Build the first working score labeler

Build only what the claim requires: three float scores and one threshold produce deterministic yes/no labels plus a positive count without repeating the rule. Prefer boring, deterministic code over frameworks you cannot yet explain. Run the path twice; identical output on this fixture is a feature, not a lack of creativity.

def label_scores(scores, threshold):
    labels = ["yes" if score >= threshold else "no" for score in scores]
    return labels, labels.count("yes")

scores = [0.2, 0.9, 0.4]
labels, positive_count = label_scores(scores, 0.5)
for score, result in zip(scores, labels):
    print(f"{score:.1f} -> {result}")
print("positive:", positive_count)

Expected output:

0.2 -> no
0.9 -> yes
0.4 -> no
positive: 1

The comparison appears once. Returning both values makes the result testable without scraping printed text.

Narrate the journey from raw input to result for a single example from scores=(0.2,0.9,0.4), threshold=0.5. If you cannot name an intermediate, the implementation is still too opaque for this lab. Only after this path is solid should you generalize data sources or UI.

While implementing the score labeler, print or log at least three intermediates that map to the claim (three float scores and one threshold produce deterministic yes/no labels plus a positive count without repeating the rule). Good intermediates are values a teammate could recompute with a calculator or diff. Bad intermediates are framework traces you cannot explain.

Re-run with scores=[0.2,0.9,0.4], threshold=0.5 twice. If the second run differs, either the path is nondeterministic (document the seed) or you have hidden global state—both are lab bugs until named.

Stop adding features once the path supports accept or reject a model score using one shared cutoff. Extra UI, extra tools, or extra models belong in later chapters. The mastery bar for this page is simply: a deterministic end-to-end path with intermediates.

Predict the output for [0.49, 0.50, 0.51] before running it. Then change only the threshold to 0.51 and explain which labels change and why.

Write flag_temperatures(readings, limit) that returns labels and a count. Equality must count as an alert. Use [29.9, 30.0, 30.1] with limit 30.0; expected labels are ["ok", "alert", "alert"].

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