Rover Front- and Backend

It's a Web-App to display dynamic status information, there is a continuous stream of incoming updates. To have the images and text dynamically appear, WebSockets are being used. WebSockets allow for a higher amount of efficiency compared to REST because they do not require the HTTP request/response overhead for each message sent and received.

Features and Credits

• Using a 73 Million parameter object detection model to detect objects from images and cut out one bounding box per image.

• Implemented asynchronous api calls to the Pl@ntNet identify API to determine plant species.

• It uses FastAPI framework for API development. FastAPI is a modern, highly performant, web framework for building APIs with Python.

• The APIs are served with Gunicorn server with multiple Uvicorn workers. Uvicorn is a lightning-fast "ASGI" server. Univorn runs asynchronous Python web code in a single process.

• Reverse-proxying with Nginx. Nginx is very good at serving static content.

• Dockerized using the uvicorn-gunicorn-fastapi-docker Docker image.

System Architecture Overview

The System consists of two docker containers: Gunicorn and Nginx. How can they communicate? Well, they use a shared volume, it's that simple. Most requests are not served by nginx directly, but passed back to the Gunicorn / FastAPI app which sits behind nginx. These are dynamic calls, rather than static content. This can be implemented by setting a proxy_pass in the nginx.conf.

Worker Threads

The number of Worker threads is variable. Generally speaking, they scale with the number of CPU cores. This configuration can be overridden in the varia/gunicorn.conf file.

Milestones

• Websocket endpoint for low-latency bidirectional communication
• Dynamically display status information
• Display pictures, updated by
• Timer, fetch Timestamp from device
• Switch between tabs
• nginx running in docker
• Specify species and similarity of plants.
• species -> API
• Show Progress bar
• image processing: image comparison algorithm

Start Local dev Environment: Linux

API Key

The app expects a constants.py in the app directory. This class contains the api-key which is needed for plant identification. Get a key for plantnet. Create a file constants.py which contains a class Constants and replace the <your-api-key> with the api key you got from plant net.

class Constants:
    def __init__(self):
        self.api_key = "<your-api-key>"

After that, startup quickly with Docker: Open the build_and_run_locally.sh file. Adjust the absolute path, it should point to the directory where you cloned the repository.

./build_and_run_locally.sh

Configure Autocomplete

If you develop in a IDE, it's much easier with auto completion. Create a virtual environment. This will create a python interpreter for your local development environment.

   conda create --name rover python=3.9.0
   conda activate rover
   pip install -r requirements-conda-for-local.txt 

Deploying

SSL

This application expects a valid certificate at ~/cert/rover. You can edit this path in the docker-compose file. To get a certificate, I followed this https://help.zerossl.com/hc/en-us/articles/360058295894-Installing-SSL-Certificate-on-NGINX. The certificate itself is is mounted as a volume in the nginx container. If you don't want SSL, you'd need to make some adjustments in the nginx/nginx.conf file.