Define the label file cleaner input contract

The accepted input remains: a UTF-8 labels.txt that may contain blanks, duplicates, and mixed case. Keep parsing and normalization in functions that do not score, train, or call a model. That split lets a test fail the boundary without blaming the core logic. The user-facing decision stays: normalize noisy training labels without destroying the source file.

from pathlib import Path
def read_labels(path):
    text=Path(path).read_text(encoding='utf-8')
    return [ln.strip() for ln in text.splitlines() if ln.strip()]
print(read_labels.__annotations__ if False else 'utf-8 lines in, list[str] out')

Expected evidence: utf-8 lines in, list(str) out. If the contract is silent on a bad value, later debugging will look like an algorithm bug when it is really a data bug.

Write one assertion for a neighboring valid input to the label file cleaner so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of python-file-handling—not as comments you plan to delete.

List every field in labels.txt with YES/yes/No/blank lines and mark each as required, optional, or forbidden. Required fields must fail loudly when missing; optional fields need defaults you can quote in a test; forbidden fields (secrets, raw PII, path escapes) must never be accepted silently. This list is the contract for the label file cleaner.

Add one sentence about encoding, units, or timezones if relevant to a UTF-8 labels.txt that may contain blanks, duplicates, and mixed case. Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to normalize noisy training labels without destroying the source file.

Write the error string you want for the most likely bad input. Prefer ValueError('threshold out of range')-style messages over generic invalid input. The contract’s job is to make FileNotFoundError, encoding errors, or accidental overwrite of the source harder to confuse with a model or algorithm bug later.

At the data contract 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 malformed inputs rejected with field-named errors 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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