Implement database integration for transactions and receipts, including CRUD operations and data retrieval endpoints

- 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.

ai_matcher.py

@@ -1,115 +1,322 @@
-import groq
-from datetime import datetime, timedelta
-from typing import List, Tuple
-import config
-from models import Receipt, Transaction, Match
-import time
 import logging
-import asyncio
+import time
+from typing import List, Tuple
+
+import groq
+
+import config
+from models import Match, Receipt, Transaction
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+
 class AIMatcher:
-    def __init__(self):
+    def __init__(self, use_batch_matching=True):
         self.client = groq.Groq(api_key=config.GROQ_API_KEY)
         self.model = "llama3-8b-8192"
         self.max_retries = 3
         self.retry_delay = 2  # seconds - increased for rate limiting
         self.rate_limit_delay = 1.0  # seconds between API calls
         self.last_api_call = 0
-    
-    def match_receipts_to_transactions(self, receipts: List[Receipt], transactions: List[Transaction]) -> List[Match]:
+        self.use_batch_matching = (
+            use_batch_matching  # Toggle between new and legacy methods
+        )
+
+    def match_receipts_to_transactions(
+        self, receipts: List[Receipt], transactions: List[Transaction]
+    ) -> List[Match]:
         """Match receipts to transactions using AI"""
-        logger.info(f"Starting AI matching for {len(receipts)} receipts against {len(transactions)} transactions")
+        logger.info(
+            f"Starting AI matching for {len(receipts)} receipts against {len(transactions)} transactions"
+        )
         matches = []
-        
+
         for i, receipt in enumerate(receipts):
-            logger.info(f"Processing receipt {i+1}/{len(receipts)}: {receipt.vendor} - ${receipt.amount}")
-            
+            logger.info(
+                f"Processing receipt {i + 1}/{len(receipts)}: {receipt.vendor} - ${receipt.amount}"
+            )
+
             # Rate limiting
             self._rate_limit()
-            
+
             # Get the BEST match for this receipt (highest confidence score)
             best_match = self._find_best_match(receipt, transactions)
             if best_match:
                 matches.append(best_match)
-                logger.info(f"Found match: {best_match.confidence_score:.3f} - {best_match.match_reason}")
+                logger.info(
+                    f"Found match: {best_match.confidence_score:.3f} - {best_match.match_reason}"
+                )
             else:
-                logger.warning(f"No match found for receipt: {receipt.vendor} - ${receipt.amount}")
-        
+                logger.warning(
+                    f"No match found for receipt: {receipt.vendor} - ${receipt.amount}"
+                )
+
         # Sort by confidence score (highest first)
         matches = sorted(matches, key=lambda x: x.confidence_score, reverse=True)
         logger.info(f"AI matching completed. Found {len(matches)} matches")
         return matches
-    
+
     def _rate_limit(self):
         """Implement rate limiting to avoid API quota exhaustion"""
         current_time = time.time()
         time_since_last_call = current_time - self.last_api_call
-        
+
         if time_since_last_call < self.rate_limit_delay:
             sleep_time = self.rate_limit_delay - time_since_last_call
             logger.debug(f"Rate limiting: sleeping for {sleep_time:.2f} seconds")
             time.sleep(sleep_time)
-        
+
         self.last_api_call = time.time()
-    
-    def _find_best_match(self, receipt: Receipt, transactions: List[Transaction]) -> Match:
-        """Find the BEST match for a receipt (highest confidence score)"""
+
+    def _find_best_match(
+        self, receipt: Receipt, transactions: List[Transaction]
+    ) -> Match:
+        """Find the BEST match for a receipt using a single AI call for all candidates"""
         candidates = self._filter_candidates(receipt, transactions)
         if not candidates:
-            logger.warning(f"No candidates found for receipt: {receipt.vendor} - ${receipt.amount}")
+            logger.warning(
+                f"No candidates found for receipt: {receipt.vendor} - ${receipt.amount}"
+            )
             return None
-            
+
         logger.info(f"Found {len(candidates)} candidates for receipt: {receipt.vendor}")
-        
-        best_match = None
-        highest_score = 0
-        
-        for transaction in candidates:
-            score, reason = self._calculate_match_score(receipt, transaction)
-            logger.debug(f"Score {score:.3f} for transaction {transaction.vendor}: {reason}")
-            
-            # Keep the match with the highest score, regardless of how low it is
-            if score > highest_score:
-                highest_score = score
-                best_match = Match(receipt, transaction, score, reason)
-        
+
+        # Choose matching method based on configuration
+        if self.use_batch_matching:
+            # New efficient method: single AI call for all candidates
+            best_match = self._find_best_match_single_call(receipt, candidates)
+        else:
+            # Legacy method: individual AI calls (fallback)
+            best_match = self._find_best_match_legacy(receipt, candidates)
+
         return best_match
-    
-    def _filter_candidates(self, receipt: Receipt, transactions: List[Transaction]) -> List[Transaction]:
+
+    def _find_best_match_single_call(
+        self, receipt: Receipt, candidates: List[Transaction]
+    ) -> Match:
+        """Find the best match using a single AI call to evaluate all candidates"""
+        if not candidates:
+            return None
+
+        # Limit candidates to avoid token limits (adjust based on your needs)
+        max_candidates = 10
+        if len(candidates) > max_candidates:
+            # Sort by amount similarity and take top candidates
+            candidates = sorted(
+                candidates, key=lambda t: abs(receipt.amount - abs(t.amount))
+            )[:max_candidates]
+            logger.info(
+                f"Limited candidates to top {max_candidates} by amount similarity"
+            )
+
+        # Build comprehensive prompt with all candidates
+        candidates_text = ""
+        for i, transaction in enumerate(candidates):
+            transaction_amount_abs = abs(transaction.amount)
+            date_diff = abs((receipt.receipt_date - transaction.transaction_date).days)
+            amount_diff = abs(receipt.amount - transaction_amount_abs)
+            amount_percent_diff = (
+                (amount_diff / receipt.amount) * 100 if receipt.amount > 0 else 0
+            )
+
+            candidates_text += f"""
+Candidate {i + 1}:
+- Vendor: {transaction.vendor}
+- Amount: ${transaction.amount} (absolute: ${transaction_amount_abs})
+- Date: {transaction.transaction_date.strftime("%Y-%m-%d")} ({date_diff} days difference)
+- Notes: {transaction.notes}
+- Amount difference: ${amount_diff} ({amount_percent_diff:.1f}%)
+"""
+
+        prompt = f"""
+You are an expert at matching receipts to bank transactions. Analyze the receipt below against ALL the candidate transactions and return the BEST match.
+
+RECEIPT TO MATCH:
+- Vendor: {receipt.vendor}
+- Amount: ${receipt.amount}
+- Date: {receipt.receipt_date.strftime("%Y-%m-%d")}
+- Description: {receipt.description}
+- Category: {receipt.category}
+
+CANDIDATE TRANSACTIONS:
+{candidates_text}
+
+SCORING CRITERIA:
+- Perfect matches (same vendor, amount, date): 0.95-1.0
