Monarch Watcher: Tracing the Decline

Inspiration

Our inspiration into going this task is the basic understanding of APIs as well as transferring and formatting files into tables to visually represent the data at hand. We also set on taking on a task that's a bit more sophisticated than for beginners to properly understand the LLM API tool better.

What it does

Our project has a chatbot assistant integrated into that can help us build upon the project in the future.

Challenges we ran into

Challenges that we ran into was not fully understanding in sophisticating the "county" data just from the Journey North data table that only had columns of just state and town. When we were able to convert the Journey North data into a .csv file, it took too much time in fully extracting all of the data and lost some of its data too. Plus, we had trouble in actually downloading the EPA AQS API due to focusing more on the Python code as well as not being able to fully display the data as a map detailing the positions of the Butterflies. Lastly, we had difficulties in the direction of which the project needed to go.

Accomplishments that we're proud of

Integrates OpenAi LLM into VS Code

What we learned

Overall, my team learned that we needed to get more assistance on the mentors and paying closer attention to what exactly the tasks are directly asking. But in return, we all learned about teamworking within a competitive nature and communicating verbally with each other. It helped us fully understand the scope of what a hackathon is meant to be and how to mentally prepare in teaming up with random peers within the event. We also learned how to incorporate LLMs into VS code.

What's next for Monarch Watcher: Tracing the Decline

With the assistance of the LLM we integrated into our code, we can continue working on how to solve the challenge of populating a CSV file with counties and getting the EPA pre-generated files. With those requirememnts met, we will be able to perform statistical analysis on our dataset.