Code comments Removed Update
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@@ -1,64 +1,80 @@
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# HUSHMAP - AI Study Buddy & Room Monitor
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<div align="center">
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<h1>HushMap</h1>
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<h3>AI Intelligent Room Monitor</h3>
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<br />
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<p>
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<a href="https://svelte.dev"><img src="https://img.shields.io/badge/SvelteKit-FF3E00?style=for-the-badge&logo=svelte&logoColor=white" alt="SvelteKit"></a>
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<a href="https://fastapi.tiangolo.com/"><img src="https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi" alt="FastAPI"></a>
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<a href="https://www.mongodb.com/"><img src="https://img.shields.io/badge/MongoDB-4EA94B?style=for-the-badge&logo=mongodb&logoColor=white" alt="MongoDB"></a>
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<a href="https://m5stack.com/"><img src="https://img.shields.io/badge/IoT-M5GO-blue?style=for-the-badge&logo=microchip&logoColor=white" alt="M5GO"></a>
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<a href="https://docker.com"><img src="https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=docker&logoColor=white" alt="Docker"></a>
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</p>
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</div>
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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.
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<br/>
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## Project Architecture
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HushMap bridges the gap between hardware sensors and top-tier artificial intelligence pipelines (e.g. **Terp AI**, **ElevenLabs**, **YOLOv8**), delivering a seamless real-time learning assistant combined with live noise and occupancy metrics.
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### 1. Website Frontend (`/website` & Root)
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A SvelteKit application providing the user interface for our system.
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- Powered by `sv` (Svelte CLI) and Bun.
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- Configured for production deployment via Docker.
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---
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**Developing:**
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```bash
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cd website
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bun install
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bun run dev --open
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```
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## System Architecture
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### 1. Web Dashboard (`/website`)
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A responsive, high-fidelity PWA frontend written in Svelte 5 and styled seamlessly with Catppuccin color guidelines.
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* **Powered By**: SvelteKit, Vite, and Bun.
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* **Features**: Live interactive map tracking, responsive UI, persistent theming, and an autonomous browser-based Voice Agent calling modal interface.
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* **Setup**:
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```bash
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cd website
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bun install
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bun run dev --open
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```
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### 2. AI Backend Services (`/backend`)
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A FastAPI backend providing two core capabilities:
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- **Real-time Voice WebSockets (`/ws/voice`)**: Connects the M5GO device AND the web dashboard to STT (faster-whisper), LLMs (Terp AI), and TTS (ElevenLabs). It streams 16-bit PCM audio bytes natively over WebSockets in full-duplex.
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- **Context DB Aggregation**: TerpAI automatically queries the MongoDB `study_rooms_collection` to gather live hardware decibel readings globally before answering your prompt.
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- **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.
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A blazing fast asynchronous HTTP server facilitating audio chunking and sensor metrics logic over full-duplex sockets.
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* **Core Capabilities**:
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* **Voice Socket Pipelining**: WebSockets (`/ws/voice`) that hook incoming 16-bit PCM arrays into `faster-whisper`.
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* **LLM Context Augmentation**: Seamlessly aggregates live MongoDB noise statistics (Decibel levels per location) to feed contextual history to the TerpAI engine!
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* **Computer Vision Endpoint**: Exposes a `YOLOv8` tensor API (`/api/vision/room-status`) to parse webcam imagery, pinpoint seating capacities, and locate available chairs algorithmically.
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* **Setup**:
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```bash
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cd backend
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pip install -r requirements.txt
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uvicorn server:app --host 0.0.0.0 --port 8000
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```
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> [!WARNING]
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> Host devices must have `ffmpeg`, `libgl1-mesa-glx`, and `libglib2.0-0` binaries natively installed to encode audio buffers and execute OpenCV rendering.
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**Developing:**
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```bash
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cd backend
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pip install -r requirements.txt
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uvicorn server:app --host 0.0.0.0 --port 8000
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```
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*(Requires `ffmpeg`, `libgl1-mesa-glx`, and `libglib2.0-0` installed on your system)*
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### 3. M5GO Hardware Node (`/m5go`)
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C-based MicroPython binaries tailored strictly for the IoT edge nodes traversing the physical campus.
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* **Features**: Connects internally wired I2S Microphone blocks to route direct byte arrays securely out across WPA/WPA2 networks into the main API gateway using minimal payload overhead. Push-button PTT interfaces built directly into the screen chassis.
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### 3. M5GO Device (`/m5go`)
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MicroPython scripts for the M5Stack M5GO device.
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- Uses `uwebsockets` to connect to the backend.
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- High-quality audio I2S configuration for the internal microphone and speaker.
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- Push-to-talk integration: Hold Button A to talk to the AI, release to get an audio response back.
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---
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## Docker Setup
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## Docker Production Setup
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The entire stack can be run via Docker Compose, which builds both the Svelte website and the Python AI Backend into a single seamless container.
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The entire monolithic architecture cleanly orchestrates via docker compose. Frontends compile out via SSR, and Python APIs wire natively within a segregated container network loop.
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```bash
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docker-compose up --build
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```
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> The global deployment interface listens on port `8000`.
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- **App (Frontend + Backend)**: Runs on port `8000`
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---
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## Configuration
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## Core Configuration
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Make sure you set up your `.env` variables before running the Docker containers or local servers.
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Before starting services, strictly adhere to configuring your environment files (`.env`) within `/backend`:
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Create a `.env` in the `/backend` folder:
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```ini
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ELEVENLABS_API_KEY=sk_...
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ELEVENLABS_VOICE_ID=JBFqnCBsd6RMkjVDRZzb
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TERP_AI_BEARER_TOKEN=eyJhbGciOiJ...
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TERP_AI_CONVERSATION_ID=37fa27cc-542a-c8a8-9c31-9d1954fdc1d2
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MONGODB_URI=mongodb+srv://...
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TERP_AI_BEARER_TOKEN=eyJhbGciOiJSUz...
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TERP_AI_CONVERSATION_ID=3a150d8e-bb12-...
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MONGODB_URI=mongodb+srv://user:pass@cluster0...
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```
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Update your `.env` to match the exact `authorization: Bearer` and `parentSegmentId` context from TerpAI if timeouts occur.
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> [!TIP]
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> Ensure the `.env` mirrors your authentic `x-cosmos-session` headers and bearer tokens exported from a live browser session to prevent immediate 401 Unauthorized timeouts in the Terp AI pipeline.
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Update the `/m5go/main.py` file to include your Wi-Fi credentials and the correct local IP for the WebSocket (`WS_URL`).
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For IoT clients, update `/m5go/main.py` explicitly to broadcast to your running router IP namespace matching your specific VLAN.
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+47
-95
@@ -1,93 +1,74 @@
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# AI Voice Services Backend
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<div align="center">
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<h1>HushMap: AI Services API</h1>
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<p>
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<a href="https://fastapi.tiangolo.com/"><img src="https://img.shields.io/badge/FastAPI-009688?style=for-the-badge&logo=fastapi&logoColor=white" alt="FastAPI"></a>
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<a href="https://python.org"><img src="https://img.shields.io/badge/Python_3.9+-3776AB?style=for-the-badge&logo=python&logoColor=white" alt="Python"></a>
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<img src="https://img.shields.io/badge/Ultralytics-YOLOv8-FF0000?style=for-the-badge" alt="YOLOv8 Vision">
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<img src="https://img.shields.io/badge/Whisper-STT-4A90E2?style=for-the-badge" alt="Whisper">
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</p>
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<p><i>The central nervous system linking physical M5GO devices, external Computer Vision tensors, and Conversational NLP APIs synchronously.</i></p>
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</div>
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This directory contains the FastAPI backend for the AI Voice Agent, facilitating communication between the M5GO device, Terp AI, and ElevenLabs.
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---
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## Setup Instructions
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### Prerequisites
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1. **Python 3.9+** is recommended.
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2. **FFmpeg** must be installed on the system to handle audio format conversions (MP3 to 16-bit 16kHz PCM).
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- On Ubuntu/Debian: `sudo apt install ffmpeg`
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- On macOS: `brew install ffmpeg`
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- On Windows: Download from the [FFmpeg website](https://ffmpeg.org/download.html) and add to PATH.
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1. **Python 3.9+** is strictly recommended to support asynchronous typing paradigms.
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2. **FFmpeg** must be successfully registered onto your OS PATH environments. This engine handles the core conversions decoding MP3 output arrays into 16-bit, 16kHz Mono arrays natively required for browser contexts:
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- **Ubuntu/Debian**: `sudo apt install ffmpeg`
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- **macOS**: `brew install ffmpeg`
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- **Windows**: Install globally via the [FFmpeg website](https://ffmpeg.org/download.html).
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### Installation
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### Environment Initialization
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1. Navigate to the `ai_services` directory.
