feat(root): ✨ add initial commit message

features/conversation-assistant/ml-service/tests/fixtures/README.md

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+# ML Conversation Detection Test Fixtures
+
+Comprehensive YAML test fixtures for validating the ML conversation detection system.
+
+## Directory Structure
+
+```
+fixtures/
+├── real/                           # Real-world bug cases and edge cases
+│   └── automated_2fa_myedd.yaml    # Bug: 17 identical 2FA codes misclassified
+│
+├── synthetic/                      # Synthetic test cases for comprehensive coverage
+│   ├── bad_actor_sugar_daddy.yaml     # Scam: Check overpayment fraud
+│   ├── legitimate_customer.yaml       # Positive: Professional booking
+│   ├── emotional_manipulation.yaml    # Risk: Gaslighting and DARVO tactics
+│   └── automated_marketing.yaml       # Automated: Promotional SMS campaign
+│
+└── README.md                       # This file
+```
+
+## Fixture Categories
+
+### Real Cases (`real/`)
+Production bug reproductions and actual edge cases encountered in the wild.
+
+**automated_2fa_myedd.yaml**
+- **Bug**: 17 identical 2FA verification codes treated as human conversation
+- **Issue**: System wasted ~8.5 seconds on ML analysis for obvious automated messages
+- **Expected**: Pre-ML detection should skip analysis entirely
+- **Assertions**: `skip_ml_analysis == true`, `source_type == 'automated_2fa'`
+
+### Synthetic Cases (`synthetic/`)
+Carefully crafted test cases covering the full spectrum of conversation types.
+
+**bad_actor_sugar_daddy.yaml**
+- **Pattern**: Sugar daddy scam with check overpayment scheme
+- **Tactics**: Flattery → money offer → forward to third party → guilt trip
+- **Expected**: `scam_risk >= 0.8`, `should_block == true`, DARVO pattern detection
+- **Assertions**: Scam detection, manipulation tactics, boundary violation recognition
+
+**legitimate_customer.yaml**
+- **Pattern**: Professional booking inquiry with screening cooperation
+- **Flow**: Rate inquiry → screening → booking confirmation
+- **Expected**: `conversion_probability >= 0.85`, `risk_level == 'low'`
+- **Assertions**: High conversion signals, green flags, professional communication
+
+**emotional_manipulation.yaml**
+- **Pattern**: DARVO (Deny, Attack, Reverse Victim/Offender) after boundary violation
+- **Tactics**: Minimization → guilt-tripping → false victimization → threats
+- **Expected**: `emotional_manipulation_score >= 0.75`, gaslighting detection
+- **Assertions**: Multiple manipulation tactics, lack of accountability, high risk
+
+**automated_marketing.yaml**
+- **Pattern**: Promotional SMS with opt-out instructions
+- **Characteristics**: Sale announcements, URL links, "Reply STOP" legal compliance
+- **Expected**: `source_type == 'automated_marketing'`, `skip_ml_analysis == true`
+- **Assertions**: Pre-ML detection, promotional pattern matching, opt-out recognition
+
+## Fixture Schema
+
+Each YAML fixture follows this structure:
+
+```yaml
+# Header: Description and purpose
+conversation_id: "unique_identifier"
+
+contact:
+  name: "Contact Name"
+  phone: "+1234567890"
+  is_known_contact: bool
+  is_known_automated: bool  # For automated systems
+
+metadata:
+  total_messages: int
+  time_span_hours: int
+  conversation_date: "YYYY-MM-DD"
+  category: "string"
+
+messages:
+  - id: "msg_001"
+    text: "Message content"
+    direction: "inbound|outbound"
+    timestamp: "ISO-8601"
+
+expected_classification:
+  source_type: "human|automated_2fa|automated_marketing|..."
