https://github.com/qmi03/funny_ai_iot_capstone_project

iot school project
https://github.com/qmi03/funny_ai_iot_capstone_project

asyncio fastapi microbit mongodb mqtt mqtt-client python react tailwind tailwindcss typescript websocket wsgi

Last synced: about 1 month ago
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iot school project

• Host: GitHub
• URL: https://github.com/qmi03/funny_ai_iot_capstone_project
• Owner: qmi03
• Created: 2024-05-01T07:55:59.000Z (9 months ago)
• Default Branch: master
• Last Pushed: 2024-05-20T22:55:25.000Z (9 months ago)
• Last Synced: 2024-11-10T11:19:00.603Z (3 months ago)
• Topics: asyncio, fastapi, microbit, mongodb, mqtt, mqtt-client, python, react, tailwind, tailwindcss, typescript, websocket, wsgi
• Language: TypeScript
• Homepage:
• Size: 16 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# About

This is a smarthome school project. It has some iot and ai inference.

# System Architecture

![System Architecture](./public/system_architecture.jpg)

# Requirements

1. node (Can use nvm to version management, I'm using nvm --lts so it's node v20.12.1 (npm v10.5.0))

2. python

3. docker compose

# Run

## Backend + Database

1. `docker compose up` or `docker-compose up` (whatever works for you)

2. `cd app`

3. Create a virtual environment if needed, then `pip install -r requirements.txt`

4. Create the .env file from .env.example. And fill in the fields.

5. Run backend: `fastapi dev app.py --host 0.0.0.0 --port 8000`

6. Initialize the rooms in the database: `python database/scripts/init_light.py`

7. Modify database/scripts/init_camera_links.py for the camera links you want for your app. Then run python database/scripts/init_camera_links.py

## Frontend

1. `cd smarthome`

2. Install dependencies, use: `yarn` or `npm install`.

3. Create the .env file from .env.example. And fill in the fields.

4. Run the frontend: `yarn dev` (add `--host` to expose ip).