bolade
1f530da7c4
- Reorganized imports in main.py for better readability and structure. - Enhanced logging configuration and added more detailed log messages throughout the application. - Improved error handling and response formatting in transaction import endpoints. - Streamlined transaction processing logic for CSV and image uploads. - Updated matching engine to enhance match results with rules and improved logging. - Refactored tax rules engine for better clarity and maintainability. - Cleaned up requirements.txt by removing specific versioning for easier dependency management.
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
from typing import Any, Dict, List
|
|
|
|
from ai_matcher import AIMatcher
|
|
from ai_rules import AIRulesEngine
|
|
from feedback_logger import FeedbackLogger
|
|
from models import Match, Receipt, Transaction
|
|
|
|
|
|
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)"
|
|
|
|
# Add tax analysis to match
|
|
if rule_results.get("tax_analysis"):
|
|
match.tax_analysis = rule_results["tax_analysis"]
|
|
|
|
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),
|
|
}
|