+- High confidence (minor differences): 0.8-0.94
+- Medium confidence (moderate differences): 0.6-0.79
+- Low confidence (significant differences): 0.4-0.59
+- Very low confidence (major differences): 0.2-0.39
+- Minimal similarity: 0.1-0.19
+- No meaningful similarity: 0.0-0.09
+
+Consider vendor name similarity, amount accuracy, date proximity, and description/notes relevance.
+
+IMPORTANT: You MUST return the candidate with the highest match score, even if it's very low. Never return NONE.
+Return ONLY the best match in this exact format:
+CANDIDATE_NUMBER|CONFIDENCE_SCORE|REASON
+
+Example: 3|0.87|Same vendor name, exact amount match, 1 day apart
+Example of low match: 5|0.15|Best available option despite significant differences in vendor and amount
+"""
+
+        for attempt in range(self.max_retries):
+            try:
+                result = self._call_groq_api_with_timeout(
+                    prompt, timeout=45
+                )  # Longer timeout for complex prompt
+
+                # Parse the single result
+                candidate_num, score, reason = self._parse_single_match_response(result)
+
+                if candidate_num == -1:  # Parsing error occurred
+                    logger.warning(
+                        f"Failed to parse AI response for receipt: {receipt.vendor}"
+                    )
+                    return None
+
+                if 0 <= candidate_num < len(candidates):
+                    best_transaction = candidates[candidate_num]
+                    logger.info(
+                        f"AI selected candidate {candidate_num + 1}: {best_transaction.vendor} (score: {score:.3f})"
+                    )
+                    return Match(receipt, best_transaction, score, reason)
+                else:
+                    logger.warning(
+                        f"AI returned invalid candidate number: {candidate_num}"
+                    )
+                    return None
+
+            except Exception as e:
+                logger.warning(
+                    f"Attempt {attempt + 1} failed for receipt {receipt.id}: {str(e)}"
+                )
+                if attempt < self.max_retries - 1:
+                    sleep_time = self.retry_delay * (2**attempt)
+                    logger.info(f"Waiting {sleep_time} seconds before retry...")
+                    time.sleep(sleep_time)
+                else:
+                    logger.error(f"All attempts failed for receipt {receipt.id}")
+                    return None
+
+        return None
+
+    def _parse_single_match_response(self, result: str) -> Tuple[int, float, str]:
+        """Parse AI response for single best match"""
+        result = result.strip()
+        logger.debug(f"Parsing single match response: {result}")
+
+        try:
+            if result.upper().startswith("NONE"):
+                # This should not happen with new prompt, but handle as parsing error
+                logger.warning(
+                    "AI returned NONE despite being instructed to always return best match"
+                )
+                return -1, 0.0, "AI returned NONE unexpectedly"
+
+            if "|" in result:
+                parts = result.split("|")
+                if len(parts) >= 3:
+                    candidate_str = parts[0].strip()
+                    score_str = parts[1].strip()
+                    reason = "|".join(parts[2:]).strip()
+
+                    # Extract candidate number
+                    import re
+
+                    candidate_match = re.search(r"\d+", candidate_str)
+                    if candidate_match:
+                        candidate_num = (
+                            int(candidate_match.group()) - 1
+                        )  # Convert to 0-based index
+                    else:
+                        raise ValueError("No candidate number found")
+
+                    # Extract score
+                    score_clean = "".join(
+                        c for c in score_str if c.isdigit() or c == "."
+                    )
+                    score = float(score_clean) if score_clean else 0.0
+
+                    # Ensure score is in valid range
+                    score = max(0.0, min(1.0, score))
+
+                    logger.debug(
+                        f"Parsed: candidate={candidate_num}, score={score}, reason={reason}"
+                    )
+                    return candidate_num, score, reason
+
+        except Exception as e:
+            logger.warning(f"Error parsing single match response: {e}")
+
+        # Fallback
+        logger.warning(f"Could not parse single match response: {result}")
+        return -1, 0.0, f"Parse error: {result[:50]}..."
+
+    def _filter_candidates(
+        self, receipt: Receipt, transactions: List[Transaction]
+    ) -> List[Transaction]:
         """Filter transactions to create a reasonable candidate list"""
         candidates = []
         amount_threshold = receipt.amount * 2.0  # 200% threshold - very inclusive
-        
+
         for transaction in transactions:
             # Use absolute value for transaction amount comparison
             transaction_amount_abs = abs(transaction.amount)
-            
+
             # Only exclude transactions with obviously different amounts
             if abs(receipt.amount - transaction_amount_abs) <= amount_threshold:
                 candidates.append(transaction)
-        
-        logger.debug(f"Filtered {len(transactions)} transactions to {len(candidates)} candidates")
+
+        logger.debug(
+            f"Filtered {len(transactions)} transactions to {len(candidates)} candidates"
+        )
         return candidates
-    
-    def _calculate_match_score(self, receipt: Receipt, transaction: Transaction) -> Tuple[float, str]:
+
+    def _find_best_match_legacy(
+        self, receipt: Receipt, transactions: List[Transaction]
+    ) -> Match:
+        """Legacy method: Find the best match using individual API calls (kept as fallback)"""
+        candidates = self._filter_candidates(receipt, transactions)
+        if not candidates:
+            return None
+
+        best_match = None
+        highest_score = 0
+
+        for transaction in candidates:
+            score, reason = self._calculate_match_score(receipt, transaction)
+            logger.debug(
+                f"Score {score:.3f} for transaction {transaction.vendor}: {reason}"
+            )
+
+            if score > highest_score:
+                highest_score = score
+                best_match = Match(receipt, transaction, score, reason)
+
+        return best_match
+
+    def _calculate_match_score(
+        self, receipt: Receipt, transaction: Transaction
+    ) -> Tuple[float, str]:
         """Calculate match score using AI"""
         # Calculate differences for the AI to consider
         date_diff = abs((receipt.receipt_date - transaction.transaction_date).days)
         transaction_amount_abs = abs(transaction.amount)
         amount_diff = abs(receipt.amount - transaction_amount_abs)
-        amount_percent_diff = (amount_diff / receipt.amount) * 100 if receipt.amount > 0 else 0
-        
+        amount_percent_diff = (
+            (amount_diff / receipt.amount) * 100 if receipt.amount > 0 else 0
+        )
+
         prompt = f"""
-        Compare this receipt with this transaction and provide a confidence score (0-1) and brief reason.
+        Compare this receipt with this transaction and provide a confidence score (0-1) and brief reason, the reason must be a single sentence without any special formatting.
         
