WIP

packages/backend/backend/domain/__init__.py

@@ -0,0 +1,25 @@
+"""
+Domain Layer
+
+Business logic separated from technical implementation.
+Contains document classification and invoice validation logic.
+"""
+from backend.domain.document_classifier import (
+    ClassificationResult,
+    DocumentClassifier,
+)
+from backend.domain.invoice_validator import (
+    InvoiceValidator,
+    ValidationIssue,
+    ValidationResult,
+)
+from backend.domain.utils import has_value
+
+__all__ = [
+    "ClassificationResult",
+    "DocumentClassifier",
+    "InvoiceValidator",
+    "ValidationIssue",
+    "ValidationResult",
+    "has_value",
+]

packages/backend/backend/domain/document_classifier.py

@@ -0,0 +1,108 @@
+"""
+Document Classifier
+
+Business logic for classifying documents based on extracted fields.
+Separates classification logic from inference pipeline.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from backend.domain.utils import has_value
+
+
+@dataclass(frozen=True)
+class ClassificationResult:
+    """
+    Immutable result of document classification.
+
+    Attributes:
+        document_type: Either "invoice" or "letter"
+        confidence: Confidence score between 0.0 and 1.0
+        reason: Human-readable explanation of classification
+    """
+
+    document_type: str
+    confidence: float
+    reason: str
+
+
+class DocumentClassifier:
+    """
+    Classifies documents as invoice or letter based on extracted fields.
+
+    Classification Rules:
+    1. If payment_line is present -> invoice (high confidence)
+    2. If 2+ invoice indicators present -> invoice (medium confidence)
+    3. If 1 invoice indicator present -> invoice (lower confidence)
+    4. Otherwise -> letter
+
+    Invoice indicator fields:
+    - payment_line (strongest indicator)
+    - OCR
+    - Amount
+    - Bankgiro
+    - Plusgiro
+    - InvoiceNumber
+    """
+
+    INVOICE_INDICATOR_FIELDS: frozenset[str] = frozenset(
+        {
+            "payment_line",
+            "OCR",
+            "Amount",
+            "Bankgiro",
+            "Plusgiro",
+            "InvoiceNumber",
+        }
+    )
+
+    def classify(self, fields: dict[str, str | None]) -> ClassificationResult:
+        """
+        Classify document type based on extracted fields.
+
+        Args:
+            fields: Dictionary of field names to extracted values.
+                    Empty strings or whitespace-only strings are treated as missing.
+
+        Returns:
+            Immutable ClassificationResult with type, confidence, and reason.
+        """
+        # Rule 1: payment_line is the strongest indicator
+        if has_value(fields.get("payment_line")):
+            return ClassificationResult(
+                document_type="invoice",
+                confidence=0.95,
+                reason="payment_line detected",
+            )
+
+        # Count present invoice indicators (excluding payment_line already checked)
+        present_indicators = [
+            field
+            for field in self.INVOICE_INDICATOR_FIELDS
+            if field != "payment_line" and has_value(fields.get(field))
+        ]
+        indicator_count = len(present_indicators)
+
+        # Rule 2: Multiple indicators -> invoice with medium-high confidence
+        if indicator_count >= 2:
+            return ClassificationResult(
+                document_type="invoice",
+                confidence=0.8,
+                reason=f"{indicator_count} invoice indicators present: {', '.join(present_indicators)}",
+            )
+
+        # Rule 3: Single indicator -> invoice with lower confidence
+        if indicator_count == 1:
+            return ClassificationResult(
+                document_type="invoice",
+                confidence=0.6,
+                reason=f"1 invoice indicator present: {present_indicators[0]}",
+            )
+
+        # Rule 4: No indicators -> letter
+        return ClassificationResult(
+            document_type="letter",
+            confidence=0.7,
+            reason="no invoice indicators found",
+        )

