Category: Lois’s Status Reports

This week, I worked on:

• Continued implementing the shopping session flow, with most features complete except for MQTT integration.
• Enhanced menu search and my menu functionalities:
  • Built a clickable ingredient list, allowing users to choose which items they want to generate a shopping list for.
  • Implemented the ability to assign specific products to menu items.
• Integrated to send to backend the selected ingredients along with user profile data (allergens, preferences, and cuisines) for filtering and product matching
• Met with the team to align the backend integration and finalize the MQTT structure

The app is now able to request product options through sending the list of necessary ingredients for menu. The team will meet tomorrow to work on receiving the product options, and this will allow completion towards menu search and my menu pages, as well as progress in shopping session page.
I am still having issues with Google OAuth when opening the app with an iOS Simulator. This is a minor issue as I am able to open the app through web, though for visuals in the future demo, I would have to fix this.

For next week, my focus will be on:

• Complete the MQTT response handling to receive and display product options
• Finalize selection UI to save chosen products per menu item
• Implement shopping session transitions (scanning screen, shopping summary page to conclude the session)

This week, I implemented Google OAuth login using Expo’s AuthSession. I also make improvements to the Shopping List UI. It now cleanly separates drafts into liked/saved drafts. I removed the recents logic to streamline the experience and reduce confusion. I’ve also been refining the UI with the demo in mind, focusing on improving clarity and layout for a smoother presentation.

I am on track. Our team will be meeting on Sunday to finalize demo preparation and align on next steps moving forward.

For next week, my focus will be on improving the UI for product substitution when suggesting or swapping items in the cart.

This week, I focused on optimizing the UI and debugging the allergen and preference based filtering system. I refined how flagged allergens and matched preferences are displayed in the app to improve clarity and user experience. I also began exploring the NLP-based allergy filtering logic.

I am a bit behind on the progress. While Aanya and I haven’t yet met to fully integrate the system, we’ve laid the groundwork for detecting and comparing keywords from user inputs and product data. Testing for real-time allergen flagging is still pending, as it will depend on completing the NLP-based integration.

For next week, my focus will be on:

• Meet with Aanya to complete NLP-based allergy filtering integration
• Begin real-time product testing to verify accurate allergen detection
• Continue refining filtering accuracy using user-defined preferences and ingredient data

This week, I met with the whole team and we successfully tested the connection between the SmartCart system and the mobile app. Now the app correctly receives and displays product information when data is sent through MQTT. The displayed product information includes product name, brand, price, and ingredients.

Additionally, I worked on designing the UI for allergen-flagged items to be displayed in a red highlighted box for visibility, and preference-matching products to appear in a green highlighted box.

I am on track with the app development, but more progress is needed on the allergy filtering system. We have defined how allergens are flagged and sent to the app, but we haven’t finished implementing the NLP-based filtering. To ensure accuracy, we will double check allergen detection by ensuring user defined allergen words match the flagged allergens, and preferences and allergens are correctly compared with the product’s ingredient list.

For next week, my focus will be on:

• Optimizing UI & debugging to refine allergen and preference-based filtering
• Work with Aanya to develop NLP-based allergy filtering
• Testing products to verify that allergy flagging works accurately in real time.

This week, I focused on refining the UI and fixing minor errors in the code. Since the camera module was unnecessary, I removed its dependencies and optimized the barcode scanning flow to rely on MQTT communication.
Additionally, I improved navigation handling to ensure smooth transitions between screens and verified that the UI correctly processes barcode data in preparation for real-time updates once the hardware is available.

I was unable to test MQTT communication between the Raspberry Pi and the mobile app due to lack of access to the hardware components. This prevented me from verifying real-time updates in the cart, as the MQTT messages are sent from the Raspberry Pi.

However, I ensured that the UI correctly handles incoming barcode data so that once the hardware is available, real-time updates can be tested with minimal changes. Also, I plan to receive the hardware components on the upcoming Monday.

Below is the updated Gantt chart:

For next week, my focus will be on:

• Testing MQTT communication between the Raspberry Pi and the mobile app
• Developing the Ingredient Substitution & Allergy Filter UI, which will include
  • Allowing users to flag allergens in their profile
  • Displaying automatic ingredient substitutions for allergens.
  • Providing alternative recommendations for scanned products.
• Working with Aanya to develop NLP-based Allergy Filtering
  • Implement keyword-based filtering to detect allergens in product ingredients lists

This week, I focused on starting the MQTT integration for real-time updates in the SmartCart system. My key accomplishments include:

• Implemented barcode scanning with expo-camera for smooth scanning functionality
• Set up the MQTT client in the mobile app, allowing it to publish scanned product data

I am on track with the project schedule. Since next week’s goal is also to integrate MQTT for real-time updates, my progress aligns with the planned timeline. While the barcode scanner may be functioning correctly and the MQTT client is set up in the mobile app, I have not yet configured the MQTT broker on the Raspberry Pi or obtained the broker IP address needed for full real-time updates. Additionally, the Camera JSX issue has not yet been resolved, which will need to be addressed moving forward.

For next week, my focus will be on:

• Set up the MQTT broker on Raspberry Pi and obtain the correct broker IP address
• Test MQTT communication between the Raspberry Pi and the mobile app
• Ensure scanned items update in the cart in real-time
• Fix the Camera JSX issue

This week, I worked on refining the UI design, testing potential workflows, and researching barcode scanning solutions. My key accomplishments include:

• Updated Figma UI Designs
• Started Developing the UI for Adding Detected Products to Cart
• Visited Aldi to Test Hypothetical Workflow
• Researched Barcode Scanning Methods

I am currently on track with the project schedule. The goals for this week (Develop UI for adding detected products to cart) was completed.

For next week, my focus will be on:

• Finishing UI Development for Adding Detected Products to the Cart
• Starting MQTT Integration for Real-Time Updates
• Testing & Refining the UI Flow to ensure a seamless user experience.

This week, I focused on the initial design and setup phase of the mobile application. My key accomplishments include:

• UI Wireframe Design using an iPad

• React Native Framework Setup in VSCode
• Figma UI Design for essential screens

I am currently on track with the project schedule. The goals for this week (Design UI Wireframe, Set up React Native Framework) were completed.

For next week, my focus will be on developing the UI for adding detected products to the cart. This will involve:

• Finishing Figma app mock-ups
• Implementing the UI Components for product detection & selection
• Simulating detected products using mock data