-        Receipt: {receipt.vendor}, ${receipt.amount}, {receipt.receipt_date.strftime('%Y-%m-%d')}
+        Receipt: {receipt.vendor}, ${receipt.amount}, {receipt.receipt_date.strftime("%Y-%m-%d")}
         Receipt Description: {receipt.description}
         Receipt Category: {receipt.category}
-        Transaction: {transaction.vendor}, ${transaction.amount} (absolute: ${transaction_amount_abs}), {transaction.transaction_date.strftime('%Y-%m-%d')}
+        Transaction: {transaction.vendor}, ${transaction.amount} (absolute: ${transaction_amount_abs}), {transaction.transaction_date.strftime("%Y-%m-%d")}
         Transaction Notes: {transaction.notes}
         
         Differences:
@@ -135,61 +342,78 @@ class AIMatcher:
         Format: [score]|[reason]
         Example: 0.85|Same vendor, same amount, 2 days apart
         """
-        
+
         for attempt in range(self.max_retries):
             try:
-                result = self._call_groq_api_with_timeout(prompt, timeout=30)  # Increased timeout
-                
+                result = self._call_groq_api_with_timeout(
+                    prompt, timeout=30
+                )  # Increased timeout
+
                 # Parse the result - handle multiple formats
                 score, reason = self._parse_ai_response(result)
-                
+
                 logger.debug(f"AI Response: {result}")
                 logger.debug(f"Parsed: score={score}, reason={reason}")
-                
+
                 return score, reason
-                
+
             except Exception as e:
-                logger.warning(f"Attempt {attempt + 1} failed for receipt {receipt.id}: {str(e)}")
+                logger.warning(
+                    f"Attempt {attempt + 1} failed for receipt {receipt.id}: {str(e)}"
+                )
                 if attempt < self.max_retries - 1:
                     # Exponential backoff for rate limiting
-                    sleep_time = self.retry_delay * (2 ** attempt)
+                    sleep_time = self.retry_delay * (2**attempt)
                     logger.info(f"Waiting {sleep_time} seconds before retry...")
                     time.sleep(sleep_time)
                 else:
                     logger.error(f"All attempts failed for receipt {receipt.id}")
                     return 0.0, f"AI error after {self.max_retries} attempts: {str(e)}"
-    
+
     def _parse_ai_response(self, result: str) -> Tuple[float, str]:
         """Parse AI response with robust error handling"""
         result = result.strip()
         logger.debug(f"Parsing AI response: {result}")
-        
+
         # Try to find score in various formats
-        if '|' in result:
-            parts = result.split('|')
+        if "|" in result:
+            parts = result.split("|")
             logger.debug(f"Split response into {len(parts)} parts: {parts}")
-            
+
             # Look for a numeric score in any part
             for i, part in enumerate(parts):
                 part = part.strip()
                 try:
                     # Remove any non-numeric characters except decimal point
-                    score_str_clean = ''.join(c for c in part if c.isdigit() or c == '.')
+                    score_str_clean = "".join(
+                        c for c in part if c.isdigit() or c == "."
+                    )
                     if score_str_clean:
                         score = float(score_str_clean)
                         if 0 <= score <= 1:  # Valid confidence score
                             # Get reason from other parts
-                            reason_parts = [p.strip() for j, p in enumerate(parts) if j != i and p.strip()]
-                            reason = ' | '.join(reason_parts) if reason_parts else "Score extracted"
-                            logger.debug(f"Found score {score} in part {i}, reason: {reason}")
+                            reason_parts = [
+                                p.strip()
+                                for j, p in enumerate(parts)
+                                if j != i and p.strip()
+                            ]
+                            reason = (
+                                " | ".join(reason_parts)
+                                if reason_parts
+                                else "Score extracted"
+                            )
+                            logger.debug(
+                                f"Found score {score} in part {i}, reason: {reason}"
+                            )
                             return score, reason
                 except ValueError:
                     continue
-        
+
         # Try to extract just a number from the response
         try:
             import re
-            numbers = re.findall(r'\d+\.?\d*', result)
+
+            numbers = re.findall(r"\d+\.?\d*", result)
             if numbers:
                 for num_str in numbers:
                     score = float(num_str)
@@ -198,11 +422,12 @@ class AIMatcher:
                         return score, f"Extracted from response: {result[:50]}..."
         except (ValueError, IndexError):
             pass
-        
+
         # Fallback - try to find any number and normalize it
         try:
             import re
-            numbers = re.findall(r'\d+\.?\d*', result)
+
+            numbers = re.findall(r"\d+\.?\d*", result)
             if numbers:
                 score = float(numbers[0])
                 # Normalize to 0-1 range if it's a percentage or other scale
@@ -213,27 +438,27 @@ class AIMatcher:
                 return score, f"Normalized from response: {result[:50]}..."
         except (ValueError, IndexError):
             pass
-        
+
         # Final fallback
         logger.warning(f"Could not parse AI response: {result}")
         return 0.0, f"Unparseable response: {result[:50]}..."
-    
+
     def _call_groq_api_with_timeout(self, prompt: str, timeout: int = 15) -> str:
         """Make API call with timeout and retry logic"""
         import concurrent.futures
-        
+
         def api_call():
             try:
                 response = self.client.chat.completions.create(
                     model=self.model,
                     messages=[{"role": "user", "content": prompt}],
                     max_tokens=200,
-                    temperature=0.1
+                    temperature=0.1,
                 )
                 return response.choices[0].message.content.strip()
             except Exception as e:
                 raise e
-        
+
         try:
             with concurrent.futures.ThreadPoolExecutor() as executor:
                 future = executor.submit(api_call)
@@ -241,4 +466,4 @@ class AIMatcher:
         except concurrent.futures.TimeoutError:
             raise Exception(f"API call timed out after {timeout} seconds")
         except Exception as e:
-            raise e 
+            raise e

ai_rules.py

@@ -1,9 +1,10 @@
 from dataclasses import dataclass
-from typing import Dict, Any, List
-import config
+from typing import Any, Dict, List
+
 from models import Receipt, Transaction
 from tax_rules_engine import TaxRulesEngine
 
