Toyo-DATA

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  Home Screen Mock-Up

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  Contact Screen Mock-Up

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  Account Screen Mock-Up

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  Data Model Screen Mock-Up

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  Login Page

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  Create Account

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  HomePage

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  Total Cars Directory

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  Sort Cars by Type

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  Data Extractor (Admins only)

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  View Uploaded Data (Admins only)

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  Chatbot answering

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  Chatbot

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  Homepage with filtered data

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  Homepage

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  Homepage (User view)

Inspiration

Our project was inspired by Toyota’s challenge statement, which invited us to create an application that could query and display fuel economy data for Toyota vehicles. This challenge motivated us to explore solutions for organizing and presenting vehicle data in an intuitive, user-friendly way. We were excited by the opportunity to make fuel economy data more accessible and to develop an app that could help users make informed choices about fuel efficiency.

What it does

Our project is designed to display Toyota vehicle fuel economy data for the years 2021-2025 in a visually appealing and accessible format. Users can view data organized in tables and graphs, allowing them to compare fuel efficiency metrics across different models and years. The app also provides filter and sort options to narrow down vehicle models based on criteria like fuel type, engine size, and transmission, enabling users to easily explore data according to their preferences.

How we built it

We built this project using Gradio and Google Colab. Gradio provided an intuitive platform for creating interactive interfaces, which allowed us to rapidly develop and deploy our application’s front end without extensive setup. Google Colab served as a powerful environment for data processing, allowing us to work collaboratively on Python code and access external datasets. We integrated both tools to create a seamless user experience, enabling users to interact with fuel economy data directly.

Challenges we ran into

One of our biggest challenges was extracting the necessary data from PDF files. The data source we chose initially was in a PDF format, and extracting information in a structured way proved to be complex due to the formatting. Additionally, we faced issues with data accuracy and structure when parsing PDFs, which required us to experiment with several libraries and techniques to ensure clean and consistent data for our app.

Accomplishments that we're proud of

As a beginner team, we’re incredibly proud of building this project from scratch. Despite our limited experience, we successfully created a functional minimum viable product by the project deadline. Learning to work together effectively, mastering new tools, and tackling the challenge of data extraction have all been major accomplishments. Meeting these goals has been a fulfilling experience, and we’re excited by the potential our app demonstrates.

What we learned

Through this project, we learned how to use Gradio to build interactive applications and explore the capabilities of Google Colab for collaborative development. Additionally, we integrated the SambaNova Cloud API, which gave us more experience in using external APIs to enhance our project’s functionality. This experience taught us valuable lessons in troubleshooting data handling issues, using new frameworks, and adapting to unforeseen challenges.

What's next for HackUTD Ripple Effect

Looking ahead, we hope to apply what we learned from this project to future endeavors. Our goal is to create more detailed and impressive projects that incorporate advanced features, such as real-time data updates, additional data visualization options, and even more refined user interface elements. By building on this foundation, we aim to continue developing our skills and creating meaningful applications that address real-world needs.

Built With

• gradio
• sambanova