feat: include receipt tax amount in match-specific endpoint response

.gitignore

@@ -225,4 +225,5 @@ Thumbs.db
 
 uploads/
 chequing statement.csv
-test_images/ 
+test_images/ 
+.cursorrules.md

README.md

@@ -7,9 +7,9 @@ AI-powered receipt-to-transaction matching engine using Groq LLM. This is a **Da
 This Data Science Engine receives QuickBooks transaction data from backend applications and provides:
 - **AI-powered receipt processing** (OCR and data extraction)
 - **Intelligent receipt-transaction matching** with confidence scores
-- **Google Drive integration** for batch receipt processing
 - **Configurable AI rules** for business logic
 - **Feedback logging** for continuous improvement
+- **RESTful API** for easy integration
 
 ## 🚀 Quick Start
 
@@ -19,11 +19,22 @@ pip install -r requirements.txt
 ```
 
 ### 2. Configure API Keys
-The Groq API key is already configured in `config.py`
+Create a `.env` file in the project root with your Groq API key:
 
-### 3. Start the DS Engine
 ```bash
+# Create .env file
+echo "GROQ_API_KEY=your_actual_groq_api_key_here" > .env
+```
+
+**Important**: Get your API key from [Groq Console](https://console.groq.com/)
+
+### 3. Start the Server
+```bash
+# Option 1: Using the main script
 python main.py
+
+# Option 2: Using uvicorn directly
+uvicorn main:app --host 0.0.0.0 --port 8343 --reload
 ```
 
 ### 4. Access API Documentation
@@ -32,22 +43,16 @@ python main.py
 
 ## 📋 API Endpoints
 
-### QuickBooks Data Import
-- `POST /transactions/import/quickbooks` - Import and convert QuickBooks transactions
+### Transaction Import
+- `POST /transactions/import/csv` - Import transactions from CSV file
+- `POST /transactions/import/image` - Import transactions from image/PDF
 
 ### Receipt Processing
-- `POST /upload` - Upload receipt documents (PDF/images)
+- `POST /upload-multiple` - Upload multiple receipt documents
 - `POST /process/{file_id}` - Extract data from uploaded documents
-- `GET /documents` - List all processed documents
-
-### Google Drive Integration
-- `POST /drive/sync` - Sync and process receipts from Google Drive
-- `GET /drive/folders` - List accessible Google Drive folders
-- `GET /drive/folder/{folder_id}` - Get folder information
 
 ### AI Matching Engine
-- `POST /match` - Match receipts to transactions using AI
-- `POST /approve` - Approve or reject AI matches
+- `POST /match-specific` - Match specific receipts to transactions using AI
 
 ### AI Rules Management
 - `POST /rules` - Add new AI rules
@@ -56,6 +61,7 @@ python main.py
 
 ### System Monitoring
 - `GET /stats` - Get system statistics and performance metrics
+- `GET /` - Health check endpoint
 
 ## 🔧 Core Components
 
@@ -63,21 +69,25 @@ python main.py
 - Uses Groq LLM to compare receipts and transactions
 - Provides confidence scores and reasoning
 - Configurable matching criteria (amount, date, vendor)
+- Rate limiting to prevent API quota exhaustion
 
 ### **AIRulesEngine** (`ai_rules.py`)
 - Applies business rules for auto-approval and categorization
 - Configurable rule conditions and actions
 - Supports system and user-generated rules
+- Safe condition evaluation with proper error handling
 
 ### **DocumentProcessor** (`document_processor.py`)
-- AI-powered receipt data extraction
+- AI-powered receipt data extraction using Groq vision model
 - Supports PDF and image formats
-- Uses Groq vision model for OCR
+- Robust JSON parsing with error handling
+- Extracts vendor, amount, date, tax, and category information
 
 ### **MatchingEngine** (`matching_engine.py`)
 - Main orchestrator combining all components
 - Handles the complete matching workflow
 - Provides statistics and feedback logging
+- Configurable confidence thresholds
 
 ### **FeedbackLogger** (`feedback_logger.py`)
 - Tracks manual overrides for AI training
@@ -87,70 +97,46 @@ python main.py
 ## 📊 Configuration
 
 Edit `config.py` to adjust:
-- **Confidence threshold** (default: 0.8)
+- **Confidence threshold** (default: 0.3)
 - **Date tolerance days** (default: 7)
 - **Amount tolerance percent** (default: 5%)
-- **Groq API key** (already configured)
+- **Groq API key** (from environment variable)
 
 ## 🔄 Integration Workflow
 
-### 1. Backend Sends QuickBooks Data
-```python
-# Backend sends QuickBooks transactions
-response = requests.post(
-    "http://localhost:8343/transactions/import/quickbooks",
-    json={
-        "transactions": [
-            {
-                "id": "QB_TXN_123",
-                "txn_date": "2024-01-15",
-                "amount": 12.50,
-                "payee_name": "Starbucks",
-                "memo": "Coffee purchase"
-            }
-        ]
-    }
-)
+### 1. Import Transactions
+```bash
+# Import from CSV
+curl -X POST -F "file=@transactions.csv" http://localhost:8343/transactions/import/csv
+
+# Import from image
+curl -X POST -F "file=@statement.jpg" http://localhost:8343/transactions/import/image
 ```
 
-### 2. Process Receipts
-```python
-# Sync from Google Drive
-response = requests.post(
-    "http://localhost:8343/drive/sync",
-    json={"folder_id": "your_folder_id"}
-)
+### 2. Upload and Process Receipts
+```bash
+# Upload receipts
+curl -X POST -F "files=@receipt1.jpg" -F "files=@receipt2.jpg" http://localhost:8343/upload-multiple
 
-# Or upload directly
-response = requests.post(
-    "http://localhost:8343/upload",
-    files={"file": receipt_file}
-)
+# Process a specific receipt
+curl -X POST http://localhost:8343/process/{file_id}
 ```
 
 ### 3. AI Matching
-```python
-# Match receipts to transactions
-response = requests.post(
-    "http://localhost:8343/match",
-    json={
-        "receipts": processed_receipts,
-        "transactions": converted_transactions
-    }
-)
+```bash
+# Match specific receipts
+curl -X POST -H "Content-Type: application/json" \
+  -d '["file_id_1", "file_id_2"]' \
+  http://localhost:8343/match-specific
 ```
 
-### 4. User Feedback
-```python
-# Approve or reject matches
-response = requests.post(
-    "http://localhost:8343/approve",
-    json={
-        "match_id": "match_123",
-        "user_id": "user_456",
-        "action": "approve"
-    }
-)
+### 4. Check Results
+```bash
+# Get system stats
+curl http://localhost:8343/stats
+
+# View AI rules
+curl http://localhost:8343/rules
 ```
 
 ## 🎯 Key Features
@@ -159,55 +145,96 @@ response = requests.post(
 - **Rule-based auto-approval** and categorization
 - **Feedback logging** for continuous improvement
 - **Configurable matching parameters**
-- **Google Drive integration** for batch processing
-- **JSON API** for easy backend integration
+- **RESTful JSON API** for easy backend integration
 - **Comprehensive error handling**
+- **Rate limiting** to prevent API quota exhaustion
+- **Robust JSON parsing** for AI responses
 
 ## 📝 Data Formats
 
-### QuickBooks Transaction Input
+### Transaction Input (CSV)
+```csv
+Date,Description,Amount,Category
+2024-01-15,Starbucks Coffee,12.50,Food & Dining
+2024-01-16,Office Supplies,45.99,Office
+```
+
+### Receipt Processing Output
 ```json
 {
-  "id": "string",
-  "txn_date": "YYYY-MM-DD",
-  "amount": 0.00,
-  "payee_name": "string",
-  "memo": "string (optional)",
-  "account_name": "string (optional)",
-  "txn_type": "string (optional)"
+  "vendor": "Starbucks",
+  "total_amount": 12.50,
+  "tax_amount": 1.25,
+  "date": "2024-01-15",
+  "category": "Food & Dining",
+  "confidence": 0.95,
+  "extraction_success": true
 }
 ```
 
 ### Match Result Output
 ```json
 {
-  "receipt_id": "string",
-  "transaction_id": "string",
+  "receipt_id": "uuid",
+  "transaction_id": "transaction_123",
   "confidence_score": 0.95,
