AdamThomasandClaude Sonnet 5
bb919554ff
- Fichier unique, processus en deux temps : POST /editions/{id}/import/analyze
(colonnes détectées, aperçu, mapping suggéré, demi-journées de l'édition)
puis POST /editions/{id}/import/execute (mapping validé)
- Parser CSV (délimiteur et encodage devinés, BOM) et XLSX (openpyxl),
détection XLSX par nombre magique
- Vocabulaire des valeurs de disponibilité (tri-état, accents/casse) et des
tailles de t-shirt ; compétences créées à la volée dans le référentiel
- Rapport ligne par ligne : codes de traduction + paramètres, jamais de
phrase. Doublons : upsert sur email, signalement sur (nom, prénom) sans
email, doublons intra-fichier. Mode simulation (rollback, rien de persisté)
- 40 tests : parser, valeurs, service (tous les cas), endpoints
- Dépendances : python-multipart, openpyxl
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017uUsRg7XzopkCPWimwg9ZM
120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
import json
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import uuid
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from sqlmodel import select
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from app.models import Volunteer
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_CSV = (
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"Prénom;Nom;Email;Compétences;Ven matin\n"
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"Alex;Martin;alex@ex.fr;SST, Permis B;x\n"
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"Sam;Nguyen;sam@ex.fr;SST;si besoin\n"
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).encode()
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def _mapping(dry_run: bool) -> str:
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return json.dumps(
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{
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"dry_run": dry_run,
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"columns": {
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"first_name": "Prénom",
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"last_name": "Nom",
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"email": "Email",
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"skills": "Compétences",
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},
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"availability_columns": [
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{
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"header": "Ven matin",
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"date": "2026-07-10",
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"day_part_code": "MORNING",
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}
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],
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}
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)
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def test_analyze_returns_headers_preview_and_day_parts(client, edition):
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resp = client.post(
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f"/editions/{edition.id}/import/analyze",
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files={"file": ("benevoles.csv", _CSV, "text/csv")},
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)
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assert resp.status_code == 200
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body = resp.json()
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assert body["format"] == "csv"
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assert body["headers"] == ["Prénom", "Nom", "Email", "Compétences", "Ven matin"]
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assert body["row_count"] == 2
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assert body["preview"][0]["values"]["Nom"] == "Martin"
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assert body["suggested_mapping"]["first_name"] == "Prénom"
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assert {d["code"] for d in body["day_parts"]} == {
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"MORNING",
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"AFTERNOON",
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"EVENING",
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}
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assert body["edition_dates"][0] == "2026-07-09"
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def test_execute_dry_run_then_real(client, session, edition):
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dry = client.post(
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f"/editions/{edition.id}/import/execute",
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files={"file": ("benevoles.csv", _CSV, "text/csv")},
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data={"mapping": _mapping(dry_run=True)},
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)
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assert dry.status_code == 200
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assert dry.json()["created"] == 2
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assert session.exec(select(Volunteer)).all() == []
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real = client.post(
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f"/editions/{edition.id}/import/execute",
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files={"file": ("benevoles.csv", _CSV, "text/csv")},
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data={"mapping": _mapping(dry_run=False)},
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)
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assert real.status_code == 200
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report = real.json()
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assert report["created"] == 2
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assert sorted(report["skills_created"]) == ["PERMIS_B", "SST"]
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assert len(session.exec(select(Volunteer)).all()) == 2
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def test_execute_rejects_invalid_mapping(client, edition):
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bad_mapping = json.dumps(
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{
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"dry_run": True,
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"columns": {"first_name": "Prénom", "last_name": "Nom"},
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"availability_columns": [
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{
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"header": "Ven matin",
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"date": "2026-07-10",
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"day_part_code": "NIGHT",
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}
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],
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}
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)
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resp = client.post(
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f"/editions/{edition.id}/import/execute",
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files={"file": ("benevoles.csv", _CSV, "text/csv")},
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data={"mapping": bad_mapping},
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)
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assert resp.status_code == 422
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detail = resp.json()["detail"]
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assert detail["code"] == "import.mapping.invalid"
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assert any(
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e["code"] == "import.mapping.unknown_day_part" for e in detail["errors"]
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)
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def test_analyze_unknown_edition_is_404(client):
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resp = client.post(
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f"/editions/{uuid.uuid4()}/import/analyze",
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files={"file": ("benevoles.csv", _CSV, "text/csv")},
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)
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assert resp.status_code == 404
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def test_analyze_rejects_empty_file(client, edition):
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resp = client.post(
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f"/editions/{edition.id}/import/analyze",
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files={"file": ("benevoles.csv", b"", "text/csv")},
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)
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assert resp.status_code == 400
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assert resp.json()["detail"]["code"] == "import.error.empty_file"
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