+
 @dataclass
 class AIRule:
     name: str
@@ -12,48 +13,88 @@ class AIRule:
     source: str
     status: str = "active"
 
+
 class AIRulesEngine:
     def __init__(self):
         self.rules: List[AIRule] = []
         self.tax_rules_engine = TaxRulesEngine()
         self._load_default_rules()
-    
+
     def _load_default_rules(self):
         self.rules = [
-            AIRule("exact_amount_match", "amount_diff <= 0.01", "auto_approve", "system"),
-            AIRule("same_vendor_same_date", "vendor_match and date_diff <= 1", "high_confidence", "system"),
-            AIRule("gas_station_pattern", "vendor_contains_gas_or_fuel", "categorize_transport", "system"),
+            AIRule(
+                "exact_amount_match", "amount_diff <= 0.01", "auto_approve", "system"
+            ),
+            AIRule(
+                "same_vendor_same_date",
+                "vendor_match and date_diff <= 1",
+                "high_confidence",
+                "system",
+            ),
+            AIRule(
+                "gas_station_pattern",
+                "vendor_contains_gas_or_fuel",
+                "categorize_transport",
+                "system",
+            ),
             # Tax-related rules
-            AIRule("fx_currency_mismatch", "currency_mismatch", "flag_fx_review", "tax_system"),
-            AIRule("meals_entertainment", "is_meals_entertainment", "apply_me_tax_rule", "tax_system"),
-            AIRule("provincial_tax_calculation", "has_address_info", "calculate_provincial_tax", "tax_system")
+            AIRule(
+                "fx_currency_mismatch",
+                "currency_mismatch",
+                "flag_fx_review",
+                "tax_system",
+            ),
+            AIRule(
+                "meals_entertainment",
+                "is_meals_entertainment",
+                "apply_me_tax_rule",
+                "tax_system",
+            ),
+            AIRule(
+                "provincial_tax_calculation",
+                "has_address_info",
+                "calculate_provincial_tax",
+                "tax_system",
+            ),
         ]
-    
+
     def apply_rules(self, receipt: Receipt, transaction: Transaction) -> Dict[str, Any]:
-        results = {"auto_approve": False, "confidence_boost": 0, "category": None, "tax_analysis": {}}
-        
+        results = {
+            "auto_approve": False,
+            "confidence_boost": 0,
+            "category": None,
+            "tax_analysis": {},
+        }
+
         for rule in self.rules:
             if rule.status != "active":
                 continue
-                
+
             if self._evaluate_condition(rule.condition, receipt, transaction):
                 self._execute_action(rule.action, results, receipt, transaction)
-        
+
         return results
-    
-    def _evaluate_condition(self, condition: str, receipt: Receipt, transaction: Transaction) -> bool:
+
+    def _evaluate_condition(
+        self, condition: str, receipt: Receipt, transaction: Transaction
+    ) -> bool:
         """Safely evaluate rule conditions without using eval()"""
         amount_diff = abs(receipt.amount - abs(transaction.amount))
         date_diff = abs((receipt.receipt_date - transaction.transaction_date).days)
-        vendor_match = receipt.vendor.lower() in transaction.vendor.lower() or transaction.vendor.lower() in receipt.vendor.lower()
+        vendor_match = (
+            receipt.vendor.lower() in transaction.vendor.lower()
+            or transaction.vendor.lower() in receipt.vendor.lower()
+        )
         vendor_lower = receipt.vendor.lower()
-        vendor_contains_gas_or_fuel = 'gas' in vendor_lower or 'fuel' in vendor_lower
-        
+        vendor_contains_gas_or_fuel = "gas" in vendor_lower or "fuel" in vendor_lower
+
         # Tax-related conditions
         currency_mismatch = receipt.currency != transaction.currency
         is_meals_entertainment = receipt.is_meals_entertainment
-        has_address_info = receipt.billing_address is not None or receipt.shipping_address is not None
-        
+        has_address_info = (
+            receipt.billing_address is not None or receipt.shipping_address is not None
+        )
+
         # Handle specific condition types safely
         if condition == "amount_diff <= 0.01":
             return amount_diff <= 0.01
@@ -86,14 +127,20 @@ class AIRulesEngine:
                     "min": min,
                     "max": max,
                     "sum": sum,
-                    "round": round
+                    "round": round,
                 }
                 return eval(condition, safe_globals, {})
             except (SyntaxError, NameError, TypeError) as e:
                 print(f"Warning: Invalid condition '{condition}': {e}")
                 return False
-    
-    def _execute_action(self, action: str, results: Dict[str, Any], receipt: Receipt, transaction: Transaction):
+
+    def _execute_action(
+        self,
+        action: str,
+        results: Dict[str, Any],
+        receipt: Receipt,
+        transaction: Transaction,
+    ):
         if action == "auto_approve":
             results["auto_approve"] = True
         elif action == "high_confidence":
@@ -114,13 +161,15 @@ class AIRulesEngine:
             # Calculate provincial tax
             tax_result = self.tax_rules_engine.apply_sales_tax_rule(receipt)
             results["tax_analysis"]["sales_tax"] = tax_result
-    
+
     def add_rule(self, rule: AIRule):
         self.rules.append(rule)
-    
+
     def remove_rule(self, rule_name: str):
         self.rules = [r for r in self.rules if r.name != rule_name]
-    
-    def apply_tax_rules(self, receipt: Receipt, transaction: Transaction = None) -> Dict[str, Any]:
+
+    def apply_tax_rules(
+        self, receipt: Receipt, transaction: Transaction = None
+    ) -> Dict[str, Any]:
         """Apply all tax rules to a receipt/transaction pair"""
-        return self.tax_rules_engine.apply_all_tax_rules(receipt, transaction) 
+        return self.tax_rules_engine.apply_all_tax_rules(receipt, transaction)

api_models.py

@@ -1,6 +1,7 @@
-from pydantic import BaseModel
 from datetime import datetime
 from typing import List, Optional
+from pydantic import BaseModel
+
 
 class AddressRequest(BaseModel):
     province: str
@@ -8,6 +9,7 @@ class AddressRequest(BaseModel):
     postal_code: str
     country: str = "Canada"
 
