Week 1

23/07/2020

During our second project session, our team discussed the specific projects we will be working on. Ryan and I have been assigned to two tasks: "Enabling Love" and "Image Labelling."

For the "Enabling Love" project, our plan is to involve Prasanth in conducting a thorough security test on the app. This will help us identify any vulnerabilities or areas that need improvement. By addressing these concerns, we aim to enhance the overall security and functionality of the application.

Regarding the "Image Labelling" project, my responsibility is to explore and familiarize myself with the Machine Learning Kit. This will enable us to understand how we can effectively incorporate it into our project. By gaining knowledge about the Machine Learning Kit, we can utilize its capabilities to improve the image labelling functionality within our application.

Furthermore, during the session, we also had a tutorial on how to integrate Firebase into the Android platform. This tutorial provided us with the necessary steps and guidance on successfully incorporating Firebase into our project. This integration will allow us to leverage the powerful features and functionalities provided by Firebase to enhance the overall performance and user experience of our Android application

Week 2

27/07/2020

The stand-up meeting for this session has concluded. Ryan is currently dedicated to the "Enabling Love" project and is actively taking a proactive approach. He aims to promptly communicate with the client, ensuring efficient progress and addressing any client requirements or concerns.

I have successfully located the APK for "Enabling Love" that can be found at this link. This APK will be provided to Prasanth, allowing him to conduct the necessary security testing on the application.

30/07/2020

Today's focus is primarily on documenting our progress and achievements in the project. I will begin compiling all my notes and organizing them into my portfolio for this semester. By starting early, I aim to ensure a comprehensive and well-documented record of our work.

Additionally, I have created the GitHub repository for the image recognition component of the project. The repository is set up, and the initial project structure is in place. The next step is to learn how to effectively incorporate Firebase into the project. This knowledge will enable us to integrate Firebase seamlessly, enhancing the functionality and capabilities of our project.

Week 3

03/08/2020

I have attempted to implement machine learning into our project, but it requires more effort than initially anticipated. I have started searching for tutorials online, but I have encountered difficulties. Many of the tutorials I have come across are either in a different language or lack the necessary depth and clarity.

While exploring the image labeling aspect, I came across a GitHub repository that caught my attention. I decided to run the provided version, and fortunately, it worked smoothly. Considering its success, I am contemplating using it as a starting point for our project.

6/08/2020

In this class, the plan is to develop another app specifically for image labeling. Rather than relying on the kit you previously worked with, the goal is to explore different approaches. One potential implementation is to incorporate the Firebase Cloud for image labeling capabilities in the new app. By utilizing Firebase Cloud, we can leverage its machine learning capabilities to determine the content of images within our application.

Week 4

10/08/2020

Unfortunately, creating another app for image labeling did not yield the desired results. As a next step, you have decided to create a new project and utilize the "ML Kit Showcase" as a reference. The plan is to copy and paste the entire showcase into the new project, allowing you to work with a pre-existing foundation and build upon it.

13/08/2020

Unfortunately, there was no opportunity to copy and paste the entire ML Kit showcase into the new project. However, you have identified the issue with the ML Kit's functionality. The problem lies in the search engine, which is currently just a placeholder and not performing the product search as intended.

To address this, you and Ryan will be working on implementing the product search function into the actual project. You plan to utilize the Vision library, specifically the Vision library's product search tutorial, to integrate the necessary functionality and enhance the application.

Week 5

17/08/2020

Exploring additional resources, Ryan has discovered another helpful tutorial. The plan is to utilize this tutorial and replace the existing searchEngine.kt file with the code provided in the tutorial. By doing so, you aim to incorporate the necessary functionality and ensure the successful operation of the image object recognition feature in the project.

20/08/2020

I am currently in the process of continuing my work on the searchEngine.kt file. My primary objective at the moment is to find an alternative tutorial specifically focused on Product search. This tutorial will guide me in replacing the existing placeholder code in the searchEngine.kt file with a more functional and appropriate implementation. By finding and following this tutorial, I aim to enhance the search functionality by incorporating a more suitable solution for the project.

Week 7

31/08/2020

Ryan has recently acquired a cloud account for our use, which enables us to proceed with the tutorial more effectively. With this newly obtained account, we can begin the tutorial with the necessary cloud services and resources readily available to us.

03/09/2020

Updating my portfolio

Week 8

07/09/2020

During the course of our project, we discovered that ML Kit has undergone an update regarding image labeling. The functionality has been divided into two distinct APIs.

The first API is the on-device labeling, which utilizes the new standalone ML Kit SDK. This SDK can be used independently, with or without Firebase integration. It offers image labeling capabilities directly on the device, without requiring an internet connection.

The second API is the cloud image labeling, which leverages Firebase ML. This API incorporates a comprehensive range of cloud-based ML features available within the Firebase ecosystem. It enables powerful image labeling capabilities by utilizing cloud resources and services.

With these updated options, we have the flexibility to choose between on-device labeling and cloud image labeling based on our project requirements and preferences.

14/09/2020

APK problem, tryna fix it