Code comments Removed Update

README.md

@@ -1,15 +1,29 @@
-# HUSHMAP - AI Study Buddy & Room Monitor
+<div align="center">
+  <h1>HushMap</h1>
+  <h3>AI Intelligent Room Monitor</h3>
+  <br />
+  <p>
+    <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>
+    <a href="https://fastapi.tiangolo.com/"><img src="https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi" alt="FastAPI"></a>
+    <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>
+    <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>
+    <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>
+  </p>
+</div>
 
-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.
+<br/>
 
-## Project Architecture
+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.
 
-### 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.
 
-**Developing:**
+## System Architecture
+
+### 1. Web Dashboard (`/website`)
+A responsive, high-fidelity PWA frontend written in Svelte 5 and styled seamlessly with Catppuccin color guidelines.
+* **Powered By**: SvelteKit, Vite, and Bun.
+* **Features**: Live interactive map tracking, responsive UI, persistent theming, and an autonomous browser-based Voice Agent calling modal interface.
+* **Setup**:
   ```bash
   cd website
   bun install
@@ -17,48 +31,50 @@ bun run dev --open
   ```
 
 ### 2. AI Backend Services (`/backend`)
-A FastAPI backend providing two core capabilities:
+A blazing fast asynchronous HTTP server facilitating audio chunking and sensor metrics logic over full-duplex sockets.
-- **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. 
+* **Core Capabilities**:
-- **Context DB Aggregation**: TerpAI automatically queries the MongoDB `study_rooms_collection` to gather live hardware decibel readings globally before answering your prompt.
+  * **Voice Socket Pipelining**: WebSockets (`/ws/voice`) that hook incoming 16-bit PCM arrays into `faster-whisper`.
-- **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.
+  * **LLM Context Augmentation**: Seamlessly aggregates live MongoDB noise statistics (Decibel levels per location) to feed contextual history to the TerpAI engine!
-
+  * **Computer Vision Endpoint**: Exposes a `YOLOv8` tensor API (`/api/vision/room-status`) to parse webcam imagery, pinpoint seating capacities, and locate available chairs algorithmically.
-**Developing:**
+* **Setup**:
   ```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)*
+  > [!WARNING]
+  > Host devices must have `ffmpeg`, `libgl1-mesa-glx`, and `libglib2.0-0` binaries natively installed to encode audio buffers and execute OpenCV rendering.
 
-### 3. M5GO Device (`/m5go`)
+### 3. M5GO Hardware Node (`/m5go`)
-MicroPython scripts for the M5Stack M5GO device.
+C-based MicroPython binaries tailored strictly for the IoT edge nodes traversing the physical campus.
-- Uses `uwebsockets` to connect to the backend.
+* **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.
-- 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 into a single seamless container.
+## Docker Production Setup
+
+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.
 
