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