from tealtiger import TealOpenAI, TealEngine, PIIDetector, PromptInjectionDetector

# Risk management system for high-risk AI
engine = TealEngine(
    guardrails=[
        PIIDetector(
            action="block",
            entities=["email", "phone", "ssn", "credit_card"],
            risk_score=9  # High risk
        ),
        PromptInjectionDetector(
            action="block",
            risk_score=8  # High risk
        )
    ],
    mode="ENFORCE"  # Strict enforcement for high-risk systems
)

client = TealOpenAI(api_key="sk-xxx", engine=engine)

from tealtiger import TealOpenAI, TealEngine, ContentModerator

# Data governance for EU AI Act compliance
engine = TealEngine(
    guardrails=[
        ContentModerator(
            action="block",
            categories=["hate", "violence", "sexual", "self-harm"],
            threshold=0.7
        )
    ],
    audit_config={
        "enabled": True,
        "redact_pii": True,  # GDPR compliance
        "retention_days": 365  # EU AI Act record-keeping
    }
)

client = TealOpenAI(api_key="sk-xxx", engine=engine)

# Export TealEngine configuration for technical documentation
config = engine.export_config()

# Save to file for EU AI Act documentation
with open("tealtiger-config-v1.0.json", "w") as f:
    json.dump(config, f, indent=2)

# Audit event schema for compliance reporting
audit_schema = engine.get_audit_schema()

# Configure record-keeping for EU AI Act compliance
engine = TealEngine(
    audit_config={
        "enabled": True,
        "retention_days": 730,  # 2 years for high-risk systems
        "storage_backend": "s3",  # Durable storage
        "bucket": "eu-ai-act-audit-logs",
        "encryption": "AES-256"  # GDPR requirement
    }
)

# Query audit logs for compliance reporting
from tealtiger.audit import AuditQuery

query = AuditQuery(
    start_date="2026-01-01",
    end_date="2026-12-31",
    event_types=["policy_violation", "guardrail_triggered"],
    risk_score_min=7
)

audit_records = engine.query_audit_logs(query)

# Transparent AI system with user notifications
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What is my account balance?"}]
)

# Check if guardrails were triggered
if response.tealtiger_decision.blocked:
    print(f"⚠️ AI Safety Notice: {response.tealtiger_decision.reason}")
    print(f"Reason Code: {response.tealtiger_decision.reason_code}")
    print(f"Risk Score: {response.tealtiger_decision.risk_score}")
    
# Show cost transparency
print(f"💰 Cost: ${response.tealtiger_decision.cost:.4f}")

# Human-in-the-loop for high-risk decisions
engine = TealEngine(
    mode="MONITOR",  # Log violations but don't block
    escalation_config={
        "enabled": True,
        "risk_threshold": 8,  # Escalate high-risk decisions
        "webhook_url": "https://api.company.com/escalate"
    }
)

# Human reviewer can approve or reject
if response.tealtiger_decision.escalated:
    approval = await request_human_approval(response)
    if not approval.approved:
        raise PolicyViolationError("Human reviewer rejected request")

# Cybersecurity for EU AI Act compliance
engine = TealEngine(
    guardrails=[
        PromptInjectionDetector(action="block"),
        InputValidator(max_length=10000, allowed_chars="alphanumeric")
    ],
    rate_limit={
        "requests_per_minute": 60,
        "requests_per_hour": 1000
    },
    budget_limit=1000.00  # Prevent runaway costs
)

# Minimal transparency for limited-risk AI (e.g., chatbots)
engine = TealEngine(
    mode="MONITOR",  # Log but don't block
    transparency={
        "enabled": True,
        "disclosure": "You are interacting with an AI assistant powered by OpenAI GPT-4."
    }
)