Week 9: hAPIness

Hello, and welcome to another edition of my blog!

This week was by far the most fulfilling, mainly because I finally got to test out a working webapp. After extensive debugging, I fixed the API network error by specifying exactly which IP the endpoints are being hosted on, both in the backend and in the frontend. I don’t know how this will affect the implementation of the web app on other computers, but that’s a problem for another time. 

So, what does my prototype look like so far? Center stage is a map centered on the grid of Butte County, with thousands of square-mile grid squares shaded in blue. The user can click any of these grids, which will pop out in a small bubble the fire proneness of that grid square. On the right side of the webapp is a feature that allows users to understand where this prediction is coming from, displaying the available data for that square.

One problem I faced this week was the time it takes for the model to run after a grid square is pressed. Remember, each time this model “predicts” on data, it’s not just predicting from features at a single snapshot in time. Instead, the LSTM sequence uses the past 10 weeks of data to predict what will happen in the current week, which leads to longer processing time. Therefore, for the sake of user-friendliness, I decided to run the model preemptively and store all predictions in a CSV, which the API simply needs to access to output the predictions. 

Next week, I’ll continue polishing my web app, along with adding a time slider so the user can observe what the proneness will look like during times of high fire risk. See you then!