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.