import json
import logging
from typing import Dict, List, Optional

import groq
from config import settings
from schemas import Match

logger = logging.getLogger(__name__)


class AIRulesMatcher:
    """
    AI-powered rules engine for post-matching evaluation.
    Uses LLM to intelligently apply custom rules and determine if matches should be:
    - Flagged for manual review (flag_for_review=True)
    - Auto-approved (auto_approve=True)
    """

    def __init__(self):
        self.client = groq.Groq(api_key=settings.GROQ_API_KEY)
        self.model = "llama-3.1-8b-instant"

    def apply_rules_to_matches(
        self, matches: List[Match], ai_rules: Optional[List[Dict]] = None
    ) -> List[Match]:
        """
        Apply AI rules to all matches and add flag_for_review and auto_approve fields.

        Args:
            matches: List of Match objects from the matching engine
            ai_rules: Optional list of custom rules (format: [{"condition": str, "action": str}])

        Returns:
            Enhanced matches with tax_analysis containing flag_for_review and auto_approve
        """
        if not matches:
            return matches

        logger.info(
            f"Applying AI rules to {len(matches)} matches with {len(ai_rules) if ai_rules else 0} custom rules"
        )

        # Built-in rule: currency mismatch should always flag for review
        builtin_rules = [
            {
                "condition": "receipt currency differs from transaction currency",
                "action": "flag_for_review",
            }
        ]

        # Combine built-in rules with user-provided rules
        all_rules = builtin_rules + (ai_rules if ai_rules else [])

        # Process each match
        for match in matches:
            try:
                rule_evaluation = self._evaluate_rules_for_match(match, all_rules)

                # Initialize or update tax_analysis
                if match.tax_analysis is None:
                    match.tax_analysis = {}

                # Add rule evaluation results
                match.tax_analysis["flag_for_review"] = rule_evaluation[
                    "flag_for_review"
                ]
                match.tax_analysis["auto_approve"] = rule_evaluation["auto_approve"]
                match.tax_analysis["rules_applied"] = rule_evaluation["rules_applied"]
                match.tax_analysis["rule_reasons"] = rule_evaluation["reasons"]

                # Update match reason with rule information
                if rule_evaluation["flag_for_review"]:
                    match.match_reason += " | 🚩 FLAGGED FOR REVIEW"
                if rule_evaluation["auto_approve"]:
                    match.match_reason += " | ✅ AUTO-APPROVED"

                logger.info(
                    f"Match {match.receipt.id} → {match.transaction.id}: "
                    f"flag_for_review={rule_evaluation['flag_for_review']}, "
                    f"auto_approve={rule_evaluation['auto_approve']}"
                )

            except Exception as e:
                logger.error(f"Error applying rules to match: {str(e)}")
                # Fail safe: flag for review if rule processing fails
                if match.tax_analysis is None:
                    match.tax_analysis = {}
                match.tax_analysis["flag_for_review"] = True
                match.tax_analysis["auto_approve"] = False
                match.tax_analysis["rule_reasons"] = [
                    f"Rule evaluation error: {str(e)}"
                ]

        return matches

    def _evaluate_rules_for_match(
        self, match: Match, rules: List[Dict]
    ) -> Dict[str, any]:
        """
        Use LLM to evaluate all rules for a single match.

        Returns:
            {
                "flag_for_review": bool,
                "auto_approve": bool,
                "rules_applied": List[str],
                "reasons": List[str]
            }
        """
        # Build context about the match
        match_context = self._build_match_context(match)

        # Build rules context
        rules_context = self._build_rules_context(rules)

        # Create prompt for LLM
        prompt = f"""You are a financial matching rules engine. Analyze the following receipt-to-transaction match and apply the specified rules.