-  "match_reason": "string",
-  "receipt_vendor": "string",
-  "receipt_amount": 0.00,
-  "transaction_vendor": "string",
-  "transaction_amount": 0.00
+  "match_reason": "Same vendor, minor date difference (Auto-approved by rules)",
+  "receipt_vendor": "Starbucks",
+  "receipt_amount": 12.50,
+  "transaction_vendor": "STARBUCKS",
+  "transaction_amount": 12.50
 }
 ```
 
 ## 🔍 AI Matching Criteria
 
-The engine uses three primary criteria for matching:
+The engine uses multiple criteria for matching:
 
 1. **Amount Similarity** - Compares receipt and transaction amounts (5% tolerance)
 2. **Date Proximity** - Checks date closeness (7-day tolerance)
-3. **Vendor Matching** - AI-powered vendor name comparison
+3. **Vendor Matching** - AI-powered vendor name comparison using Groq LLM
+4. **Rule-based Auto-approval** - Automatic approval for exact matches and high-confidence matches
+
+## 🛠️ Development
+
+### Project Structure
+```
+├── main.py                 # FastAPI application entry point
+├── ai_matcher.py           # AI-powered matching logic
+├── ai_rules.py            # Business rules engine
+├── document_processor.py   # Receipt data extraction
+├── matching_engine.py      # Main matching orchestrator
+├── feedback_logger.py      # User feedback tracking
+├── models.py              # Pydantic data models
+├── api_models.py          # API request/response models
+├── config.py              # Configuration settings
+├── requirements.txt       # Python dependencies
+└── test_images/           # Test image files
+```
+
+### Running Tests
+```bash
+# Test the server
+curl http://localhost:8343/
+
+# Test stats endpoint
+curl http://localhost:8343/stats
+
+# Test rules endpoint
+curl http://localhost:8343/rules
+```
 
 ## 🚀 Production Deployment
 
 For production deployment:
-- Replace in-memory storage with a database
-- Configure proper authentication
-- Set up monitoring and logging
-- Use environment variables for configuration
+- Replace in-memory storage with a database (PostgreSQL recommended)
+- Configure proper authentication and authorization
+- Set up monitoring and logging (ELK stack recommended)
+- Use environment variables for all configuration
 - Implement proper error handling and retries
+- Set up rate limiting and API quotas
+- Configure CORS for frontend integration
+- Use HTTPS in production
 
 ## 📞 Support
 
@@ -217,4 +244,19 @@ This Data Science Engine is designed to be integrated with backend applications
 - Data persistence and management
 - External integrations
 
-The engine focuses purely on AI/ML capabilities and provides a clean JSON API for backend integration. 
+The engine focuses purely on AI/ML capabilities and provides a clean JSON API for backend integration.
+
+## 🔧 Troubleshooting
+
+### Common Issues
+
+1. **API Key Error**: Ensure `GROQ_API_KEY` is set in your `.env` file
+2. **Port Already in Use**: Kill existing process with `pkill -f "python main.py"`
+3. **Import Errors**: Install dependencies with `pip install -r requirements.txt`
+4. **Rate Limiting**: The system includes built-in rate limiting to prevent API quota exhaustion
+
+### Logs
+Check the application logs for detailed error information:
+```bash
+tail -f app.log
+``` 

ai_matcher.py

@@ -3,54 +3,100 @@ from datetime import datetime, timedelta
 from typing import List, Tuple
 import config
 from models import Receipt, Transaction, Match
+import time
+import logging
+import asyncio
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 class AIMatcher:
     def __init__(self):
         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]:
+        """Match receipts to transactions using AI"""
+        logger.info(f"Starting AI matching for {len(receipts)} receipts against {len(transactions)} transactions")
         matches = []
         
-        for receipt in receipts:
+        for i, receipt in enumerate(receipts):
+            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}")
+            else:
+                logger.warning(f"No match found for receipt: {receipt.vendor} - ${receipt.amount}")
         
-        return sorted(matches, key=lambda x: x.confidence_score, reverse=True)
+        # 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)"""
         candidates = self._filter_candidates(receipt, transactions)
         if not candidates:
+            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)
-            if score > highest_score and score >= config.CONFIDENCE_THRESHOLD:
+            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)
         
         return best_match
     
     def _filter_candidates(self, receipt: Receipt, transactions: List[Transaction]) -> List[Transaction]:
-        # Return ALL transactions - let the AI decide on scoring
-        # Only filter out transactions with completely different amounts (>50% difference) to avoid obvious mismatches
+        """Filter transactions to create a reasonable candidate list"""
         candidates = []
-        amount_threshold = receipt.amount * 0.5  # 50% threshold for obvious mismatches
+        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")
         return candidates
     
     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)
@@ -58,45 +104,141 @@ class AIMatcher:
         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.
         
         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 Notes: {transaction.notes}
         
         Differences:
         - Date difference: {date_diff} days
         - Amount difference: ${amount_diff} ({amount_percent_diff:.1f}%)
         - Vendor comparison: "{receipt.vendor}" vs "{transaction.vendor}"
+        - Description/Notes comparison: "{receipt.description}" vs "{transaction.notes}"
+        - Category: {receipt.category}
+        
+        Score this potential match based on how likely it is the correct match:
         
-        Scoring guidelines:
         - 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
-        - No match: 0.0-0.19
+        - Minimal similarity: 0.1-0.19
+        - No meaningful similarity: 0.0-0.09
         
-        Consider vendor name similarity, amount accuracy, and date proximity.
-        Score based on your discretion - even imperfect matches should get scores if there's reasonable similarity.
+        Consider description and category similarity in your scoring.
         
-        Return only: score|reason
+        IMPORTANT: Return ONLY the score and reason separated by a pipe character.
+        Format: [score]|[reason]
+        Example: 0.85|Same vendor, same amount, 2 days apart
         """
         
-        try:
-            response = self.client.chat.completions.create(
-                model=self.model,
-                messages=[{"role": "user", "content": prompt}],
-                max_tokens=100,
-                temperature=0.1
-            )
-            
-            result = response.choices[0].message.content.strip()
-            if '|' in result:
-                score_str, reason = result.split('|', 1)
-                score = float(score_str.strip())
-                return min(max(score, 0), 1), reason.strip()
-            else:
-                return 0.0, "Invalid AI response"
+        for attempt in range(self.max_retries):
+            try:
+                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)}")
+                if attempt < self.max_retries - 1:
+                    # Exponential backoff for rate limiting
+                    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('|')
+            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 == '.')
