[FastAPI][React][Google Gemini][OCR (Tesseract)][Python]
01 Context & Problem
Consumers struggle to understand complex chemical names in ingredient lists and identify hidden health risks like sugar aliases.
02 Architecture & Design
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A pipeline that OCRs ingredient text from images, classifies additives using a hybrid approach (Static DB + LLM fallback), and generates plain-English health scores.
03 Key Technical Decisions
- Decision: Hybrid Classification: Prioritized local DB for speed/cost, used LLM only for unknown ingredients.
- Decision: FastAPI: Chosen for high-performance async handling of OCR tasks.
- Decision: Gemini Flash: Selected for low latency structured data extraction.
04 Challenges & Resolutions
! Warning: OCR Accuracy: Improved text extraction from curved packaging surfaces using OpenCV preprocessing.
! Warning: Latency: Parallelized database lookups to return "Safe" ingredients immediately while processing "Unknowns" in background.