packages/backend/backend/domain/invoice_validator.py

@@ -0,0 +1,141 @@
+"""
+Invoice Validator
+
+Business logic for validating extracted invoice fields.
+Checks for required fields, format validity, and confidence thresholds.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from backend.domain.utils import has_value
+
+
+@dataclass(frozen=True)
+class ValidationIssue:
+    """
+    Single validation issue.
+
+    Attributes:
+        field: Name of the field with the issue
+        severity: One of "error", "warning", "info"
+        message: Human-readable description of the issue
+    """
+
+    field: str
+    severity: str
+    message: str
+
+
+@dataclass(frozen=True)
+class ValidationResult:
+    """
+    Immutable result of invoice validation.
+
+    Attributes:
+        is_valid: True if no errors (warnings are allowed)
+        issues: Tuple of validation issues found
+        confidence: Average confidence score of validated fields
+    """
+
+    is_valid: bool
+    issues: tuple[ValidationIssue, ...]
+    confidence: float
+
+
+class InvoiceValidator:
+    """
+    Validates extracted invoice fields for completeness and consistency.
+
+    Validation Rules:
+    1. Required fields must be present (Amount)
+    2. At least one payment reference should be present (warning if missing)
+    3. Field confidence should be above threshold (warning if below)
+
+    Required fields:
+    - Amount
+
+    Payment reference fields (at least one expected):
+    - OCR
+    - Bankgiro
+    - Plusgiro
+    - payment_line
+    """
+
+    REQUIRED_FIELDS: tuple[str, ...] = ("Amount",)
+    PAYMENT_REF_FIELDS: tuple[str, ...] = ("OCR", "Bankgiro", "Plusgiro", "payment_line")
+    DEFAULT_MIN_CONFIDENCE: float = 0.5
+
+    def __init__(self, min_confidence: float = DEFAULT_MIN_CONFIDENCE) -> None:
+        """
+        Initialize validator.
+
+        Args:
+            min_confidence: Minimum confidence threshold for valid fields.
+                           Fields below this threshold produce warnings.
+        """
+        self._min_confidence = min_confidence
+
+    def validate(
+        self,
+        fields: dict[str, str | None],
+        confidence: dict[str, float],
+    ) -> ValidationResult:
+        """
+        Validate extracted invoice fields.
+
+        Args:
+            fields: Dictionary of field names to extracted values
+            confidence: Dictionary of field names to confidence scores
+
+        Returns:
+            Immutable ValidationResult with validity status and issues
+        """
+        issues: list[ValidationIssue] = []
+
+        # Check required fields
+        for field in self.REQUIRED_FIELDS:
+            if not has_value(fields.get(field)):
+                issues.append(
+                    ValidationIssue(
+                        field=field,
+                        severity="error",
+                        message=f"Required field '{field}' is missing",
+                    )
+                )
+
+        # Check payment reference (at least one expected)
+        has_payment_ref = any(
+            has_value(fields.get(f)) for f in self.PAYMENT_REF_FIELDS
+        )
+        if not has_payment_ref:
+            issues.append(
+                ValidationIssue(
+                    field="payment_reference",
+                    severity="warning",
+                    message="No payment reference (OCR, Bankgiro, Plusgiro, or payment_line)",
+                )
+            )
+
+        # Check confidence thresholds
+        for field, conf in confidence.items():
+            if conf < self._min_confidence:
+                issues.append(
+                    ValidationIssue(
+                        field=field,
+                        severity="warning",
+                        message=f"Low confidence ({conf:.2f}) for field '{field}'",
+                    )
+                )
+
+        # Calculate overall validity
+        has_errors = any(i.severity == "error" for i in issues)
+        avg_confidence = (
+            sum(confidence.values()) / len(confidence) if confidence else 0.0
+        )
+
+        return ValidationResult(
+            is_valid=not has_errors,
+            issues=tuple(issues),
+            confidence=avg_confidence,
+        )

packages/backend/backend/domain/utils.py

@@ -0,0 +1,23 @@
+"""
+Domain Layer Utilities
+
+Shared helper functions for domain layer classes.
+"""
+from __future__ import annotations
+
+
+def has_value(value: str | None) -> bool:
+    """
+    Check if a field value is present and non-empty.
+
+    Args:
+        value: Field value to check
+
+    Returns:
+        True if value is a non-empty, non-whitespace string
+    """
+    if value is None:
+        return False
+    if not isinstance(value, str):
+        return bool(value)
+    return bool(value.strip())

packages/backend/backend/web/services/inference.py

@@ -1,21 +1,24 @@
 """
-Inference Service
+Inference Service (Adapter Layer)
 
