Lekha’s Status Report for 3/29/25
Work Accomplished:
This week, I finished printing the 3D case that makes up the whole hardware system and attaches to the cart. In addition, I finished the connection between the USB barcode scanner, rasberry pi, and the front end mobile app updates. This finishes the basic functionality of scanning a product, retrieving its product details and displaying it on the mobile app.
Progress Status:
I am currently on track in our schedule. We adjusted our schedule based on design changes. My focus now is to make the device completely portable and able to attach to a shopping cart for our final user experience
Next Week’s Goal:
- Make UPC scanning work with remote ssh
- make device completely portable
- initial testing with aldi products
Lois’s Status Report for 03/29/2025
Work Accomplished:
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.
Progress Status:
I am on track. Our team will be meeting on Sunday to finalize demo preparation and align on next steps moving forward.
Next Week’s Goal:
For next week, my focus will be on improving the UI for product substitution when suggesting or swapping items in the cart.
Team Status Report for 3/29/2025
Risks & Management
- Risk: With multiple components (LangChain, Spoonacular, barcode scanning, MQTT) now functioning independently, the risk lies in delays or errors during integration of the full end-to-end workflow.
- Management: A system-level integration plan is being developed. Tasks have been divided to ensure frontend and backend components communicate seamlessly through MQTT, and fallback logs are in place to trace errors.
Design Changes & Justification:
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Reorganized backend into modular components for easier testing and debugging.
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Added a persistent product object structure for better real-time tracking and removal from cart UI.
Progress:
- 3D printed outer shell for device
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Completed LangChain + OpenAI integration for dietary-specific meal plan generation
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Developed object structure for in-store ingredient tracking and real-time tally
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Frontend tested receiving product data
Aanya’s Status Report for 3/29/2025
Work Accomplished:
- Completed a modular backend design that outlines the full data flow, from user profile intake to product tracking and budgeting. The system now supports clear separation of concerns across user_profile, meal_planner, ingredient_substitutor, grocery_tracker, product_scanner, and mqtt_handler
- Successfully integrated LangChain with the GPT-4 model to extract dietary preferences, allergens, and cultural requirements from user input and generate personalized meal plans
- Wrote standalone test files for each backend component to ensure correctness and maintainability
Progress Status:
I am now on track with the backend development roadmap. The foundation is built and tested. Modules are independently working, with API integrations functional. Code is modular and testable.
Goals:
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Complete MQTT-to-frontend integration with real hardware input.
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Connect product substitution logic with real scanned products.
Lekha’s Status Report for 3/22/25
Work Accomplished:
This week, I was working on the connection between the rasberry pi and the mobile app. Unfortunately, I ran into some networking issues with the rasberry pi. I was unable to connect it to to my desktop due to networking issues. I set up the server on the rasberry pi, but need to have the mobile app connect to the rasberry pi server to obtain the upc info. I also put in the 3D printing request to tech spark for the overall case.
Progress Status:
I expected the last connection between mobile app and rasberry pi to be finished last week, but unfortunately ran into issues. This is the last connection, however, and should be completed in the beginning of this week.
Next Week’s Goal:
- Retrieve 3D printed case
- Finish last system connection
- integration testing
Team Status Report for 03/22/2025
Risks & Management:
NLP Integration Delay
- Risk: The integration of the NLP-based allergen filtering is still in progress. While initial development has started, coordination is needed between team members to complete the logic and begin testing.
- Management: Our team meeting is scheduled to align on integration tasks and finalize implementation. Progress is being tracked to ensure real-time testing can begin as soon as integration is complete.
Design Changes & Justification:
- No design changes have been made
Progress:
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- Partially completed UI optimizations
- Debugged and stabilized preference/allergen comparison logic in the product card UI.
- Initial work on NLP-based filtering has started, focusing on extracting keywords from user inputs.
Lois’s Status Report for 03/22/2025
Work Accomplished:
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.
Progress Status:
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.
Next Week’s Goal:
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
Aanya’s Status Report for 03/22/25
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Worked on Allergen Filtering Module
Developed and tested an NLP-based allergen detection function to parse ingredient lists and flag common allergens. Wrote a modular script that supports updates to allergen keywords and is usable with incoming Spoonacular ingredient strings. -
Tools Used: Python, Regular Expressions, unittest
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Next Week Goals:
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Integrate the allergen detection with the existing barcode -> Spoonacular pipeline.
- Connect to personalized allergens
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Send flags via MQTT to frontend.
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Begin testing on dietary profile filtering logic.
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Team Status Report for 03/15/2025
Risks & Management:
- NLP-based Allergy Detection Accuracy
- Risk
- The NLP-based allergy detection may fail to accurately flag allergens due to variations in ingredient names, formatting inconsistencies, or missing data in product ingredient lists.
- Risk management
- Implement keyword based filtering as a baseline before integrating AI-driven NLP
- Cross-check allergens with multiple data sources (Spoonacular, Open Food Facts API)
- Display AI-identified allergens and preferences on the User Settings page, beneath the profile text input, where users describe their allergens and dietary habits in natural language, allowing users to review and confirm which items are correctly flagged as allergens or preferred products.
- Contingency Plan
- Allow users to manually confirm flagged items
- Implement a highlighting feature for uncertain matches for additional user validation
- Risk
Design Changes & Justification:
- UI Enhancement for Allergy & Preference Filtering for clarification of product selection and improvements in user accessibility of managing allergens
– Allergen flagged items now appear in red highlighted boxes for visibility
– User preferred products based on dietary preferences are displayed in green highlighted boxes
Progress:
- MQTT connection & data transmission
- Ingredient Substitution & allergy filtering UI
- Obtain product UPC value
- Spoonacular API requests