LeetCode Contest Rating Prediction
MIT License
This is a LeetCode weekly and biweekly contest rating predictor. The APP is available online at 🔗 lccn.lbao.site
Hopefully, you can get the predicted result within 15-30 minutes after the contest has finished.
EXPAND_SIZE=1
) completes predictions in under 0.25 seconds, maintaining an impressively low Mean Squared Error (MSE) of approximately 0.027.git clone [email protected]:baoliay2008/lccn_predictor.git
cd lccn_predictor
# write your mongodb environment config
cp config.yaml.template config.yaml
vi config.yaml
python3.10 -m virtualenv venv/
source venv/bin/activate
pip3 install -r requirements.txt
python main.py
uvicorn api.entry:app --host 0.0.0.0 --port 55555
git clone [email protected]:baoliay2008/lccn_predictor.git
cd lccn_predictor
# write production environment mongodb config
cp config.yaml.template config.yaml
vi config.yaml
# build docker image
docker image build -t lccn_predictor:0.1.2 .
# create docker volume
docker volume create lccn_predictor
# run container
docker run -d -v lccn_predictor:/lccn_predictor -p 55555:55555 --name lp lccn_predictor:0.1.2
docker exec -it lp bash
docker container stop lp
docker container start lp
cd client
# install dependencies
npm install
# change `baseUrl` to your local backend process
vi src/data/constants.js
# if you followed instruction above
# it should be "http://localhost:55555/api/v1"
# local test
npm run dev
# publish
npm run build
make this repo public, first release.
first version in production
change frontend from server-side rendering(Jinja + Materialize) to client-side rendering(React).
refine backend logic to enhance robustness and clean up deprecated static site rendering code
last version prior to the rewrite of the Elo rating algorithm