+
 class ReceiptRequest(BaseModel):
     id: str
     file_name: str
@@ -24,6 +26,7 @@ class ReceiptRequest(BaseModel):
     currency: str = "CAD"
     is_meals_entertainment: bool = False
 
+
 class TransactionRequest(BaseModel):
     id: str
     transaction_date: datetime
@@ -34,6 +37,7 @@ class TransactionRequest(BaseModel):
     currency: str = "CAD"
     fx_rate: Optional[float] = None
 
+
 class AssetRequest(BaseModel):
     id: str
     name: str
@@ -44,42 +48,51 @@ class AssetRequest(BaseModel):
     cca_rate: float
     asset_class: str
 
+
 class MatchingRequest(BaseModel):
     receipt_ids: List[str]
     transaction_ids: List[str]
 
+
 class MatchResponse(BaseModel):
     receipt_id: str
     transaction_id: str
     confidence_score: float
     match_reason: str
-    tax_analysis: Optional[dict] = None
-    # Currency information
-    receipt_currency: str = "CAD"
-    transaction_currency: str = "CAD"
-    currency_match: bool = True
+    receipt_vendor: str
+    receipt_amount: float
+    receipt_description: str
+    receipt_category: str
+    receipt_tax_amount: float
+    transaction_vendor: str
+    transaction_amount: float
+
 
 class MatchingResponse(BaseModel):
     matches: List[MatchResponse]
     stats: dict
 
+
 class ApprovalRequest(BaseModel):
     match_id: str
     approved: bool
     reason: Optional[str] = None
 
+
 class RuleRequest(BaseModel):
     name: str
     condition: str
     action: str
     source: str = "user"
 
+
 class DocumentUploadResponse(BaseModel):
     file_id: str
     filename: str
     upload_date: datetime
     status: str
 
+
 class DocumentProcessResponse(BaseModel):
     file_id: str
     extraction_success: bool
@@ -92,11 +105,13 @@ class DocumentProcessResponse(BaseModel):
     confidence: Optional[float] = None
     error: Optional[str] = None
 
+
 # New tax-related models
 class TaxCalculationRequest(BaseModel):
     receipt_id: str
     transaction_id: Optional[str] = None
 
+
 class TaxCalculationResponse(BaseModel):
     receipt_id: str
     rules_applied: List[str]
@@ -104,11 +119,13 @@ class TaxCalculationResponse(BaseModel):
     fx_analysis: Optional[dict] = None
     meals_entertainment: dict
 
+
 class DepreciationRequest(BaseModel):
     asset: AssetRequest
     year: int
     method: str  # "straight_line" or "cca"
 
+
 class DepreciationResponse(BaseModel):
     asset_id: str
     year: int
@@ -117,4 +134,9 @@ class DepreciationResponse(BaseModel):
     book_value: float
     total_depreciation: Optional[float] = None
     success: bool
-    error: Optional[str] = None 
+    error: Optional[str] = None
+
+class MatchSpecificRequest(BaseModel):
+    file_ids: List[str]
+    categorization_id: str
+    

database.py

@@ -0,0 +1,75 @@
+from typing import Annotated
+
+from fastapi import Depends
+from sqlalchemy import Column, DateTime, Float, Integer, String, create_engine
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import Session, sessionmaker
+
+SQLALCHEMY_DATABASE_URL = "sqlite:///./sql_app.db"
+
+engine = create_engine(
+    SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
+)
+
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+db_dependency = Annotated[Session, Depends(get_db)]
+Base = declarative_base()
+
+
+def create_db_tables():
+    Base.metadata.create_all(bind=engine)
+
+
+def clear_all_data():
+    """Clear all data from the database (useful for testing)"""
+    db = SessionLocal()
+    try:
+        db.query(Transaction).delete()
+        db.query(Receipt).delete()
+        db.commit()
+    finally:
+        db.close()
+
+
+# Transactions table
+class Transaction(Base):
+    __tablename__ = "transactions"
+
+    id = Column(Integer, primary_key=True, index=True)
+    transaction_id = Column(String, unique=True, index=True)
+    amount = Column(Float, nullable=False)
+    date = Column(DateTime, nullable=False)
+    vendor = Column(String, nullable=False)
+    description = Column(String, nullable=True)
+    category = Column(String, nullable=True)
+    tax_amount = Column(Float, nullable=True)
+    categorisation_id = Column(String, nullable=True)
+    user_id = Column(String, nullable=True)
+
+
+# Receipts table
+class Receipt(Base):
+    __tablename__ = "receipts"
+
+    id = Column(Integer, primary_key=True, index=True)
+    receipt_id = Column(String, unique=True, index=True)
+    file_id = Column(String, unique=True, index=True)
+    amount = Column(Float, nullable=False)
+    date = Column(DateTime, nullable=False)
+    vendor = Column(String, nullable=False)
+    description = Column(String, nullable=True)
+    category = Column(String, nullable=True)
+    tax_amount = Column(Float, nullable=True)
+    confidence = Column(Float, nullable=True)
+    extraction_success = Column(String, nullable=True)
+    error_message = Column(String, nullable=True)

feedback_logger.py

@@ -1,8 +1,9 @@
-from dataclasses import dataclass
-from datetime import datetime, timedelta
-from typing import List, Optional
 import json
 import os
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from typing import List
+
 
 @dataclass
 class FeedbackLog:
@@ -13,48 +14,63 @@ class FeedbackLog:
     timestamp: datetime
     user_id: str
 
