This simple streamlit app is used to deplonstrate the Azure restaurant recommender webservice.
- aimple_app.py contains the streamlit app
- create_recommender_tables1.ipynb is used to generate the artificial training data
- Procfile and setup.sh are used for launching streamlit on Heroku
- requirements.txt is used to create the conda environment used by this project.
git clone https://github.com/professorkazarinoff/simple-streamlit-app.git
cd simple-streamlit-app
conda create --name simple-streamlit-app python=3.9.7
pip install -r requirements.txt
conda activate simple-streamlit-app
streamlit run simple_app.py
Create a Heroku account and install the Heroku CLI.
You may need to save all the code in this repo to your own repo. Make sure to git add .
, git commit -m "commit message"
, git push origin master
up to GitHub.com
cd simple-streamlit-app
git init
git add .
git commit -m "Initial commit"
git remote add origin https://github.com/<your-account>/simple-streamlit-app.git
git push origin main```