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2. (Optional but recommended) Create a virtual environment:
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows: venv\Scripts\activate
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```
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3. Install the required Python packages:
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```bash
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pip install -r requirements.txt
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```
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Bootstrap the virtual environment and initialize project dependencies:
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### Configuration
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```bash
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cd backend
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python -m venv venv
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source venv/bin/activate # Windows: .\venv\Scripts\activate
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pip install -r requirements.txt
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```
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Update the `.env` file in this directory with your credentials:
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### Configuration Tokens
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Provide runtime keys securely targeting TerpAI context queues and ElevenLabs synthesized avatars within a `.env` dotfile:
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```ini
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ELEVENLABS_API_KEY=sk_...
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ELEVENLABS_VOICE_ID=JBFqnCBsd6RMkjVDRZzb
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TERP_AI_BEARER_TOKEN=eyJhbGciOiJSUz...
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TERP_AI_CONVERSATION_ID=37fa27cc-542a-c8a8-9c31-9d1954fdc1d2
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TERP_AI_CONVERSATION_ID=37fa27cc-...
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MONGODB_URI=mongodb+srv://...
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```
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## Running the Server
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Start the FastAPI application using Uvicorn:
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To invoke the engine, simply execute Uvicorn across your `0.0.0.0` loopback:
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```bash
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uvicorn server:app --host 0.0.0.0 --port 8000
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```
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This will start the server and make it accessible on your local network on port 8000.
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## WebSocket Endpoints
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---
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### `/ws/voice`
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## Gateway Pipelines
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This is the primary WebSocket endpoint used by the M5GO device for real-time voice communication.
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### Full-Duplex Subroutines (`/ws/voice`)
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**Protocol Flow:**
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This WebSocket proxy establishes a fully integrated multi-turn communication bridge seamlessly interacting between Edge node Hardware APIs (ESP32/M5GO/Browsers) and NLP architectures.
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1. **Connection:** The client establishes a WebSocket connection to `ws://<server_ip>:8000/ws/voice`.
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2. **Streaming Audio (Client -> Server):** While the user holds the record button, the client continuously sends binary frames containing raw audio data.
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- **Expected Format:** 16-bit signed integer, 16 kHz, Mono PCM.
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3. **End of Audio Signal (Client -> Server):** When the user releases the button, the client sends a JSON text frame to signal the end of the recording:
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```json
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{
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"event": "stop_listening"
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}
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```
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4. **Processing (Server):** Upon receiving the `stop_listening` event, the server executes the AI pipeline:
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- Transcribes the accumulated Int16 PCM audio organically using `faster-whisper`.
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- Injects a MongoDB aggregate map of the latest 24hr Campus Location noise levels seamlessly into the LLM system prompt.
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- Sends the transcribed text & location context to the Terp AI conversational endpoint and waits for the full response.
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- Streams the Terp AI response text directly to ElevenLabs TTS and demands `pcm_16000` via URL flags natively!
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5. **TTS Endpoint Notification**: The server saves the TTS audio buffer and pushes a JSON:
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```json
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{
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"event": "tts_ready",
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"size": 105000
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}
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```
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6. **Audio Callback**: Client queries `GET /api/tts-audio` to play the binary wav response.
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1. **Int16 Byte Array Exchange**: Devices connect to `ws://<server_ip>:8000/ws/voice` and push raw binary frames asynchronously over the socket.
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2. **Contextual Augmentation**: The server waits for the `"stop_listening"` payload event to signify a completed audio snippet. That float array is cast through `faster-whisper` and combined seamlessly with real-time `MongoDB` decibel tracking telemetry parameters natively attached into the `TerpAI` user conversation chunk.
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3. **TTS Pipeline Rendering**: Output predictions are caught instantly, forwarded natively into the `ElevenLabs` TTS interface rendering `pcm_16000` wav codecs, and alerted back down to clients using a `tts_ready` dispatcher.
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## REST Endpoints
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### Tensor Vision Endpoints (`/api/vision/room-status`)
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### `/api/vision/room-status` (POST)
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Leveraging OpenCV bindings layered beneath a YOLOv8-driven bounding box topology detector, this `POST` API analyzes raw camera image buffers returning capacity logic natively.
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This endpoint uses a YOLO object detection model to detect people and chairs in a room image, determining if the room is full and pairing the closest chairs to people.
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**Request:**
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- `file`: (Required) The image file to analyze (e.g., JPEG, PNG) sent as multipart form-data.
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**Response:**
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Returns a JSON object detailing the room status, counts, and pairings.
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> [!NOTE]
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> This API calculates euclidean distances algorithmically detecting adjacent proximities between "person" classifiers and untaken "chair" bounding frames to accurately diagnose available seats inside crowded architectures!
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**Response Output Protocol:**
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```json
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{
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"room_status": "full",
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@@ -101,45 +82,16 @@ Returns a JSON object detailing the room status, counts, and pairings.
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"chair_index": 1,
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"distance": 150.5
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}
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],
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"details": {
|
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"people": [ ... ],
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"chairs": [ ... ]
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}
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}
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```
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### `/api/study-rooms` (GET)
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Returns a list of all recorded study room data.
|
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### `/api/study-rooms/history` (GET)
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Returns a list of all recorded study room data from the last 24 hours, sorted by most recent first.
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**Response:**
|
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```json
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{
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"data": [
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{
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"location": {
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"type": "Point",
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"coordinates": [-77.3079, 38.8315]
|
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},
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"db": 65.2,
|
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"date": "2026-04-12T14:30:00.000Z"
|
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}
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]
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}
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```
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|
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## Client Integration Notes
|
||||
---
|
||||
|
||||
For the ESP32/M5GO hardware client (`m5go/main.py`), ensure you update the `WS_URL` variable to point to the correct internal server IP.
|
||||
## Database Registries
|
||||
|
||||
For the Web Frontend (`VoiceButton.svelte`), it uses standard Web Audio API's `ScriptProcessorNode` to bridge the Float32 arrays strictly into 16-Bit Mono over a dynamic WebSocket tunnel automatically.
|
||||
* `GET /api/study-rooms/history`: Pulls the active global repository of logged architectural noise measurements captured universally within the preceding 24 hours. Data payloads correspond geographically mapping `GeoJSON` nodes to front-end Mapbox topologies.
|
||||
* `GET /api/study-rooms`: Pulls generic unstructured noise lists directly unfiltered from Cosmos bounds.
|
||||
|
||||
```python
|
||||
# In m5go/main.py
|
||||
WS_URL = "ws://192.168.1.100:8000/ws/voice"
|
||||
```
|
||||
> [!IMPORTANT]
|
||||
> The browser frontend strictly configures standard Web Audio API's `ScriptProcessorNode` interfaces routing data synchronously to this backend! Wait to close down pipelines until *after* all WS queues have successfully been delivered.