+
 class FeedbackLogger:
     def __init__(self, log_file: str = "feedback_logs.json"):
         self.log_file = log_file
         self.logs: List[FeedbackLog] = self._load_logs()
-    
+
     def _load_logs(self) -> List[FeedbackLog]:
         if not os.path.exists(self.log_file):
             return []
-        
+
         try:
-            with open(self.log_file, 'r') as f:
+            with open(self.log_file, "r") as f:
                 data = json.load(f)
                 return [FeedbackLog(**log) for log in data]
-        except:
+        except Exception:
             return []
-    
+
     def _save_logs(self):
-        with open(self.log_file, 'w') as f:
-            json.dump([{
-                'transaction_id': log.transaction_id,
-                'original_match': log.original_match,
-                'correction': log.correction,
-                'reason': log.reason,
-                'timestamp': log.timestamp.isoformat(),
-                'user_id': log.user_id
-            } for log in self.logs], f, indent=2)
-    
-    def log_override(self, transaction_id: str, original_match: str, correction: str, reason: str, user_id: str):
+        with open(self.log_file, "w") as f:
+            json.dump(
+                [
+                    {
+                        "transaction_id": log.transaction_id,
+                        "original_match": log.original_match,
+                        "correction": log.correction,
+                        "reason": log.reason,
+                        "timestamp": log.timestamp.isoformat(),
+                        "user_id": log.user_id,
+                    }
+                    for log in self.logs
+                ],
+                f,
+                indent=2,
+            )
+
+    def log_override(
+        self,
+        transaction_id: str,
+        original_match: str,
+        correction: str,
+        reason: str,
+        user_id: str,
+    ):
         log = FeedbackLog(
             transaction_id=transaction_id,
             original_match=original_match,
             correction=correction,
             reason=reason,
             timestamp=datetime.now(),
-            user_id=user_id
+            user_id=user_id,
         )
         self.logs.append(log)
         self._save_logs()
-    
+
     def get_logs_by_transaction(self, transaction_id: str) -> List[FeedbackLog]:
         return [log for log in self.logs if log.transaction_id == transaction_id]
-    
+
     def get_recent_logs(self, days: int = 30) -> List[FeedbackLog]:
         cutoff = datetime.now() - timedelta(days=days)
-        return [log for log in self.logs if log.timestamp > cutoff] 
+        return [log for log in self.logs if log.timestamp > cutoff]

google_drive_sync.py

@@ -1,13 +1,13 @@
 import os
-import io
-from typing import List, Dict, Any, Optional
 from datetime import datetime, timedelta
+from typing import Any, Dict, List
+
 
 class GoogleDriveSync:
     def __init__(self):
         self.service = None
         self.processed_files = set()
-        
+
     def authenticate(self):
         """Authenticate with Google Drive API"""
         try:
@@ -15,111 +15,130 @@ class GoogleDriveSync:
             from google.oauth2.credentials import Credentials
             from google_auth_oauthlib.flow import InstalledAppFlow
             from googleapiclient.discovery import build
-            
-            SCOPES = ['https://www.googleapis.com/auth/drive.readonly']
-            
+
+            SCOPES = ["https://www.googleapis.com/auth/drive.readonly"]
+
             # Load existing credentials
-            if os.path.exists('token.json'):
-                self.creds = Credentials.from_authorized_user_file('token.json', SCOPES)
-            
+            if os.path.exists("token.json"):
+                self.creds = Credentials.from_authorized_user_file("token.json", SCOPES)
+
             # If no valid credentials available, let user log in
             if not self.creds or not self.creds.valid:
                 if self.creds and self.creds.expired and self.creds.refresh_token:
                     self.creds.refresh(Request())
                 else:
-                    if not os.path.exists('credentials.json'):
-                        raise Exception("credentials.json not found. Please download from Google Cloud Console.")
-                    
-                    flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES)
+                    if not os.path.exists("credentials.json"):
+                        raise Exception(
+                            "credentials.json not found. Please download from Google Cloud Console."
+                        )
+
+                    flow = InstalledAppFlow.from_client_secrets_file(
+                        "credentials.json", SCOPES
+                    )
                     self.creds = flow.run_local_server(port=0)
-                
+
                 # Save credentials for next run
-                with open('token.json', 'w') as token:
+                with open("token.json", "w") as token:
                     token.write(self.creds.to_json())
-            
+
             # Build the Drive service
-            self.service = build('drive', 'v3', credentials=self.creds)
+            self.service = build("drive", "v3", credentials=self.creds)
             return True
-            
+
         except Exception as e:
             print(f"Authentication error: {e}")
             return False
-    
+
     def list_folders(self) -> List[Dict[str, Any]]:
         """List all folders in Google Drive"""
         if not self.service:
             if not self.authenticate():
                 return []
-        
+
         try:
-            results = self.service.files().list(
-                q="mimeType='application/vnd.google-apps.folder'",
-                pageSize=100,
-                fields="nextPageToken, files(id, name, createdTime, modifiedTime)"
-            ).execute()
-            
-            return results.get('files', [])
-            
+            results = (
+                self.service.files()
+                .list(
+                    q="mimeType='application/vnd.google-apps.folder'",
+                    pageSize=100,
+                    fields="nextPageToken, files(id, name, createdTime, modifiedTime)",
+                )
+                .execute()
+            )
+
+            return results.get("files", [])
+
         except Exception as e:
             print(f"Error listing folders: {e}")
             return []
-    
+
     def get_folder_info(self, folder_id: str) -> Dict[str, Any]:
         """Get information about a Google Drive folder"""
         if not self.service:
             if not self.authenticate():
                 return {}
-        
+
         try:
-            folder = self.service.files().get(
-                fileId=folder_id,
-                fields="id, name, createdTime, modifiedTime"
-            ).execute()
-            
+            folder = (
+                self.service.files()
+                .get(fileId=folder_id, fields="id, name, createdTime, modifiedTime")
+                .execute()
+            )
+
             return folder
-            
+
         except Exception as e:
             print(f"Error getting folder info: {e}")
             return {}
-    
+
     async def process_drive_files(self, folder_id: str = None) -> List[Dict[str, Any]]:
         """Process all receipt files from Google Drive"""
         if not self.service:
             if not self.authenticate():
                 return []
-        
+
         results = []
-        
+
         try:
             # File types to look for
-            file_types = ["'application/pdf'", "'image/jpeg'", "'image/png'", "'image/gif'", "'image/bmp'"]
+            file_types = [
+                "'application/pdf'",
+                "'image/jpeg'",
+                "'image/png'",
+                "'image/gif'",
+                "'image/bmp'",
+            ]
             mime_types = " or ".join(file_types)
-            
+
             # Build query
             query = f"mimeType contains {mime_types}"
             if folder_id:
                 query += f" and '{folder_id}' in parents"
-            
+
             # Add date filter (last 30 days)
-            thirty_days_ago = (datetime.now() - timedelta(days=30)).isoformat() + 'Z'
+            thirty_days_ago = (datetime.now() - timedelta(days=30)).isoformat() + "Z"
             query += f" and modifiedTime > '{thirty_days_ago}'"
-            
-            results_files = self.service.files().list(
-                q=query,
-                pageSize=100,
-                fields="nextPageToken, files(id, name, mimeType, modifiedTime, size)"
-            ).execute()
-            
-            files = results_files.get('files', [])
-            files = [file for file in files if file['id'] not in self.processed_files]
-            
+
+            results_files = (
+                self.service.files()
+                .list(
+                    q=query,
+                    pageSize=100,
+                    fields="nextPageToken, files(id, name, mimeType, modifiedTime, size)",
+                )
+                .execute()
+            )
+
+            files = results_files.get("files", [])
+            files = [file for file in files if file["id"] not in self.processed_files]
+
             # For demo purposes, return mock results
             for file in files[:3]:  # Process first 3 files
                 mock_result = {
-                    "file_id": file['id'],
-                    "filename": file['name'],
-                    "drive_modified": file['modifiedTime'],
-                    "file_size": file.get('size', 0),
+                    "file_id": file["id"],
+                    "filename": file["name"],
+                    "drive_modified": file["modifiedTime"],
+                    "file_size": file.get("size", 0),
                     "extraction_success": True,
                     "vendor": "Demo Vendor",
                     "description": "Coffee and sandwich",
@@ -127,12 +146,12 @@ class GoogleDriveSync:
                     "tax_amount": 2.04,
                     "date": "2024-01-15",
                     "category": "Food",
-                    "confidence": 0.95
+                    "confidence": 0.95,
                 }
                 results.append(mock_result)
-                self.processed_files.add(file['id'])
-            
+                self.processed_files.add(file["id"])
+
         except Exception as e:
             print(f"Error processing Drive files: {e}")
-        
-        return results 
+
+        return results