 ```bash
 docker-compose up --build
 ```
+> The global deployment interface listens on port `8000`.
 
-- **App (Frontend + Backend)**: Runs on port `8000`
+---
 
-## Configuration
+## Core Configuration
 
-Make sure you set up your `.env` variables before running the Docker containers or local servers.
+Before starting services, strictly adhere to configuring your environment files (`.env`) within `/backend`:
 
-Create a `.env` in the `/backend` folder:
 ```ini
 ELEVENLABS_API_KEY=sk_...
 ELEVENLABS_VOICE_ID=JBFqnCBsd6RMkjVDRZzb
-TERP_AI_BEARER_TOKEN=eyJhbGciOiJ...
+TERP_AI_BEARER_TOKEN=eyJhbGciOiJSUz...
-TERP_AI_CONVERSATION_ID=37fa27cc-542a-c8a8-9c31-9d1954fdc1d2
+TERP_AI_CONVERSATION_ID=3a150d8e-bb12-...
-MONGODB_URI=mongodb+srv://...
+MONGODB_URI=mongodb+srv://user:pass@cluster0...
 ```
 
-Update your `.env` to match the exact `authorization: Bearer` and `parentSegmentId` context from TerpAI if timeouts occur.
+> [!TIP]
+> 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.
 
-Update the `/m5go/main.py` file to include your Wi-Fi credentials and the correct local IP for the WebSocket (`WS_URL`).
+For IoT clients, update `/m5go/main.py` explicitly to broadcast to your running router IP namespace matching your specific VLAN.

backend/README.md

@@ -1,93 +1,74 @@
-# AI Voice Services Backend
+<div align="center">
+  <h1>HushMap: AI Services API</h1>
+  <p>
+    <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>
+    <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>
+    <img src="https://img.shields.io/badge/Ultralytics-YOLOv8-FF0000?style=for-the-badge" alt="YOLOv8 Vision">
+    <img src="https://img.shields.io/badge/Whisper-STT-4A90E2?style=for-the-badge" alt="Whisper">
+  </p>
+  <p><i>The central nervous system linking physical M5GO devices, external Computer Vision tensors, and Conversational NLP APIs synchronously.</i></p>
+</div>
 
-This directory contains the FastAPI backend for the AI Voice Agent, facilitating communication between the M5GO device, Terp AI, and ElevenLabs.
+---
 
 ## Setup Instructions
 
 ### Prerequisites
-1. **Python 3.9+** is recommended.
+1. **Python 3.9+** is strictly recommended to support asynchronous typing paradigms.
-2. **FFmpeg** must be installed on the system to handle audio format conversions (MP3 to 16-bit 16kHz PCM).
+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:
-   - On Ubuntu/Debian: `sudo apt install ffmpeg`
+   - **Ubuntu/Debian**: `sudo apt install ffmpeg`
-   - On macOS: `brew install ffmpeg`
+   - **macOS**: `brew install ffmpeg`
-   - On Windows: Download from the [FFmpeg website](https://ffmpeg.org/download.html) and add to PATH.
+   - **Windows**: Install globally via the [FFmpeg website](https://ffmpeg.org/download.html).
 
-### Installation
+### Environment Initialization
+
+Bootstrap the virtual environment and initialize project dependencies:
 
-1. Navigate to the `ai_services` directory.
-2. (Optional but recommended) Create a virtual environment:
 ```bash
+cd backend
 python -m venv venv
-   source venv/bin/activate  # On Windows: venv\Scripts\activate
+source venv/bin/activate  # Windows: .\venv\Scripts\activate
-   ```
-3. Install the required Python packages:
-   ```bash
 pip install -r requirements.txt
 ```
 
-### Configuration
+### Configuration Tokens
 
-Update the `.env` file in this directory with your credentials:
+Provide runtime keys securely targeting TerpAI context queues and ElevenLabs synthesized avatars within a `.env` dotfile:
 
 ```ini
 ELEVENLABS_API_KEY=sk_...
 ELEVENLABS_VOICE_ID=JBFqnCBsd6RMkjVDRZzb
 TERP_AI_BEARER_TOKEN=eyJhbGciOiJSUz...
-TERP_AI_CONVERSATION_ID=37fa27cc-542a-c8a8-9c31-9d1954fdc1d2
+TERP_AI_CONVERSATION_ID=37fa27cc-...
 MONGODB_URI=mongodb+srv://...
 ```
 
-## Running the Server
+To invoke the engine, simply execute Uvicorn across your `0.0.0.0` loopback:
-
-Start the FastAPI application using Uvicorn:
 
