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