https://github.com/pnmartinez/ai-dashboard-builder
Throw your data, let AI make a dashboard
https://github.com/pnmartinez/ai-dashboard-builder
ai dashboard dashboard-application dashboards data-visualization
Last synced: 9 months ago
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Throw your data, let AI make a dashboard
- Host: GitHub
- URL: https://github.com/pnmartinez/ai-dashboard-builder
- Owner: pnmartinez
- License: other
- Created: 2024-11-16T12:32:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-09T16:51:17.000Z (12 months ago)
- Last Synced: 2025-02-09T17:30:13.517Z (12 months ago)
- Topics: ai, dashboard, dashboard-application, dashboards, data-visualization
- Language: Python
- Homepage:
- Size: 306 KB
- Stars: 8
- Watchers: 1
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
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README
# AI Dashboard Builder 📊
> We welcome contributions! Please fork and submit PRs! See our [CONTRIBUTING.md](CONTRIBUTING.md) guide for details.
### Focus on
* **Simplicity** 🌱: strong focus on keeping the project easy to use and the codebase simple.
* **Privacy** 🛡️: use local LLMs through Ollama for your private data.
* **Flexibility** 🤸: flexible features like:
- KPI-directed: Prompt the LLMs to prioritize your KPIs in the dataset,
- Preview what you are passing: data importer wizard preview and filter the dataset by columns and/or rows,
- Dynamic filters: the LLM infers the most relevant filters for your dataset dynamically,
- Custom LLM: use your favourite 3rd party LLM (or local through Ollama).
* **Reusability** 🔄: each dashboard generates a reusable "viz_spec" JSON file in the "llm_responses" folder, accessible for future use through the "Import Previous Viz Specs" feature. Each plot includes a "Code" tab to reproduce it anywhere.
https://github.com/user-attachments/assets/02152b49-3d83-4382-9437-81704af40590
## Setup
### Option 1: Docker Compose (Recommended)
1. Start the application with Docker Compose:
```bash
docker-compose up --build
```
2. Access the dashboard at http://localhost:8050
### Option 2: All-in-One Deployment (includes Ollama)
1. Start both Ollama and the dashboard:
```bash
docker-compose -f docker-compose.all-in-one.yml up --build
```
2. Access the dashboard at http://localhost:8050
### Option 3: Manual Deployment
1. Start Ollama separately (if using local models)
2. Run the application:
```bash
python src/app.py
```
## Development
To run the application in development mode:
```bash
pip install uv # if you don't have it already
uv run ai_dashboard_builder --dev
```
## Setting API KEYS and/or Olama
In the project root folder, you can create a `.env` file and set the API keys for the LLMs you want to use, or pass them through the webapp.
- `OLLAMA_HOST`: Ollama server address (default: host.docker.internal)
- `OPENAI_API_KEY`: OpenAI API key (for GPT models)
- `ANTHROPIC_API_KEY`: Anthropic API key (for Claude models)
- `GROQ_API_KEY`: Groq API key (for Mixtral/LLaMA models)
## Contributing
We welcome contributions! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) guide for details on how to get started.
## License
This project is licensed under a form of MIT License - see the [LICENSE.md](LICENSE.md) file for details.