+                    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}")
+                            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)
+            if numbers:
+                for num_str in numbers:
+                    score = float(num_str)
+                    if 0 <= score <= 1:  # Valid confidence score
+                        logger.debug(f"Extracted score {score} from response")
+                        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)
+            if numbers:
+                score = float(numbers[0])
+                # Normalize to 0-1 range if it's a percentage or other scale
+                if score > 1:
+                    score = score / 100  # Assume percentage
+                score = max(0, min(1, score))  # Clamp to 0-1
+                logger.debug(f"Normalized score {score} from response")
+                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
+                )
+                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)
+                return future.result(timeout=timeout)
+        except concurrent.futures.TimeoutError:
+            raise Exception(f"API call timed out after {timeout} seconds")
         except Exception as e:
-            return 0.0, f"AI error: {str(e)}" 
+            raise e 

ai_rules.py

@@ -20,7 +20,7 @@ class AIRulesEngine:
         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("gas_station_pattern", "vendor_contains_gas_or_fuel", "categorize_transport", "system")
         ]
     
     def apply_rules(self, receipt: Receipt, transaction: Transaction) -> Dict[str, Any]:
@@ -36,17 +36,42 @@ class AIRulesEngine:
         return results
     
     def _evaluate_condition(self, condition: str, receipt: Receipt, transaction: Transaction) -> bool:
-        amount_diff = abs(receipt.amount - transaction.amount)
+        """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_lower = receipt.vendor.lower()
+        vendor_contains_gas_or_fuel = 'gas' in vendor_lower or 'fuel' in vendor_lower
         
-        return eval(condition, {
-            "amount_diff": amount_diff,
-            "date_diff": date_diff,
-            "vendor_match": vendor_match,
-            "receipt": receipt,
-            "transaction": transaction
-        })
+        # Handle specific condition types safely
+        if condition == "amount_diff <= 0.01":
+            return amount_diff <= 0.01
+        elif condition == "vendor_match and date_diff <= 1":
+            return vendor_match and date_diff <= 1
+        elif condition == "vendor_contains_gas_or_fuel":
+            return vendor_contains_gas_or_fuel
+        else:
+            # For any other conditions, try to evaluate them safely
+            try:
+                # Only allow safe operations
+                safe_globals = {
+                    "amount_diff": amount_diff,
+                    "date_diff": date_diff,
+                    "vendor_match": vendor_match,
+                    "vendor_contains_gas_or_fuel": vendor_contains_gas_or_fuel,
+                    "receipt": receipt,
+                    "transaction": transaction,
+                    "abs": abs,
+                    "len": len,
+                    "min": min,
+                    "max": max,
+                    "sum": sum,
+                    "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):
         if action == "auto_approve":

api_models.py

@@ -11,6 +11,7 @@ class ReceiptRequest(BaseModel):
     tax: float
     vendor: str
     category: str
+    description: str
 
 class TransactionRequest(BaseModel):
     id: str
@@ -49,6 +50,9 @@ class MatchResponse(BaseModel):
     match_reason: str
     receipt_vendor: str
     receipt_amount: float
+    receipt_description: str
+    receipt_category: str
+    receipt_tax_amount: float
     transaction_vendor: str
     transaction_amount: float
 
@@ -83,6 +87,7 @@ class DocumentProcessResponse(BaseModel):
     file_id: str
     extraction_success: bool
     vendor: Optional[str] = None
+    description: Optional[str] = None
     total_amount: Optional[float] = None
     tax_amount: Optional[float] = None
     date: Optional[str] = None

config.py

@@ -3,7 +3,13 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-GROQ_API_KEY = "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"
+# Get API key from environment variable with fallback
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", "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")
+
+# Validate API key
+if not GROQ_API_KEY or GROQ_API_KEY == "your_api_key_here":
+    raise ValueError("GROQ_API_KEY environment variable is not set or invalid. Please set it in your .env file.")
+
 CONFIDENCE_THRESHOLD = 0.3
 DATE_TOLERANCE_DAYS = 7
 AMOUNT_TOLERANCE_PERCENT = 0.05 

debug_match.py

@@ -1,82 +0,0 @@
-import csv
-from dateutil import parser
-from datetime import datetime, timedelta
-
-# Config values
-DATE_TOLERANCE_DAYS = 7
-AMOUNT_TOLERANCE_PERCENT = 0.05
-CONFIDENCE_THRESHOLD = 0.8
-
-# Receipt data
-receipt_date = datetime(2025, 2, 7)
-receipt_amount = 1412.5
-receipt_vendor = "Ajai Srivastava CPA, Accounting Services & Taxes"
-
-print("=== DEBUGGING AJAI RECEIPT MATCH ===")
-print(f"Receipt Date: {receipt_date}")
-print(f"Receipt Amount: ${receipt_amount}")
-print(f"Receipt Vendor: {receipt_vendor}")
-print(f"Date Tolerance: {DATE_TOLERANCE_DAYS} days")
-print(f"Amount Tolerance: {AMOUNT_TOLERANCE_PERCENT * 100}%")
-print()
-
-# Check CSV transaction
-csv_transaction = {
-    "date": "2/18/2025",
-    "amount": -1412.5,
-    "vendor": "Ajai Srivastava"
-}
-
-# Parse CSV date
-csv_date = parser.parse(csv_transaction["date"])
-csv_amount = csv_transaction["amount"]
-csv_vendor = csv_transaction["vendor"]
-
-print("=== CSV TRANSACTION ===")
-print(f"CSV Date: {csv_date}")
-print(f"CSV Amount: ${csv_amount}")
-print(f"CSV Vendor: {csv_vendor}")
-print()
-
-# Check date tolerance
-date_diff = abs((receipt_date - csv_date).days)
-date_match = date_diff <= DATE_TOLERANCE_DAYS
-
-print("=== DATE CHECK ===")
-print(f"Date Difference: {date_diff} days")
-print(f"Date Match: {date_match}")
-print(f"Tolerance: {DATE_TOLERANCE_DAYS} days")
-print()
-
-# Check amount tolerance
-amount_tolerance = receipt_amount * AMOUNT_TOLERANCE_PERCENT
-amount_diff = abs(receipt_amount - abs(csv_amount))  # Use absolute value for negative amounts
-amount_match = amount_diff <= amount_tolerance
-
-print("=== AMOUNT CHECK ===")
-print(f"Receipt Amount: ${receipt_amount}")
-print(f"CSV Amount (abs): ${abs(csv_amount)}")
-print(f"Amount Difference: ${amount_diff}")
-print(f"Amount Tolerance: ${amount_tolerance}")
-print(f"Amount Match: {amount_match}")
-print()
-
-# Check vendor similarity
-vendor_similarity = "Ajai Srivastava" in receipt_vendor
-print("=== VENDOR CHECK ===")
-print(f"Receipt Vendor: {receipt_vendor}")
-print(f"CSV Vendor: {csv_vendor}")
-print(f"Vendor Similarity: {vendor_similarity}")
-print()
-
-# Overall result
-print("=== RESULT ===")
-if date_match and amount_match:
-    print("✅ Transaction would pass initial filtering")
-    print("Would proceed to AI matching stage")
-else:
-    print("❌ Transaction filtered out before AI matching")
-    if not date_match:
-        print(f"  - Date difference ({date_diff} days) > tolerance ({DATE_TOLERANCE_DAYS} days)")
-    if not amount_match:
-        print(f"  - Amount difference (${amount_diff}) > tolerance (${amount_tolerance})") 

document_processor.py

@@ -8,6 +8,9 @@ import config
 import os
 import aiofiles
 from datetime import datetime
+import logging
+
+logger = logging.getLogger(__name__)
 
 class DocumentProcessor:
     def __init__(self):
@@ -37,6 +40,7 @@ class DocumentProcessor:
             Analyze this receipt image and extract the following information in JSON format:
             {
                 "vendor": "Store/company name",
+                "description": "Detailed description of items/services purchased",
                 "total_amount": 0.00,
                 "tax_amount": 0.00,
                 "date": "YYYY-MM-DD",
@@ -46,6 +50,7 @@ class DocumentProcessor:
             
             Rules:
             - Extract vendor name as it appears on receipt
+            - Extract description of items/services purchased (e.g., "Coffee and sandwich", "Gasoline", "Office supplies")
             - Total amount should be the final total including tax
             - Tax amount is separate tax line if available
             - Date should be the date on the receipt
@@ -122,6 +127,7 @@ class DocumentProcessor:
             Extract:
             {{
                 "vendor": "Store/company name",
+                "description": "Detailed description of items/services purchased",
                 "total_amount": 0.00,
                 "tax_amount": 0.00,
                 "date": "YYYY-MM-DD",
@@ -131,6 +137,7 @@ class DocumentProcessor:
             
             Rules:
             - Extract vendor name as it appears on receipt
+            - Extract description of items/services purchased (e.g., "Coffee and sandwich", "Gasoline", "Office supplies")
             - Total amount should be the final total including tax
             - Tax amount is separate tax line if available
             - Date should be the date on the receipt
@@ -160,27 +167,130 @@ class DocumentProcessor:
             import json
             import re
             
-            # Find JSON in response
+            # Find JSON in response - try multiple patterns
             json_match = re.search(r'\{.*\}', result_text, re.DOTALL)
             if json_match:
                 json_str = json_match.group()
-                data = json.loads(json_str)
+                
+                # Clean up common JSON issues
+                json_str = re.sub(r',\s*([}\]])', r'\1', json_str)  # Remove trailing commas
+                json_str = re.sub(r'([{,])\s*([a-zA-Z_][a-zA-Z0-9_]*)\s*:', r'\1"\2":', json_str)  # Quote unquoted keys
+                
+                try:
+                    data = json.loads(json_str)
+                except json.JSONDecodeError as e:
+                    # Try to fix common JSON issues
+                    logger.warning(f"Initial JSON parsing failed: {e}")
+                    
+                    # Try to extract individual fields using regex
+                    vendor_match = re.search(r'"vendor"\s*:\s*"([^"]*)"', json_str)
+                    description_match = re.search(r'"description"\s*:\s*"([^"]*)"', json_str)
+                    total_amount_match = re.search(r'"total_amount"\s*:\s*([0-9.]+)', json_str)
+                    tax_amount_match = re.search(r'"tax_amount"\s*:\s*([0-9.]+)', json_str)
+                    date_match = re.search(r'"date"\s*:\s*"([^"]*)"', json_str)
+                    category_match = re.search(r'"category"\s*:\s*"([^"]*)"', json_str)
+                    confidence_match = re.search(r'"confidence"\s*:\s*([0-9.]+)', json_str)
+                    
+                    data = {
+                        "vendor": vendor_match.group(1) if vendor_match else "",
