fegger
da37e91c86
- Refactors process_single into inner pipeline with guaranteed client close. - Runs classifier -> OCR branch -> extraction -> review -> XML/sidecar. - Uses review-corrected invoice when available. - Confidence >= 0.75 produces XML; < 0.90 adds a human-review comment. - Adds end-to-end CLI tests covering confidence bands, corrections, and batch mode.
134 lines
3.9 KiB
Python
134 lines
3.9 KiB
Python
"""End-to-end CLI pipeline tests using the fake VLM client."""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any
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import pytest
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from lxml import etree
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from PIL import Image
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from odoo_ocr import cli
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from odoo_ocr.config import Settings
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from tests.conftest import FakeVLMClient
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EXTRACTION: dict[str, Any] = {
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"vendor_name": "Acme",
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"vendor_address": None,
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"vendor_vat": None,
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"invoice_number": "INV-9",
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"invoice_date": "2024-01-01",
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"due_date": None,
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"currency": "EUR",
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"payment_terms": None,
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"line_items": [
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{
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"description": "Widget",
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"quantity": 1,
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"unit_price": 100.0,
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"total_price": 100.0,
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"tax_rate": 0,
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}
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],
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"subtotal": 100.0,
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"tax_total": 0.0,
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"total": 100.0,
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"iban": None,
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"raw_ocr_text": "",
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}
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def _responses(confidence: float) -> dict[str, Any]:
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return {
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"document classifier": {
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"category": "scanned_print",
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"confidence": 0.9,
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"reasoning": "printed invoice",
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},
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"OCR engine": {"text": "Invoice INV-9 from Acme, total 100.00 EUR"},
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"invoice data extraction": EXTRACTION,
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"invoice review assistant": {
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"valid": True,
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"confidence": confidence,
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"issues": [],
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"corrected_invoice": None,
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},
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}
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def _write_invoice(tmp_path: Path) -> Path:
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src = tmp_path / "invoice.jpg"
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Image.new("RGB", (100, 100), "white").save(src)
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return src
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def _install_fake(monkeypatch: pytest.MonkeyPatch, responses: dict[str, Any]) -> FakeVLMClient:
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fake = FakeVLMClient(responses=responses)
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monkeypatch.setattr(cli, "OllamaClient", lambda _settings: fake)
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return fake
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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("confidence", "expect_xml", "expect_review_comment"),
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[(0.95, True, False), (0.80, True, True), (0.70, False, False)],
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)
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async def test_confidence_bands(
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tmp_path: Path,
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monkeypatch: pytest.MonkeyPatch,
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confidence: float,
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expect_xml: bool,
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expect_review_comment: bool,
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) -> None:
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src = _write_invoice(tmp_path)
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out_dir = tmp_path / "out"
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_install_fake(monkeypatch, _responses(confidence))
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sidecar = await cli.process_single(src, out_dir, Settings())
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assert sidecar["review"]["confidence"] == confidence
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assert (out_dir / "invoice_sidecar.json").exists()
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data = json.loads((out_dir / "invoice_sidecar.json").read_text(encoding="utf-8"))
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assert data["extracted_invoice"]["vendor_name"] == "Acme"
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xml_file = out_dir / "invoice.xml"
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assert xml_file.exists() is expect_xml
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if expect_xml:
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xml = xml_file.read_text(encoding="utf-8")
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assert ("HUMAN REVIEW ADVISED" in xml) is expect_review_comment
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root = etree.fromstring(xml.encode("utf-8"))
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models = {record.get("model") for record in root.iter("record")}
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assert {"res.partner", "account.move", "account.move.line", "account.tax"} <= models
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@pytest.mark.asyncio
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async def test_uses_corrected_invoice_from_review(
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tmp_path: Path, monkeypatch: pytest.MonkeyPatch
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) -> None:
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src = _write_invoice(tmp_path)
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responses = _responses(0.95)
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responses["invoice review assistant"]["corrected_invoice"] = {
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**EXTRACTION,
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"invoice_number": "INV-999",
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}
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_install_fake(monkeypatch, responses)
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await cli.process_single(src, tmp_path / "out", Settings())
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xml = (tmp_path / "out" / "invoice.xml").read_text(encoding="utf-8")
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assert "<field name=\"ref\">INV-999</field>" in xml
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@pytest.mark.asyncio
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async def test_process_batch(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
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_install_fake(monkeypatch, _responses(0.95))
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(tmp_path / "a.jpg").touch()
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Image.new("RGB", (10, 10), "white").save(tmp_path / "b.jpg")
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results = await cli.process_batch(tmp_path, tmp_path / "out", Settings())
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assert len(results) == 1
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assert (tmp_path / "out" / "b.xml").exists()
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