-Business logic for invoice field extraction.
+Orchestrates technical pipeline and business domain logic.
+Acts as adapter between API layer and internal components.
 """
 
 from __future__ import annotations
 
+import io
 import logging
 import time
 import uuid
+from contextlib import contextmanager
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import TYPE_CHECKING, Callable
+from typing import TYPE_CHECKING, Callable, Generator
 
-import numpy as np
 from PIL import Image
 
+from backend.domain.document_classifier import DocumentClassifier
 from backend.web.services.storage_helpers import get_storage_helper
 
 if TYPE_CHECKING:
@@ -50,9 +53,12 @@ class ServiceResult:
 
 class InferenceService:
     """
-    Service for running invoice field extraction.
+    Service for running invoice field extraction (Adapter Pattern).
+
+    Orchestrates:
+    - Technical layer: InferencePipeline, YOLODetector
+    - Business layer: DocumentClassifier
 
-    Encapsulates YOLO detection and OCR extraction logic.
     Supports dynamic model loading from database.
     """
 
@@ -61,6 +67,7 @@ class InferenceService:
         model_config: ModelConfig,
         storage_config: StorageConfig,
         model_path_resolver: ModelPathResolver | None = None,
+        document_classifier: DocumentClassifier | None = None,
     ) -> None:
         """
         Initialize inference service.
@@ -71,12 +78,19 @@ class InferenceService:
             model_path_resolver: Optional function to resolve model path from database.
                                 If provided, will be called to get active model path.
                                 If returns None, falls back to model_config.model_path.
+            document_classifier: Optional custom classifier (uses default if None)
         """
         self.model_config = model_config
         self.storage_config = storage_config
         self._model_path_resolver = model_path_resolver
+
+        # Technical layer (lazy initialized)
         self._pipeline = None
         self._detector = None
+
+        # Business layer (eagerly initialized, no heavy resources)
+        self._classifier = document_classifier or DocumentClassifier()
+
         self._is_initialized = False
         self._current_model_path: Path | None = None
         self._business_features_enabled = False
@@ -219,22 +233,12 @@ class InferenceService:
             result.success = pipeline_result.success
             result.errors = pipeline_result.errors
 
-            # Determine document type based on payment_line presence
+            # Business layer: classify document type
-            # If no payment_line found, it's likely a letter, not an invoice
+            classification = self._classifier.classify(result.fields)
-            if not result.fields.get('payment_line'):
+            result.document_type = classification.document_type
-                result.document_type = "letter"
-            else:
-                result.document_type = "invoice"
 
             # Get raw detections for visualization
-            result.detections = [
+            result.detections = self._format_detections(pipeline_result.raw_detections)
-                {
-                    "field": d.class_name,
-                    "confidence": d.confidence,
-                    "bbox": list(d.bbox),
-                }
-                for d in pipeline_result.raw_detections
-            ]
 
             # Save visualization if requested
             if save_visualization and pipeline_result.raw_detections:
@@ -293,22 +297,12 @@ class InferenceService:
             result.success = pipeline_result.success
             result.errors = pipeline_result.errors
 
-            # Determine document type based on payment_line presence
+            # Business layer: classify document type
-            # If no payment_line found, it's likely a letter, not an invoice
+            classification = self._classifier.classify(result.fields)
-            if not result.fields.get('payment_line'):
+            result.document_type = classification.document_type
-                result.document_type = "letter"
-            else:
-                result.document_type = "invoice"
 
             # Get raw detections
-            result.detections = [
+            result.detections = self._format_detections(pipeline_result.raw_detections)
-                {
-                    "field": d.class_name,
-                    "confidence": d.confidence,
-                    "bbox": list(d.bbox),
-                }
-                for d in pipeline_result.raw_detections
-            ]
 
             # Include business features if extracted
             if extract_line_items:
@@ -329,10 +323,19 @@ class InferenceService:
         result.processing_time_ms = (time.time() - start_time) * 1000
         return result
 
