class ComplianceScraper:
    def __init__(self, respect_robots=True, rate_limit=True):
        self.respect_robots = respect_robots
        self.rate_limit = rate_limit
        self.session_headers = {
            'User-Agent': 'ComplianceBot/1.0 (+https://yourcompany.com/bot-policy)',
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
            'Accept-Language': 'en-US,en;q=0.5',
            'Accept-Encoding': 'gzip, deflate',
            'Connection': 'keep-alive',
        }
    
    def check_robots_txt(self, url):
        """Verify robots.txt compliance before scraping"""
        import urllib.robotparser
        
        robots_url = urljoin(url, '/robots.txt')
        rp = urllib.robotparser.RobotFileParser()
        rp.set_url(robots_url)
        rp.read()
        
        user_agent = self.session_headers['User-Agent']
        return rp.can_fetch(user_agent, url)
    
    def implement_rate_limiting(self, domain, requests_per_minute=60):
        """Implement respectful rate limiting"""
        import time
        from collections import defaultdict
        
        if not hasattr(self, 'request_times'):
            self.request_times = defaultdict(list)
        
        now = time.time()
        domain_requests = self.request_times[domain]
        
        # Remove requests older than 1 minute
        domain_requests[:] = [req_time for req_time in domain_requests 
                             if now - req_time < 60]
        
        # Check rate limit
        if len(domain_requests) >= requests_per_minute:
            sleep_time = 60 - (now - domain_requests[0])
            time.sleep(sleep_time)
        
        domain_requests.append(now)
    
    def respect_meta_tags(self, soup):
        """Check for meta tags indicating scraping preferences"""
        
        # Check for noindex, nofollow, noarchive directives
        robots_meta = soup.find('meta', attrs={'name': 'robots'})
        if robots_meta:
            content = robots_meta.get('content', '').lower()
            if 'noindex' in content or 'noarchive' in content:
                return False
        
        return True

class DataMinimizationScraper:
    def __init__(self, purpose, retention_period):
        self.purpose = purpose
        self.retention_period = retention_period
        self.collected_fields = self._define_necessary_fields()
    
    def _define_necessary_fields(self):
        """Define only fields necessary for stated purpose"""
        
        field_definitions = {
            'competitive_analysis': [
                'product_name', 'price', 'features', 'availability'
            ],
            'market_research': [
                'company_name', 'industry', 'location', 'size_indicators'
            ],
            'lead_generation': [
                'company_name', 'contact_info', 'business_indicators'
            ]
        }
        
        return field_definitions.get(self.purpose, [])
    
    def extract_minimal_data(self, content):
        """Extract only necessary data fields"""
        
        extracted_data = {}
        
        for field in self.collected_fields:
            field_value = self._extract_field(content, field)
            if field_value:
                extracted_data[field] = field_value
        
        # Add metadata for compliance tracking
        extracted_data['_metadata'] = {
            'collection_date': datetime.now().isoformat(),
            'purpose': self.purpose,
            'retention_until': (datetime.now() + self.retention_period).isoformat(),
            'legal_basis': self._determine_legal_basis(field)
        }
        
        return extracted_data

class DataSubjectRightsManager:
    def __init__(self, data_store):
        self.data_store = data_store
        self.deletion_requests = []
        self.opt_out_registry = set()
    
    def process_deletion_request(self, identifier, identifier_type='email'):
        """Process right to deletion requests"""
        
        # Find all records associated with the identifier
        records_to_delete = self.data_store.find_records(
            identifier_type, identifier
        )
        
        # Log the deletion request for audit
        deletion_log = {
            'request_id': generate_uuid(),
            'identifier': identifier,
            'identifier_type': identifier_type,
            'request_date': datetime.now(),
            'records_found': len(records_to_delete),
            'status': 'processing'
        }
        
        # Execute deletion
        deleted_count = self.data_store.delete_records(records_to_delete)
        
        # Update log
        deletion_log['status'] = 'completed'
        deletion_log['records_deleted'] = deleted_count
        deletion_log['completion_date'] = datetime.now()
        
        self.deletion_requests.append(deletion_log)
        
        return deletion_log
    
    def register_opt_out(self, identifier):
        """Register opt-out preferences"""
        
        self.opt_out_registry.add(identifier)
        
