, etc.) can be configured in the app; the same environment variables used for Docker apply when running the bundled backend.
**Prerequisites:** Node.js (LTS), npm, Python 3.11, and [uv](https://docs.astral.sh/uv/) (for the Electron FastAPI backend in `electron/servers/fastapi`).
**Setup (first time):**
```bash
cd electron
npm run setup:env
```
This installs Node dependencies, runs `uv sync` in the FastAPI server, and installs Next.js dependencies.
**Run in development:**
```bash
npm run dev
```
This compiles TypeScript and starts Electron; the backend and UI run locally in the desktop window.
**Build distributable (optional):**
To create installers for Windows, macOS, or Linux:
```bash
npm run build:all
npm run dist
```
Outputs are written to `electron/dist` (or as per your electron-builder config).
## Deployment Configurations
These settings apply to both Docker and the Electron app's backend. 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/anthropic/ollama/custom]**: Select **LLM** of your choice.
- **OPENAI_API_KEY=[Your OpenAI API Key]**: Provide this if **LLM** is set to **openai**
- **OPENAI_MODEL=[OpenAI Model ID]**: Provide this if **LLM** is set to **openai** (default: "gpt-4.1")
- **GOOGLE_API_KEY=[Your Google API Key]**: Provide this if **LLM** is set to **google**
- **GOOGLE_MODEL=[Google Model ID]**: Provide this if **LLM** is set to **google** (default: "models/gemini-2.0-flash")
- **ANTHROPIC_API_KEY=[Your Anthropic API Key]**: Provide this if **LLM** is set to **anthropic**
- **ANTHROPIC_MODEL=[Anthropic Model ID]**: Provide this if **LLM** is set to **anthropic** (default: "claude-3-5-sonnet-20241022")
- **OLLAMA_URL=[Custom Ollama URL]**: Provide this if you want to custom Ollama URL and **LLM** is set to **ollama**
- **OLLAMA_MODEL=[Ollama Model ID]**: Provide this if **LLM** is set to **ollama**
- **CUSTOM_LLM_URL=[Custom OpenAI Compatible URL]**: Provide this if **LLM** is set to **custom**
- **CUSTOM_LLM_API_KEY=[Custom OpenAI Compatible API KEY]**: Provide this if **LLM** is set to **custom**
- **CUSTOM_MODEL=[Custom Model ID]**: Provide this if **LLM** is set to **custom**
- **TOOL_CALLS=[Enable/Disable Tool Calls on Custom LLM]**: If **true**, **LLM** will use Tool Call instead of Json Schema for Structured Output.
- **DISABLE_THINKING=[Enable/Disable Thinking on Custom LLM]**: If **true**, Thinking will be disabled.
- **WEB_GROUNDING=[Enable/Disable Web Search for OpenAI, Google And Anthropic]**: If **true**, LLM will be able to search web for better results.
You can also set the following environment variables to customize the image generation provider and API keys:
- **DISABLE_IMAGE_GENERATION**: If **true**, Image Generation will be disabled for slides.
- **IMAGE_PROVIDER=[dall-e-3/gpt-image-1.5/gemini_flash/nanobanana_pro/pexels/pixabay/comfyui]**: Select the image provider of your choice.
- Required if **DISABLE_IMAGE_GENERATION** is not set to **true**.
- **OPENAI_API_KEY=[Your OpenAI API Key]**: Required if using **dall-e-3** or **gpt-image-1.5** as the image provider.
- **DALL_E_3_QUALITY=[standard/hd]**: Optional quality setting for **dall-e-3** (default: `standard`).
- **GPT_IMAGE_1_5_QUALITY=[low/medium/high]**: Optional quality setting for **gpt-image-1.5** (default: `medium`).
- **GOOGLE_API_KEY=[Your Google API Key]**: Required if using **gemini_flash** or **nanobanana_pro** as the image provider.
- **PEXELS_API_KEY=[Your Pexels API Key]**: Required if using **pexels** as the image provider.
- **PIXABAY_API_KEY=[Your Pixabay API Key]**: Required if using **pixabay** as the image provider.
- **COMFYUI_URL=[Your ComfyUI server URL]** and **COMFYUI_WORKFLOW=[Workflow JSON]**: Required if using **comfyui** to route prompts to a self-hosted ComfyUI workflow.