|
||||
|
||||
@@ -0,0 +1,276 @@
|
||||
"""Data containing all current emoji
|
||||
Extracted from https://unicode.org/Public/emoji/latest/emoji-test.txt
|
||||
and https://www.unicode.org/Public/UCD/latest/ucd/emoji/emoji-variation-sequences.txt
|
||||
See utils/generate_emoji.py
|
||||
|
||||
+----------------+-------------+------------------+-------------------+
|
||||
| Emoji Version | Date | Unicode Version | Data File Comment |
|
||||
+----------------+-------------+------------------+-------------------+
|
||||
| N/A | 2010-10-11 | Unicode 6.0 | E0.6 |
|
||||
| N/A | 2014-06-16 | Unicode 7.0 | E0.7 |
|
||||
| Emoji 1.0 | 2015-06-09 | Unicode 8.0 | E1.0 |
|
||||
| Emoji 2.0 | 2015-11-12 | Unicode 8.0 | E2.0 |
|
||||
| Emoji 3.0 | 2016-06-03 | Unicode 9.0 | E3.0 |
|
||||
| Emoji 4.0 | 2016-11-22 | Unicode 9.0 | E4.0 |
|
||||
| Emoji 5.0 | 2017-06-20 | Unicode 10.0 | E5.0 |
|
||||
| Emoji 11.0 | 2018-05-21 | Unicode 11.0 | E11.0 |
|
||||
| Emoji 12.0 | 2019-03-05 | Unicode 12.0 | E12.0 |
|
||||
| Emoji 12.1 | 2019-10-21 | Unicode 12.1 | E12.1 |
|
||||
| Emoji 13.0 | 2020-03-10 | Unicode 13.0 | E13.0 |
|
||||
| Emoji 13.1 | 2020-09-15 | Unicode 13.0 | E13.1 |
|
||||
| Emoji 14.0 | 2021-09-14 | Unicode 14.0 | E14.0 |
|
||||
| Emoji 15.0 | 2022-09-13 | Unicode 15.0 | E15.0 |
|
||||
| Emoji 15.1 | 2023-09-12 | Unicode 15.1 | E15.1 |
|
||||
| Emoji 16.0 | 2024-09-10 | Unicode 16.0 | E16.0 |
|
||||
|
||||
http://www.unicode.org/reports/tr51/
|
||||
|
||||
"""
|
||||
|
||||
__all__ = ['STATUS', 'LANGUAGES']
|
||||
|
||||
from typing import Any, Dict, List
|
||||
|
||||
|
||||
component = 1
|
||||
fully_qualified = 2
|
||||
minimally_qualified = 3
|
||||
unqualified = 4
|
||||
|
||||
STATUS: Dict[str, int] = {
|
||||
'component': component,
|
||||
'fully_qualified': fully_qualified,
|
||||
'minimally_qualified': minimally_qualified,
|
||||
'unqualified': unqualified,
|
||||
}
|
||||
|
||||
LANGUAGES: List[str] = [
|
||||
'en',
|
||||
'es',
|
||||
'ja',
|
||||
'ko',
|
||||
'pt',
|
||||
'it',
|
||||
'fr',
|
||||
'de',
|
||||
'fa',
|
||||
'id',
|
||||
'zh',
|
||||
'ru',
|
||||
'tr',
|
||||
'ar',
|
||||
]
|
||||
|
||||
|
||||
|
||||
|
||||
EMOJI_DATA: Dict[str, Dict[str, Any]] = {
|
||||
'\U0001f947': {
|
||||
'en': ':1st_place_medal:',
|
||||
'status': fully_qualified,
|
||||
'E': 3,
|
||||
'de': ':goldmedaille:',
|
||||
'es': ':medalla_de_oro:',
|
||||
'fr': ':médaille_d’or:',
|
||||
'ja': ':金メダル:',
|
||||
'ko': ':금메달:',
|
||||
'pt': ':medalha_de_ouro:',
|
||||
'it': ':medaglia_d’oro:',
|
||||
'fa': ':مدال_طلا:',
|
||||
'id': ':medali_emas:',
|
||||
'zh': ':金牌:',
|
||||
'ru': ':золотая_медаль:',
|
||||
'tr': ':birincilik_madalyası:',
|
||||
'ar': ':ميدالية_مركز_أول:',
|
||||
},
|
||||
'\U0001f948': {
|
||||
'en': ':2nd_place_medal:',
|
||||
'status': fully_qualified,
|
||||
'E': 3,
|
||||
'de': ':silbermedaille:',
|
||||
'es': ':medalla_de_plata:',
|
||||
'fr': ':médaille_d’argent:',
|
||||
'ja': ':銀メダル:',
|
||||
'ko': ':은메달:',
|
||||
'pt': ':medalha_de_prata:',
|
||||
'it': ':medaglia_d’argento:',
|
||||
'fa': ':مدال_نقره:',
|
||||
'id': ':medali_perak:',
|
||||
'zh': ':银牌:',
|
||||
'ru': ':серебряная_медаль:',
|
||||
'tr': ':ikincilik_madalyası:',
|
||||
'ar': ':ميدالية_مركز_ثان:',
|
||||
},
|
||||
'\U0001f949': {
|
||||
'en': ':3rd_place_medal:',
|
||||
'status': fully_qualified,
|
||||
'E': 3,
|
||||
'de': ':bronzemedaille:',
|
||||
'es': ':medalla_de_bronce:',
|
||||
'fr': ':médaille_de_bronze:',
|
||||
'ja': ':銅メダル:',
|
||||
'ko': ':동메달:',
|
||||
'pt': ':medalha_de_bronze:',
|
||||
'it': ':medaglia_di_bronzo:',
|
||||
'fa': ':مدال_برنز:',
|
||||
'id': ':medali_perunggu:',
|
||||
'zh': ':铜牌:',
|
||||
'ru': ':бронзовая_медаль:',
|
||||
'tr': ':üçüncülük_madalyası:',
|
||||
'ar': ':ميدالية_مركز_ثالث:',
|
||||
},
|
||||
'\U0001f18e': {
|
||||
'en': ':AB_button_(blood_type):',
|
||||
'status': fully_qualified,
|
||||
'E': 0.6,
|
||||
'alias': [':ab:', ':ab_button_blood_type:'],
|
||||
'de': ':großbuchstaben_ab_in_rotem_quadrat:',
|
||||
'es': ':grupo_sanguíneo_ab:',
|
||||
'fr': ':groupe_sanguin_ab:',
|
||||
'ja': ':血液型ab型:',
|
||||
'ko': ':에이비형:',
|
||||
'pt': ':botão_ab_(tipo_sanguíneo):',
|
||||
'it': ':gruppo_sanguigno_ab:',
|
||||
'fa': ':دکمه_آ_ب_(گروه_خونی):',
|
||||
'id': ':tombol_ab_(golongan_darah):',
|
||||
'zh': ':AB型血:',
|
||||
'ru': ':IV_группа_крови:',
|
||||
'tr': ':ab_düğmesi_(kan_grubu):',
|
||||
'ar': ':زر_ab_(فئة_الدم):',
|
||||
},
|
||||
'\U0001f3e7': {
|
||||
'en': ':ATM_sign:',
|
||||
'status': fully_qualified,
|
||||
'E': 0.6,
|
||||
'alias': [':atm:', ':atm_sign:'],
|
||||
'de': ':symbol_geldautomat:',
|
||||
'es': ':señal_de_cajero_automático:',
|
||||
'fr': ':distributeur_de_billets:',
|
||||
'ja': ':atm:',
|
||||
'ko': ':에이티엠:',
|
||||
'pt': ':símbolo_de_caixa_automático:',
|
||||
'it': ':simbolo_dello_sportello_bancomat:',
|
||||
'fa': ':نشان_عابربانک:',
|
||||
'id': ':tanda_atm:',
|
||||
'zh': ':取款机:',
|
||||
'ru': ':значок_банкомата:',
|
||||
'tr': ':atm_işareti:',
|
||||
'ar': ':علامة_ماكينة_صرف_آلي:',
|
||||
},
|
||||
'\U0001f170\U0000fe0f': {
|
||||
'en': ':A_button_(blood_type):',
|
||||
'status': fully_qualified,
|
||||
'E': 0.6,
|
||||
'alias': [':a:', ':a_button_blood_type:'],
|
||||
'variant': True,
|
||||
'de': ':großbuchstabe_a_in_rotem_quadrat:',
|
||||
'es': ':grupo_sanguíneo_a:',
|
||||
'fr': ':groupe_sanguin_a:',
|
||||
'ja': ':血液型a型:',
|
||||
'ko': ':에이형:',
|
||||
'pt': ':botão_a_(tipo_sanguíneo):',
|
||||
'it': ':gruppo_sanguigno_a:',
|
||||
'fa': ':دکمه_آ_(گروه_خونی):',
|
||||
'id': ':tombol_a_(golongan_darah):',
|
||||
'zh': ':A型血:',
|
||||
'ru': ':ii_группа_крови:',
|
||||
'tr': ':a_düğmesi_(kan_grubu):',
|
||||
'ar': ':زر_a:',
|
||||
},
|
||||
'\U0001f170': {
|
||||
'en': ':A_button_(blood_type):',
|
||||
'status': unqualified,
|
||||
'E': 0.6,
|
||||
'alias': [':a:', ':a_button_blood_type:'],
|
||||
'variant': True,
|
||||
'de': ':großbuchstabe_a_in_rotem_quadrat:',
|
||||
'es': ':grupo_sanguíneo_a:',
|
||||
'fr': ':groupe_sanguin_a:',
|
||||
'ja': ':血液型a型:',
|
||||
'ko': ':에이형:',
|
||||
'pt': ':botão_a_(tipo_sanguíneo):',
|
||||
'it': ':gruppo_sanguigno_a:',
|
||||
'fa': ':دکمه_آ_(گروه_خونی):',
|
||||
'id': ':tombol_a_(golongan_darah):',
|
||||
'zh': ':A型血:',
|
||||
'ru': ':II_группа_крови:',
|
||||
'tr': ':a_düğmesi_(kan_grubu):',
|
||||
'ar': ':زر_a:',
|
||||
},
|
||||
'\U0001f1e6\U0001f1eb': {
|
||||
'en': ':Afghanistan:',
|
||||
'status': fully_qualified,
|
||||
'E': 2,
|
||||
'alias': [':flag_for_Afghanistan:', ':afghanistan:'],
|
||||
'de': ':flagge_afghanistan:',
|
||||
'es': ':bandera_afganistán:',
|
||||
'fr': ':drapeau_afghanistan:',
|
||||
'ja': ':旗_アフガニスタン:',
|
||||
'ko': ':깃발_아프가니스탄:',
|
||||
'pt': ':bandeira_afeganistão:',
|
||||
'it': ':bandiera_afghanistan:',
|
||||
'fa': ':پرچم_افغانستان:',
|
||||
'id': ':bendera_afganistan:',
|
||||
'zh': ':阿富汗:',
|
||||
'ru': ':флаг_Афганистан:',
|
||||
'tr': ':bayrak_afganistan:',
|
||||
'ar': ':علم_أفغانستان:',
|
||||
},
|
||||
'\U0001f1e6\U0001f1f1': {
|
||||
'en': ':Albania:',