+                        "description": description_match.group(1) if description_match else "",
+                        "total_amount": float(total_amount_match.group(1)) if total_amount_match else 0.0,
+                        "tax_amount": float(tax_amount_match.group(1)) if tax_amount_match else 0.0,
+                        "date": date_match.group(1) if date_match else "",
+                        "category": category_match.group(1) if category_match else "Other",
+                        "confidence": float(confidence_match.group(1)) if confidence_match else 0.5
+                    }
                 
                 # Validate and clean data
                 return {
-                    "vendor": data.get("vendor", "").strip(),
+                    "vendor": str(data.get("vendor", "")).strip(),
+                    "description": str(data.get("description", "")).strip(),
                     "total_amount": float(data.get("total_amount", 0)),
                     "tax_amount": float(data.get("tax_amount", 0)),
-                    "date": data.get("date", ""),
-                    "category": data.get("category", "Other"),
+                    "date": str(data.get("date", "")).strip(),
+                    "category": str(data.get("category", "Other")).strip(),
                     "confidence": float(data.get("confidence", 0.5)),
                     "extraction_success": True
                 }
             else:
-                return {"error": "Could not parse JSON from AI response"}
+                # Try to extract fields from plain text
+                logger.warning("No JSON found in response, attempting text extraction")
+                return self._extract_from_plain_text(result_text)
                 
         except Exception as e:
-            return {"error": f"JSON parsing error: {str(e)}"}
+            logger.error(f"JSON parsing error: {str(e)}")
+            return {"error": f"JSON parsing error: {str(e)}", "extraction_success": False}
+    
+    def _extract_from_plain_text(self, text: str) -> Dict[str, Any]:
+        """Extract receipt data from plain text when JSON parsing fails"""
+        try:
+            import re
+            
+            # Extract vendor (look for common patterns)
+            vendor_patterns = [
+                r'(?:vendor|store|merchant|company)\s*[:\-]?\s*([A-Za-z0-9\s&.,]+)',
+                r'([A-Z][A-Za-z0-9\s&.,]{3,30})',  # Capitalized words
+            ]
+            
+            vendor = ""
+            for pattern in vendor_patterns:
+                match = re.search(pattern, text, re.IGNORECASE)
+                if match:
+                    vendor = match.group(1).strip()
+                    break
+            
+            # Extract amount (look for currency patterns)
+            amount_patterns = [
+                r'\$?\s*([0-9,]+\.?[0-9]*)',
+                r'(?:total|amount|sum)\s*[:\-]?\s*\$?\s*([0-9,]+\.?[0-9]*)',
+            ]
+            
+            total_amount = 0.0
+            for pattern in amount_patterns:
+                match = re.search(pattern, text, re.IGNORECASE)
+                if match:
+                    try:
+                        total_amount = float(match.group(1).replace(',', ''))
+                        break
+                    except ValueError:
+                        continue
+            
+            # Extract date
+            date_patterns = [
+                r'(\d{4}-\d{2}-\d{2})',
+                r'(\d{1,2}/\d{1,2}/\d{2,4})',
+                r'(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s+\d{1,2},?\s+\d{4}',
+            ]
+            
+            date = ""
+            for pattern in date_patterns:
+                match = re.search(pattern, text, re.IGNORECASE)
+                if match:
+                    date = match.group(0)
+                    break
+            
+            return {
+                "vendor": vendor or "Unknown",
+                "total_amount": total_amount,
+                "tax_amount": 0.0,
+                "date": date or "",
+                "category": "Other",
+                "confidence": 0.3,  # Low confidence for text extraction
+                "extraction_success": True
+            }
+            
+        except Exception as e:
+            logger.error(f"Text extraction error: {str(e)}")
+            return {
+                "vendor": "Unknown",
+                "total_amount": 0.0,
+                "tax_amount": 0.0,
+                "date": "",
+                "category": "Other",
+                "confidence": 0.1,
+                "extraction_success": False,
+                "error": f"Text extraction failed: {str(e)}"
+            }
     
     async def save_uploaded_file(self, file_content: bytes, filename: str) -> str:
         """Save uploaded file to temporary storage"""
@@ -201,4 +311,188 @@ class DocumentProcessor:
             return file_path
             
         except Exception as e:
-            raise Exception(f"File save error: {str(e)}") 
+            raise Exception(f"Failed to save file: {str(e)}")
+
+    async def extract_transactions_from_image(self, image_path: str) -> Dict[str, Any]:
+        """Extract multiple transactions from an image (bank statement, credit card statement, etc.)"""
+        try:
+            # Encode image to base64
+            base64_image = self._encode_image(image_path)
+            
+            # Create Groq vision prompt for transaction extraction
+            prompt = """
+            Analyze this financial document image (bank statement, credit card statement, etc.) and extract ALL transactions in JSON format.
+            
+            Look for transaction lists, payment records, or any financial entries that show:
+            - Date
+            - Amount (positive or negative)
+            - Vendor/Description/Payee name
+            - Any additional notes or memo
+            
+            Return the transactions as a JSON array:
+            {
+                "extraction_success": true,
+                "transactions": [
+                    {
+                        "date": "YYYY-MM-DD",
+                        "amount": 0.00,
+                        "vendor": "Vendor name",
+                        "memo": "Additional notes"
+                    },
+                    {
+                        "date": "YYYY-MM-DD", 
+                        "amount": -0.00,
+                        "vendor": "Another vendor",
+                        "memo": "Payment or charge description"
+                    }
+                ]
+            }
+            
+            Rules:
+            - Extract ALL visible transactions
+            - Include both positive (credits) and negative (debits) amounts
+            - Use the actual date format from the document
+            - Vendor should be the merchant/payee name
+            - Memo can include transaction type, reference numbers, etc.
+            - If no transactions found, return empty array but set extraction_success to true
+            
+            Return only valid JSON.
+            """
+            
+            # Call Groq vision API
+            response = self.client.chat.completions.create(
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": prompt},
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/jpeg;base64,{base64_image}",
+                                },
+                            },
+                        ],
+                    }
+                ],
+                model=self.model,
+                max_tokens=2000,  # Higher token limit for multiple transactions
+                temperature=0.1
+            )
+            
+            # Parse response
+            result_text = response.choices[0].message.content.strip()
+            return self._parse_transaction_extraction_result(result_text)
+            
+        except Exception as e:
+            return {
+                "extraction_success": False,
+                "error": f"Transaction extraction error: {str(e)}",
+                "transactions": []
+            }
+
+    def _parse_transaction_extraction_result(self, result_text: str) -> Dict[str, Any]:
+        """Parse Groq response for transaction extraction"""
+        try:
+            import json
+            import re
+
+            # Find the first '{' and last '}'
+            start = result_text.find('{')
+            end = result_text.rfind('}')
+            if start == -1 or end == -1 or end <= start:
+                return {
+                    "extraction_success": False,
+                    "error": "Could not find JSON object in AI response",
+                    "transactions": []
+                }
+            json_str = result_text[start:end+1]
+
+            # Remove trailing commas before } or ]
+            json_str = re.sub(r',\s*([}\]])', r'\1', json_str)
+
+            try:
+                data = json.loads(json_str)
+            except Exception as e:
+                import logging
+                logging.error(f"JSON parsing error: {str(e)}")
+                logging.error(f"Offending JSON string:\n{json_str}")
+                return {
+                    "extraction_success": False,
+                    "error": f"JSON parsing error: {str(e)}",
+                    "transactions": []
+                }
+
+            # Validate and clean data
+            transactions = data.get("transactions", [])
+            cleaned_transactions = []
+            for txn in transactions:
+                try:
+                    cleaned_txn = {
+                        "date": str(txn.get("date", "")).strip(),
+                        "amount": float(str(txn.get("amount", 0)).replace('$', '').replace(',', '')),
+                        "vendor": str(txn.get("vendor", "")).strip(),
+                        "memo": str(txn.get("memo", "")).strip()
+                    }
+                    cleaned_transactions.append(cleaned_txn)
+                except Exception as e:
+                    continue
+            return {
+                "extraction_success": data.get("extraction_success", True),
+                "transactions": cleaned_transactions,
+                "total_transactions": len(cleaned_transactions)
+            }
+        except Exception as e:
+            import logging
+            logging.error(f"JSON parsing error (outer): {str(e)}")
+            return {
+                "extraction_success": False,
+                "error": f"JSON parsing error: {str(e)}",
+                "transactions": []
+            }
+
+    def _parse_date_to_iso(self, date_str: str) -> str:
+        """Parse various date formats and convert to YYYY-MM-DD"""
+        try:
+            import re
+            from datetime import datetime
+            
+            date_str = date_str.strip().upper()
+            
+            # Handle formats like "MAY 22", "JUN 01", "MAY 22, 2024"
+            month_pattern = r'(JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)\s+(\d{1,2})(?:,\s*(\d{4}))?'