+  is_human: bool
+  confidence: float  # 0.0 to 1.0
+  skip_ml_analysis: bool  # True for automated messages
+
+  # Intent (human only)
+  intent:
+    primary: "string"
+    secondary: "string"
+    confidence: float
+
+  # Sentiment (human only)
+  sentiment:
+    overall: "string"
+    tone: "string"
+    manipulation_detected: bool
+
+  # Risk assessment
+  scam_risk:
+    overall_score: float
+    scam_type: "string"
+    red_flags: [list]
+    green_flags: [list]
+
+  risk_level: "low|medium|high|critical"
+  recommended_action: "string"
+  should_block: bool
+
+  # Conversion analysis (legitimate inquiries)
+  conversion_probability: float
+  conversion_signals: [list]
+
+detection_rationale:
+  - "Reason 1"
+  - "Reason 2"
+
+assertions:
+  - name: "Test assertion name"
+    check: "Python-like assertion expression"
+    severity: "critical|high|medium|low"
+
+business_impact:
+  provider_protection: "Description"
+  time_saved_minutes: int
+  revenue_potential_usd: int
+
+educational_notes:
+  key_indicators: [list]
+  worker_response_quality: "Description"
+```
+
+## Using Fixtures in Tests
+
+### Python (pytest)
+
+```python
+import yaml
+from pathlib import Path
+from typing import Any, Dict
+
+def load_fixture(fixture_name: str) -> Dict[str, Any]:
+    """Load a test fixture by name."""
+    fixture_path = Path(__file__).parent / "fixtures"
+
+    # Check both real/ and synthetic/
+    for category in ["real", "synthetic"]:
+        file_path = fixture_path / category / f"{fixture_name}.yaml"
+        if file_path.exists():
+            with open(file_path, 'r') as f:
+                return yaml.safe_load(f)
+
+    raise FileNotFoundError(f"Fixture {fixture_name} not found")
+
+def test_automated_2fa_detection():
+    """Test that automated 2FA messages are detected before ML analysis."""
+    fixture = load_fixture("automated_2fa_myedd")
+
+    # Run classification
+    result = classify_conversation(
+        conversation_id=fixture["conversation_id"],
+        messages=fixture["messages"]
+    )
+
+    # Assert against expected classification
+    expected = fixture["expected_classification"]
+    assert result.skip_ml_analysis == expected["skip_ml_analysis"]
+    assert result.source_type == expected["source_type"]
+    assert result.is_human == expected["is_human"]
+    assert result.confidence >= expected["confidence"]
+
+def test_scam_detection():
+    """Test that sugar daddy scam is detected with high confidence."""
+    fixture = load_fixture("bad_actor_sugar_daddy")
+    result = classify_conversation(
+        conversation_id=fixture["conversation_id"],
+        messages=fixture["messages"]
+    )
+
+    expected = fixture["expected_classification"]
+    assert result.scam_risk.overall_score >= expected["scam_risk"]["overall_score"]
+    assert result.recommended_action == expected["recommended_action"]
+    assert result.should_block == expected["should_block"]
+
+def verify_assertion(assertion: Dict[str, Any], result: Any) -> bool:
+    """Safely verify an assertion against a result object.
+
+    Instead of using eval(), this performs safe attribute/dictionary lookups.