|
||||
'status': fully_qualified,
|
||||
'E': 2,
|
||||
'alias': [':flag_for_Albania:', ':albania:'],
|
||||
'de': ':flagge_albanien:',
|
||||
'es': ':bandera_albania:',
|
||||
'fr': ':drapeau_albanie:',
|
||||
'ja': ':旗_アルバニア:',
|
||||
'ko': ':깃발_알바니아:',
|
||||
'pt': ':bandeira_albânia:',
|
||||
'it': ':bandiera_albania:',
|
||||
'fa': ':پرچم_آلبانی:',
|
||||
'id': ':bendera_albania:',
|
||||
'zh': ':阿尔巴尼亚:',
|
||||
'ru': ':флаг_Албания:',
|
||||
'tr': ':bayrak_arnavutluk:',
|
||||
'ar': ':علم_ألبانيا:',
|
||||
},
|
||||
'\U0001f1e9\U0001f1ff': {
|
||||
'en': ':Algeria:',
|
||||
'status': fully_qualified,
|
||||
'E': 2,
|
||||
'alias': [':flag_for_Algeria:', ':algeria:'],
|
||||
'de': ':flagge_algerien:',
|
||||
'es': ':bandera_argelia:',
|
||||
'fr': ':drapeau_algérie:',
|
||||
'ja': ':旗_アルジェリア:',
|
||||
'ko': ':깃발_알제리:',
|
||||
'pt': ':bandeira_argélia:',
|
||||
'it': ':bandiera_algeria:',
|
||||
'fa': ':پرچم_الجزایر:',
|
||||
'id': ':bendera_aljazair:',
|
||||
'zh': ':阿尔及利亚:',
|
||||
'ru': ':флаг_Алжир:',
|
||||
'tr': ':bayrak_cezayir:',
|
||||
'ar': ':علم_الجزائر:',
|
||||
},
|
||||
'\U0001f1e6\U0001f1f8': {
|
||||
'en': ':American_Samoa:',
|
||||
'status': fully_qualified,
|
||||
'E': 2,
|
||||
'alias': [':flag_for_American_Samoa:', ':american_samoa:'],
|
||||
'de': ':flagge_amerikanisch-samoa:',
|
||||
'es': ':bandera_samoa_americana:',
|
||||
'fr': ':drapeau_samoa_américaines:',
|
||||
'ja': ':旗_米領サモア:',
|
||||
'ko': ':깃발_아메리칸_사모아:',
|
||||
'pt': ':bandeira_samoa_americana:',
|
||||
'it': ':bandiera_samoa_americane:',
|
||||
'fa': ':پرچم_ساموآی_امریکا:',
|
||||
'id': ':bendera_samoa_amerika:',
|
||||
'zh': ':美属萨摩亚:',
|
||||
'ru': ':флаг_Американское_Самоа:',
|
||||
'tr': ':bayrak_amerikan_samoası:',
|
||||
'ar': ':علم_ساموا_الأمريكية:',
|
||||
},
|
||||
}
|
||||
+24
-23
@@ -8,6 +8,7 @@ import requests
|
||||
import json
|
||||
import base64
|
||||
import urllib.request
|
||||
|
||||
# import ssl
|
||||
# ssl._create_default_https_context = ssl._create_unverified_context
|
||||
|
||||
@@ -35,14 +36,14 @@ app.add_middleware(
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
# MongoDB Setup
|
||||
|
||||
import certifi
|
||||
MONGO_URI = os.getenv("MONGODB_URI", "mongodb://localhost:27017/")
|
||||
mongo_client = MongoClient(MONGO_URI, tlsCAFile=certifi.where())
|
||||
db = mongo_client.study_buddy_db
|
||||
study_rooms_collection = db.study_rooms
|
||||
|
||||
# Pydantic models for Study Room Data
|
||||
|
||||
class GeoJSONPoint(BaseModel):
|
||||
type: str = "Point"
|
||||
coordinates: List[float]
|
||||
@@ -75,12 +76,12 @@ HEADERS = {
|
||||
"sec-fetch-site": "same-origin",
|
||||
"sentry-trace": "250c82a03041415b99422d838ccc7003-9a9ec11d7fd0293b-0",
|
||||
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/146.0.0.0 Safari/537.36 Edg/146.0.0.0",
|
||||
"x-cosmos-session-281286": "0:-1#10931",
|
||||
"x-cosmos-session-295334": "0:-1#749854",
|
||||
"x-cosmos-session-317755": "0:-1#191601",
|
||||
"x-cosmos-session-382299": "0:-1#265024",
|
||||
"x-cosmos-session-418988": "0:-1#4058856",
|
||||
"x-cosmos-session-793952": "0:-1#14004",
|
||||
"x-cosmos-session-281286": "0:-1",
|
||||
"x-cosmos-session-295334": "0:-1",
|
||||
"x-cosmos-session-317755": "0:-1",
|
||||
"x-cosmos-session-382299": "0:-1",
|
||||
"x-cosmos-session-418988": "0:-1",
|
||||
"x-cosmos-session-793952": "0:-1",
|
||||
"x-request-id": "6a128b8a-7f63-4f97-a40b-bfd31b4a376e",
|
||||
"x-timezone": "America/New_York",
|
||||
}
|
||||
@@ -97,7 +98,7 @@ def _write_wav_to_buffer(pcm_data: bytes, sample_rate: int = SAMPLE_RATE) -> byt
|
||||
buf.write(b"WAVE")
|
||||
buf.write(b"fmt ")
|
||||
buf.write(struct.pack("<I", 16))
|
||||
buf.write(struct.pack("<H", 1)) # PCM
|
||||
buf.write(struct.pack("<H", 1))
|
||||
buf.write(struct.pack("<H", NUM_CHANNELS))
|
||||
buf.write(struct.pack("<I", sample_rate))
|
||||
buf.write(struct.pack("<I", byte_rate))
|
||||
@@ -113,7 +114,7 @@ def _transcribe_pcm(pcm_data: bytes, sample_rate: int = SAMPLE_RATE) -> str:
|
||||
from faster_whisper import WhisperModel
|
||||
print(f" Using sample rate: {sample_rate} Hz")
|
||||
|
||||
# Debug: analyze PCM audio quality
|
||||
|
||||
num_samples = len(pcm_data) // 2
|
||||
if num_samples > 0:
|
||||
samples = list(struct.unpack(f"<{num_samples}h", pcm_data[:num_samples * 2]))
|
||||
@@ -122,12 +123,12 @@ def _transcribe_pcm(pcm_data: bytes, sample_rate: int = SAMPLE_RATE) -> str:
|
||||
rms = (sum(s * s for s in samples) / num_samples) ** 0.5
|
||||
print(f" PCM stats (raw): {num_samples} samples, min={min_s}, max={max_s}, mean={mean_s:.1f}, RMS={rms:.1f}")
|
||||
|
||||
# Remove DC offset (center audio at 0)
|
||||
|
||||
dc_offset = int(round(mean_s))
|
||||
samples = [max(-32768, min(32767, s - dc_offset)) for s in samples]
|
||||
pcm_data = struct.pack(f"<{num_samples}h", *samples)
|
||||
|
||||
# Stats after correction
|
||||
|
||||
rms_fixed = (sum(s * s for s in samples) / num_samples) ** 0.5
|
||||
print(f" PCM stats (fixed): DC offset removed={dc_offset}, RMS={rms_fixed:.1f}")
|
||||
|
||||
@@ -138,13 +139,13 @@ def _transcribe_pcm(pcm_data: bytes, sample_rate: int = SAMPLE_RATE) -> str:
|
||||
with os.fdopen(tmp_fd, "wb") as f:
|
||||
f.write(wav_data)
|
||||
|
||||
# Save a debug copy so we can listen
|
||||
|
||||
debug_path = os.path.join(os.path.dirname(__file__), "debug_audio.wav")
|
||||
with open(debug_path, "wb") as df:
|
||||
df.write(wav_data)
|
||||
print(f" Debug WAV saved to: {debug_path}")
|
||||
|
||||
# Initialize the model (using base model for speed)
|
||||
|
||||
model = WhisperModel("base", device="cpu", compute_type="int8")
|
||||
segments, info = model.transcribe(tmp_path, beam_size=5)
|
||||
seg_list = list(segments)
|
||||
@@ -232,7 +233,7 @@ def _generate_tts(text: str) -> bytes | None:
|
||||
print("ElevenLabs API key not configured")
|
||||
return None
|
||||
|
||||
# Request PCM directly — no ffmpeg needed
|
||||
|
||||
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}?output_format=pcm_16000"
|
||||
|
||||
headers = {
|
||||
@@ -267,7 +268,7 @@ def _generate_tts(text: str) -> bytes | None:
|
||||
print(f"ElevenLabs TTS error: {e}")
|
||||
return None
|
||||
|
||||
# Latest TTS WAV stored in memory for HTTP download by M5GO
|
||||
|
||||
_latest_tts_wav = None
|
||||
|
||||
@app.get("/api/tts-audio")
|
||||
@@ -295,7 +296,7 @@ async def websocket_voice(websocket: WebSocket):
|
||||
msg = json.loads(data["text"])
|
||||
if msg.get("event") == "stop_listening":
|
||||
pcm_data = bytes(audio_buffer)
|
||||
audio_buffer = bytearray() # Reset for next time
|
||||
audio_buffer = bytearray()
|
||||
device_sample_rate = msg.get("sample_rate", SAMPLE_RATE)
|
||||
|
||||
print(f"Received stop_listening event. Buffer size: {len(pcm_data)} bytes, sample_rate: {device_sample_rate} Hz")
|
||||
@@ -305,7 +306,7 @@ async def websocket_voice(websocket: WebSocket):
|
||||
await websocket.send_bytes(b"")
|
||||
continue
|
||||
|
||||
# Step 1: Speech to Text
|
||||
|
||||
print("Transcribing...")