-    def _save_visualization(self, image_path: Path, doc_id: str) -> Path:
+    def _format_detections(self, raw_detections: list) -> list[dict]:
-        """Save visualization image with detections."""
+        """Format raw detections for response."""
-        from ultralytics import YOLO
+        return [
+            {
+                "field": d.class_name,
+                "confidence": d.confidence,
+                "bbox": list(d.bbox),
+            }
+            for d in raw_detections
+        ]
 
+    def _save_visualization(self, image_path: Path, doc_id: str) -> Path | None:
+        """Save visualization image with detections using existing detector."""
         # Get storage helper for results directory
         storage = get_storage_helper()
         results_dir = storage.get_results_base_path()
@@ -340,9 +343,8 @@ class InferenceService:
             logger.warning("Cannot save visualization: local storage not available")
             return None
 
-        # Load model and run prediction with visualization
+        # Reuse self._detector instead of creating new YOLO instance
-        model = YOLO(str(self.model_config.model_path))
+        results = self._detector.model.predict(str(image_path), verbose=False)
-        results = model.predict(str(image_path), verbose=False)
 
         # Save annotated image
         output_path = results_dir / f"{doc_id}_result.png"
@@ -351,11 +353,20 @@ class InferenceService:
 
         return output_path
 
-    def _save_pdf_visualization(self, pdf_path: Path, doc_id: str) -> Path:
+    @contextmanager
-        """Save visualization for PDF (first page)."""
+    def _temp_image_file(
+        self, results_dir: Path, doc_id: str
+    ) -> Generator[Path, None, None]:
+        """Context manager for temporary image file with guaranteed cleanup."""
+        temp_path = results_dir / f"{doc_id}_temp.png"
+        try:
+            yield temp_path
+        finally:
+            temp_path.unlink(missing_ok=True)
+
+    def _save_pdf_visualization(self, pdf_path: Path, doc_id: str) -> Path | None:
+        """Save visualization for PDF (first page) using existing detector."""
         from shared.pdf.renderer import render_pdf_to_images
-        from ultralytics import YOLO
-        import io
 
         # Get storage helper for results directory
         storage = get_storage_helper()
@@ -369,19 +380,18 @@ class InferenceService:
             pdf_path, dpi=self.model_config.dpi
         ):
             image = Image.open(io.BytesIO(image_bytes))
-            temp_path = results_dir / f"{doc_id}_temp.png"
+
+            # Use context manager for temp file to guarantee cleanup
+            with self._temp_image_file(results_dir, doc_id) as temp_path:
                 image.save(temp_path)
 
-            # Run YOLO and save visualization
+                # Reuse self._detector instead of creating new YOLO instance
-            model = YOLO(str(self.model_config.model_path))
+                results = self._detector.model.predict(str(temp_path), verbose=False)
-            results = model.predict(str(temp_path), verbose=False)
 
                 output_path = results_dir / f"{doc_id}_result.png"
                 for r in results:
                     r.save(filename=str(output_path))
 
-            # Cleanup temp file
-            temp_path.unlink(missing_ok=True)
                 return output_path
 