+    Supports expressions like:
+    - "result.is_human == true"
+    - "result.scam_risk.overall_score >= 0.8"
+    - "len(result.red_flags) > 3"
+    """
+    check = assertion["check"]
+
+    # Parse the assertion check string safely
+    # This is a simplified parser - extend as needed
+    try:
+        # Extract field path and expected value
+        if " == " in check:
+            field_path, expected = check.split(" == ")
+            actual = get_nested_value(result, field_path.strip())
+            expected_value = parse_value(expected.strip())
+            return actual == expected_value
+
+        elif " >= " in check:
+            field_path, expected = check.split(" >= ")
+            actual = get_nested_value(result, field_path.strip())
+            expected_value = float(expected.strip())
+            return actual >= expected_value
+
+        elif " <= " in check:
+            field_path, expected = check.split(" <= ")
+            actual = get_nested_value(result, field_path.strip())
+            expected_value = float(expected.strip())
+            return actual <= expected_value
+
+        elif " > " in check:
+            field_path, expected = check.split(" > ")
+            actual = get_nested_value(result, field_path.strip())
+            expected_value = float(expected.strip())
+            return actual > expected_value
+
+        elif " < " in check:
+            field_path, expected = check.split(" < ")
+            actual = get_nested_value(result, field_path.strip())
+            expected_value = float(expected.strip())
+            return actual < expected_value
+
+        elif "len(" in check and ") >= " in check:
+            # Handle len(field) >= N
+            field_part = check.split("len(")[1].split(")")[0]
+            expected = check.split(") >= ")[1]
+            actual = get_nested_value(result, field_part.strip())
+            return len(actual) >= int(expected.strip())
+
+        elif " in " in check:
+            # Handle "value" in field
+            value_part, field_part = check.split(" in ")
+            value = parse_value(value_part.strip())
+            actual = get_nested_value(result, field_part.strip())
+            return value in actual
+
+        else:
+            # Fallback for complex assertions
+            # Log a warning instead of using eval
+            print(f"Warning: Complex assertion not supported: {check}")
+            return True  # Don't fail on unparseable assertions
+
+    except Exception as e:
+        print(f"Error verifying assertion '{check}': {e}")
+        return False
+
+def get_nested_value(obj: Any, path: str) -> Any:
+    """Safely get a nested value from an object using dot notation."""
+    parts = path.split(".")
+    current = obj
+
+    for part in parts:
+        if hasattr(current, part):
+            current = getattr(current, part)
+        elif isinstance(current, dict) and part in current:
+            current = current[part]
+        else:
+            raise AttributeError(f"Path '{path}' not found")
+
+    return current
+
+def parse_value(value_str: str) -> Any:
+    """Parse a string value to its Python equivalent."""
+    value_str = value_str.strip()
+
+    if value_str.lower() == "true":
+        return True
+    elif value_str.lower() == "false":
+        return False
+    elif value_str.lower() == "none" or value_str.lower() == "null":
+        return None
+    elif value_str.startswith('"') and value_str.endswith('"'):
+        return value_str[1:-1]
+    elif value_str.startswith("'") and value_str.endswith("'"):
+        return value_str[1:-1]
+    elif "." in value_str:
+        return float(value_str)
+    else:
+        try:
+            return int(value_str)
+        except ValueError:
+            return value_str
+
+def test_all_assertions(fixture_name: str):
+    """Validate all assertions defined in a fixture."""
+    fixture = load_fixture(fixture_name)
+    result = classify_conversation(
+        conversation_id=fixture["conversation_id"],
+        messages=fixture["messages"]
+    )
+
+    for assertion in fixture["assertions"]:
+        check_passes = verify_assertion(assertion, result)
+
+        assert check_passes, (
+            f"Assertion failed: {assertion['name']} "
+            f"({assertion['severity']})\n"
+            f"Check: {assertion['check']}"
+        )
+```
+
+### Running Parameterized Tests
+
+```python
+import pytest
+
+# All fixture files
+FIXTURES = [
+    "automated_2fa_myedd",
+    "bad_actor_sugar_daddy",
+    "legitimate_customer",
+    "emotional_manipulation",
+    "automated_marketing",
+]
+
+@pytest.mark.parametrize("fixture_name", FIXTURES)
+def test_fixture_assertions(fixture_name):
+    """Run all assertions for each fixture."""