        # Also process as deletion request for existing data
        self.process_deletion_request(identifier)
    
    def check_opt_out_status(self, identifier):
        """Check if identifier has opted out"""
        
        return identifier in self.opt_out_registry

class InternationalComplianceManager:
    def __init__(self):
        self.adequacy_decisions = {
            'EU_approved': ['US', 'CA', 'JP', 'KR', 'NZ', 'CH', 'UK'],
            'restricted': ['CN', 'RU'],
            'requires_safeguards': ['IN', 'BR', 'MX']
        }
        
        self.data_localization_requirements = {
            'RU': 'personal_data_local_storage',
            'CN': 'critical_data_local_processing',
            'IN': 'sensitive_data_local_storage'
        }
    
    def assess_transfer_legality(self, data_origin, data_destination, data_type):
        """Assess legality of international data transfer"""
        
        assessment = {
            'transfer_allowed': False,
            'safeguards_required': [],
            'restrictions': [],
            'legal_basis': None
        }
        
        # Check adequacy decisions
        if data_destination in self.adequacy_decisions['EU_approved']:
            assessment['transfer_allowed'] = True
            assessment['legal_basis'] = 'adequacy_decision'
        
        # Check for restricted destinations
        elif data_destination in self.adequacy_decisions['restricted']:
            assessment['transfer_allowed'] = False
            assessment['restrictions'].append('destination_restricted')
        
        # Check for safeguard requirements
        elif data_destination in self.adequacy_decisions['requires_safeguards']:
            assessment['transfer_allowed'] = True
            assessment['safeguards_required'].append('standard_contractual_clauses')
            assessment['legal_basis'] = 'appropriate_safeguards'
        
        # Check data localization requirements
        if data_destination in self.data_localization_requirements:
            localization_req = self.data_localization_requirements[data_destination]
            assessment['restrictions'].append(localization_req)
        
        return assessment
    
    def implement_transfer_safeguards(self, transfer_assessment):
        """Implement required safeguards for data transfer"""
        
        safeguards_implemented = []
        
        for safeguard in transfer_assessment['safeguards_required']:
            if safeguard == 'standard_contractual_clauses':
                # Implement SCC framework
                scc_implementation = self._implement_scc()
                safeguards_implemented.append(scc_implementation)
            
            elif safeguard == 'binding_corporate_rules':
                # Implement BCR framework
                bcr_implementation = self._implement_bcr()
                safeguards_implemented.append(bcr_implementation)
        
        return safeguards_implemented

class ComplianceMonitoringSystem:
    def __init__(self):
        self.compliance_logs = []
        self.risk_assessments = []
        self.audit_trail = []
    
    def log_scraping_activity(self, activity_details):
        """Log all scraping activities for audit purposes"""
        
        compliance_entry = {
            'timestamp': datetime.now().isoformat(),
            'activity_id': generate_uuid(),
            'target_url': activity_details['url'],
            'data_types_collected': activity_details['data_types'],
            'legal_basis': activity_details['legal_basis'],
            'volume_collected': activity_details['record_count'],
            'purpose': activity_details['purpose'],
            'retention_period': activity_details['retention'],
            'geographic_scope': activity_details['geography'],
            'compliance_checks': {
                'robots_txt_respected': activity_details.get('robots_respected', False),
                'rate_limits_applied': activity_details.get('rate_limited', False),
                'consent_verified': activity_details.get('consent_checked', False),
                'opt_outs_respected': activity_details.get('opt_outs_checked', False)
            }
        }
        
        self.compliance_logs.append(compliance_entry)
        return compliance_entry
    
    def conduct_privacy_impact_assessment(self, scraping_project):
        """Conduct privacy impact assessment for scraping projects"""
        
        pia = {
            'assessment_id': generate_uuid(),
            'project_name': scraping_project['name'],
            'assessment_date': datetime.now(),
            'data_protection_officer': scraping_project.get('dpo'),
            'privacy_risks': [],
            'mitigation_measures': [],
            'residual_risks': [],
            'approval_status': 'pending'
        }
        