         # If no pages rendered

tests/domain/__init__.py

@@ -0,0 +1 @@
+# Domain layer tests

tests/domain/test_document_classifier.py

@@ -0,0 +1,176 @@
+"""
+Tests for DocumentClassifier - TDD RED phase.
+
+Test document type classification based on extracted fields.
+"""
+import pytest
+
+from backend.domain.document_classifier import DocumentClassifier, ClassificationResult
+
+
+class TestDocumentClassifier:
+    """Test document classification logic."""
+
+    @pytest.fixture
+    def classifier(self) -> DocumentClassifier:
+        """Create classifier instance."""
+        return DocumentClassifier()
+
+    # ==================== Invoice Detection Tests ====================
+
+    def test_classify_with_payment_line_returns_invoice(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """Payment line is the strongest invoice indicator."""
+        fields = {"payment_line": "# 123456 # 100 00 5 > 308-2963#"}
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "invoice"
+        assert result.confidence >= 0.9
+        assert "payment_line" in result.reason
+
+    def test_classify_with_multiple_indicators_returns_invoice(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """Multiple invoice indicators -> invoice with medium confidence."""
+        fields = {
+            "Amount": "1200.00",
+            "Bankgiro": "123-4567",
+            "payment_line": None,
+        }
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "invoice"
+        assert result.confidence >= 0.7
+
+    def test_classify_with_ocr_and_amount_returns_invoice(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """OCR + Amount is typical invoice pattern."""
+        fields = {
+            "OCR": "123456789012",
+            "Amount": "500.00",
+        }
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "invoice"
+        assert result.confidence >= 0.7
+
+    def test_classify_with_single_indicator_returns_invoice_lower_confidence(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """Single indicator -> invoice but lower confidence."""
+        fields = {"Amount": "100.00"}
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "invoice"
+        assert 0.5 <= result.confidence < 0.8
+
+    def test_classify_with_invoice_number_only(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """Invoice number alone suggests invoice."""
+        fields = {"InvoiceNumber": "INV-2024-001"}
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "invoice"
+
+    # ==================== Letter Detection Tests ====================
+
+    def test_classify_with_no_indicators_returns_letter(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """No invoice indicators -> letter."""
+        fields: dict[str, str | None] = {}
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "letter"
+        assert result.confidence >= 0.5
+
+    def test_classify_with_empty_fields_returns_letter(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """All fields empty or None -> letter."""
+        fields = {
+            "payment_line": None,
+            "OCR": None,
+            "Amount": None,
+            "Bankgiro": None,
+        }
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "letter"
+
+    def test_classify_with_only_non_indicator_fields_returns_letter(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """Fields that don't indicate invoice -> letter."""
+        fields = {
+            "CustomerNumber": "C12345",
+            "SupplierOrgNumber": "556677-8899",
+        }
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "letter"
+
+    # ==================== Edge Cases ====================
+
+    def test_classify_with_empty_string_fields_returns_letter(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """Empty strings should be treated as missing."""
+        fields = {
+            "payment_line": "",
+            "Amount": "",
+        }
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "letter"
+
+    def test_classify_with_whitespace_only_fields_returns_letter(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """Whitespace-only strings should be treated as missing."""
+        fields = {
+            "payment_line": "   ",
+            "Amount": "\t\n",
+        }
+
+        result = classifier.classify(fields)
+
+        assert result.document_type == "letter"
+
+    # ==================== ClassificationResult Immutability ====================
+
+    def test_classification_result_is_immutable(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """ClassificationResult should be a frozen dataclass."""
+        fields = {"payment_line": "test"}
+        result = classifier.classify(fields)
+
+        with pytest.raises((AttributeError, TypeError)):
+            result.document_type = "modified"  # type: ignore
+
+    def test_classification_result_has_required_fields(
+        self, classifier: DocumentClassifier
+    ) -> None:
+        """ClassificationResult must have document_type, confidence, reason."""
+        fields = {"Amount": "100.00"}
+        result = classifier.classify(fields)
+
+        assert hasattr(result, "document_type")
+        assert hasattr(result, "confidence")
+        assert hasattr(result, "reason")
+        assert isinstance(result.document_type, str)
+        assert isinstance(result.confidence, float)
+        assert isinstance(result.reason, str)