 ```bash
 uvicorn server:app --host 0.0.0.0 --port 8000
 ```
-This will start the server and make it accessible on your local network on port 8000.
 
-## WebSocket Endpoints
+---
 
-### `/ws/voice`
+## Gateway Pipelines
 
-This is the primary WebSocket endpoint used by the M5GO device for real-time voice communication.
+### Full-Duplex Subroutines (`/ws/voice`)
 
-**Protocol Flow:**
+This WebSocket proxy establishes a fully integrated multi-turn communication bridge seamlessly interacting between Edge node Hardware APIs (ESP32/M5GO/Browsers) and NLP architectures.
 
-1. **Connection:** The client establishes a WebSocket connection to `ws://<server_ip>:8000/ws/voice`.
+1. **Int16 Byte Array Exchange**: Devices connect to `ws://<server_ip>:8000/ws/voice` and push raw binary frames asynchronously over the socket.
-2. **Streaming Audio (Client -> Server):** While the user holds the record button, the client continuously sends binary frames containing raw audio data.
+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.
-   - **Expected Format:** 16-bit signed integer, 16 kHz, Mono PCM.
+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.
-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:
-   ```json
-   {
-       "event": "stop_listening"
-   }
-   ```
-4. **Processing (Server):** Upon receiving the `stop_listening` event, the server executes the AI pipeline:
-   - Transcribes the accumulated Int16 PCM audio organically using `faster-whisper`.
-   - Injects a MongoDB aggregate map of the latest 24hr Campus Location noise levels seamlessly into the LLM system prompt.
-   - Sends the transcribed text & location context to the Terp AI conversational endpoint and waits for the full response.
-   - Streams the Terp AI response text directly to ElevenLabs TTS and demands `pcm_16000` via URL flags natively!
-5. **TTS Endpoint Notification**: The server saves the TTS audio buffer and pushes a JSON:
-   ```json
-   {
-       "event": "tts_ready",
-       "size": 105000
-   }
-   ```
-6. **Audio Callback**: Client queries `GET /api/tts-audio` to play the binary wav response.
 
-## REST Endpoints
+### Tensor Vision Endpoints (`/api/vision/room-status`)
 
-### `/api/vision/room-status` (POST)
+Leveraging OpenCV bindings layered beneath a YOLOv8-driven bounding box topology detector, this `POST` API analyzes raw camera image buffers returning capacity logic natively. 
 
-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.
+> [!NOTE] 
-
+> 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!
-**Request:**
-- `file`: (Required) The image file to analyze (e.g., JPEG, PNG) sent as multipart form-data.
-
-**Response:**
-Returns a JSON object detailing the room status, counts, and pairings.
 
+**Response Output Protocol:**
 ```json
 {
   "room_status": "full",
@@ -101,45 +82,16 @@ Returns a JSON object detailing the room status, counts, and pairings.
       "chair_index": 1,
       "distance": 150.5
     }
-  ],
-  "details": {
-    "people": [ ... ],
-    "chairs": [ ... ]
-  }
-}
-```
-
-### `/api/study-rooms` (GET)
-
-Returns a list of all recorded study room data.
-
-### `/api/study-rooms/history` (GET)
-
-Returns a list of all recorded study room data from the last 24 hours, sorted by most recent first.
-
-**Response:**
-```json
-{
-  "data": [
-    {
-      "location": {
-        "type": "Point",
-        "coordinates": [-77.3079, 38.8315]
-      },
-      "db": 65.2,
-      "date": "2026-04-12T14:30:00.000Z"
-    }
   ]
 }
 ```
 
-## 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
+> [!IMPORTANT]
-# In m5go/main.py
+> 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.
-WS_URL = "ws://192.168.1.100:8000/ws/voice" 
-```

backend/emoji_data.py

@@ -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:',
+    },
+}

backend/server.py

@@ -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-281286": "0:-1",
-    "x-cosmos-session-295334": "0:-1#749854",
+    "x-cosmos-session-295334": "0:-1",
-    "x-cosmos-session-317755": "0:-1#191601",
+    "x-cosmos-session-317755": "0:-1",
-    "x-cosmos-session-382299": "0:-1#265024",
+    "x-cosmos-session-382299": "0:-1",
-    "x-cosmos-session-418988": "0:-1#4058856",
+    "x-cosmos-session-418988": "0:-1",
-    "x-cosmos-session-793952": "0:-1#14004",
+    "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

backend/vision.py

@@ -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 {

m5go/main.py

@@ -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:

scripts/ai.py

@@ -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-281286": "0:-1
-    "x-cosmos-session-295334": "0:-1#745295",
+    "x-cosmos-session-295334": "0:-1
-    "x-cosmos-session-317755": "0:-1#190559",
+    "x-cosmos-session-317755": "0:-1
-    "x-cosmos-session-382299": "0:-1#264026",
+    "x-cosmos-session-382299": "0:-1
-    "x-cosmos-session-418988": "0:-1#4052014",
+    "x-cosmos-session-418988": "0:-1
-    "x-cosmos-session-793952": "0:-1#13832",
+    "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")

scripts/generate_fake_data.py

@@ -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

website/src/app.d.ts

@@ -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 {}
+
 	}
 }
 

website/src/lib/components/AICallModal.svelte

@@ -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();
 	});
 

website/src/lib/components/InteractiveMap.svelte

@@ -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>

website/src/lib/components/LocationPopup.svelte

@@ -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();
 	});

website/src/lib/index.ts

@@ -1 +1 @@
-// place files you want to import through the `$lib` alias in this folder.
+

website/src/lib/states/alerts.svelte.ts

@@ -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();
                         }

website/src/lib/states/map.svelte.ts

@@ -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();

website/src/routes/history/+page.svelte

@@ -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(() => {

website/src/routes/layout.css

@@ -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;

website/src/routes/settings/+page.svelte

@@ -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">

website/svelte.config.js

@@ -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: {

website/vite.config.ts

@@ -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: [