We shifted our whole focus into our demo and getting our individual components to work. A big thing that we discovered in all our components were a lot of underlying issues that came when integrating into our project. All the testing was local and independent, so we ran into a lot of complications.
A few complications arose from our ML design. The first being the difficulty of using the ChatGPT API to take in images and process them. It was slow and hard to input an image efficiently. Furthermore, our local tests for some of the ML classification schemes were difficult to integrate into the Django web app. As a result, we had to shift base and adjust on the fly. This included using some backup plans we had such as using pre-trained data sets and classification APIs. The big issue with these were the configuration issues that we spent hours dealing with and making sure we had everything installed to the right versions. Lastly, we decided to change our fruit testing data to oranges, bananas, and strawberries instead of apples. We hope this change allows us to move on from classification at the moment and shift our focus towards label reading and OCR design as well as hardware integration.
Surya made major progress in integrating the Raspberry Pi and configuring it with our internet. The major issue is configuring it with CMU wifi which is the primary issue right now. However, he was able to set it up with the ssh server and download required packages for the video stream. We shifted focus to doing a lot of computation on the Raspberry Pi itself to add into our designed trade studies. We hope to show all of these in the demo to showcase our experimentation process. Surya lastly did a lot of work configuring MacBook settings to run our website with all the required packages. There were many hardware issues that he had to resolve and fix to even get the website to run. Ultimately, the website was able to run successfully on his computer and classify images to an acceptable accuracy.
Lastly, Grace was able to create a new Gantt chart schedule that reflected changes in our schedule that had to be made since several technical challenges were encountered during our testing process. While there were some unexpected delays with the OCR libraries and hardware configurations, we remained on track in terms of our original project schedule with the appropriate allocated slack time and abilities to adjust to such changes. Since we added additional features and ideas to our project throughout the capstone process including the in-class ethics discussion, some slack was allocated towards those features and some extra time was spent handling unanticipated technical issues. Ultimately while schedule changes to our project were necessary, they ended up contributed a lot towards our ability to work together as teammates and also learn to adapt to necessary changes to adapt to our project framework.
We hope to have a productive demo next week and take in the feedback of everyone to get closer to completing our final project. Likewise, we will start drafting the final report.