tests/domain/test_invoice_validator.py

@@ -0,0 +1,232 @@
+"""
+Tests for InvoiceValidator - TDD RED phase.
+
+Test invoice field validation logic.
+"""
+import pytest
+
+from backend.domain.invoice_validator import (
+    InvoiceValidator,
+    ValidationResult,
+    ValidationIssue,
+)
+
+
+class TestInvoiceValidator:
+    """Test invoice validation logic."""
+
+    @pytest.fixture
+    def validator(self) -> InvoiceValidator:
+        """Create validator instance with default settings."""
+        return InvoiceValidator()
+
+    @pytest.fixture
+    def validator_strict(self) -> InvoiceValidator:
+        """Create validator with strict confidence threshold."""
+        return InvoiceValidator(min_confidence=0.8)
+
+    # ==================== Valid Invoice Tests ====================
+
+    def test_validate_complete_invoice_is_valid(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Complete invoice with all required fields is valid."""
+        fields = {
+            "Amount": "1200.00",
+            "OCR": "123456789012",
+            "Bankgiro": "123-4567",
+        }
+        confidence = {
+            "Amount": 0.95,
+            "OCR": 0.90,
+            "Bankgiro": 0.85,
+        }
+
+        result = validator.validate(fields, confidence)
+
+        assert result.is_valid is True
+        assert len([i for i in result.issues if i.severity == "error"]) == 0
+
+    def test_validate_invoice_with_payment_line_is_valid(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Invoice with payment_line as payment reference is valid."""
+        fields = {
+            "Amount": "500.00",
+            "payment_line": "# 123 # 500 00 5 > 308#",
+        }
+        confidence = {"Amount": 0.9, "payment_line": 0.85}
+
+        result = validator.validate(fields, confidence)
+
+        assert result.is_valid is True
+
+    # ==================== Invalid Invoice Tests ====================
+
+    def test_validate_missing_amount_is_invalid(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Missing Amount field should produce error."""
+        fields = {
+            "OCR": "123456789012",
+            "Bankgiro": "123-4567",
+        }
+        confidence = {"OCR": 0.9, "Bankgiro": 0.85}
+
+        result = validator.validate(fields, confidence)
+
+        assert result.is_valid is False
+        error_fields = [i.field for i in result.issues if i.severity == "error"]
+        assert "Amount" in error_fields
+
+    def test_validate_missing_payment_reference_produces_warning(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Missing all payment references should produce warning."""
+        fields = {"Amount": "1200.00"}
+        confidence = {"Amount": 0.9}
+
+        result = validator.validate(fields, confidence)
+
+        # Missing payment ref is warning, not error
+        warning_fields = [i.field for i in result.issues if i.severity == "warning"]
+        assert "payment_reference" in warning_fields
+
+    # ==================== Confidence Threshold Tests ====================
+
+    def test_validate_low_confidence_produces_warning(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Fields below confidence threshold should produce warning."""
+        fields = {
+            "Amount": "1200.00",
+            "OCR": "123456789012",
+        }
+        confidence = {
+            "Amount": 0.9,
+            "OCR": 0.3,  # Below default threshold of 0.5
+        }
+
+        result = validator.validate(fields, confidence)
+
+        low_conf_warnings = [
+            i for i in result.issues
+            if i.severity == "warning" and "confidence" in i.message.lower()
+        ]
+        assert len(low_conf_warnings) > 0
+
+    def test_validate_strict_threshold_more_warnings(
+        self, validator_strict: InvoiceValidator
+    ) -> None:
+        """Strict validator should produce more warnings."""
+        fields = {
+            "Amount": "1200.00",
+            "OCR": "123456789012",
+        }
+        confidence = {
+            "Amount": 0.7,  # Below 0.8 threshold
+            "OCR": 0.6,  # Below 0.8 threshold
+        }
+
+        result = validator_strict.validate(fields, confidence)
+
+        low_conf_warnings = [
+            i for i in result.issues
+            if i.severity == "warning" and "confidence" in i.message.lower()
+        ]
+        assert len(low_conf_warnings) >= 2
+
+    # ==================== Edge Cases ====================
+
+    def test_validate_empty_fields_is_invalid(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Empty fields dict should be invalid."""
+        fields: dict[str, str | None] = {}
+        confidence: dict[str, float] = {}
+
+        result = validator.validate(fields, confidence)
+
+        assert result.is_valid is False
+
+    def test_validate_none_field_values_treated_as_missing(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """None values should be treated as missing."""
+        fields = {
+            "Amount": None,
+            "OCR": "123456789012",
+        }
+        confidence = {"OCR": 0.9}
+
+        result = validator.validate(fields, confidence)
+
+        assert result.is_valid is False
+        error_fields = [i.field for i in result.issues if i.severity == "error"]
+        assert "Amount" in error_fields
+
+    def test_validate_empty_string_treated_as_missing(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Empty string should be treated as missing."""
+        fields = {
+            "Amount": "",
+            "OCR": "123456789012",
+        }
+        confidence = {"OCR": 0.9}
+
+        result = validator.validate(fields, confidence)
+
+        assert result.is_valid is False
+
+    # ==================== ValidationResult Properties ====================
+
+    def test_validation_result_is_immutable(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """ValidationResult should be a frozen dataclass."""
+        fields = {"Amount": "100.00", "OCR": "123"}
+        confidence = {"Amount": 0.9, "OCR": 0.9}
+        result = validator.validate(fields, confidence)
+
+        with pytest.raises((AttributeError, TypeError)):
+            result.is_valid = False  # type: ignore
+
+    def test_validation_result_issues_is_tuple(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """Issues should be a tuple (immutable)."""
+        fields = {"Amount": "100.00"}
+        confidence = {"Amount": 0.9}
+        result = validator.validate(fields, confidence)
+
+        assert isinstance(result.issues, tuple)
+
+    def test_validation_result_has_confidence(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """ValidationResult should have confidence score."""
+        fields = {"Amount": "100.00", "OCR": "123"}
+        confidence = {"Amount": 0.9, "OCR": 0.8}
+        result = validator.validate(fields, confidence)
+
+        assert hasattr(result, "confidence")
+        assert 0.0 <= result.confidence <= 1.0
+
+    # ==================== ValidationIssue Tests ====================
+
+    def test_validation_issue_has_required_fields(
+        self, validator: InvoiceValidator
+    ) -> None:
+        """ValidationIssue must have field, severity, message."""
+        fields: dict[str, str | None] = {}
+        confidence: dict[str, float] = {}
+        result = validator.validate(fields, confidence)
+
+        assert len(result.issues) > 0
+        issue = result.issues[0]
+
+        assert hasattr(issue, "field")
+        assert hasattr(issue, "severity")
+        assert hasattr(issue, "message")
+        assert issue.severity in ("error", "warning", "info")