You can disable anonymous telemetry using the following environment variable:
- **DISABLE_ANONYMOUS_TELEMETRY=[true/false]**: Set this to **true** to disable anonymous telemetry.
> **Note:** You can freely choose both the LLM (text generation) and the image provider. Supported image providers: **dall-e-3**, **gpt-image-1.5** (OpenAI), **gemini_flash**, **nanobanana_pro** (Google), **pexels**, **pixabay**, and **comfyui** (self-hosted).
### Using OpenAI
```bash
docker run -it --name presenton -p 5000:80 -e LLM="openai" -e OPENAI_API_KEY="******" -e IMAGE_PROVIDER="dall-e-3" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
### Using Google
```bash
docker run -it --name presenton -p 5000:80 -e LLM="google" -e GOOGLE_API_KEY="******" -e IMAGE_PROVIDER="gemini_flash" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
### Using Ollama
```bash
docker run -it --name presenton -p 5000:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
### Using Anthropic
```bash
docker run -it --name presenton -p 5000:80 -e LLM="anthropic" -e ANTHROPIC_API_KEY="******" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
### Using OpenAI Compatible API
```bash
docker run -it -p 5000:80 -e CAN_CHANGE_KEYS="false" -e LLM="custom" -e CUSTOM_LLM_URL="http://*****" -e CUSTOM_LLM_API_KEY="*****" -e CUSTOM_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="********" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
#### 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:
```bash
docker run -it --name presenton --gpus=all -p 5000:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
```
> **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.
## Generate Presentation over API
### Generate Presentation
Endpoint: `/api/v1/ppt/presentation/generate`
Method: `POST`
Content-Type: `application/json`
#### Request Body
| Parameter | Type | Required | Description |
| ------------------------- | ---------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| content | string | Yes | The content for generating the presentation |
| slides_markdown | string[] \| null | No | The markdown for the slides |
| instructions | string \| null | No | The instruction for generating the presentation |
| tone | string | No | The tone to use for the text (default: "default"). Available options: "default", "casual", "professional", "funny", "educational", "sales_pitch" |
| verbosity | string | No | How verbose the text should be (default: "standard"). Available options: "concise", "standard", "text-heavy" |
| web_search | boolean | No | Whether to enable web search (default: false) |
| n_slides | integer | No | Number of slides to generate (default: 8) |
| language | string | No | Language for the presentation (default: "English") |
| template | string | No | Template to use for the presentation (default: "general") |
| include_table_of_contents | boolean | No | Whether to include a table of contents (default: false) |
| include_title_slide | boolean | No | Whether to include a title slide (default: true) |
| files | string[] \| null | No | Files to use for the presentation. Use /api/v1/ppt/files/upload to upload files |
| export_as | string | No | Export format (default: "pptx"). Available options: "pptx", "pdf" |
#### Response
```json
{
"presentation_id": "string",
"path": "string",
"edit_path": "string"
}
```
#### Example Request
```bash
curl -X POST http://localhost:5000/api/v1/ppt/presentation/generate \
-H "Content-Type: application/json" \
-d '{
"content": "Introduction to Machine Learning",
"n_slides": 5,
"language": "English",
"template": "general",
"export_as": "pptx"
}'
```
#### Example Response
```json
{
"presentation_id": "d3000f96-096c-4768-b67b-e99aed029b57",
"path": "/app_data/d3000f96-096c-4768-b67b-e99aed029b57/Introduction_to_Machine_Learning.pptx",
"edit_path": "/presentation?id=d3000f96-096c-4768-b67b-e99aed029b57"
}
```
> **Note:** Make sure to prepend your server's root URL to the path and edit_path fields in the response to construct valid links.
For detailed info checkout [API documentation](https://docs.presenton.ai/using-presenton-api).
### API Tutorials
- [Generate Presentations via API in 5 minutes](https://docs.presenton.ai/tutorial/generate-presentation-over-api)
- [Create Presentations from CSV using AI](https://docs.presenton.ai/tutorial/generate-presentation-from-csv)
- [Create Data Reports Using AI](https://docs.presenton.ai/tutorial/create-data-reports-using-ai)
## Roadmap
- [x] Support for custom HTML templates by developers
- [x] Support for accessing custom templates over API
- [x] Implement MCP server
- [ ] Ability for users to change system prompt
- [x] Support external SQL database
## UI 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](https://discord.gg/9ZsKKxudNE)
## License
Apache 2.0