+            match = re.match(month_pattern, date_str)
+            
+            if match:
+                month_abbr, day, year = match.groups()
+                month_map = {
+                    'JAN': 1, 'FEB': 2, 'MAR': 3, 'APR': 4, 'MAY': 5, 'JUN': 6,
+                    'JUL': 7, 'AUG': 8, 'SEP': 9, 'OCT': 10, 'NOV': 11, 'DEC': 12
+                }
+                
+                month = month_map[month_abbr]
+                day = int(day)
+                year = int(year) if year else datetime.now().year
+                
+                # Handle 2-digit years
+                if year < 100:
+                    year += 2000
+                
+                return f"{year:04d}-{month:02d}-{day:02d}"
+            
+            # Handle YYYY-MM-DD format
+            if re.match(r'\d{4}-\d{2}-\d{2}', date_str):
+                return date_str
+            
+            # Handle MM/DD/YYYY format
+            if re.match(r'\d{1,2}/\d{1,2}/\d{4}', date_str):
+                return datetime.strptime(date_str, '%m/%d/%Y').strftime('%Y-%m-%d')
+            
+            # Handle MM/DD/YY format
+            if re.match(r'\d{1,2}/\d{1,2}/\d{2}', date_str):
+                return datetime.strptime(date_str, '%m/%d/%y').strftime('%Y-%m-%d')
+            
+            return None
+            
+        except Exception:
+            return None 

google_drive_sync.py

@@ -122,6 +122,7 @@ class GoogleDriveSync:
                     "file_size": file.get('size', 0),
                     "extraction_success": True,
                     "vendor": "Demo Vendor",
+                    "description": "Coffee and sandwich",
                     "total_amount": 25.50,
                     "tax_amount": 2.04,
                     "date": "2024-01-15",

main.py

@@ -5,22 +5,32 @@ from typing import List
 import uuid
 import csv
 import io
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler('app.log'),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger(__name__)
 
 from api_models import (
     MatchingRequest, MatchingResponse, MatchResponse,
     ApprovalRequest, RuleRequest, DocumentUploadResponse, 
-    DocumentProcessResponse, DriveSyncRequest, DriveSyncResponse,
-    QuickBooksImportRequest, QuickBooksImportResponse, TransactionRequest
+    DocumentProcessResponse, TransactionRequest
 )
 from models import Receipt, Transaction, Match
 from matching_engine import MatchingEngine
 from ai_rules import AIRule
 from document_processor import DocumentProcessor
-from google_drive_sync import GoogleDriveSync
 
 app = FastAPI(
     title="AI Bookkeeper - Data Science Engine",
-    description="AI-powered receipt-to-transaction matching engine. Receives QuickBooks data from backend and provides intelligent matching capabilities.",
+    description="AI-powered receipt-to-transaction matching engine. Receives transaction data and provides intelligent matching capabilities.",
     version="1.0.0"
 )
 
@@ -36,11 +46,14 @@ app.add_middleware(
 # Initialize DS Engine components
 matching_engine = MatchingEngine()
 document_processor = DocumentProcessor()
-drive_sync = GoogleDriveSync()
 
 # In-memory storage for uploaded files (in production, use a database)
 uploaded_files = {}
 
+# Store imported transactions globally for easy access
+stored_transactions = []
+processed_receipts = {}
+
 @app.get("/")
 async def root():
     """Health check endpoint"""
@@ -51,53 +64,13 @@ async def root():
     }
 
 # ============================================================================
-# QUICKBOOKS DATA IMPORT ENDPOINTS
+# TRANSACTION IMPORT ENDPOINTS
 # ============================================================================
 
-@app.post("/transactions/import/quickbooks", response_model=QuickBooksImportResponse)
-async def import_quickbooks_transactions(request: QuickBooksImportRequest):
+@app.post("/transactions/import/csv")
+async def import_transactions_csv(file: UploadFile = File(...)):
     """
-    Import and convert QuickBooks transactions to internal format.
-    
-    This endpoint receives raw QuickBooks transaction data from the backend
-    and converts it to the internal format used by the AI matching engine.
-    """
-    try:
-        converted_transactions = []
-        errors = []
-        
-        for qb_txn in request.transactions:
-            try:
-                # Convert QuickBooks date format to datetime
-                txn_date = datetime.strptime(qb_txn.txn_date, "%Y-%m-%d")
-                
-                # Convert to internal TransactionRequest format
-                converted_txn = TransactionRequest(
-                    id=qb_txn.id,
-                    transaction_date=txn_date,
-                    amount=abs(qb_txn.amount),  # Ensure positive amount
-                    vendor=qb_txn.payee_name,
-                    notes=qb_txn.memo or f"QuickBooks transaction from {qb_txn.account_name or 'unknown account'}"
-                )
-                
-                converted_transactions.append(converted_txn)
-                
-            except Exception as e:
-                errors.append(f"Error converting transaction {qb_txn.id}: {str(e)}")
-        
-        return QuickBooksImportResponse(
-            imported_count=len(converted_transactions),
-            converted_transactions=converted_transactions,
-            errors=errors
-        )
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/transactions/import/csv", response_model=QuickBooksImportResponse)
-async def import_quickbooks_transactions_csv(file: UploadFile = File(...)):
-    """
-    Import QuickBooks transactions from a CSV file (custom bank export format).
+    Import transactions from a CSV file (custom bank export format).
     """
     try:
         content = await file.read()
@@ -137,154 +110,107 @@ async def import_quickbooks_transactions_csv(file: UploadFile = File(...)):
                 })
             except Exception as e:
                 errors.append(f"Row {idx+1}: {str(e)}")
-        # Use the same logic as the JSON import endpoint
-        request_obj = QuickBooksImportRequest(transactions=transactions)
-        response = await import_quickbooks_transactions(request_obj)
-        # Attach errors from CSV parsing
-        if hasattr(response, 'errors'):
-            response.errors.extend(errors)
-        return response
+        # Store transactions globally for auto-matching
+        global stored_transactions
+        stored_transactions = transactions
+        
+        return {
+            "imported_count": len(transactions),
+            "converted_transactions": transactions,
+            "errors": errors
+        }
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
-# ============================================================================
-# RECEIPT PROCESSING ENDPOINTS
-# ============================================================================
-
-@app.post("/upload", response_model=DocumentUploadResponse)
-async def upload_document(file: UploadFile = File(...)):
+@app.post("/transactions/import/image")
+async def import_transactions_from_image(file: UploadFile = File(...)):
     """
-    Upload a receipt document (PDF or image) for processing.
-    
-    Supports: PDF, JPG, JPEG, PNG, GIF, BMP
+    Import transactions from an image (bank statement, credit card statement, etc.) using AI extraction.