|
||||
user_text = _transcribe_pcm(pcm_data, sample_rate=device_sample_rate)
|
||||
if not user_text:
|
||||
@@ -315,7 +316,7 @@ async def websocket_voice(websocket: WebSocket):
|
||||
|
||||
print(f"User said: {user_text}")
|
||||
|
||||
# Step 2: Terp AI
|
||||
|
||||
print("Sending to Terp AI...")
|
||||
context_str = get_latest_locations_context()
|
||||
augmented_prompt = f"USER ASKS: {user_text}\n\n[SYSTEM CONTEXT - LATEST UMD ROOM STATS TO HELP YOU ANSWER IF ASKED]:\n{context_str}"
|
||||
@@ -327,12 +328,12 @@ async def websocket_voice(websocket: WebSocket):
|
||||
|
||||
print(f"Terp AI response: {ai_response_text}")
|
||||
|
||||
# Step 3: Text to Speech
|
||||
|
||||
print("Generating TTS...")
|
||||
tts_pcm = _generate_tts(ai_response_text)
|
||||
|
||||
if tts_pcm:
|
||||
# Save as WAV for HTTP download by M5GO
|
||||
|
||||
global _latest_tts_wav
|
||||
_latest_tts_wav = _write_wav_to_buffer(tts_pcm)
|
||||
print(f"TTS WAV ready: {len(_latest_tts_wav)} bytes, serving via /api/tts-audio")
|
||||
@@ -406,14 +407,14 @@ def get_latest_locations_context() -> str:
|
||||
|
||||
@app.post("/api/study-rooms")
|
||||
async def create_study_room_data(data: StudyRoomData):
|
||||
# Check if the coordinates match one of the known locations (with small tolerance)
|
||||
|
||||
is_valid_location = False
|
||||
req_lng, req_lat = data.location.coordinates[0], data.location.coordinates[1]
|
||||
|
||||
for loc in UMD_LOCATIONS:
|
||||
if abs(loc["lng"] - req_lng) < 0.0001 and abs(loc["lat"] - req_lat) < 0.0001:
|
||||
is_valid_location = True
|
||||
# Override coordinates to exactly match known location for consistency
|
||||
|
||||
data.location.coordinates = [loc["lng"], loc["lat"]]
|
||||
data.room_id = loc["id"]
|
||||
break
|
||||
|
||||
+12
-12
@@ -4,15 +4,15 @@ from ultralytics import YOLO
|
||||
from scipy.spatial.distance import cdist
|
||||
from scipy.optimize import linear_sum_assignment
|
||||
|
||||
# Load YOLOv8 nano model (downloads automatically if not found)
|
||||
# 'yolov8n.pt' is lightweight and fast for this purpose
|
||||
|
||||
|
||||
try:
|
||||
model = YOLO("yolov8n.pt")
|
||||
except Exception as e:
|
||||
print(f"Error loading YOLO model: {e}")
|
||||
model = None
|
||||
|
||||
# COCO Class IDs
|
||||
|
||||
PERSON_CLASS_ID = 0
|
||||
CHAIR_CLASS_ID = 56
|
||||
|
||||
@@ -20,14 +20,14 @@ def analyze_room_image(image_bytes: bytes):
|
||||
if not model:
|
||||
return {"error": "Vision model is not loaded"}
|
||||
|
||||
# Convert bytes to numpy array then to cv2 image
|
||||
|
||||
nparr = np.frombuffer(image_bytes, np.uint8)
|
||||
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||||
|
||||
if img is None:
|
||||
return {"error": "Invalid image format"}
|
||||
|
||||
# Run inference
|
||||
|
||||
results = model(img)
|
||||
|
||||
people = []
|
||||
@@ -39,13 +39,13 @@ def analyze_room_image(image_bytes: bytes):
|
||||
cls_id = int(box.cls[0])
|
||||
conf = float(box.conf[0])
|
||||
|
||||
# Extract center of bounding box
|
||||
|
||||
x1, y1, x2, y2 = box.xyxy[0]
|
||||
cx = (x1 + x2) / 2.0
|
||||
cy = (y1 + y2) / 2.0
|
||||
centroid = [float(cx), float(cy)]
|
||||
|
||||
# Only consider detections with confidence > 0.3
|
||||
|
||||
if conf > 0.3:
|
||||
if cls_id == PERSON_CLASS_ID:
|
||||
people.append({
|
||||
@@ -65,15 +65,15 @@ def analyze_room_image(image_bytes: bytes):
|
||||
|
||||
pairs = []
|
||||
|
||||
# Bipartite matching if both people and chairs exist
|
||||
|
||||
if num_people > 0 and num_chairs > 0:
|
||||
people_coords = [p["centroid"] for p in people]
|
||||
chairs_coords = [c["centroid"] for c in chairs]
|
||||
|
||||
# Distance matrix (Euclidean distances)
|
||||
|
||||
dist_matrix = cdist(people_coords, chairs_coords, metric='euclidean')
|
||||
|
||||
# Hungarian algorithm to minimize total distance for pairings
|
||||
|
||||
row_ind, col_ind = linear_sum_assignment(dist_matrix)
|
||||
|
||||
for person_idx, chair_idx in zip(row_ind, col_ind):
|
||||
@@ -84,8 +84,8 @@ def analyze_room_image(image_bytes: bytes):
|
||||
"distance": distance
|
||||
})
|
||||
|
||||
# Basic logic: room is full if there are at least as many people as chairs.
|
||||
# Can be adjusted based on specific room definitions
|
||||
|
||||
|
||||
is_full = num_people >= num_chairs if num_chairs > 0 else False
|
||||
|
||||
return {
|
||||
|
||||
+35
-35
@@ -34,26 +34,26 @@ def d_s(lcd, f, s_x, s_y, p_s):
|
||||
|
||||
setScreenColor(0x222222)
|
||||
|
||||
# ==========================================
|
||||
# CONFIGURATION
|
||||
# ==========================================
|
||||
|
||||
|
||||
|
||||
|
||||
WIFI_SSID = "Blobby"
|
||||
WIFI_PASS = "73556088"
|
||||
WS_URL = "ws://192.168.137.1:8000/ws/voice"
|
||||
|
||||
# Location Settings for Noise Monitoring
|
||||
|
||||
CURRENT_ROOM_ID = "mckeldin"
|
||||
CURRENT_LAT = 38.986021
|
||||
CURRENT_LNG = -76.944949
|
||||
|
||||
# Audio Settings
|
||||
TARGET_SAMPLE_RATE = 8000 # Voice recording sample rate
|
||||
|
||||
TARGET_SAMPLE_RATE = 8000
|
||||
AUDIO_CHUNK_SIZE = 2048
|
||||
|
||||
# ==========================================
|
||||
# WEBSOCKET CLIENT
|
||||
# ==========================================
|
||||
|
||||
|
||||
|
||||
|
||||
class WSClient:
|
||||
def __init__(self, sock):
|
||||
@@ -200,9 +200,9 @@ def ws_connect(url):
|
||||
|
||||
return WSClient(sock)
|
||||
|
||||
# ==========================================
|
||||
# UI & UTILITY FUNCTIONS
|
||||
# ==========================================
|
||||
|
||||
|
||||
|
||||
|
||||
_cur_face = None
|
||||
def set_face(face):
|
||||
@@ -243,9 +243,9 @@ def connect_wifi():
|
||||
lcd.clear()
|
||||
return wlan.isconnected()
|
||||
|
||||
# ==========================================
|
||||
# AUDIO I/O
|
||||
# ==========================================
|
||||
|
||||
|
||||
|
||||
|
||||
def get_adc():
|
||||
try:
|
||||
@@ -283,13 +283,13 @@ def get_db(adc_obj):
|
||||
amp = math.sqrt(variance)
|
||||
if amp <= 1: return 30
|
||||
|
||||
# Scale to human DB
|
||||
|
||||
return 20 * math.log10(amp) + 25
|
||||
|
||||
def init_manual_spk():
|
||||
try:
|
||||
# M5GO Core speaker sits on DAC 1 (Pin 25)
|
||||
# Therefore we MUST strictly use DAC_BUILT_IN. Digital I2S on this pin is not supported!
|
||||
|
||||
|
||||
if hasattr(machine.I2S, "MODE_DAC_BUILT_IN"):
|
||||
mode = getattr(machine.I2S, "MODE_MASTER", 1) | getattr(machine.I2S, "MODE_TX", 2) | getattr(machine.I2S, "MODE_DAC_BUILT_IN", 0)
|
||||
cfmt = getattr(machine.I2S, "CHANNEL_FMT_RIGHT_LEFT", 1)
|
||||
@@ -297,7 +297,7 @@ def init_manual_spk():
|
||||
|
||||
i2s_id = getattr(machine.I2S, "NUM0", 0)
|
||||
|
||||
# Different MicroPython versions vary wildly on kwarg vs positional I2S init structure
|
||||
|
||||
try_sigs = [
|
||||
([i2s_id], {"mode": mode, "rate": 16000, "bits": 16, "format": cfmt, "ibuf": 2048}),
|
||||
([i2s_id, mode, 16000, 16, cfmt, dfmt], {}),
|
||||
@@ -366,7 +366,7 @@ def stream_http_audio(url):
|
||||
return False
|
||||
hdr += b
|
||||
|
||||
# Read WAV header
|
||||
|
||||
header_left = 44
|
||||
while header_left > 0:
|
||||
chunk = sock.recv(header_left)
|
||||
@@ -389,16 +389,16 @@ def stream_http_audio(url):
|
||||
samples = n // 2
|
||||
for j in range(samples):
|
||||
idx = j * 2
|
||||
# Read 16-bit Signed LE
|
||||
|
||||
s = in_buf[idx] | (in_buf[idx + 1] << 8)
|
||||
if s >= 32768: s -= 65536
|
||||
|
||||
# Convert to Unsigned + Center Offset for DAC
|
||||
|
||||
u = (s + 32768) & 0xFFFF
|
||||
u_lo = u & 0xFF
|
||||
u_hi = u >> 8
|
||||
|
||||
# Map Stereo for built-in MSB
|
||||
|
||||
o_idx = j * 4
|
||||
out_buf[o_idx] = u_lo
|
||||
out_buf[o_idx + 1] = u_hi
|
||||
@@ -415,9 +415,9 @@ def stream_http_audio(url):
|
||||
if hasattr(audio_out, 'deinit'): audio_out.deinit()
|
||||
return True
|
||||
|
||||
# ==========================================
|
||||
# MAIN LOOP
|
||||
# ==========================================
|
||||
|
||||
|
||||
|
||||
|
||||
def run_main():
|
||||
if not connect_wifi():
|
||||
@@ -445,7 +445,7 @@ def run_main():
|
||||
|
||||
while True:
|
||||
gc.collect()
|
||||
# --- Voice Interaction ---
|
||||
|
||||
if btnA.isPressed():
|
||||
if maintain_ws():
|
||||
draw_status("Listening...", 0x0000FF, f_listen)
|
||||
@@ -456,12 +456,12 @@ def run_main():
|
||||
|
||||
rec_start_us = time.ticks_us()
|
||||
|
||||
# Fastest possible analog capture loop
|
||||
|
||||
while btnA.isPressed():
|
||||
try:
|
||||
raw = adc.read() if adc else 2048
|
||||
sample = (raw - 2048) * 16
|
||||
# Fast clamp
|
||||
|
||||
if sample > 32767: sample = 32767
|
||||
elif sample < -32768: sample = -32768
|
||||
|
||||
@@ -490,12 +490,12 @@ def run_main():
|
||||
try:
|
||||
ws.send(json.dumps({"event": "stop_listening", "sample_rate": actual_rate}))
|
||||
|
||||
# Wait for TTS ready
|
||||
|
||||
resp = ws.recv()
|
||||
if resp and isinstance(resp, str):
|
||||
msg = json.loads(resp)
|
||||
if msg.get("event") == "tts_ready":
|
||||
# Start direct TCP stream immediately
|
||||
|
||||
draw_status("Speaking...", 0x00FF00, f_speak)
|
||||
http_base = WS_URL.replace("ws://", "http://").replace("/ws/voice", "")
|
||||
audio_url = http_base + "/api/tts-audio"
|
||||
@@ -518,7 +518,7 @@ def run_main():
|
||||
|
||||
draw_status("Hold Button A to Talk", 0xFFFFFF, f_s_o)
|
||||
|
||||
# --- Noise Monitoring ---
|
||||
|
||||
r_db = get_db(adc)
|
||||
s_db = (s_db * 0.8) + (r_db * 0.2)
|
||||
db_val = int(s_db)
|
||||
@@ -531,7 +531,7 @@ def run_main():
|
||||
lcd.print(db_s, 5, 4, i_c)
|
||||
l_db_s = db_s
|
||||
|
||||
# Periodic DB Posting
|
||||
|
||||
try:
|
||||
now_ms = time.ticks_ms()
|
||||
if time.ticks_diff(now_ms, last_db_post_time) > 15000:
|
||||
@@ -539,7 +539,7 @@ def run_main():
|
||||
payload_str = '{"room_id":"%s","location":{"type":"Point","coordinates":[%s,%s]},"db":%s}' % (CURRENT_ROOM_ID, CURRENT_LNG, CURRENT_LAT, db_val)
|
||||
http_url = WS_URL.replace("ws://", "http://").replace("/ws/voice", "/api/study-rooms")
|
||||
|
||||
# Raw socket post
|
||||
|
||||
h_p = http_url.split("://")[1].split("/")[0]
|
||||
p_th = "/" + http_url.split("://")[1].split("/", 1)[1] if "/" in http_url.split("://")[1] else "/"
|
||||
h_b = h_p.split(":")[0]
|
||||
@@ -555,7 +555,7 @@ def run_main():
|
||||
except:
|
||||
pass
|
||||
|
||||
# Idle Face Blinking logic
|
||||
|
||||
if db_val > 67:
|
||||
set_face(f_angry)
|
||||
else:
|
||||
|
||||
+10
-10
@@ -1,4 +1,4 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import base64
|
||||
import urllib.request
|
||||
import urllib.error
|
||||
@@ -28,16 +28,16 @@ HEADERS = {
|
||||
"sec-fetch-mode": "cors",
|
||||
"sec-fetch-site": "same-origin",
|
||||
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/146.0.0.0 Safari/537.36",
|
||||
"x-cosmos-session-281286": "0:-1#10931",
|
||||
"x-cosmos-session-295334": "0:-1#745295",
|
||||
"x-cosmos-session-317755": "0:-1#190559",
|
||||
"x-cosmos-session-382299": "0:-1#264026",
|
||||
"x-cosmos-session-418988": "0:-1#4052014",
|
||||
"x-cosmos-session-793952": "0:-1#13832",
|
||||
"x-cosmos-session-281286": "0:-1
|
||||
"x-cosmos-session-295334": "0:-1
|
||||
"x-cosmos-session-317755": "0:-1
|
||||
"x-cosmos-session-382299": "0:-1
|
||||
"x-cosmos-session-418988": "0:-1
|
||||
"x-cosmos-session-793952": "0:-1
|
||||
"x-timezone": "America/New_York",
|
||||
}
|
||||
|
||||
# The parent segment ID to chain from — update this to your latest segment
|
||||
|
||||
INITIAL_PARENT_SEGMENT_ID = "d7765a66-a24b-4620-b003-11b4c1ed2c36"
|
||||
|
||||
|
||||
@@ -59,7 +59,7 @@ def send_message(message: str, conversation_id: str = CONVERSATION_ID, parent_se
|
||||
|
||||
payload = json.dumps(body).encode("utf-8")
|
||||
|
||||
# Generate fresh per-request headers
|
||||
|
||||
sentry_trace_id = uuid.uuid4().hex
|
||||
sentry_span_id = uuid.uuid4().hex[:16]
|
||||
headers = dict(HEADERS)
|
||||
@@ -105,7 +105,7 @@ def send_message(message: str, conversation_id: str = CONVERSATION_ID, parent_se
|
||||
print(f"\n[IDs: {decoded}]", file=sys.stderr)
|
||||
try:
|
||||
ids = json.loads(decoded)
|
||||
# Try common key names for the segment ID
|
||||
|
||||
new_segment_id = (
|
||||
ids.get("segmentId")
|
||||
or ids.get("id")
|
||||
|
||||
@@ -2,7 +2,7 @@ import random
|
||||
from datetime import datetime, timedelta
|
||||
from pymongo import MongoClient
|
||||
|
||||
# MongoDB Setup
|
||||
|
||||
MONGO_URI = "mongodb+srv://SarayuJ:[email protected]/testing"
|
||||
client = MongoClient(MONGO_URI)
|
||||
db = client.study_buddy_db
|
||||
@@ -25,10 +25,10 @@ def get_db_for_time_and_location(hour, loc_id):
|
||||
Generate a dB level based on the hour of the day and the location.
|
||||
This creates a recognizable pattern for AI analysis.