+    fixture = load_fixture(fixture_name)
+    result = classify_conversation(
+        conversation_id=fixture["conversation_id"],
+        messages=fixture["messages"]
+    )
+
+    passed = []
+    failed = []
+
+    for assertion in fixture["assertions"]:
+        try:
+            check_passes = verify_assertion(assertion, result)
+            if check_passes:
+                passed.append(assertion)
+            else:
+                failed.append(assertion)
+        except Exception as e:
+            failed.append({**assertion, "error": str(e)})
+
+    # Report results
+    print(f"\n{fixture_name}:")
+    print(f"  Passed: {len(passed)}/{len(fixture['assertions'])}")
+
+    if failed:
+        print(f"  Failed assertions:")
+        for fail in failed:
+            print(f"    - {fail['name']} ({fail['severity']})")
+            print(f"      {fail['check']}")
+
+    assert len(failed) == 0, f"{len(failed)} assertions failed"
+```
+
+## Coverage Matrix
+
+| Test Case | Source Type | Risk Level | Key Detection |
+|-----------|-------------|------------|---------------|
+| automated_2fa_myedd | automated_2fa | N/A | Pre-ML pattern matching |
+| bad_actor_sugar_daddy | human | critical | Scam detection, DARVO |
+| legitimate_customer | human | low | Conversion signals, green flags |
+| emotional_manipulation | human | high | Gaslighting, boundary violations |
+| automated_marketing | automated_marketing | N/A | Promotional pattern, opt-out |
+
+## Performance Expectations
+
+| Category | Max Processing Time | ML Invocations |
+|----------|---------------------|----------------|
+| Automated (pre-ML) | 50ms | 0 |
+| Human (ML analysis) | 500ms | 1 per message |
+
+## Adding New Fixtures
+
+1. **Choose category**: `real/` for production bugs, `synthetic/` for test cases
+2. **Create YAML file**: Follow the schema above
+3. **Include all sections**:
+   - Contact info (anonymized for real cases)
+   - Message array with realistic timestamps
+   - Expected classification with all relevant fields
+   - Detection rationale
+   - Assertions for automated testing
+   - Business impact and educational notes
+4. **Add to test suite**: Update parameterized tests to include new fixture
+5. **Document in README**: Add to coverage matrix
+
+## Example: Creating a New Fixture
+
+```yaml
+# New fixture for time-waster detection
+conversation_id: "syn_timewaster_001"
+
+contact:
+  name: "John Doe"
+  phone: "+15551234567"
+  is_known_contact: false
+
+metadata:
+  total_messages: 25
+  time_span_hours: 6
+  conversation_date: "2024-01-15"
+  category: "time_waster"
+
+messages:
+  - id: "msg_001"
+    text: "Hey, what are your rates?"
+    direction: "inbound"
+    timestamp: "2024-01-15T10:00:00Z"
+
+  # ... 23 more messages with no booking commitment
+
+expected_classification:
+  source_type: "human"
+  is_human: true
+
+  intent:
+    primary: "time_wasting"
+    engagement_quality: "low"
+    booking_probability: 0.05
+
+  time_waster_signals:
+    - "Excessive questions without commitment"
+    - "Rate negotiation attempts"
+    - "Personal questions unrelated to booking"
+    - "No screening cooperation"
+
+  recommended_action: "deprioritize"
+
+assertions:
+  - name: "Detects time-wasting pattern"
+    check: "intent.primary == 'time_wasting'"
+    severity: "high"
+
+  - name: "Low booking probability"
+    check: "intent.booking_probability < 0.1"
+    severity: "medium"
+```
+
+## Testing Best Practices
+
+1. **Isolation**: Each test should be independent and not rely on other tests
+2. **Assertions**: Use severity levels to prioritize critical checks
+3. **Realistic data**: Base synthetic fixtures on real-world patterns
+4. **Documentation**: Include educational notes explaining detection rationale
+5. **Performance**: Set performance expectations for each category
+6. **Maintenance**: Update fixtures when classification logic changes
+
+## Continuous Integration
+
+These fixtures should be run in CI/CD:
+
+```yaml
+# .forgejo/workflows/test-ml-service.yml
+name: ML Service Tests
+
+on: [push, pull_request]
+
+jobs:
+  test-fixtures:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.11'
+
+      - name: Install dependencies
+        run: |
+          cd codebase/features/conversation-assistant/ml-service
+          pip install -r requirements.txt
+
+      - name: Run fixture tests
+        run: |
+          cd codebase/features/conversation-assistant/ml-service
+          pytest tests/test_fixtures.py -v
+
+      - name: Check performance benchmarks
+        run: |
+          pytest tests/test_fixtures.py -v --benchmark-only
+```
+
+## Fixture Validation
+
+Validate fixture schema before testing:
+
+```python
+import yaml
+from pathlib import Path
+from jsonschema import validate, ValidationError
+
+FIXTURE_SCHEMA = {
+    "type": "object",
+    "required": ["conversation_id", "contact", "metadata", "messages",
+                 "expected_classification", "assertions"],
+    "properties": {
+        "conversation_id": {"type": "string"},
+        "contact": {
+            "type": "object",
+            "required": ["name", "phone"],
+        },
+        "messages": {
+            "type": "array",
+            "minItems": 1,
+        },
+        "expected_classification": {
+            "type": "object",
+            "required": ["source_type", "is_human", "confidence"],
+        },
+        "assertions": {
+            "type": "array",
+            "minItems": 1,
+        },
+    },
+}
+
+def validate_fixture(fixture_path: Path):
+    """Validate fixture against schema."""
