Backend Vision Update

README.md

@@ -1,42 +1,61 @@
-# sv
+# BitCamp 2026 - AI Study Buddy & Room Monitor
 
-Everything you need to build a Svelte project, powered by [`sv`](https://github.com/sveltejs/cli).
+This project is a comprehensive solution featuring an M5GO smart device integration, an AI Voice and Vision Backend, and a Svelte frontend dashboard. It connects physical hardware to advanced AI models (Terp AI, ElevenLabs, YOLOv8) to provide a real-time study buddy experience and a study room occupancy monitor.
 
-## Creating a project
+## Project Architecture
 
-If you're seeing this, you've probably already done this step. Congrats!
+### 1. Website Frontend (`/website` & Root)
+A SvelteKit application providing the user interface for our system.
+- Powered by `sv` (Svelte CLI) and Bun.
+- Configured for production deployment via Docker.
 
-```sh
-# create a new project
-npx sv create my-app
+**Developing:**
+```bash
+cd website
+bun install
+bun run dev --open
 ```
 
-To recreate this project with the same configuration:
+### 2. AI Backend Services (`/backend`)
+A FastAPI backend providing two core capabilities:
+- **Real-time Voice WebSockets (`/ws/voice`)**: Connects the M5GO device to STT (faster-whisper), an LLM (Terp AI), and TTS (ElevenLabs). It streams audio bytes natively over WebSockets.
+- **Vision Occupancy API (`/api/vision/room-status`)**: Uses YOLOv8 object detection to identify people and chairs in a room image, determining if a study room is fully occupied and pairing the closest person to an available chair.
 
-```sh
-# recreate this project
-bun x [email protected] create --template minimal --types ts --add tailwindcss="plugins:typography,forms" --install bun ./
+**Developing:**
+```bash
+cd backend
+pip install -r requirements.txt
+uvicorn server:app --host 0.0.0.0 --port 8000
+```
+*(Requires `ffmpeg`, `libgl1-mesa-glx`, and `libglib2.0-0` installed on your system)*
+
+### 3. M5GO Device (`/m5go`)
+MicroPython scripts for the M5Stack M5GO device.
+- Uses `uwebsockets` to connect to the backend.
+- High-quality audio I2S configuration for the internal microphone and speaker.
+- Push-to-talk integration: Hold Button A to talk to the AI, release to get an audio response back.
+
+## Docker Setup
+
+The entire stack can be run via Docker Compose, which builds both the Svelte website and the Python AI Backend.
+
+```bash
+docker-compose up --build
 ```
 
-## Developing
+- **Web Frontend**: Runs on port `3000`
+- **AI Backend**: Runs on port `8000`
 
-Once you've created a project and installed dependencies with `npm install` (or `pnpm install` or `yarn`), start a development server:
+## Configuration
 
-```sh
-npm run dev
+Make sure you set up your `.env` variables before running the Docker containers or local servers.
 
-# or start the server and open the app in a new browser tab
-npm run dev -- --open
+Create a `.env` in the `/backend` folder:
+```ini
+ELEVENLABS_API_KEY=your_elevenlabs_api_key_here
+ELEVENLABS_VOICE_ID=JBFqnCBsd6RMkjVDRZzb
+TERP_AI_BEARER_TOKEN=your_jwt_token_here
+TERP_AI_CONVERSATION_ID=5e752e56-06c6-ec73-1f13-456029ce1299
 ```
 
-## Building
-
-To create a production version of your app:
-
-```sh
-npm run build
-```
-
-You can preview the production build with `npm run preview`.
-
-> To deploy your app, you may need to install an [adapter](https://svelte.dev/docs/kit/adapters) for your target environment.
+Update the `/m5go/main.py` file to include your Wi-Fi credentials and the correct local IP for the WebSocket (`WS_URL`).