Refactor main application structure and improve logging

- 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.

matching_engine.py

@@ -1,46 +1,53 @@
-from typing import List, Dict, Any
-from datetime import datetime
+from typing import Any, Dict, List
+
 from ai_matcher import AIMatcher
 from ai_rules import AIRulesEngine
 from feedback_logger import FeedbackLogger
-from models import Receipt, Transaction, Match
+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]:
+
+    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)
-        
+        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"])
-        
+            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(
@@ -48,9 +55,9 @@ class MatchingEngine:
             original_match=f"AI Score: {match.confidence_score}",
             correction="Approved",
             reason="User approved match",
-            user_id=user_id
+            user_id=user_id,
         )
-    
+
     def reject_match(self, match: Match, reason: str, user_id: str):
         # Log the rejection
         self.feedback_logger.log_override(
@@ -58,20 +65,25 @@ class MatchingEngine:
             original_match=f"AI Score: {match.confidence_score}",
             correction="Rejected",
             reason=reason,
-            user_id=user_id
+            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}
-        
+            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)
-        } 
+            "avg_score": round(avg_score, 3),
+        }