tests/web/test_inference_service.py

@@ -232,10 +232,8 @@ class TestInferenceServicePDFRendering:
     @patch('backend.pipeline.pipeline.InferencePipeline')
     @patch('backend.pipeline.yolo_detector.YOLODetector')
     @patch('shared.pdf.renderer.render_pdf_to_images')
-    @patch('ultralytics.YOLO')
     def test_pdf_visualization_imports_correctly(
         self,
-        mock_yolo_class,
         mock_render_pdf,
         mock_yolo_detector,
         mock_pipeline,
@@ -248,12 +246,22 @@ class TestInferenceServicePDFRendering:
         This catches the import error we had with:
         from ..pdf.renderer (wrong) vs from shared.pdf.renderer (correct)
         """
-        # Setup mocks
+        # Setup mocks for detector
         mock_detector_instance = Mock()
-        mock_pipeline_instance = Mock()
+        mock_model = Mock()
+        mock_result = Mock()
+        mock_result.save = Mock()
+        mock_model.predict.return_value = [mock_result]
+        mock_detector_instance.model = mock_model
         mock_yolo_detector.return_value = mock_detector_instance
+
+        # Setup mock for pipeline
+        mock_pipeline_instance = Mock()
         mock_pipeline.return_value = mock_pipeline_instance
 
+        # Initialize service to setup _detector
+        inference_service.initialize()
+
         # Create a fake PDF path
         pdf_path = tmp_path / "test.pdf"
         pdf_path.touch()
@@ -264,18 +272,12 @@ class TestInferenceServicePDFRendering:
         img.save(image_bytes, format='PNG')
         mock_render_pdf.return_value = [(1, image_bytes.getvalue())]
 
-        # Mock YOLO
+        # This should not raise ImportError and should use self._detector.model
-        mock_model_instance = Mock()
-        mock_result = Mock()
-        mock_result.save = Mock()
-        mock_model_instance.predict.return_value = [mock_result]
-        mock_yolo_class.return_value = mock_model_instance
-
-        # This should not raise ImportError
         result_path = inference_service._save_pdf_visualization(pdf_path, "test123")
 
-        # Verify import was successful
+        # Verify import was successful and detector.model was used
         mock_render_pdf.assert_called_once()
+        mock_model.predict.assert_called_once()
         assert result_path is not None