---
title: ThoughtSpot Demo Builder
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "4.44.1"
app_file: app.py
pinned: false
license: mit
python_version: "3.11"
---

# DemoPrep — AI-Powered ThoughtSpot Demo Builder

A Gradio application that generates and deploys complete ThoughtSpot demo environments end to end — company research, an LLM-authored data blueprint, deterministic data generation, a Snowflake schema + data load, and a ThoughtSpot semantic model and liveboard.

> **Deployed on Hugging Face Spaces.** `app.py` is the entry point. See [DEPLOYMENT.md](DEPLOYMENT.md) for setup.

## 🖥️ The interface

The app is a **tabbed Gradio UI**, and the **App tab is the primary way to build a demo**: fill in a short form and press **GO**, and the full pipeline runs automatically with live progress.

> **Naming note:** the whole application lives in **`chat_interface.py`** — that name is *historical*. It is **not** just a chat interface; that single file hosts every tab (App, Chat, Admin, Settings, Run History, …), and the **App tab is the main path**. A **Chat tab** offers an alternative conversational flow that runs the same pipeline. `app.py` simply configures and launches this app.

## 🚀 Features

- **AI-powered research** — researches the company and industry with an LLM
- **Single blueprint pipeline** — one path, no keyword routing or fallback: research → LLM-authored `DemoBlueprint` → deterministic engine → validation → derived DDL → load
- **Realistic data** — bounded/related measures (a part never exceeds its whole, rates stay ≤ 1, prices are stable per entity) plus planted, discoverable "Demo to Win" insights the validator proves are visible
- **ThoughtSpot integration** — deploys the connection, tables, semantic model, and an enhanced liveboard
- **Interactive UI** — tabbed Gradio app with real-time progress

## 🛠️ Tech Stack

- **Frontend**: Gradio (Python web UI)
- **Backend**: Python 3.11
- **Database**: Snowflake (keypair auth)
- **Analytics**: ThoughtSpot (per-environment trusted auth)
- **AI**: multiple LLM providers (OpenAI and Anthropic/Claude), routed via `llm_config.py`
- **Settings**: Supabase (admin + per-user settings)

## 📋 Prerequisites

- Python 3.11
- Snowflake account (keypair configured)
- ThoughtSpot Cloud account
- An LLM API key (OpenAI and/or Anthropic)
- Supabase project (settings storage)

## 🚀 Quick Start

1. **Clone the repository**
   ```bash
   git clone <repo-url>
   cd demoprep
   ```

2. **Set up a virtual environment**
   ```bash
   python -m venv .venv
   source .venv/bin/activate   # Windows: .venv\Scripts\activate
   ```

3. **Install dependencies**
   ```bash
   pip install -r requirements.txt
   ```

4. **Configure bootstrap environment variables** (see Configuration)
   ```bash
   cp .env.example .env
   # edit .env
   ```

5. **Run the application**
   ```bash
   python app.py
   ```
   `app.py` launches the full Gradio app defined in `chat_interface.py`.

6. **Open your browser**
   Navigate to `http://localhost:7860` and use the **App** tab.

## ⚙️ Configuration

`.env` holds only the **bootstrap** secrets needed to start the app and reach Supabase and the LLM providers. All other credentials — the Snowflake keypair and the ThoughtSpot **per-environment trusted-auth keys** — live in **Supabase admin settings** and are loaded at runtime.

```env
# Supabase (settings storage)
SUPABASE_URL=...
SUPABASE_ANON_KEY=...

# LLM providers
OPENAI_API_KEY=...
GOOGLE_API_KEY=...            # optional (Gemini)

# Slack deployment notifications (optional, outbound-only)
SLACK_BOT_TOKEN=xoxb-...
SLACK_DEPLOYMENT_CHANNEL_ID=C0123456789
```

Slack notifications use the Slack Web API to post deployment status into one approved channel. This path is outbound-only: no Socket Mode, event subscriptions, slash commands, or public request URL. The Slack app needs only the `chat:write` bot scope and must be invited to the target channel.

## 🎯 Usage

The **App tab** is the primary interface — fill the form, press **GO**, and the pipeline runs end to end:

1. **Fill the form** — vertical / line of business / function (or a custom use case), the company URL, and the target **ThoughtSpot environment**
2. **Press GO** — research → blueprint → data generation → validation → Snowflake load → ThoughtSpot model + liveboard
3. **Review** — model and liveboard links, a Demo Pack, and a Spotter Viz story appear on completion

## 📁 Project Structure

```
demoprep/
├── app.py                    # Entry point — launches the Gradio app on :7860
├── chat_interface.py         # The full Gradio app (App/Chat/Admin/Settings/…) — historical name
├── thoughtspot_deployer.py   # ThoughtSpot deploy: connection, tables, model, liveboard
├── liveboard_creator.py      # MCP liveboard creation + TML post-processing (enhance_mcp_liveboard)
├── snowflake_auth.py         # Snowflake keypair authentication
├── llm_config.py             # LLM provider/model routing (single source of truth)
├── demo_personas.py          # Vertical × Function use-case configs
├── demoprep_app/             # The demo-generation pipeline package
│   ├── pipeline/build_demo.py    #   the single build entry point
│   ├── scenario/                 #   blueprint contract + LLM authoring + directives
│   ├── dataset/                  #   deterministic engine + validator
│   ├── ddl/                      #   Snowflake DDL derived from the dataset
│   └── integrations/snowflake/   #   Snowflake row loader
├── requirements.txt          # Python dependencies
├── docs/                     # Documentation
├── tests/                    # Unit tests + the e2e quality harness (e2e_quality.py)
└── results/                  # Generated demo results
```

## 🧪 Testing

```bash
# Unit tests (pytest collects test_*.py)
python -m pytest tests/

# End-to-end quality harness — drives a running app via the browser and grades the output
python tests/e2e_quality.py --env-name test
```

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- ThoughtSpot for the analytics platform
- Snowflake for the data warehouse
- OpenAI and Anthropic for the AI capabilities
- Gradio for the web interface

## 📚 Development Notes

Sprint planning and working notes live in `dev_notes/` (gitignored); architecture and handoff docs are in `docs/`.

## 📞 Support

For support, create an issue in this repository.

---

**Built with ❤️ for the ThoughtSpot community**