6a78a0059b
Enchaine ocr_fn -> tokens_from_grid -> map_roles -> assess_quality. OCR et client VLM injectables (testable hors-ligne, import OCR lazy = module reste pur). C'est la brique que le handler runtime extract_dossier appellera. 4 tests (35 au total role_mapper). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""Tests de l'orchestrateur extract_dossier_from_image.
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Enchaîne OCR → tokens_from_grid → map_roles → assess_quality. L'OCR (`ocr_fn`)
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et le client VLM (`vlm_client`) sont INJECTABLES → testable sans réseau ni OCR
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réel. C'est cette fonction que le handler runtime `_handle_extract_dossier_action`
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appellera (avec le vrai OCR et le vrai client vLLM).
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"""
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from core.extraction.role_mapper import extract_dossier_from_image
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def _cell(text, x0, conf=0.9, row=0, col=0):
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return {"text": text, "bbox": [[x0, 0], [x0 + 10, 0], [x0 + 10, 8], [x0, 8]],
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"confidence": conf, "row": row, "col": col}
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def _fake_vlm(response):
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def client(image_path, prompt):
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return response
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return client
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def test_orchestre_ocr_vlm_qualite():
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grid = [[_cell("DUPONT", 0, conf=0.95, col=0), _cell("Jean", 20, conf=0.9, col=1)]]
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res = extract_dossier_from_image(
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"img.png",
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_fake_vlm('{"champs":[{"label":"Nom complet","value_ids":[0,1]}]}'),
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ocr_fn=lambda path: grid,
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)
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assert len(res["fields"]) == 1
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assert res["fields"][0].value == "DUPONT Jean"
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assert res["fields"][0].anchored is True
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assert res["status"] in ("complete", "partial", "needs_review", "failed")
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assert res["n_tokens"] == 2
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def test_ocr_vide_donne_failed():
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res = extract_dossier_from_image(
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"img.png",
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_fake_vlm('{"champs":[]}'),
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ocr_fn=lambda path: [],
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)
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assert res["status"] == "failed"
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assert res["fields"] == []
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def test_status_needs_review_si_role_requis_absent():
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grid = [[_cell("X", 0)]]
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res = extract_dossier_from_image(
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"img.png",
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_fake_vlm('{"champs":[{"label":"Autre","value_ids":[0]}]}'),
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ocr_fn=lambda path: grid,
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required_roles=["Nom"],
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)
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assert res["status"] == "needs_review"
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def test_roles_transmis_au_vlm():
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grid = [[_cell("X", 0)]]
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captured = {}
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def client(image_path, prompt):
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captured["prompt"] = prompt
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return '{"champs":[]}'
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extract_dossier_from_image(
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"img.png", client, ocr_fn=lambda path: grid, roles=["Diagnostic", "GEMSA"],
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)
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assert "Diagnostic" in captured["prompt"] and "GEMSA" in captured["prompt"]
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