MATCH DETAILS:
{match_context}

RULES TO APPLY:
{rules_context}

INSTRUCTIONS:
1. Evaluate each rule's condition against the match details
2. If a rule's condition is TRUE, apply the action:
   - If action is "flag_for_review" or "review" → set flag_for_review = true
   - If action is "auto_approve" or "approve" → set auto_approve = true
   - For other actions, determine if they imply review or approval
3. If BOTH flag_for_review and auto_approve are triggered, flag_for_review takes priority
4. If NO rules match, set both to false (default behavior)

IMPORTANT BUILT-IN RULE:
- If receipt currency differs from transaction currency → ALWAYS set flag_for_review = true

Return ONLY a valid JSON object with this exact format:
{{
    "flag_for_review": boolean,
    "auto_approve": boolean,
    "rules_applied": ["list of rule conditions that matched"],
    "reasons": ["list of reasons for the decisions"]
}}
"""

        try:
            # Call LLM
            response = self.client.chat.completions.create(
                model=self.model,
                messages=[
                    {
                        "role": "system",
                        "content": "You are a financial rules evaluation assistant. You analyze transaction matches and apply business rules. Always respond with valid JSON only.",
                    },
                    {"role": "user", "content": prompt},
                ],
                temperature=0.1,
                max_tokens=500,
            )

            result_text = response.choices[0].message.content.strip()

            # Parse JSON response
            result = self._parse_llm_response(result_text)

            # Validate and enforce constraints
            if result["flag_for_review"] and result["auto_approve"]:
                logger.warning(
                    "Both flag_for_review and auto_approve were true, prioritizing flag_for_review"
                )
                result["auto_approve"] = False
                result["reasons"].append(
                    "Conflicting rules: prioritized manual review over auto-approval"
                )

            return result

        except Exception as e:
            logger.error(f"LLM evaluation failed: {str(e)}")
            # Fail safe: flag for review
            return {
                "flag_for_review": True,
                "auto_approve": False,
                "rules_applied": [],
                "reasons": [f"Error evaluating rules: {str(e)}"],
            }

    def _build_match_context(self, match: Match) -> str:
        """Build a text description of the match for the LLM"""
        receipt = match.receipt
        transaction = match.transaction

        context = f"""Receipt Information:
- ID: {receipt.id}
- Vendor: {receipt.vendor}
- Amount: ${receipt.amount:.2f}
- Tax: ${receipt.tax:.2f}
- Category: {receipt.category}
- Description: {receipt.description}
- Date: {receipt.receipt_date}
- Currency: {receipt.currency}

Transaction Information:
- ID: {transaction.id}
- Vendor: {transaction.vendor}
- Amount: ${transaction.amount:.2f}
- Date: {transaction.transaction_date}
- Notes: {transaction.notes}
- Currency: {transaction.currency}

Match Quality:
- Confidence Score: {match.confidence_score:.2%}
- Match Reason: {match.match_reason}
"""

        # Add tax analysis if available
        if match.tax_analysis:
            context += f"\nTax Analysis:\n{json.dumps(match.tax_analysis, indent=2)}"

        return context

    def _build_rules_context(self, rules: List[Dict]) -> str:
        """Build a formatted list of rules for the LLM"""
        if not rules:
            return "No custom rules provided. Apply default evaluation."

        rules_text = ""
        for idx, rule in enumerate(rules, 1):
            condition = rule.get("condition", "")
            action = rule.get("action", "")
            rules_text += f"{idx}. IF {condition} → THEN {action}\n"

        return rules_text

    def _parse_llm_response(self, response_text: str) -> Dict:
        """Parse and validate LLM JSON response"""
        try:
            # Remove markdown code blocks if present
            if "```json" in response_text:
                response_text = response_text.split("```json")[1].split("```")[0]
            elif "```" in response_text:
                response_text = response_text.split("```")[1].split("```")[0]

            # Parse JSON
            result = json.loads(response_text.strip())

            # Validate required fields
            if "flag_for_review" not in result:
                result["flag_for_review"] = False
            if "auto_approve" not in result:
                result["auto_approve"] = False
            if "rules_applied" not in result:
                result["rules_applied"] = []
            if "reasons" not in result:
                result["reasons"] = []

            # Ensure boolean types
            result["flag_for_review"] = bool(result["flag_for_review"])
            result["auto_approve"] = bool(result["auto_approve"])

            return result

        except json.JSONDecodeError as e:
            logger.error(f"Failed to parse LLM response as JSON: {str(e)}")
            logger.error(f"Response text: {response_text}")
            # Return safe defaults
            return {
                "flag_for_review": True,  # Fail safe to manual review
                "auto_approve": False,
                "rules_applied": [],
                "reasons": ["Failed to parse LLM response"],
            }