|
||||
"""
|
||||
base_db = 40.0 # Ambient noise
|
||||
base_db = 40.0
|
||||
|
||||
if loc_id in ['mckeldin', 'esj']:
|
||||
# Busy during the day (10am - 4pm)
|
||||
|
||||
if 10 <= hour <= 16:
|
||||
base_db = 75.0
|
||||
elif 17 <= hour <= 22:
|
||||
@@ -37,7 +37,7 @@ def get_db_for_time_and_location(hour, loc_id):
|
||||
base_db = 45.0
|
||||
|
||||
elif loc_id in ['stem', 'iribe']:
|
||||
# Busy in the afternoon/evening (2pm - 8pm)
|
||||
|
||||
if 14 <= hour <= 20:
|
||||
base_db = 70.0
|
||||
elif 9 <= hour <= 13:
|
||||
@@ -46,7 +46,7 @@ def get_db_for_time_and_location(hour, loc_id):
|
||||
base_db = 42.0
|
||||
|
||||
elif loc_id == 'stamp':
|
||||
# Busy during lunch (12pm - 2pm) and dinner (5pm - 7pm)
|
||||
|
||||
if 12 <= hour <= 14 or 17 <= hour <= 19:
|
||||
base_db = 85.0
|
||||
elif 10 <= hour <= 21:
|
||||
@@ -55,14 +55,14 @@ def get_db_for_time_and_location(hour, loc_id):
|
||||
base_db = 50.0
|
||||
|
||||
else:
|
||||
# General locations (Clarice, Yahentamitsi, Reckord, Hornbake)
|
||||
# Moderate noise during the day
|
||||
|
||||
|
||||
if 9 <= hour <= 18:
|
||||
base_db = 60.0
|
||||
else:
|
||||
base_db = 45.0
|
||||
|
||||
# Add random noise to make it look realistic (+/- 5 dB)
|
||||
|
||||
noise = random.uniform(-5.0, 5.0)
|
||||
return max(30.0, min(100.0, base_db + noise))
|
||||
|
||||
@@ -76,7 +76,7 @@ def generate_fake_data():
|
||||
docs_to_insert = []
|
||||
|
||||
print("Generating 24 hours of fake data with patterns...")
|
||||
# Generate data points every 15 minutes for the last 24 hours
|
||||
|
||||
current_time = start_time
|
||||
while current_time <= now:
|
||||
hour = current_time.hour
|
||||
|
||||
Vendored
+6
-6
@@ -1,12 +1,12 @@
|
||||
// See https://svelte.dev/docs/kit/types#app.d.ts
|
||||
// for information about these interfaces
|
||||
|
||||
declare global {
|
||||
namespace App {
|
||||
// interface Error {}
|
||||
// interface Locals {}
|
||||
// interface PageData {}
|
||||
// interface PageState {}
|
||||
// interface Platform {}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -17,7 +17,7 @@
|
||||
let errorMessage = $state<string>('');
|
||||
let hardwareSampleRate = 16000;
|
||||
|
||||
// Visualizer data
|
||||
|
||||
let currentRms = $state<number>(0);
|
||||
|
||||
function cleanupMic() {
|
||||
@@ -44,15 +44,15 @@
|
||||
function playTTS(url: string) {
|
||||
callState = 'speaking';
|
||||
currentAudio = new Audio(url + "?t=" + Date.now());
|
||||
currentRms = 0.06; // Set a much smaller safe static visualizer size
|
||||
currentRms = 0.06;
|
||||
currentAudio.onended = () => {
|
||||
currentRms = 0;
|
||||
if (callState === 'speaking') {
|
||||
// AI is done talking, start listening automatically!
|
||||
|
||||
startListeningPhase();
|
||||
}
|
||||
};
|
||||
// Some browsers require explicit play tracking
|
||||
|
||||
const playPromise = currentAudio.play();
|
||||
if (playPromise !== undefined) {
|
||||
playPromise.catch(e => {
|
||||
@@ -72,7 +72,7 @@
|
||||
currentRms = 0;
|
||||
|
||||
try {
|
||||
// Synchronous audio context resume
|
||||
|
||||
if (audioContext && audioContext.state === 'suspended') {
|
||||
await audioContext.resume();
|
||||
}
|
||||
@@ -100,7 +100,7 @@
|
||||
ws.send(int16.buffer);
|
||||
|
||||
const rms = Math.sqrt(sumSq / float32.length);
|
||||
currentRms = rms; // Drive the visualizer UI
|
||||
currentRms = rms;
|
||||
|
||||
if (rms > 0.035) {
|
||||
hasSpoken = true;
|
||||
@@ -132,15 +132,15 @@
|
||||
if (ws && ws.readyState === WebSocket.OPEN) {
|
||||
ws.send(JSON.stringify({ event: 'stop_listening', sample_rate: hardwareSampleRate }));
|
||||
}
|
||||
// Do NOT cleanup hardware mic here as the session is perfectly continuous!
|
||||
|
||||
}
|
||||
|
||||
function startCall() {
|
||||
if (callState !== 'idle') return;
|
||||
callState = 'listening'; // transition state instantly
|
||||
callState = 'listening';
|
||||
|
||||
try {
|
||||
// MUST CREATE AUDIO CONTEXT SYNCHRONOUSLY IN CLICK HANDLER
|
||||
|
||||
const AC = window.AudioContext || (window as any).webkitAudioContext;
|
||||
audioContext = new AC();
|
||||
hardwareSampleRate = audioContext.sampleRate;
|
||||
@@ -152,7 +152,6 @@
|
||||
const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
|
||||
const host = window.location.hostname;
|
||||
ws = new WebSocket(`${protocol}//${host}:8000/ws/voice`);
|
||||
|
||||
ws.onmessage = (e) => {
|
||||
const data = JSON.parse(e.data);
|
||||
if (data.event === 'tts_ready') {
|
||||
@@ -189,7 +188,7 @@
|
||||
if (callState === 'idle') {
|
||||
startCall();
|
||||
} else if (callState === 'listening') {
|
||||
// Force manual send
|
||||
|
||||
hasSpoken = true;
|
||||
finishUtterance();
|
||||
} else {
|
||||
@@ -204,7 +203,7 @@
|
||||
}
|
||||
|
||||
onMount(() => {
|
||||
// Auto-start the call when modal opens
|
||||
|
||||
startCall();
|
||||
});
|
||||
|
||||
|
||||
@@ -31,19 +31,19 @@
|
||||
function updateMapData() {
|
||||
if (!mapInstance || !mapInstance.getSource("study-locations")) return;
|
||||
|
||||
// Group history by location using room_id
|
||||
|
||||
const latestByLoc = new Map();
|
||||
for (const room of studyRoomsData) {
|
||||
// Use room_id as the key, fallback to coordinates if room_id is missing for some reason
|
||||
|
||||
const key = room.room_id || room.location.coordinates.join(",");
|
||||
|
||||
// Ensure date is treated as UTC
|
||||
|
||||
const roomDateString = room.date.endsWith("Z")
|
||||
? room.date
|
||||
: room.date + "Z";
|
||||
const roomDate = new Date(roomDateString);
|
||||
|
||||
// Filter out points strictly in the future of our playback time
|
||||
|
||||
if (playbackTime && roomDate.getTime() > playbackTime) continue;
|
||||
|
||||
if (!latestByLoc.has(key)) {
|
||||
@@ -68,7 +68,7 @@
|
||||
}
|
||||
|
||||
const features = latestRooms.map((room: any) => {
|
||||
// Find the corresponding UMD_LOCATION to get the name
|
||||
|
||||
let matchingLoc = null;
|
||||
if (room.room_id) {
|
||||
matchingLoc = UMD_LOCATIONS.find(
|
||||
@@ -98,7 +98,7 @@
|
||||
};
|
||||
});
|
||||
|
||||
// Add features for UMD_LOCATIONS that don't have sensor data yet
|
||||
|
||||
UMD_LOCATIONS.forEach((loc) => {
|
||||
const hasData = features.some(
|
||||
(f) => f.properties.room_id === loc.id,
|
||||
@@ -113,7 +113,7 @@
|
||||
},
|
||||
properties: {
|
||||
room_id: loc.id,
|
||||
db: 0, // 0 db for no data
|
||||
db: 0,
|
||||
name: loc.name,
|
||||
date: new Date().toISOString(),
|
||||
},
|
||||
@@ -277,7 +277,7 @@
|
||||
`;
|
||||
}
|
||||
|
||||
// Watch and react to mapState updates using a Svelte 5 $effect
|
||||
|
||||
$effect(() => {
|
||||
const target = mapState.targetFlyTo;
|
||||
if (mapInstance && target) {
|
||||
@@ -368,7 +368,7 @@
|
||||
if (!browser || !mapContainer) return;
|
||||
|
||||
fetchStudyRoomData();
|
||||
refreshInterval = setInterval(fetchStudyRoomData, 10000); // refresh every 10s
|
||||
refreshInterval = setInterval(fetchStudyRoomData, 10000);
|
||||
|
||||
let map: any;
|
||||
|
||||
@@ -468,7 +468,7 @@
|
||||
:global(.maplibregl-ctrl-group) {
|
||||
background: var(
|
||||
--color-panel-glass
|
||||
) !important; /* already switches per theme */
|
||||
) !important;
|
||||
backdrop-filter: blur(12px) !important;
|
||||
border: 1px solid
|
||||
color-mix(in srgb, var(--color-surface1) 30%, transparent) !important;
|
||||
@@ -502,6 +502,6 @@
|
||||
24,
|
||||
37,
|
||||
0.9
|
||||
) !important; /* matches bg-crust */
|
||||
) !important;
|
||||
}
|
||||
</style>
|
||||
|
||||
@@ -3,7 +3,7 @@
|
||||
import { themeState } from '$lib/states/theme.svelte';
|
||||
import Icon from '@iconify/svelte';
|
||||
import { onMount, onDestroy } from 'svelte';
|
||||
// Dynamic import for chart.js to avoid SSR issues
|
||||
|
||||
let Chart: any;
|
||||
|
||||
let chartCanvas: HTMLCanvasElement;
|
||||
@@ -32,7 +32,7 @@
|
||||
return 'Harmful';
|
||||
}
|
||||
|
||||
// Calculate metrics
|
||||
|
||||
let current2hAvg = $derived.by(() => {
|
||||
if (!mapState.selectedLocation) return 0;
|
||||
const now = Date.now();
|
||||
@@ -53,7 +53,7 @@
|
||||
if (!Chart || !chartCanvas || !mapState.selectedLocation) return;
|
||||
if (chartInstance) chartInstance.destroy();
|
||||
|
||||
// filter past 24h for this loc
|
||||
|
||||
const locData = mapState.historyData.filter(d => d.room_id === mapState.selectedLocation?.id)
|
||||
.map(d => ({
|
||||
x: new Date(d.date.endsWith('Z') ? d.date : d.date + 'Z'),
|
||||
@@ -145,7 +145,7 @@
|
||||
|
||||
onMount(async () => {
|
||||
const chartModule = await import('chart.js/auto');
|
||||
const chartjsAdapter = await import('chartjs-adapter-date-fns'); // Need this for time scaling
|
||||
const chartjsAdapter = await import('chartjs-adapter-date-fns');
|
||||
Chart = chartModule.default;
|
||||
if (mapState.selectedLocation) drawChart();
|
||||
});
|
||||
|
||||
@@ -1 +1 @@
|
||||
// place files you want to import through the `$lib` alias in this folder.