        # Assess privacy risks
        risks = self._assess_privacy_risks(scraping_project)
        pia['privacy_risks'] = risks
        
        # Identify mitigation measures
        mitigations = self._identify_mitigations(risks)
        pia['mitigation_measures'] = mitigations
        
        # Calculate residual risks
        residual = self._calculate_residual_risks(risks, mitigations)
        pia['residual_risks'] = residual
        
        self.risk_assessments.append(pia)
        return pia
    
    def generate_compliance_report(self, time_period):
        """Generate compliance report for specified time period"""
        
        start_date, end_date = time_period
        
        relevant_logs = [
            log for log in self.compliance_logs
            if start_date <= datetime.fromisoformat(log['timestamp']) <= end_date
        ]
        
        report = {
            'report_period': f"{start_date} to {end_date}",
            'total_activities': len(relevant_logs),
            'compliance_summary': {
                'robots_txt_compliance_rate': self._calculate_compliance_rate(
                    relevant_logs, 'robots_txt_respected'
                ),
                'rate_limiting_compliance_rate': self._calculate_compliance_rate(
                    relevant_logs, 'rate_limits_applied'
                ),
                'consent_verification_rate': self._calculate_compliance_rate(
                    relevant_logs, 'consent_verified'
                ),
                'opt_out_respect_rate': self._calculate_compliance_rate(
                    relevant_logs, 'opt_outs_respected'
                )
            },
            'data_subject_requests': {
                'deletion_requests': len([r for r in self.deletion_requests 
                                        if start_date <= r['request_date'] <= end_date]),
                'opt_out_requests': len([r for r in self.opt_out_registry 
                                       if start_date <= r.get('date', start_date) <= end_date])
            },
            'geographic_distribution': self._analyze_geographic_distribution(relevant_logs),
            'risk_assessments_completed': len([r for r in self.risk_assessments 
                                             if start_date <= r['assessment_date'] <= end_date])
        }
        
        return report

class FinancialServicesCompliance(ComplianceScraper):
    def __init__(self):
        super().__init__()
        self.material_information_filters = [
            'earnings_data', 'merger_announcements', 'regulatory_actions'
        ]
        self.restricted_sources = [
            'insider_networks', 'non_public_databases', 'private_communications'
        ]
    
    def assess_material_information_risk(self, content):
        """Assess risk of collecting material non-public information"""
        
        risk_indicators = {
            'earnings_guidance': ['guidance', 'forecast', 'projection'],
            'merger_activity': ['acquisition', 'merger', 'takeover'],
            'regulatory_issues': ['investigation', 'enforcement', 'violation']
        }
        
        detected_risks = []
        
        for risk_type, keywords in risk_indicators.items():
            if any(keyword.lower() in content.lower() for keyword in keywords):
                detected_risks.append(risk_type)
        
        return {
            'has_material_risk': len(detected_risks) > 0,
            'risk_types': detected_risks,
            'recommendation': 'review_legal' if detected_risks else 'proceed'
        }

class HealthcareCompliance(ComplianceScraper):
    def __init__(self):
        super().__init__()
        self.phi_identifiers = [
            'name', 'address', 'phone', 'email', 'ssn', 'medical_record_number',
            'health_plan_number', 'account_number', 'certificate_number',
            'vehicle_identifier', 'device_identifier', 'web_url', 'ip_address',
            'biometric_identifier', 'photo', 'unique_identifier'
        ]
    
    def detect_phi(self, content):
        """Detect potential PHI in scraped content"""
        
        phi_detected = {}
        
        # Pattern-based detection
        patterns = {
            'ssn': r'\b\d{3}-\d{2}-\d{4}\b',
            'phone': r'\b\d{3}-\d{3}-\d{4}\b',
            'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
            'medical_record': r'\bMRN\s*:?\s*\d+\b'
        }
        
        for phi_type, pattern in patterns.items():
            matches = re.findall(pattern, content)
            if matches:
                phi_detected[phi_type] = len(matches)
        
        return {
            'phi_detected': len(phi_detected) > 0,
            'phi_types': list(phi_detected.keys()),
            'recommendation': 'exclude_content' if phi_detected else 'proceed'
        }
    