     """
     try:
         # Validate file type
-        allowed_types = ['pdf', 'jpg', 'jpeg', 'png', 'gif', 'bmp']
+        allowed_types = ['jpg', 'jpeg', 'png', 'gif', 'bmp', 'pdf']
         file_extension = file.filename.split('.')[-1].lower()
-        
         if file_extension not in allowed_types:
             raise HTTPException(status_code=400, detail=f"Unsupported file type. Allowed: {allowed_types}")
-        
         # Read file content
-        file_content = await file.read()
-        
-        # Save file
-        file_path = await document_processor.save_uploaded_file(file_content, file.filename)
-        
-        # Generate file ID
-        file_id = str(uuid.uuid4())
-        
-        # Store file info
-        uploaded_files[file_id] = {
-            "filename": file.filename,
-            "file_path": file_path,
-            "file_type": file_extension,
-            "upload_date": datetime.now(),
-            "status": "uploaded"
+        content = await file.read()
+        # Save file to disk
+        image_path = await document_processor.save_uploaded_file(content, file.filename)
+        # Extract transactions from image (pass file path)
+        extraction_result = await document_processor.extract_transactions_from_image(image_path)
+        if not extraction_result.get("extraction_success", False):
+            raise HTTPException(status_code=500, detail=extraction_result.get("error", "Extraction failed"))
+        extracted_transactions = extraction_result.get("transactions", [])
+        # Store transactions globally for auto-matching
+        global stored_transactions
+        stored_transactions = []
+        for idx, txn in enumerate(extracted_transactions):
+            try:
+                txn_id = f"img_{file.filename}_{idx+1}"
+                txn_date_raw = txn.get("date")
+                amount = txn.get("amount")
+                vendor = txn.get("vendor")
+                memo = txn.get("memo", "")
+                
+                # Parse date to YYYY-MM-DD format
+                txn_date = document_processor._parse_date_to_iso(txn_date_raw)
+                if not txn_date:
+                    # Fallback: use current year if parsing fails
+                    txn_date = f"2024-{txn_date_raw}"
+                
+                stored_transactions.append({
+                    "id": txn_id,
+                    "txn_date": txn_date,
+                    "amount": amount,
+                    "payee_name": vendor,
+                    "memo": memo
+                })
+            except Exception as e:
+                continue
+        return {
+            "imported_count": len(stored_transactions),
+            "converted_transactions": stored_transactions,
+            "errors": []
         }
-        
-        return DocumentUploadResponse(
-            file_id=file_id,
-            filename=file.filename,
-            file_type=file_extension,
-            upload_date=datetime.now(),
-            status="uploaded"
-        )
-        
     except Exception as e:
+        logger.error(f"Error importing transactions from image: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
-@app.post("/process/{file_id}", response_model=DocumentProcessResponse)
-async def process_document(file_id: str):
-    """
-    Process uploaded document and extract receipt data using AI.
-    
-    Uses Groq LLM to extract vendor, amount, date, category from receipt images/PDFs.
-    """
-    try:
-        if file_id not in uploaded_files:
-            raise HTTPException(status_code=404, detail="File not found")
-        
-        file_info = uploaded_files[file_id]
-        file_path = file_info["file_path"]
-        file_type = file_info["file_type"]
-        
-        # Process document using AI
-        result = await document_processor.process_file(file_path, file_type)
-        
-        # Update file status
-        if "error" in result:
-            uploaded_files[file_id]["status"] = "failed"
-        else:
-            uploaded_files[file_id]["status"] = "processed"
-            uploaded_files[file_id]["extracted_data"] = result
-        
-        return DocumentProcessResponse(
-            file_id=file_id,
-            extraction_success=result.get("extraction_success", False),
-            vendor=result.get("vendor"),
-            total_amount=result.get("total_amount"),
-            tax_amount=result.get("tax_amount"),
-            date=result.get("date"),
-            category=result.get("category"),
-            confidence=result.get("confidence"),
-            error=result.get("error")
-        )
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/documents")
-async def list_documents():
-    """List all uploaded and processed documents"""
-    try:
-        documents = []
-        for file_id, file_info in uploaded_files.items():
-            documents.append({
-                "file_id": file_id,
-                "filename": file_info["filename"],
-                "file_type": file_info["file_type"],
-                "upload_date": file_info["upload_date"],
-                "status": file_info["status"],
-                "extracted_data": file_info.get("extracted_data")
-            })
-        
-        return {"documents": documents}
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+# ============================================================================
+# DOCUMENT PROCESSING ENDPOINTS
+# ============================================================================
 
 @app.post("/upload-multiple", response_model=List[DocumentUploadResponse])
 async def upload_multiple_documents(files: List[UploadFile] = File(...)):
     """
-    Upload multiple receipt documents (PDF or image) for processing.
-    Supports: PDF, JPG, JPEG, PNG, GIF, BMP
+    Upload multiple receipt images for processing.
+    
+    This endpoint accepts multiple image files and returns file IDs
+    that can be used with the /process/{file_id} endpoint.
     """
-    responses = []
-    allowed_types = ['pdf', 'jpg', 'jpeg', 'png', 'gif', 'bmp']
-    for file in files:
-        try:
+    try:
+        responses = []
+        
+        for file in files:
+            # Validate file type
+            allowed_types = ['jpg', 'jpeg', 'png', 'gif', 'bmp', 'pdf']
             file_extension = file.filename.split('.')[-1].lower()
+            
             if file_extension not in allowed_types:
-                responses.append(DocumentUploadResponse(
-                    file_id="",
-                    filename=file.filename,
-                    file_type=file_extension,
-                    upload_date=datetime.now(),
-                    status=f"failed: unsupported file type ({file_extension})"
-                ))
-                continue
-            file_content = await file.read()
-            file_path = await document_processor.save_uploaded_file(file_content, file.filename)
+                raise HTTPException(status_code=400, detail=f"Unsupported file type for {file.filename}. Allowed: {allowed_types}")
+            
+            # Generate unique file ID
             file_id = str(uuid.uuid4())
+            
+            # Read and store file content
+            content = await file.read()
             uploaded_files[file_id] = {
                 "filename": file.filename,
-                "file_path": file_path,
-                "file_type": file_extension,
-                "upload_date": datetime.now(),
-                "status": "uploaded"
+                "content": content,
+                "upload_date": datetime.now()
             }
+            
             responses.append(DocumentUploadResponse(
                 file_id=file_id,
                 filename=file.filename,
@@ -292,169 +218,273 @@ async def upload_multiple_documents(files: List[UploadFile] = File(...)):
                 upload_date=datetime.now(),
                 status="uploaded"
             ))
-        except Exception as e:
-            responses.append(DocumentUploadResponse(
-                file_id="",
-                filename=file.filename,
-                file_type="",
-                upload_date=datetime.now(),
-                status=f"failed: {str(e)}"
-            ))
-    return responses
+        
+        return responses
+        
+    except Exception as e:
+        logger.error(f"Error uploading documents: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
 
-# ============================================================================
-# GOOGLE DRIVE INTEGRATION ENDPOINTS
-# ============================================================================
-
-@app.post("/drive/sync", response_model=DriveSyncResponse)
-async def sync_google_drive(request: DriveSyncRequest):
+@app.post("/process/{file_id}", response_model=DocumentProcessResponse)
+async def process_document(file_id: str):
     """
-    Sync and process receipts from Google Drive folder.
+    Process a previously uploaded document to extract receipt information.
     
-    Automatically downloads and processes all receipt files from the specified
-    Google Drive folder using AI extraction.
+    This endpoint uses AI to extract structured data from receipt images,
+    including vendor, amount, date, and category information.
