Welcome to the PTP data source repository! This repository serves as the primary source of data for the participants of PTP problem statement. Here, you can find the necessary image dataset to develop an AI vision model on Power Apps.

PTP is in need of a modern Fuel Management System (FMS) that can effectively keep track of fuel consumption for its equipment (Prime Mover, RTG, etc). The current system is manual and prone to errors, making it difficult for the company to accurately analyze the fuel consumption trends and costs involved. To address this issue, PTP is seeking solutions from skilled developers and data scientists to create a new FMS that will be equipped with advanced features such as Power Apps and AI Vision. The goal of the hackathon is to:

Develop an online Power Apps platform for pump station operators to use, which will allow them to scan equipment IDs (on the vehicle), input operator IDs, scan fuel consumption values (on the pump station), and store the data in a database. The requirement for the new FMS is as below:

1. The Power Apps platform should be equipped with AI Vision that can convert images of Equipment ID and Fuel consumption values captured into a text input. The sample image can be obtained in this repository.
2. Developers must design the system in a way that the system allows for analytics on trend, consumption, and other potential related analysis
3. The database used must be a SQL database. (Preferably MSSQL, but any SQL DB will do)
4. Provide simple data visualizations (reports/dashboards) using the data obtained from the system to monitor fuel input-output.

PTP is eager to implement a modern Fuel Management System that will help them to make better business decisions and reduce its carbon footprint.

We have provided a dataset of images for participants to train and test their models. The dataset is stored in the data directory of the repository. It contains images of various equipment available in PTP. Participants may refer the image of each equipment based on the folder name.

29 March 2023 : CSV File containing the dummy dataset, sample of employee ID, and sample of employee ID format stored in the data directory.

To get started with the hackathon, you will need to clone this repository to your local machine using the following command:

git clone https://github.com/PTPITDiv/CWBHackathon_Folder.git

Once you have cloned the repository, you can start exploring the dataset and begin building your machine learning model.

We hope this repository helps you get started with the hackathon and build an outstanding product. If you have any questions or need further assistance, please feel free to reach out to our Hackathon mentor.

Happy hacking!