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Lekha’s Status Report for 3/15/25

Work Accomplished:

This week, I worked on connecting the barcode scanner upc values to the mobile app and rasberry pi. This is used in order to automatically retrieve the product information from the UPC code. I also updated the 3D model’s dimensions to start printing

Progress Status:

The main data pipeline has been set up, so the additional features are the focus now in addition to testing.

Next Week’s Goal:

  • Test full barcode scanner -> rasberry pi -> product query -> mobile app using real grocery products
  • 3D printing techspark request
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Lois’s Status Report for 03/15/2025

Work Accomplished:

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.

Progress Status:

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.

Next Week’s Goal:

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.
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Aanya’s Status Report for 03/15/25

 

Progress:

  • Successfully connected the Raspberry Pi to the HiveMQ Cloud MQTT broker.
  • Implemented a robust publish-subscribe system to send scanned UPC data and receive product details.
  • Developed and tested API calls to Spoonacular for retrieving product details, including title, brand, price, image, and ingredients.
  • Published test UPC codes to HiveMQ Web Client to verify data flow from backend to frontend.
  • Ensured that product information updates in real-time upon scanning new items.

Goals:

  • Work closely with the frontend to integrate the correct API response format
  • Implement caching for frequent scans to minimize redundant API requests

 

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Aanya’s Status Report for 03/08/2025

Progress:

Backend API Integration:

  • Implemented barcode scanning API using Spoonacular UPC lookup instead of Open Food Facts, reducing API latency and improving product data accuracy.
  • Optimized API usage by implementing Redis caching to store frequently scanned products and reduce redundant API calls.

Goals for Next Week:

Refine Allergen Detection & Database Structure

  • Implement database indexing for faster allergen lookup queries.
  • Test OpenAI API’s NLP model for edge cases in ingredient parsing.

Optimize Barcode Processing & API Calls

  • Fine-tune Redis caching logic to prioritize frequently scanned items.
  • Implement error handling & API fallback strategy if Spoonacular is unavailable.
  • Expand the database to store scanned products for historical tracking.
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Lekha’s Status Report for 3/8/25

Work Accomplished:

This week, I was able to successfully set up the barcode scanner connected to the rasberry pi. I created code that is able to send the UPC value to the Rasberry Pi for processing. I tested this on various items and barcodes. I was also able to create the CAD design for the 3D printed case.

Progress Status:

I am on progress as now it is able to successfully scan barcodes and send it to the rasberry pi. Now, I will focus on connecting this with Aanya’s software system to retrieve the specific product using the UPC.

Next Week’s Goal:

  • Identify the product using the UPC code
  • Begin 3D printing case
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Team’s Status Report for 03/08/2025

Risks & Management:

    • Risk
      • We are a bit worried that the case and hardware components will be too heavy to be equipped to the case
    • Risk Mitigation
      • Start creating prototypes of the case before the final 3D printed design
      • Test it on the shopping cart attachment clip

Design Changes & Justification:

    • No design changes have been made

Progress:

      • Since we were unable to test MQTT communication due to hardware availability, the timeline for testing real-time cart updates has been slightly pushed back. However, this is planned to be done on the following week, along with developing the allergy filtering. This will allow us to stay on track.
    • Updated Schedule:
    • So far, we have made progress on
      • Developed UI for all main pages
      • Retrieving UPC code from barcode scanner and rasberry pi connection
      • CAD design for 3D printed case

A: Consideration of Global Factors

The smart cart attachment has consideration for global factors as it makes grocery shopping more efficient and accessible for people from all backgrounds. By accommodating diverse dietary needs, preferences, and shopping habits, it ensures that every shopper can quickly find products that align with their lifestyle. Features like barcode scanning, automated list creation, and real-time price comparisons help reduce time spent in stores and prevent unnecessary purchases. Whether someone is looking for healthier options, managing a strict diet, or simply trying to shop more efficiently, the smart cart provides a seamless and intuitive solution that adapts to individual needs and is not limited to any specific area or group of people.

 

B: Consideration of Cultural Factors

SmartCart has consideration for cultural factors as it enhances the grocery shopping experience by accommodating a wide range of dietary needs and nutritional preferences. By integrating databases of religious, cultural, and health-based dietary restrictions, the device can help users make informed choices aligned with their personal or religious beliefs. For instance, it can flag non-halal or non-kosher items, suggest vegetarian or vegan alternatives, or highlight gluten-free products. This functionality ensures that users not only shop efficiently but also make choices that align with their dietary restrictions, health goals, and cultural preferences, making the shopping experience more personalized and culturally inclusive.

C: Consideration of Environmental Factors

SmartCart contributes to reducing food waste through smart meal planning and ingredient substation features, enabling users to maximize existing resources rather than discard usable items. Additionally, by providing real-time inventory tracking and budget management, SmartCart eliminates unnecessary purchases and excessive consumption that typically lead to waste generation.

From a sustainability perspective, SmartCart reduces paper waste by replacing traditional receipts with digital purchase tracking. While our current model includes budget tracking with price estimates accurate within ±5% (due to variations for weight-based produce), future improvements could enable completely paperless checkout, eliminating receipt waste and further enhancing eco-friendly shopping experience.

A was written by Aanya Rustogi, B was written by Lekha Punya, and C was written by Lois Yun.

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Lois’s Status Report for 03/08/2025

Work Accomplished:

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.

Progress Status:

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:

Next Week’s Goal:

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