Instrument the label file cleaner

Instrument the label cleaner writing clean-labels.txt so a run records enough structure to debug offline: counts, latency if relevant, pass/fail of source byte-identical after run, and a stable stage name. Redact secrets and raw credentials from every event.

import json
print(json.dumps({'src_lines':4,'kept':2,'dropped_blank':1,'dropped_dup':1}))

Expected evidence: line accounting JSON. Prefer JSON or structured text you can grep in CI over prose logs for python-file-handling.

Turn one historical failure—especially FileNotFoundError—into a test that fails if the signal disappears for the label file cleaner. Observability without a failing test is optional decoration; observability with a test is part of the python-file-handling artifact.

Draft a three-field event for the label file cleaner: stage, ok, and one domain field derived from source byte-identical after run; clean file has unique lowercase lines. Add fixture_id or docs_version when content can change. Explicitly list fields that must never appear (tokens, passwords, raw prompts) because writing cleaned labels into a world-writable shared path or reading untrusted paths via ../ is in scope for this lab.

Wire one assertion that fails if the label file cleaner event is missing after a run. Observability that cannot fail a test will not survive contact with a busy python-file-handling repository.

Imagine a teammate opens only your event stream after a bad deploy. Could they tell whether labels.txt with YES/yes/No/blank lines was wrong, whether FileNotFoundError, encoding errors, or accidental overwrite of the source returned, or whether writing cleaned labels into a world-writable shared path or reading untrusted paths via ../ slipped through? If not, rename fields until those three stories are distinguishable.

At the testing and observability stage for python-file-handling, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about labels.txt with YES/yes/No/blank lines that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: manual unique-lower count of the fixture before coding.

For this page specifically, success looks like a structured event schema locked by a test while still centering the user decision to normalize noisy training labels without destroying the source file. If you cannot point to a file, command, or assertion that proves that for the label file cleaner, stay on this page instead of advancing.

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