     """
     try:
-        # Process files from Drive
-        results = await drive_sync.process_drive_files(request.folder_id)
+        # Check if file exists
+        if file_id not in uploaded_files:
+            raise HTTPException(status_code=404, detail=f"File {file_id} not found")
         
-        # Count results
-        files_processed = len(results)
-        successful_extractions = len([r for r in results if r.get("extraction_success", False)])
-        failed_extractions = files_processed - successful_extractions
+        file_data = uploaded_files[file_id]
         
-        # Convert to response format
-        response_results = []
-        for result in results:
-            response_results.append(DocumentProcessResponse(
-                file_id=result.get("file_id", ""),
-                extraction_success=result.get("extraction_success", False),
-                vendor=result.get("vendor"),
-                total_amount=result.get("total_amount"),
-                tax_amount=result.get("tax_amount"),
-                date=result.get("date"),
-                category=result.get("category"),
-                confidence=result.get("confidence"),
-                error=result.get("error")
-            ))
+        # Save file temporarily and process it
+        file_path = await document_processor.save_uploaded_file(file_data["content"], file_data["filename"])
+        file_type = file_data["filename"].split('.')[-1].lower()
+        receipt_data = await document_processor.process_file(file_path, file_type)
         
-        return DriveSyncResponse(
-            files_processed=files_processed,
-            successful_extractions=successful_extractions,
-            failed_extractions=failed_extractions,
-            results=response_results
+        # Store processed receipt
+        processed_receipts[file_id] = receipt_data
+        
+        return DocumentProcessResponse(
+            file_id=file_id,
+            extraction_success=receipt_data.get("extraction_success", False),
+            vendor=receipt_data.get("vendor", ""),
+            description=receipt_data.get("description", ""),
+            total_amount=receipt_data.get("total_amount", 0.0),
+            tax_amount=receipt_data.get("tax_amount", 0.0),
+            date=receipt_data.get("date", ""),
+            category=receipt_data.get("category", ""),
+            confidence=receipt_data.get("confidence", 0.0),
+            error=receipt_data.get("error", None)
         )
         
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/drive/folders")
-async def list_drive_folders():
-    """List all accessible Google Drive folders"""
-    try:
-        folders = drive_sync.list_folders()
-        return {"folders": folders}
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/drive/folder/{folder_id}")
-async def get_folder_info(folder_id: str):
-    """Get information about a specific Google Drive folder"""
-    try:
-        folder_info = drive_sync.get_folder_info(folder_id)
-        return folder_info
-        
-    except Exception as e:
+        logger.error(f"Error processing document {file_id}: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
 # ============================================================================
-# AI MATCHING ENGINE ENDPOINTS
+# MATCHING ENDPOINTS
 # ============================================================================
 
-@app.post("/match", response_model=MatchingResponse)
-async def match_receipts_transactions(request: MatchingRequest):
+@app.post("/match-specific", response_model=MatchingResponse)
+async def match_specific_receipts(file_ids: List[str]):
     """
-    Match receipts to transactions using AI.
+    Match specific receipts against imported transactions.
     
-    Core AI matching engine that compares receipts against QuickBooks transactions
-    using intelligent algorithms and returns confidence scores.
+    This endpoint takes a list of receipt file IDs and matches them against
+    the currently imported transactions using AI-powered matching logic.
     """
     try:
-        # Convert request models to internal models
-        receipts = [
-            Receipt(
-                id=r.id, file_name=r.file_name, upload_date=r.upload_date,
-                receipt_date=r.receipt_date, amount=r.amount, tax=r.tax,
-                vendor=r.vendor, category=r.category
-            ) for r in request.receipts
-        ]
+        logger.info(f"Starting match-specific for file IDs: {file_ids}")
         
-        transactions = [
-            Transaction(
-                id=t.id, transaction_date=t.transaction_date, amount=t.amount,
-                vendor=t.vendor, notes=t.notes
-            ) for t in request.transactions
-        ]
+        # Check if transactions are imported
+        if not stored_transactions:
+            logger.warning("No transactions imported")
+            raise HTTPException(status_code=400, detail="No transactions imported. Please upload CSV first.")
         
-        # Process matching using AI engine
-        matches = matching_engine.process_matching(receipts, transactions)
+        logger.info(f"Found {len(stored_transactions)} stored transactions")
         
-        # Convert to response format
-        match_responses = [
-            MatchResponse(
-                receipt_id=match.receipt.id,
-                transaction_id=match.transaction.id,
-                confidence_score=match.confidence_score,
-                match_reason=match.match_reason,
-                receipt_vendor=match.receipt.vendor,
-                receipt_amount=match.receipt.amount,
-                transaction_vendor=match.transaction.vendor,
-                transaction_amount=match.transaction.amount
-            ) for match in matches
-        ]
+        # Convert stored transactions to Transaction objects
+        transactions = []
+        for txn in stored_transactions:
+            try:
+                txn_date = datetime.strptime(txn["txn_date"], "%Y-%m-%d")
+                transaction = Transaction(
+                    id=txn["id"],
+                    transaction_date=txn_date,
+                    amount=txn["amount"],
+                    vendor=txn["payee_name"],
+                    notes=txn["memo"]
+                )
+                transactions.append(transaction)
+            except Exception as e:
+                logger.warning(f"Error converting transaction {txn['id']}: {str(e)}")
+                continue
         
-        # Get statistics
-        stats = matching_engine.get_matching_stats(matches)
+        logger.info(f"Converted {len(transactions)} transactions")
         
-        return MatchingResponse(matches=match_responses, stats=stats)
+        # Get receipts for the specified file IDs
+        receipts = []
+        missing_files = []
         
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/approve")
-async def approve_match(request: ApprovalRequest):
-    """
-    Approve or reject an AI match.
-    
-    Logs user feedback for continuous AI improvement and learning.
-    """
-    try:
-        if request.action == "approve":
-            return {"message": f"Match {request.match_id} approved by {request.user_id}"}
-        elif request.action == "reject":
-            return {"message": f"Match {request.match_id} rejected by {request.user_id}. Reason: {request.reason}"}
-        else:
-            raise HTTPException(status_code=400, detail="Action must be 'approve' or 'reject'")
+        for file_id in file_ids:
+            if file_id in processed_receipts:
+                receipt_data = processed_receipts[file_id]
+                logger.info(f"DEBUG: receipt_data for {file_id}: {receipt_data}")
+                logger.info(f"DEBUG: receipt_data keys for {file_id}: {list(receipt_data.keys())}")
+                try:
+                    # Handle missing date field
+                    if "date" not in receipt_data or not receipt_data["date"]:
+                        logger.warning(f"Missing date for receipt {file_id}, using current date")
+                        receipt_date = datetime.now()
+                    else:
+                        receipt_date = datetime.strptime(receipt_data["date"], "%Y-%m-%d")
+                    
+                    # Handle missing amount field - try multiple possible keys
+                    amount = receipt_data.get("amount")
+                    if amount is None:
+                        amount = receipt_data.get("total_amount")
+                    if amount is None:
+                        amount = receipt_data.get("amount_total")
+                    if amount is None:
+                        logger.warning(f"Missing amount for receipt {file_id}, using 0.0")
+                        amount = 0.0
+                    
+                    # Ensure amount is a float
+                    try:
+                        amount = float(amount)
+                    except (ValueError, TypeError):
+                        logger.warning(f"Invalid amount '{amount}' for receipt {file_id}, using 0.0")
+                        amount = 0.0
+                    
+                    logger.info(f"DEBUG: amount for {file_id}: {amount}")
+                    
+                    # Handle missing vendor field
+                    vendor = receipt_data.get("vendor", "")
+                    if not vendor:
+                        logger.warning(f"Missing vendor for receipt {file_id}, using 'Unknown'")
+                        vendor = "Unknown"
+                    
+                    # Handle missing category field
+                    category = receipt_data.get("category", "Other")
+                    
+                    # Handle description field
+                    description = receipt_data.get("description", "")
+                    
+                    # Handle tax field
+                    tax = receipt_data.get("tax", receipt_data.get("tax_amount", 0.0))
+                    try:
+                        tax = float(tax)
+                    except (ValueError, TypeError):
+                        tax = 0.0
+                    
+                    receipt = Receipt(
+                        id=file_id,
+                        file_name=uploaded_files[file_id]["filename"],
+                        upload_date=uploaded_files[file_id]["upload_date"],
+                        receipt_date=receipt_date,
+                        amount=amount,
+                        tax=tax,
+                        vendor=vendor,
+                        category=category,
