where2charge

Course project for 'CSE583 Software Development for Data Scientists' during the autumn 2024 quarter at the University of Washington.

Team members: Arsalan Esmaili, Soheil Keshavarz

Description:

where2charge is a platform that is aimed to suggest reliable charging station options to EV owners.

This repository contains four main components as below:

• User interface (app.py)

• Server handler (server.py)

• Control logic (recommender.py)

• Unit tests

More information on user requirements, component design, structure of this work, and future work can be seen at doc/README.md and doc/CSE583 where2charge presentation.pptx

How to use

This project can be used in three ways: as a web application, as an API, and as a python package.

Before using our codes, you need to have:

1. A valid Google API key (https://developers.google.com/maps) with access to Places, Distance Matrix, and Directions APIs.
2. A valid OpenAI API key (https://platform.openai.com/api-keys)

Streamlit based web app

1. Update config.yaml file with your api keys

GOOGLE_API_KEY: "your_google_api_key"
OpenAI_API_KEY: "your_openai_api_key"

2. run src/main.py from root directory of this repo.

Please do not click or drag on the map when you see the message below on the app:

API

in case you want to use this work directly as an api instead of a UI, you can get connected to the server. Since this project is deployed on cloud yet, server address is you localhost. To run the server individually, run uvicorn src.server:app --reload --port {selected-port} on terminal and on the root directory. Then open a browser and type http://localhost:{selected-port}/docs in the address bar. selected-port, 8000 for example, is determined by the user.

a Python package

pip install where2charge

Sample code:

import where2charge

google_key = 'your_google_api_key'
openai_key = 'your_openai_api_key'

lat, lng = selected_latitude, selected_longitude
connector_type = 'Tesla'
number_of_suggestions = 3

recommender = where2charge.Recommender(google_key, openai_key)
suggestions = recommender.get_suggestions(lat, lng, number_of_suggestions, connector_type)