Inspiration
We were inspired to solve the challenge of the company's lack of ability to analyze the data associated with manufacturing autoparts. The Yazaki Challenge was to create a visual tool which allows their planning team to set the program constraints and identify the best plant options to be able to increase plant efficiency and utilization, reduce paperwork, and become more optimized with their business.
What it does
Excelerate is an analytical automation tool that seeks to plot data provided by the user and allow them to communicate with a GPT-POWERED AI Agent to ask questions about the data. This project also tackles the automobile organization Yazaki's Challenge statement by implementing a heatmap visualization of its Excel data with adaptive filtering also with its ability to answer users' questions in real-time.
How we built it
We built it with html, css along with streamlit framework and OpenAI API and Langchain framework.
Challenges we ran into
The main challenge during the development was the transformation of the data in order to visualize them. We tried to put the data into arrays for each column, but found a better way with using dataframe object.
Accomplishments that we're proud of
We are proud of learning new stacks and making good user interface for data visualization and AI assistance with answering questions about data analysis. Also, we are proud of having a feedback form to use the feedbacks to better our service further.
What we learned
We learned a lot of tech stacks including streamlit framework, OpenAI API and Langchain framework and python libraries including plotly, panda.
What's next for Excelerator
We are going to examine new methods of observing the data from the plots and generating a response that best answers the user's queries with the langchain framework. In addition, we will use the feedbacks from our feedback form to better identify the customers' needs and improve our service in the future.
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