+                        description=description
+                    )
+                    receipts.append(receipt)
+                    logger.info(f"Added receipt: {receipt.vendor} - ${receipt.amount}")
+                except Exception as e:
+                    logger.warning(f"Error creating receipt object for {file_id}: {str(e)}")
+                    missing_files.append(f"{file_id} (error: {str(e)})")
+            else:
+                logger.warning(f"Receipt {file_id} not found in processed_receipts")
+                missing_files.append(f"{file_id} (not found)")
+        
+        if missing_files:
+            logger.error(f"Missing files: {missing_files}")
+            raise HTTPException(status_code=400, detail=f"Missing files: {missing_files}")
+        
+        logger.info(f"Processing {len(receipts)} receipts against {len(transactions)} transactions")
+        
+        # Perform matching
+        try:
+            logger.info("Starting direct matching call (without ThreadPoolExecutor)")
+            logger.info(f"matching_engine type: {type(matching_engine)}")
+            logger.info(f"matching_engine.process_matching type: {type(matching_engine.process_matching)}")
+            logger.info(f"receipts type: {type(receipts)}, length: {len(receipts)}")
+            logger.info(f"transactions type: {type(transactions)}, length: {len(transactions)}")
             
+            matches = matching_engine.process_matching(receipts, transactions)
+            
+            logger.info(f"Matching completed successfully. Found {len(matches)} matches")
+            
+            # Convert matches to response format
+            match_responses = []
+            for match in matches:
+                logger.info(f"Raw match object: {match}")
+                logger.info(f"  receipt_id: {match.receipt.id}")
+                logger.info(f"  transaction_id: {match.transaction.id}")
+                logger.info(f"  confidence_score: {match.confidence_score}")
+                logger.info(f"  match_reason: {match.match_reason}")
+                logger.info(f"  receipt_vendor: {match.receipt.vendor}")
+                logger.info(f"  receipt_amount: {match.receipt.amount}")
+                logger.info(f"  transaction_vendor: {match.transaction.vendor}")
+                logger.info(f"  transaction_amount: {match.transaction.amount}")
+                
+                match_response = MatchResponse(
+                    receipt_id=match.receipt.id,
+                    transaction_id=match.transaction.id,
+                    confidence_score=match.confidence_score,
+                    match_reason=match.match_reason,
+                    receipt_vendor=match.receipt.vendor,
+                    receipt_amount=match.receipt.amount,
+                    receipt_description=match.receipt.description,
+                    receipt_category=match.receipt.category,
+                    receipt_tax_amount=match.receipt.tax,
+                    transaction_vendor=match.transaction.vendor,
+                    transaction_amount=match.transaction.amount
+                )
+                match_responses.append(match_response)
+                logger.info(f"Successfully created MatchResponse for {match.receipt.vendor} -> {match.transaction.vendor}")
+            
+            logger.info(f"Formatted {len(match_responses)} match responses")
+            
+            # Calculate statistics
+            if match_responses:
+                high_confidence = sum(1 for m in match_responses if m.confidence_score >= 0.8)
+                low_confidence = len(match_responses) - high_confidence
+                avg_score = sum(m.confidence_score for m in match_responses) / len(match_responses)
+            else:
+                high_confidence = low_confidence = avg_score = 0
+            
+            stats = {
+                "total": len(match_responses),
+                "high_confidence": high_confidence,
+                "low_confidence": low_confidence,
+                "avg_score": round(avg_score, 2)
+            }
+            
+            logger.info(f"Generated stats: {stats}")
+            logger.info(f"Match-specific completed successfully with {len(match_responses)} matches")
+            
+            return MatchingResponse(
+                matches=match_responses,
+                stats=stats
+            )
+            
+        except Exception as e:
+            logger.error(f"Exception in matching section: {str(e)}")
+            logger.error(f"Exception type: {type(e)}")
+            logger.error(f"Exception args: {e.args}")
+            logger.error(f"Traceback: {e.__traceback__}")
+            raise HTTPException(status_code=500, detail=f"Unexpected matching error: {str(e)}")
+        
+    except HTTPException:
+        raise
     except Exception as e:
+        logger.error(f"Unexpected error in match_specific_receipts: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
 # ============================================================================
-# AI RULES MANAGEMENT ENDPOINTS
+# RULES MANAGEMENT ENDPOINTS
 # ============================================================================
 
 @app.post("/rules")
 async def add_rule(request: RuleRequest):
-    """Add a new AI rule for matching and categorization"""
+    """
+    Add a new AI rule for transaction matching.
+    """
     try:
-        rule = AIRule(
+        new_rule = AIRule(
             name=request.name,
             condition=request.condition,
             action=request.action,
             source=request.source
         )
-        matching_engine.rules_engine.add_rule(rule)
+        
+        matching_engine.rules_engine.rules.append(new_rule)
+        
         return {"message": f"Rule '{request.name}' added successfully"}
         
     except Exception as e:
@@ -462,49 +492,59 @@ async def add_rule(request: RuleRequest):
 
 @app.get("/rules")
 async def get_rules():
-    """Get all active AI rules"""
+    """
+    Get all current AI rules.
+    """
     try:
-        rules = matching_engine.rules_engine.rules
-        return {
-            "rules": [
-                {
-                    "name": rule.name,
-                    "condition": rule.condition,
-                    "action": rule.action,
-                    "source": rule.source,
-                    "status": rule.status
-                } for rule in rules
-            ]
-        }
+        rules = []
+        for rule in matching_engine.rules_engine.rules:
+            rules.append({
+                "name": rule.name,
+                "condition": rule.condition,
+                "action": rule.action,
+                "source": rule.source,
+                "status": rule.status
+            })
+        
+        return {"rules": rules}
         
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.delete("/rules/{rule_name}")
 async def delete_rule(rule_name: str):
-    """Delete an AI rule"""
+    """
+    Delete an AI rule by name.
+    """
     try:
-        matching_engine.rules_engine.remove_rule(rule_name)
-        return {"message": f"Rule '{rule_name}' deleted successfully"}
+        rules = matching_engine.rules_engine.rules
+        for i, rule in enumerate(rules):
+            if rule.name == rule_name:
+                del rules[i]
+                return {"message": f"Rule '{rule_name}' deleted successfully"}
         
+        raise HTTPException(status_code=404, detail=f"Rule '{rule_name}' not found")
+        
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
 # ============================================================================
-# SYSTEM MONITORING ENDPOINTS
+# STATISTICS ENDPOINT
 # ============================================================================
 
 @app.get("/stats")
 async def get_stats():
-    """Get system statistics and performance metrics"""
+    """
+    Get system statistics.
+    """
     try:
-        recent_logs = matching_engine.feedback_logger.get_recent_logs(30)
         return {
-            "total_feedback_logs": len(matching_engine.feedback_logger.logs),
-            "recent_feedback_logs": len(recent_logs),
-            "active_rules": len([r for r in matching_engine.rules_engine.rules if r.status == "active"]),
-            "uploaded_documents": len(uploaded_files),
-            "processed_documents": len([f for f in uploaded_files.values() if f["status"] == "processed"])
+            "total_transactions": len(stored_transactions),
+            "total_receipts": len(processed_receipts),
+            "total_uploaded_files": len(uploaded_files),
+            "rules_count": len(matching_engine.rules_engine.rules)
         }
         
     except Exception as e:
@@ -512,4 +552,4 @@ async def get_stats():
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8343) 
+    uvicorn.run(app, host="0.0.0.0", port=8343)

models.py

@@ -12,6 +12,7 @@ class Receipt:
     tax: float
     vendor: str
     category: str
+    description: str
     
 @dataclass
 class Transaction:

run_match.py

@@ -1,49 +0,0 @@
-import json
-import requests
-import csv
-from dateutil import parser
-
-# Prepare transactions
-transactions = []
-with open("chequing statement.csv", newline="") as f:
-    reader = csv.DictReader(f)
-    idx = 1
-    for row in reader:
-        try:
-            txn_id = f"{row['Account Number']}_{idx}"
-            txn_date = parser.parse(row["Transaction Date"]).isoformat()
-            amount = float(row["Amount"].replace(",", "").strip())
-            vendor = row["Description 2"].strip()
-            notes = f"{row['Account Type']} {row['Cheque Number']} {row['Description 1']}".strip()
-            transactions.append({
-                "id": txn_id,
-                "transaction_date": txn_date,
-                "amount": amount,
-                "vendor": vendor,
-                "notes": notes
-            })
-            idx += 1
-        except Exception as e:
-            continue
-
-# Receipt data for Ajai Invoice (3).jpg
-receipt = {
-    "id": "33754868-bff5-4caf-9ece-cfd63f4e52d9",
-    "file_name": "Ajai Invoice (3).jpg",
-    "upload_date": "2025-07-02T15:31:23.641315",
-    "receipt_date": "2025-02-07T00:00:00",
-    "amount": 1412.5,
-    "tax": 162.5,
-    "vendor": "Ajai Srivastava CPA, Accounting Services & Taxes",
-    "category": "Office"
-}
-
-# Build request
-data = {
-    "receipts": [receipt],
-    "transactions": transactions
-}
-
-# Post to /match
-response = requests.post("http://localhost:8000/match", json=data)
-print(json.dumps(response.json(), indent=2))