bolade
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
from typing import List, Dict, Any
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from datetime import datetime
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from ai_matcher import AIMatcher
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from ai_rules import AIRulesEngine
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from feedback_logger import FeedbackLogger
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from models import Receipt, Transaction, Match
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class MatchingEngine:
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def __init__(self):
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self.ai_matcher = AIMatcher()
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self.rules_engine = AIRulesEngine()
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self.feedback_logger = FeedbackLogger()
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def process_matching(self, receipts: List[Receipt], transactions: List[Transaction]) -> List[Match]:
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# Get AI matches
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ai_matches = self.ai_matcher.match_receipts_to_transactions(receipts, transactions)
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# Apply rules and enhance matches
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enhanced_matches = []
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for match in ai_matches:
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enhanced_match = self._enhance_match_with_rules(match)
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enhanced_matches.append(enhanced_match)
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return enhanced_matches
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def _enhance_match_with_rules(self, match: Match) -> Match:
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rule_results = self.rules_engine.apply_rules(match.receipt, match.transaction)
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# Apply confidence boost from rules
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if rule_results["confidence_boost"] > 0:
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match.confidence_score = min(1.0, match.confidence_score + rule_results["confidence_boost"])
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# Auto-approve if rules say so
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if rule_results["auto_approve"]:
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match.confidence_score = 1.0
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match.match_reason += " (Auto-approved by rules)"
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# Add tax analysis to match
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if rule_results.get("tax_analysis"):
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match.tax_analysis = rule_results["tax_analysis"]
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return match
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def approve_match(self, match: Match, user_id: str):
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# Log the approval
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self.feedback_logger.log_override(
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transaction_id=match.transaction.id,
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original_match=f"AI Score: {match.confidence_score}",
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correction="Approved",
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reason="User approved match",
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user_id=user_id
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)
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def reject_match(self, match: Match, reason: str, user_id: str):
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# Log the rejection
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self.feedback_logger.log_override(
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transaction_id=match.transaction.id,
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original_match=f"AI Score: {match.confidence_score}",
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correction="Rejected",
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reason=reason,
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user_id=user_id
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)
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def get_matching_stats(self, matches: List[Match]) -> Dict[str, Any]:
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if not matches:
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return {"total": 0, "high_confidence": 0, "low_confidence": 0, "avg_score": 0}
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high_confidence = len([m for m in matches if m.confidence_score >= 0.8])
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low_confidence = len([m for m in matches if m.confidence_score < 0.8])
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avg_score = sum(m.confidence_score for m in matches) / len(matches)
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return {
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"total": len(matches),
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"high_confidence": high_confidence,
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"low_confidence": low_confidence,
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"avg_score": round(avg_score, 3)
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} |