dschlueter
89b61d8c62
- Prompt-Beispiel '1 → 2: 3' durch Schema 'A → B: Wert' ersetzt: eine Lesung hatte nur die Beispielzeile übernommen statt aller fünf Pfeile; alle Datenpunkte verlangt - canonical_figure_id: '1' und 'Abbildung 1' gelten als dieselbe Abbildung – beim Vergleich, in der Pipeline und im Report (Formfehler im zweiten Testlauf) Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
165 lines
8.1 KiB
Python
165 lines
8.1 KiB
Python
"""Aufgaben mit Abbildungen (docling-Markdown): abtrennen, als Bild an die Verstehensstufe, doppelt ablesen."""
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import base64
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import pytest
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from checks.model_checks import check_figure_readings, numbers_in
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from core.model_schema import (FigureReading, FigureTranscription, SolverCode, SubQuestion, TaskUnderstanding)
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from reporting.report import build_document, to_html, to_markdown
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from TextToModel import run_pipeline
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from understanding.figures import MAX_FIGURES, FigureError, split_figures, with_transcriptions
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from understanding.understand import understand_task
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# 1×1-PNG – Inhalt egal, es geht um den Weg durch die Pipeline
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PNG = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
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AUFGABE = f"Gesucht sind die minimalen Fahrtkosten von Knoten 1 nach Knoten 4.\n\n\n"
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# Graph aus test_Image.md
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GRAPH = FigureTranscription(id="Abbildung 1", shows="gerichteter Graph mit 4 Knoten",
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transcription="Pfeil 1 → 2: 3\nPfeil 1 → 3: 1\nPfeil 3 → 2: 1\nPfeil 2 → 4: 3\nPfeil 3 → 4: 5")
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VERLESEN = GRAPH.model_copy(update={"transcription": GRAPH.transcription.replace("2 → 4: 3", "2 → 4: 5")})
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# --- Abtrennen ohne LLM ---
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def test_embedded_image_is_replaced_by_a_placeholder():
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text, figuren = split_figures(AUFGABE)
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assert "[Abbildung 1]" in text and "base64" not in text
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assert len(figuren) == 1 and figuren[0].mime == "image/png" and figuren[0].data == PNG
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assert numbers_in(text) == {"1", "4"} # keine Zahlen aus dem Base64-Zeichensalat
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def test_referenced_image_file_is_loaded_relative_to_the_task(tmp_path):
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(tmp_path / "bilder").mkdir()
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(tmp_path / "bilder" / "graph.png").write_bytes(base64.b64decode(PNG))
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text, figuren = split_figures("Siehe  und  und ", tmp_path)
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assert "[Abbildung 1: Netz]" in text
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assert "" in text and "https://x.de/a.png" in text # nicht vorhanden bzw. keine Datei: bleibt
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assert len(figuren) == 1 and figuren[0].data == PNG and figuren[0].source.endswith("graph.png")
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def test_limits_and_broken_base64_are_errors_not_silent_cuts():
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zu_viele = "\n".join(f"" for _ in range(MAX_FIGURES + 1))
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with pytest.raises(FigureError, match="höchstens"):
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split_figures(zu_viele)
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with pytest.raises(FigureError, match="Base64"):
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split_figures("")
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def test_task_without_images_is_unchanged():
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assert split_figures("Minimiere 3x + 2y.") == ("Minimiere 3x + 2y.", [])
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def test_transcription_is_appended_with_uncertain_parts():
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unsicher = GRAPH.model_copy(update={"uncertain": ["Richtung des Pfeils zwischen 2 und 3"]})
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text = with_transcriptions("Aufgabe [Abbildung 1]", [unsicher])
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assert "Pfeil 3 → 4: 5" in text and "Unsicher gelesen: Richtung des Pfeils" in text
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# --- Nachricht an das Bildmodell ---
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class Aufzeichner:
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def __init__(self, antwort): self.antwort, self.nachrichten = antwort, []
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def create(self, response_model, messages, step="unbenannt"):
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self.nachrichten.append((step, messages)); return self.antwort
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def test_understanding_sends_images_as_image_parts():
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text, figuren = split_figures(AUFGABE)
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session = Aufzeichner("ok")
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understand_task(session, text, figures=figuren)
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inhalt = session.nachrichten[0][1][-1]["content"]
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assert isinstance(inhalt, list) and inhalt[0]["type"] == "text" and "1 ABBILDUNG" in inhalt[0]["text"]
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assert inhalt[-1] == {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{PNG}"}}
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def test_understanding_without_images_stays_plain_text():
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session = Aufzeichner("ok")
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understand_task(session, "Minimiere 3x.")
