Download code/backend/tests/test_correction_efficacy.py from Arko007/Yoga-1M: direct link, hf CLI and curl.
- Browser
- Download file 3.29 kB
-
https://huggingface.co/datasets/Arko007/Yoga-1M/resolve/main/code/backend/tests/test_correction_efficacy.py
- Command line
-
hf download hf://datasets/Arko007/Yoga-1M/code/backend/tests/test_correction_efficacy.py
-
curl -L -o test_correction_efficacy.py https://huggingface.co/datasets/Arko007/Yoga-1M/resolve/main/code/backend/tests/test_correction_efficacy.py
3.29 kB
| """The correction-efficacy loop's server side. | |
| An open-loop coach repeats the same sentence at a user who did not respond to | |
| it, which is the single most obviously "deaf" behaviour automated coaching | |
| has. These tests pin the three things the client needs for the loop to work: | |
| * the response names WHICH joint the cue is trying to move, so the client can | |
| measure that joint specifically rather than inferring from the whole-body | |
| score (which drifts for unrelated reasons); | |
| * repeating a cue that did not work escalates instead of repeating verbatim; | |
| * escalation ends in BACKING OFF, not in pushing harder -- a cue that has | |
| failed twice usually means the range of motion is not there today, and | |
| "try harder" is exactly the injury this system exists to prevent. | |
| """ | |
| import os | |
| import sys | |
| sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) | |
| from app.services.correction import generate_safe_correction # noqa: E402 | |
| # warrior_2/knee_l has a real template, so the target joint is deterministic | |
| POSE = "warrior_2" | |
| DEVS = {"knee_l": 24.0, "knee_r": 2.0} | |
| def _text(attempt, language="en"): | |
| t, safe, joint = generate_safe_correction( | |
| pose_id=POSE, deviations=DEVS, language=language, | |
| groq_api_key=None, attempt=attempt) | |
| return t, safe, joint | |
| def test_response_names_the_targeted_joint(): | |
| _t, _s, joint = _text(0) | |
| assert joint == "knee_l", joint | |
| def test_joint_is_the_worst_deviation_not_the_first(): | |
| t, _s, joint = generate_safe_correction( | |
| pose_id=POSE, deviations={"knee_l": 3.0, "knee_r": 30.0}, | |
| language="en", groq_api_key=None, attempt=0) | |
| assert joint == "knee_r", joint | |
| def test_first_attempt_is_the_plain_cue(): | |
| t0, _s, _j = _text(0) | |
| assert "degrees off" not in t0 | |
| assert "Ease out" not in t0 | |
| def test_second_attempt_adds_the_measured_magnitude(): | |
| t0, _, _ = _text(0) | |
| t1, _, _ = _text(1) | |
| assert t1 != t0, "a cue that did not work must not be repeated verbatim" | |
| assert "24 degrees" in t1, t1 | |
| assert t0.rstrip() in t1, "the escalated cue should still contain the original instruction" | |
| def test_third_attempt_backs_off_rather_than_pushing(): | |
| t2, safe, joint = _text(2) | |
| low = t2.lower() | |
| assert "ease out" in low, t2 | |
| for word in ("more", "deeper", "further", "harder"): | |
| assert word not in low, f"escalation must not push harder: {t2}" | |
| assert safe is True | |
| assert joint == "knee_l" | |
| def test_escalation_is_capped_and_does_not_crash(): | |
| for a in (3, 10, 99): | |
| t, safe, _j = _text(a) | |
| assert "ease out" in t.lower() | |
| assert safe is True | |
| def test_escalation_is_localised(): | |
| for lang in ("hi", "bn"): | |
| t1, _, _ = _text(1, lang) | |
| t2, _, _ = _text(2, lang) | |
| assert t1 and t2 | |
| # the localised strings must not silently fall back to English | |
| assert "degrees off" not in t1, (lang, t1) | |
| assert "Ease out" not in t2, (lang, t2) | |
| def test_no_target_joint_when_nothing_exceeds_threshold(): | |
| t, safe, joint = generate_safe_correction( | |
| pose_id=POSE, deviations={"knee_l": 1.0, "knee_r": 2.0}, | |
| language="en", groq_api_key=None, attempt=0) | |
| assert joint is None | |
| assert safe is True | |
| assert t # still returns the generic encouragement | |