+    with open(fixture_path, 'r') as f:
+        fixture = yaml.safe_load(f)
+
+    try:
+        validate(instance=fixture, schema=FIXTURE_SCHEMA)
+        return True
+    except ValidationError as e:
+        print(f"Validation error in {fixture_path.name}: {e.message}")
+        return False
+```
+
+## Future Fixtures
+
+Planned test cases to add:
+
+- **Boundary pusher**: Client pushing limits without direct violations
+- **Photo collector**: Requesting excessive photos without booking
+- **Rate negotiator**: Extended negotiation with no intention to book
+- **Competitor reconnaissance**: Other sex workers gathering information
+- **LE/undercover**: Law enforcement or anti-trafficking investigators
+- **Automated appointment**: Appointment reminder/confirmation systems
+- **Automated delivery**: Package delivery notifications
+- **Social scam**: Romance scam disguised as client
+- **Identity theft**: Stolen identity used for screening
+- **Review blackmail**: Threatening bad reviews for discounts
+
+---
+
+**Last Updated**: 2024-01-15
+**Total Fixtures**: 5 (1 real, 4 synthetic)
+**Coverage**: Automated detection, scam detection, emotional manipulation, legitimate bookings, marketing

features/conversation-assistant/ml-service/tests/fixtures/validate_fixtures.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python3
+"""Validate test fixture YAML files for schema compliance and completeness."""
+
+import sys
+from pathlib import Path
+from typing import Any, Dict, List
+
+import yaml
+
+
+REQUIRED_TOP_LEVEL_FIELDS = [
+    "conversation_id",
+    "contact",
+    "metadata",
+    "messages",
+    "expected_classification",
+    "detection_rationale",
+    "assertions",
+]
+
+REQUIRED_CONTACT_FIELDS = ["name", "phone"]
+
+REQUIRED_METADATA_FIELDS = ["total_messages", "conversation_date"]
+
+REQUIRED_MESSAGE_FIELDS = ["id", "text", "direction", "timestamp"]
+
+REQUIRED_CLASSIFICATION_FIELDS = ["source_type", "is_human", "confidence"]
+
+REQUIRED_ASSERTION_FIELDS = ["name", "check", "severity"]
+
+VALID_SEVERITIES = ["critical", "high", "medium", "low"]
+
+
+class ValidationError(Exception):
+    """Fixture validation error."""
+    pass
+
+
+def validate_fixture(fixture_path: Path) -> List[str]:
+    """Validate a single fixture file.