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),
+        }

requirements.txt

@@ -1,16 +1,16 @@
-groq>=0.5.0
-python-dotenv==1.0.0
-pandas==2.1.4
-numpy==1.24.3
-fastapi==0.104.1
-uvicorn==0.24.0
-pydantic==2.5.0
-requests==2.31.0
-python-multipart==0.0.6
-Pillow==10.0.1
-PyPDF2==3.0.1
-aiofiles==23.2.1
-google-auth==2.23.4
-google-auth-oauthlib==1.1.0
-google-auth-httplib2==0.1.1
-google-api-python-client==2.108.0 
+groq
+python-dotenv
+pandas
+numpy
+fastapi
+uvicorn
+pydantic
+requests
+python-multipart
+Pillow
+PyPDF2
+aiofiles
+google-auth
+google-auth-oauthlib
+google-auth-httplib2
+google-api-python-client

tax_rules_engine.py

@@ -1,13 +1,14 @@
-from typing import Dict, Any, Optional, Tuple
-from datetime import datetime
-from models import Receipt, Transaction, Address, Asset
 import logging
+from typing import Any, Dict, Optional
+
+from models import Address, Asset, Receipt, Transaction
 
 logger = logging.getLogger(__name__)
 
+
 class TaxRulesEngine:
     """Engine to handle tax calculations based on the four tax rules"""
-    
+
     # Provincial tax rates (simplified - in production, use a tax rate API)
     PROVINCIAL_TAX_RATES = {
         "ON": 0.13,  # Ontario HST
@@ -24,10 +25,10 @@ class TaxRulesEngine:
         "NU": 0.05,  # Nunavut
         "YT": 0.05,  # Yukon
     }
-    
+
     def __init__(self):
         self.logger = logging.getLogger(__name__)
-    
+
     def apply_sales_tax_rule(self, receipt: Receipt) -> Dict[str, Any]:
         """
         Sales Tax Rule: Apply correct sales tax based on billing vs shipping addresses
@@ -35,43 +36,45 @@ class TaxRulesEngine:
         try:
             # Determine which address to use for tax calculation
             tax_address = self._get_tax_address(receipt)
-            
+
             if not tax_address:
                 return {
                     "success": False,
                     "error": "No valid address found for tax calculation",
                     "calculated_tax": 0.0,
-                    "tax_rate": 0.0
+                    "tax_rate": 0.0,
                 }
-            
+
             # Get tax rate for the province
             tax_rate = self.PROVINCIAL_TAX_RATES.get(tax_address.province, 0.0)
-            
+
             # Calculate tax amount
             calculated_tax = receipt.amount * tax_rate
-            
+
             return {
                 "success": True,
                 "calculated_tax": calculated_tax,
                 "tax_rate": tax_rate,
                 "tax_address": tax_address.province,
-                "rule_applied": "Sales Tax Rule"
+                "rule_applied": "Sales Tax Rule",
             }
-            
+
         except Exception as e:
             self.logger.error(f"Error applying sales tax rule: {str(e)}")
             return {
                 "success": False,
                 "error": str(e),
                 "calculated_tax": 0.0,
-                "tax_rate": 0.0
+                "tax_rate": 0.0,
             }
-    
+
     def _get_tax_address(self, receipt: Receipt) -> Optional[Address]:
         """Determine which address to use for tax calculation"""
         # Rule: Use shipping address if different from billing, otherwise use billing
         if receipt.shipping_address and receipt.billing_address:
-            if self._addresses_different(receipt.billing_address, receipt.shipping_address):
+            if self._addresses_different(
+                receipt.billing_address, receipt.shipping_address
+            ):
                 return receipt.shipping_address
             else:
                 return receipt.billing_address
@@ -81,14 +84,18 @@ class TaxRulesEngine:
             return receipt.billing_address
         else:
             return None
-    
+
     def _addresses_different(self, billing: Address, shipping: Address) -> bool:
         """Check if billing and shipping addresses are different"""
-        return (billing.province != shipping.province or 
-                billing.city != shipping.city or 
-                billing.postal_code != shipping.postal_code)
-    
-    def apply_fx_rule(self, receipt: Receipt, transaction: Transaction) -> Dict[str, Any]:
+        return (
+            billing.province != shipping.province
+            or billing.city != shipping.city
+            or billing.postal_code != shipping.postal_code
+        )
+
+    def apply_fx_rule(
+        self, receipt: Receipt, transaction: Transaction
+    ) -> Dict[str, Any]:
         """
         Foreign Exchange Rule: Handle currency mismatches
         """
@@ -96,7 +103,7 @@ class TaxRulesEngine:
             # Check for currency mismatch
             if receipt.currency != transaction.currency:
                 fx_discrepancy = abs(receipt.amount - abs(transaction.amount))
-                
+
                 return {
                     "success": True,
                     "fx_discrepancy": fx_discrepancy,
@@ -105,26 +112,28 @@ class TaxRulesEngine:
                     "receipt_amount": receipt.amount,
                     "transaction_amount": abs(transaction.amount),
                     "requires_manual_review": True,
-                    "rule_applied": "Foreign Exchange Rule"
+                    "rule_applied": "Foreign Exchange Rule",
                 }
             else:
                 return {
                     "success": True,
                     "fx_discrepancy": 0.0,
                     "requires_manual_review": False,
-                    "rule_applied": "No FX Rule (same currency)"
+                    "rule_applied": "No FX Rule (same currency)",
                 }
-                
+
         except Exception as e:
             self.logger.error(f"Error applying FX rule: {str(e)}")
             return {
                 "success": False,
                 "error": str(e),
                 "fx_discrepancy": 0.0,
-                "requires_manual_review": False
+                "requires_manual_review": False,
             }
-    
-    def calculate_straight_line_depreciation(self, asset: Asset, year: int) -> Dict[str, Any]:
+
+    def calculate_straight_line_depreciation(
+        self, asset: Asset, year: int
+    ) -> Dict[str, Any]:
         """
         Straight-Line Depreciation for accounting purposes
         """