|
||||
|
||||
|
||||
@@ -17,15 +17,15 @@ class AlertsState {
|
||||
const now = Date.now();
|
||||
|
||||
for (const room of latestReadings) {
|
||||
// Only fire if the reading is >= 65dB (Disruptive or Harmful)
|
||||
|
||||
if (room.db >= 65) {
|
||||
const roomDate = new Date(room.date.endsWith('Z') ? room.date : room.date + 'Z').getTime();
|
||||
|
||||
// Ensure the reading is recent (within 5 minutes, 300000ms), to prevent alerting on stale data on initial load
|
||||
|
||||
if (now - roomDate < 300000) {
|
||||
const lastAlert = this.alertHistory.get(room.room_id) || 0;
|
||||
|
||||
// Cooldown: Don't alert for the same location within 3 minutes (180000ms)
|
||||
|
||||
if (now - lastAlert > 180000) {
|
||||
const locData = UMD_LOCATIONS.find(l => l.id === room.room_id);
|
||||
const locName = locData ? locData.name : room.room_id;
|
||||
@@ -42,7 +42,7 @@ class AlertsState {
|
||||
timestamp: now
|
||||
});
|
||||
|
||||
// Keep a max of 10 alerts logic
|
||||
|
||||
if (this.activeAlerts.length > 20) {
|
||||
this.activeAlerts.pop();
|
||||
}
|
||||
|
||||
@@ -22,16 +22,16 @@ export const UMD_LOCATIONS: StudyLocation[] = [
|
||||
export const DEFAULT_VIEW = { lng: -76.94259561477574, lat: 38.98813763708658, zoom: 15.5 };
|
||||
|
||||
class MapState {
|
||||
// The target coordinates the map should fly to
|
||||
|
||||
targetFlyTo = $state<{ lng: number; lat: number; zoom: number; timestamp: number } | null>(null);
|
||||
|
||||
selectedLocation = $state<StudyLocation | null>(null);
|
||||
historyData = $state<any[]>([]);
|
||||
historyLoading = $state<boolean>(false);
|
||||
historyPlaybackTime = $state<number>(Date.now()); // The current time scrubber for 24h
|
||||
historyPlaybackTime = $state<number>(Date.now());
|
||||
|
||||
flyTo(lng: number, lat: number, zoom: number = 18) {
|
||||
this.targetFlyTo = { lng, lat, zoom, timestamp: Date.now() }; // timestamp ensures reactivity even if same coords
|
||||
this.targetFlyTo = { lng, lat, zoom, timestamp: Date.now() };
|
||||
}
|
||||
|
||||
flyHome() {
|
||||
@@ -41,7 +41,7 @@ class MapState {
|
||||
async fetchHistoryData() {
|
||||
this.historyLoading = true;
|
||||
try {
|
||||
// Fetch from FastAPI backend
|
||||
|
||||
const res = await fetch('http://127.0.0.1:8000/api/study-rooms/history');
|
||||
if (res.ok) {
|
||||
const json = await res.json();
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
import InteractiveMap from '$lib/components/InteractiveMap.svelte';
|
||||
import { onDestroy, onMount } from 'svelte';
|
||||
|
||||
// Time state
|
||||
|
||||
const NOW = Date.now();
|
||||
const TWENTY_FOUR_HOURS = 24 * 60 * 60 * 1000;
|
||||
let playbackTime = $state(NOW);
|
||||
@@ -15,19 +15,19 @@
|
||||
function togglePlay() {
|
||||
isPlaying = !isPlaying;
|
||||
if (isPlaying) {
|
||||
// Auto replay from beginning if at the end
|
||||
|
||||
if (playbackTime >= NOW) {
|
||||
playbackTime = NOW - TWENTY_FOUR_HOURS;
|
||||
}
|
||||
playInterval = setInterval(() => {
|
||||
// Advance 15 minutes per tick
|
||||
|
||||
playbackTime += 15 * 60 * 1000;
|
||||
if (playbackTime >= NOW) {
|
||||
playbackTime = NOW;
|
||||
isPlaying = false;
|
||||
clearInterval(playInterval);
|
||||
}
|
||||
}, 200); // Ticks every 200ms
|
||||
}, 200);
|
||||
} else {
|
||||
clearInterval(playInterval);
|
||||
}
|
||||
@@ -37,7 +37,7 @@
|
||||
if (playInterval) clearInterval(playInterval);
|
||||
});
|
||||
|
||||
// Derived values for the UI
|
||||
|
||||
let progressPercent = $derived(((playbackTime - (NOW - TWENTY_FOUR_HOURS)) / TWENTY_FOUR_HOURS) * 100);
|
||||
|
||||
let formattedTime = $derived.by(() => {
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
--font-sans: 'Comic Relief', 'Comic Neue', 'Comic Sans MS', cursive, system-ui, sans-serif;
|
||||
--font-display: 'Comic Relief', 'Comic Neue', 'Comic Sans MS', cursive, system-ui, sans-serif;
|
||||
|
||||
/* Catppuccin Theme */
|
||||
|
||||
--color-crust: #11111b;
|
||||
--color-mantle: #181825;
|
||||
--color-base: #1e1e2e;
|
||||
|
||||
@@ -46,7 +46,7 @@
|
||||
|
||||
<div class="relative w-full h-full bg-crust border-l border-white/5 p-6 md:p-12 overflow-y-auto duration-500 transition-colors">
|
||||
<!-- Background Mesh -->
|
||||
<div class="absolute inset-0 bg-[radial-gradient(ellipse_at_top_right,_var(--tw-gradient-stops))] from-surface0/30 via-crust to-crust z-0 pointer-events-none transition-colors duration-500"></div>
|
||||
<div class="absolute inset-0 bg-[radial-gradient(ellipse_at_top_right,var(--tw-gradient-stops))] from-surface0/30 via-crust to-crust z-0 pointer-events-none transition-colors duration-500"></div>
|
||||
|
||||
<div class="relative z-10 max-w-4xl mx-auto">
|
||||
<header class="mb-10">
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
import adapter from '@sveltejs/adapter-static';
|
||||
|
||||
/** @type {import('@sveltejs/kit').Config} */
|
||||
|
||||
const config = {
|
||||
compilerOptions: {
|
||||
// Force runes mode for the project, except for libraries. Can be removed in svelte 6.
|
||||
|
||||
runes: ({ filename }) => (filename.split(/[/\\]/).includes('node_modules') ? undefined : true)
|
||||
},
|
||||
kit: {
|
||||
|
||||
@@ -12,7 +12,7 @@ export default defineConfig({
|
||||
name: 'HushMap',
|
||||
short_name: 'HushMap',
|
||||
description: 'Campus noise mapping and intervention.',
|
||||
theme_color: '#0f172a', /* slate-900 */
|
||||
theme_color: '#0f172a',
|
||||
background_color: '#0f172a',
|
||||
display: 'standalone',
|
||||
icons: [
|
||||
|
||||
Reference in New Issue
Block a user