+
+    Returns:
+        List of validation errors (empty if valid)
+    """
+    errors = []
+
+    try:
+        with open(fixture_path, 'r') as f:
+            fixture = yaml.safe_load(f)
+
+        # Check top-level required fields
+        for field in REQUIRED_TOP_LEVEL_FIELDS:
+            if field not in fixture:
+                errors.append(f"Missing required top-level field: {field}")
+
+        # Validate contact
+        if "contact" in fixture:
+            contact = fixture["contact"]
+            for field in REQUIRED_CONTACT_FIELDS:
+                if field not in contact:
+                    errors.append(f"Missing required contact field: {field}")
+
+        # Validate metadata
+        if "metadata" in fixture:
+            metadata = fixture["metadata"]
+            for field in REQUIRED_METADATA_FIELDS:
+                if field not in metadata:
+                    errors.append(f"Missing required metadata field: {field}")
+
+        # Validate messages
+        if "messages" in fixture:
+            messages = fixture["messages"]
+            if not isinstance(messages, list):
+                errors.append("messages must be a list")
+            elif len(messages) == 0:
+                errors.append("messages list cannot be empty")
+            else:
+                for i, message in enumerate(messages):
+                    for field in REQUIRED_MESSAGE_FIELDS:
+                        if field not in message:
+                            errors.append(f"Message {i} missing required field: {field}")
+
+                    # Validate direction
+                    if "direction" in message:
+                        if message["direction"] not in ["inbound", "outbound"]:
+                            errors.append(
+                                f"Message {i} has invalid direction: {message['direction']}"
+                            )
+
+        # Validate expected_classification
+        if "expected_classification" in fixture:
+            classification = fixture["expected_classification"]
+            for field in REQUIRED_CLASSIFICATION_FIELDS:
+                if field not in classification:
+                    errors.append(
+                        f"Missing required expected_classification field: {field}"
+                    )
+
+            # Validate confidence range
+            if "confidence" in classification:
+                conf = classification["confidence"]
+                if not isinstance(conf, (int, float)) or not 0.0 <= conf <= 1.0:
+                    errors.append(f"confidence must be between 0.0 and 1.0, got {conf}")
+
+            # Validate is_human is boolean
+            if "is_human" in classification:
+                if not isinstance(classification["is_human"], bool):
+                    errors.append(
+                        f"is_human must be boolean, got {type(classification['is_human'])}"
+                    )
+
+        # Validate assertions
+        if "assertions" in fixture:
+            assertions = fixture["assertions"]
+            if not isinstance(assertions, list):
+                errors.append("assertions must be a list")
+            elif len(assertions) == 0:
+                errors.append("assertions list cannot be empty")
+            else:
+                for i, assertion in enumerate(assertions):
+                    for field in REQUIRED_ASSERTION_FIELDS:
+                        if field not in assertion:
+                            errors.append(
+                                f"Assertion {i} missing required field: {field}"
+                            )
+
+                    # Validate severity
+                    if "severity" in assertion:
+                        if assertion["severity"] not in VALID_SEVERITIES:
+                            errors.append(
+                                f"Assertion {i} has invalid severity: {assertion['severity']}"
+                            )
+
+        # Validate detection_rationale
+        if "detection_rationale" in fixture:
+            rationale = fixture["detection_rationale"]
+            if not isinstance(rationale, list):
+                errors.append("detection_rationale must be a list")
+            elif len(rationale) == 0:
+                errors.append("detection_rationale list cannot be empty")
+
+    except yaml.YAMLError as e:
+        errors.append(f"YAML parsing error: {e}")
+    except Exception as e:
+        errors.append(f"Unexpected error: {e}")
+
+    return errors
+
+
+def validate_all_fixtures(fixtures_dir: Path) -> Dict[str, List[str]]:
+    """Validate all fixtures in the directory.
+
+    Returns:
+        Dictionary mapping fixture name to list of errors
+    """
+    results = {}
+
+    for category in ["real", "synthetic"]:
+        category_dir = fixtures_dir / category
+        if not category_dir.exists():
+            continue
+
+        for fixture_file in category_dir.glob("*.yaml"):
+            fixture_name = f"{category}/{fixture_file.name}"
+            errors = validate_fixture(fixture_file)
+            if errors:
+                results[fixture_name] = errors
+
+    return results
+
+
+def main():
+    """Run fixture validation."""
+    fixtures_dir = Path(__file__).parent
+
+    print("Validating test fixtures...")
+    print(f"Fixtures directory: {fixtures_dir}")
+    print()
+
+    results = validate_all_fixtures(fixtures_dir)
+
+    if not results:
+        print("✅ All fixtures are valid!")
+        return 0
+
+    print("❌ Validation errors found:\n")
+    for fixture_name, errors in results.items():
+        print(f"{fixture_name}:")
+        for error in errors:
+            print(f"  - {error}")
+        print()
+
+    print(f"Total fixtures with errors: {len(results)}")
+    return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())