@@ -133,28 +142,26 @@ class TaxRulesEngine:
                 return {
                     "success": False,
                     "error": f"Year {year} exceeds useful life of {asset.useful_life_years} years",
-                    "depreciation": 0.0
+                    "depreciation": 0.0,
                 }
-            
+
             # Straight-line formula: (Cost - Residual Value) / Useful Life
-            annual_depreciation = (asset.purchase_amount - asset.residual_value) / asset.useful_life_years
-            
+            annual_depreciation = (
+                asset.purchase_amount - asset.residual_value
+            ) / asset.useful_life_years
+
             return {
                 "success": True,
                 "depreciation": annual_depreciation,
                 "book_value": asset.purchase_amount - (annual_depreciation * year),
                 "method": "Straight-Line",
-                "rule_applied": "Depreciation Rule (Accounting)"
+                "rule_applied": "Depreciation Rule (Accounting)",
             }
-            
+
         except Exception as e:
             self.logger.error(f"Error calculating straight-line depreciation: {str(e)}")
-            return {
-                "success": False,
-                "error": str(e),
-                "depreciation": 0.0
-            }
-    
+            return {"success": False, "error": str(e), "depreciation": 0.0}
+
     def calculate_cca_depreciation(self, asset: Asset, year: int) -> Dict[str, Any]:
         """
         CCA (Capital Cost Allowance) Depreciation for tax purposes
@@ -164,40 +171,36 @@ class TaxRulesEngine:
                 return {
                     "success": False,
                     "error": "Year must be at least 1",
-                    "depreciation": 0.0
+                    "depreciation": 0.0,
                 }
-            
+
             # CCA uses declining balance method
             book_value = asset.purchase_amount
             total_depreciation = 0.0
-            
+
             for current_year in range(1, year + 1):
                 # CCA is calculated on the declining balance
                 cca_amount = book_value * asset.cca_rate
                 book_value -= cca_amount
                 total_depreciation += cca_amount
-                
+
                 # Stop if book value reaches residual value
                 if book_value <= asset.residual_value:
                     break
-            
+
             return {
                 "success": True,
                 "depreciation": cca_amount,  # Current year depreciation
                 "total_depreciation": total_depreciation,
                 "book_value": max(book_value, asset.residual_value),
                 "method": "CCA Declining Balance",
-                "rule_applied": "Depreciation Rule (Tax)"
+                "rule_applied": "Depreciation Rule (Tax)",
             }
-            
+
         except Exception as e:
             self.logger.error(f"Error calculating CCA depreciation: {str(e)}")
-            return {
-                "success": False,
-                "error": str(e),
-                "depreciation": 0.0
-            }
-    
+            return {"success": False, "error": str(e), "depreciation": 0.0}
+
     def apply_meals_entertainment_rule(self, receipt: Receipt) -> Dict[str, Any]:
         """
         Meals & Entertainment Tax Deduction Rule
@@ -208,36 +211,38 @@ class TaxRulesEngine:
                     "success": True,
                     "tax_deduction": receipt.amount,
                     "accounting_deduction": receipt.amount,
-                    "rule_applied": "No M&E Rule (not meals/entertainment)"
+                    "rule_applied": "No M&E Rule (not meals/entertainment)",
                 }
-            
+
             # For tax purposes: 50% deductible
             tax_deduction = receipt.amount * 0.5
-            
+
             # For accounting purposes: 100% deductible
             accounting_deduction = receipt.amount
-            
+
             # Sales tax is fully deductible for accounting
             tax_on_meal = receipt.tax
-            
+
             return {
                 "success": True,
                 "tax_deduction": tax_deduction,
                 "accounting_deduction": accounting_deduction,
                 "tax_on_meal": tax_on_meal,
-                "rule_applied": "Meals & Entertainment Rule"
+                "rule_applied": "Meals & Entertainment Rule",
             }
-            
+
         except Exception as e:
             self.logger.error(f"Error applying meals & entertainment rule: {str(e)}")
             return {
                 "success": False,
                 "error": str(e),
                 "tax_deduction": 0.0,
-                "accounting_deduction": 0.0
+                "accounting_deduction": 0.0,
             }
-    
-    def apply_all_tax_rules(self, receipt: Receipt, transaction: Transaction = None) -> Dict[str, Any]:
+
+    def apply_all_tax_rules(
+        self, receipt: Receipt, transaction: Transaction = None
+    ) -> Dict[str, Any]:
         """
         Apply all tax rules to a receipt
         """
@@ -246,26 +251,26 @@ class TaxRulesEngine:
             "rules_applied": [],
             "sales_tax": {},
             "fx_analysis": {},
-            "meals_entertainment": {}
+            "meals_entertainment": {},
         }
-        
+
         # Apply Sales Tax Rule
         sales_tax_result = self.apply_sales_tax_rule(receipt)
         results["sales_tax"] = sales_tax_result
         if sales_tax_result["success"]:
             results["rules_applied"].append("Sales Tax Rule")
-        
+
         # Apply FX Rule (if transaction provided)
         if transaction:
             fx_result = self.apply_fx_rule(receipt, transaction)
             results["fx_analysis"] = fx_result
             if fx_result["success"]:
                 results["rules_applied"].append("Foreign Exchange Rule")
-        
+
         # Apply Meals & Entertainment Rule
         me_result = self.apply_meals_entertainment_rule(receipt)
         results["meals_entertainment"] = me_result
         if me_result["success"]:
             results["rules_applied"].append("Meals & Entertainment Rule")
-        
-        return results 
+
+        return results