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assert isinstance(session.nachrichten[0][1][-1]["content"], str)
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# --- Doppelt ablesen ---
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def test_identical_readings_have_no_finding():
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assert check_figure_readings([GRAPH], [GRAPH.model_copy()]) == []
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def test_misread_value_is_found_with_both_numbers():
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befunde = check_figure_readings([GRAPH], [VERLESEN])
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assert len(befunde) == 1 and befunde[0].code == "FIGURE_READINGS_DIFFER"
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assert "nur in Lesung 1: (2 4 3)" in befunde[0].message and "nur in Lesung 2: (2 4 5)" in befunde[0].message
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def test_figure_missing_in_one_reading_is_found():
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assert "nur in einer" in check_figure_readings([GRAPH], [])[0].message
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# --- Durch die ganze Pipeline (ohne LLM) ---
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def _verstaendnis(figur):
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return TaskUnderstanding(is_optimization_task=True, reason="-", facts=[], ignored_numbers=[],
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sub_questions=[SubQuestion(id="1", quote="-", kind="rechnen", needs_solver=True)],
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figures=[figur])
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def _lauf(erste, lesungen):
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from test_pipeline import GOOD, OK, FakeSession
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from test_script_export import STRUCTURED_CODE
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session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)], understandings=[_verstaendnis(erste)],
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readings=[FigureReading(figures=[f]) for f in lesungen])
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return run_pipeline(AUFGABE, session=session, max_revisions=0, max_restarts=0), session
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def test_only_the_understanding_sees_the_image_later_stages_get_the_transcription():
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result, session = _lauf(GRAPH, [GRAPH])
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bilder = [schritt for schritt, inhalt in session.contents if isinstance(inhalt, list)]
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assert set(bilder) == {"Verstehen", "Abbildungen"}
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spaeter = [inhalt for schritt, inhalt in session.contents if schritt not in ("Verstehen", "Abbildungen")]
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assert spaeter and all("data:image" not in str(i) for i in spaeter)
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assert any("Pfeil 3 → 4: 5" in str(i) for i in spaeter)
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assert result.reliable and len(result.figures) == 1 and "Pfeil 2 → 4: 3" in result.task
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def test_two_of_three_agreeing_readings_win():
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result, _ = _lauf(VERLESEN, [GRAPH, GRAPH]) # erste Lesung verlesen, zweite und dritte stimmen überein
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assert result.reliable and "Pfeil 2 → 4: 3" in result.task
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def test_persistently_differing_readings_make_the_result_unreliable():
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dritte = GRAPH.model_copy(update={"transcription": GRAPH.transcription.replace("1 → 3: 1", "1 → 3: 7")})
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result, _ = _lauf(GRAPH, [VERLESEN, dritte])
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assert not result.reliable
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assert any(p.startswith("Abbildung:") and "uneinheitlich" in p for p in result.reliability_issues)
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def test_report_shows_original_and_transcription():
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result, _ = _lauf(GRAPH.model_copy(update={"uncertain": ["Pfeil 3 → 2 schwer lesbar"]}), [GRAPH])
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html = to_html(build_document(result))
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assert f'<img src="data:image/png;base64,{PNG}"' in html
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assert "Pfeil 1 → 3: 1" in html and "unsicher gelesen" in html
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assert f"" in to_markdown(build_document(result))
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def test_reading_comparison_ignores_wording_but_not_assignment():
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# erster echter Lauf: Knotenaufzählung und doppelte Pfeilbeschreibung lösten einen Fehlalarm aus
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anders_formuliert = GRAPH.model_copy(update={"transcription": "(1,2): 3; (1,3): 1; (3,2): 1; (2,4): 3; (3,4): 5"})
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assert check_figure_readings([GRAPH], [anders_formuliert]) == []
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vertauscht = GRAPH.model_copy(update={"transcription": GRAPH.transcription.replace("1 → 2: 3", "1 → 2: 1")
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.replace("1 → 3: 1", "1 → 3: 3")}) # gleiche Zahlen, falsche Pfeile
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assert check_figure_readings([GRAPH], [vertauscht])
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richtung = GRAPH.model_copy(update={"transcription": GRAPH.transcription.replace("3 → 2: 1", "2 → 3: 1")})
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assert check_figure_readings([GRAPH], [richtung])
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def test_figure_ids_are_compared_by_number():
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# beobachtet: Lesung 1 schrieb als Kennung nur „1“, Lesung 2 „Abbildung 1“ – Inhalt identisch
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from understanding.figures import canonical_figure_id
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assert check_figure_readings([GRAPH.model_copy(update={"id": "1"})], [GRAPH]) == []
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assert canonical_figure_id("Abb. 2") == "Abbildung 2"
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def test_short_id_in_understanding_still_reaches_the_report():
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result, _ = _lauf(GRAPH.model_copy(update={"id": "1"}), [GRAPH])
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html = to_html(build_document(result))
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assert "nicht übertragen" not in html and "Pfeil 3 → 4: 5" in html
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