Interactive web apps created using leafmap and streamlit
Steps to run –
- Git clone the repository.
- Install dependencies from requirements.txt.
- Run in the terminal as “Stream-lit run app.py” Although currently, it is deployed on a streamlit shared platform.
Future steps can be taken –
- Streamlit application can be easily deployed on the Heroku platform too.
- Another deep learning model based on time-series data can be used to improve performance.
- Sequential series-based models like RNN and LSTM may show some improvement.
- Representation can be made more effective by showing how many trees are available in the area for particle availability.
Challenges come so far –
- Sometimes, openAQ API doesn’t respond well.
- openAQ API has its limitation with range and response time etc.
- Since pm2.5 particle prediction is not formulated using ML models, it does not show any pattern or recognizable reference.
The following can be accessed via - Streamlit (deepesh-sherwal-surrey-airnode-surrey-new--app-g3fiuk.streamlit.app)
Please raise a pull request for any other questions.
This app is created using the template from upraneelnihar/streamlit-multiapps.