    def implement_minimum_necessary(self, data_request):
        """Implement minimum necessary standard"""
        
        necessary_fields = self._determine_necessary_fields(
            data_request['purpose']
        )
        
        filtered_request = {
            field: value for field, value in data_request.items()
            if field in necessary_fields
        }
        
        return filtered_request

from scrapegraphai.graphs import SmartScraperGraph
 
# Configure compliance-first scraping
compliance_config = {
    "llm": {
        "model": "openai/gpt-4",
        "api_key": "your-api-key"
    },
    "compliance": {
        "respect_robots_txt": True,
        "rate_limiting": {
            "requests_per_minute": 60,
            "respect_crawl_delay": True
        },
        "data_minimization": {
            "collect_only_necessary": True,
            "purpose": "competitive_analysis"
        },
        "privacy_protection": {
            "exclude_personal_data": True,
            "anonymization_level": "high"
        },
        "audit_logging": {
            "log_all_requests": True,
            "retain_logs_days": 365
        }
    }
}
 
# Create compliant scraper
compliant_scraper = SmartScraperGraph(
    prompt="Extract only business-relevant product information, excluding any personal data",
    source="https://competitor-website.com/products",
    config=compliance_config
)
 
# Execute with automatic compliance monitoring
result = compliant_scraper.run()

class AutomatedDataClassification:
    def __init__(self):
        self.classification_rules = {
            'personal_data': [
                'name', 'email', 'phone', 'address', 'id_number'
            ],
            'sensitive_data': [
                'health', 'financial', 'biometric', 'political', 'religious'
            ],
            'business_data': [
                'company_name', 'product_info', 'pricing', 'features'
            ],
            'public_data': [
                'press_releases', 'public_filings', 'published_content'
            ]
        }
    
    def classify_extracted_data(self, extracted_data):
        """Automatically classify extracted data for compliance purposes"""
        
        classified_data = {
            'personal_data': [],
            'sensitive_data': [],
            'business_data': [],
            'public_data': []
        }
        
        for field, value in extracted_data.items():
            classification = self._classify_field(field, value)
            classified_data[classification].append({
                'field': field,
                'value': value,
                'confidence': self._get_classification_confidence(field, value)
            })
        
        return classified_data
    
    def apply_protection_measures(self, classified_data):
        """Apply appropriate protection measures based on data classification"""
        
        protected_data = {}
        
        # Handle personal data
        for item in classified_data['personal_data']:
            if item['confidence'] > 0.8:
                # Exclude high-confidence personal data
                continue
            else:
                # Anonymize uncertain cases
                protected_data[item['field']] = self._anonymize_data(item['value'])
        
        # Handle sensitive data
        for item in classified_data['sensitive_data']:
            # Exclude all sensitive data
            continue
        
        # Include business and public data
        for category in ['business_data', 'public_data']:
            for item in classified_data[category]:
                protected_data[item['field']] = item['value']
        
        return protected_data

class ComplianceIncidentResponse:
    def __init__(self):
        self.incident_types = {
            'cease_and_desist': self._handle_cease_and_desist,
            'data_breach': self._handle_data_breach,
            'privacy_complaint': self._handle_privacy_complaint,
            'technical_blocking': self._handle_technical_blocking
        }
    
    def handle_incident(self, incident_type, incident_details):
        """Handle compliance incidents with appropriate response"""
        
        if incident_type in self.incident_types:
            response = self.incident_types[incident_type](incident_details)
        else:
            response = self._handle_general_incident(incident_details)
        
        # Log incident for audit purposes
        self._log_incident(incident_type, incident_details, response)
        
        return response
    
    def _handle_cease_and_desist(self, details):
        """Handle cease and desist requests"""
        
        response_plan = {
            'immediate_actions': [
                'stop_all_scraping_from_domain',
                'notify_legal_team',
                'document_incident'
            ],
            'investigation_steps': [
                'review_legal_basis',
                'analyze_terms_of_service',
                'assess_public_data_nature'
            ],
            'response_options': [
                'comply_with_request',
                'negotiate_limited_access',
                'challenge_if_legally_justified'
            ],
            'timeline': '48_hours_initial_response'
        }
        
        return response_plan