57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
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"""Tests für {{key|d}}, ai_caption und Unbekannt-Erkennung."""
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from placeholder_registry import (
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PlaceholderMetadata,
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PlaceholderType,
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OutputType,
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build_ai_placeholder_caption,
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)
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import placeholder_resolver as pr
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def test_build_ai_caption_prefers_business_meaning():
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m = PlaceholderMetadata(
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key="test_x",
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category="Test",
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description="Kurzbeschreibung",
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resolver_module="m",
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resolver_function="f",
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semantic_contract="Lang Vertrag " * 50,
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business_meaning="Kernbedeutung für die KI.",
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unit="g/day",
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placeholder_type=PlaceholderType.INTERPRETED,
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output_type=OutputType.NUMERIC,
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)
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cap = build_ai_placeholder_caption(m)
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assert "Kernbedeutung" in cap
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def test_build_ai_caption_score_adds_scale():
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m = PlaceholderMetadata(
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key="test_score",
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category="Test",
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description="Score",
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resolver_module="m",
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resolver_function="f",
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business_meaning="Gewichteter Gesamtscore.",
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unit="Score (0-100)",
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placeholder_type=PlaceholderType.SCORE,
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output_type=OutputType.NUMERIC,
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)
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cap = build_ai_placeholder_caption(m)
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assert "0–100" in cap or "0-100" in cap
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assert "Gewichteter" in cap
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def test_placeholder_token_regex_optional_modifier():
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m0 = pr._PLACEHOLDER_TOKEN_RE.search("{{fat_avg}}")
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assert m0 and m0.group(1) == "fat_avg" and m0.group(2) is None
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m1 = pr._PLACEHOLDER_TOKEN_RE.search("{{fat_avg|d}}")
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assert m1 and m1.group(1) == "fat_avg" and m1.group(2).strip() == "d"
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m2 = pr._PLACEHOLDER_TOKEN_RE.search("{{ protein_avg | d }}")
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assert m2 and m2.group(1) == "protein_avg" and m2.group(2).strip() == "d"
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def test_get_unknown_placeholders_strips_modifier():
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unk = pr.get_unknown_placeholders("{{not_a_real_key|d}}")
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assert set(unk) == {"not_a_real_key"}
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