Label Parser Utility

Overview:

The parser converts raw Rekognition detect_labels responses into a normalized and reusable tag format. It outputs a deterministic list of label names and confidence scores for consistent use across backend and frontend systems.

Prerequisites

• Raw JSON responses from the lab harness
• Defined tag schema: [{ "name": string, "confidence": number }]
• Unit testing setup
• Established normalization policy

Implementation/Steps

1. The system extracts label names and confidence values from the raw response.
2. The system normalizes label names using a consistent casing policy (lowercase).
3. The system returns a list of tag objects following the defined schema.
4. The system sorts tags in descending order by confidence.
5. The system validates output using fixture-based tests without AWS calls.
6. The system routes lab harness output through the parser for consistency.

Key Takeaways

• The parser remains pure Python for reuse and testing.
• Normalization ensures consistent output across consumers.
• Deterministic ordering supports downstream reliability.
• All transformation logic is centralized in one place.