from core.domain_classifier import domain_classifier
from core.legal_intelligence import legal_intelligence
from layers.layer_4_logic.smart_money import smart_money
from layers.layer_4_logic.pos_engine import pos_engine
import re

def analyze(user_query: str, context: dict, domain_info: dict):
    domain = domain_info['primary_domain']
    jurisdiction = context['country_code']
    
    # 1. Response Strategy
    strategy = domain_classifier.get_response_strategy(
        domain, jurisdiction, legal_intelligence
    )
    
    # 2. Financial Logic
    analysis = {
        "domain": domain,
        "strategy": strategy,
        "actions_requested": []
    }
    
    # Check for specific actions (POS, payment requests, etc.)
    if domain == "PAYMENTS" or domain == "BUSINESS_FINANCE":
        sale_record = pos_engine.parse_sale(user_query)
        if sale_record:
            analysis['actions_requested'].append({
                "type": "record_sale",
                "data": sale_record
            })
            
            # Check for SMS receipt/payment request
            phone_match = re.search(
                r'(?:tuma|send|receipt to|risiti|omba malipo)\s+(?:kwa\s+)?(\+?0?[17]\d{8})',
                user_query,
                re.IGNORECASE
            )
            if phone_match:
                phone = phone_match.group(1)
                payment_trigger = any(
                    trigger in user_query.lower()
                    for trigger in ['charge', 'collect', 'omba malipo', 'request payment']
                )
                if payment_trigger:
                    analysis['actions_requested'].append({
                        "type": "request_payment",
                        "data": {"phone": phone, "amount": sale_record['revenue']}
                    })
                else:
                    analysis['actions_requested'].append({
                        "type": "send_receipt",
                        "data": {"phone": phone, "sale_record": sale_record}
                    })

    # 3. Calculations
    if domain == "TAXATION":
        # Simplified: identify if it's PAYE or Turnover
        if "paye" in user_query.lower() or "salary" in user_query.lower():
            analysis['calculation_type'] = "PAYE"
        elif "turnover" in user_query.lower() or "business" in user_query.lower():
            analysis['calculation_type'] = "TURNOVER_TAX"

    return analysis

def run_calculators(calculators: list, query: str, jurisdiction: str, currency: str):
    from core.financial_calculator import calculator
    # Extract numbers
    numbers = [float(n.replace(',', '')) for n in re.findall(r'\b\d+(?:,\d+)*(?:\.\d+)?\b', query)]
    val = numbers[0] if numbers else 0
    
    results = []
    for c in calculators:
        if c == 'paye':
            res = calculator.run(c, monthly_salary=val, currency=currency)
        elif c == 'turnover_tax':
            res = calculator.run(c, annual_revenue=val * 12, currency=currency)
        elif c == 'fuliza_cost':
            res = calculator.run(c, amount=val, days=30, currency=currency)
        else:
            res = {}
        if res and res.get("formatted"):
            results.append(res.get("formatted"))
    
    return "\n\n".join(results) if results else None

def check_fraud(transactions, user_hash):
    return {"aml_flagged": False, "flags": []}

def apply_legal_rules(rules, domain, jurisdiction):
    return {"disclaimer": None, "rules_used": [], "refer_professional": False}

def detect_stress(query):
    return False

def query_user_data(query, transactions, profile, language, currency):
    from core.data_conversation import data_conversation
    data_query = data_conversation.classify_query(query)
    if data_query.get('is_data_query'):
        result = data_conversation.execute_query(
            query_classification=data_query,
            transactions=transactions,
            user_goals=[],
            language=language
        )
        if result and 'error' not in result:
            return data_conversation.format_response(
                result=result,
                original_query=query,
                language=language,
                currency=currency
            )
    return None

def assess_risk(profile, query):
    return None