Add /transactions/import/image endpoint to extract transactions from images using AI

document_processor.py

@@ -201,4 +201,131 @@ 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 JSON in response
+            json_match = re.search(r'\{.*\}', result_text, re.DOTALL)
+            if json_match:
+                json_str = json_match.group()
+                data = json.loads(json_str)
+                
+                # Validate and clean data
+                transactions = data.get("transactions", [])
+                cleaned_transactions = []
+                
+                for txn in transactions:
+                    try:
+                        # Clean and validate each transaction
+                        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:
+                        # Skip invalid transactions
+                        continue
+                
+                return {
+                    "extraction_success": data.get("extraction_success", True),
+                    "transactions": cleaned_transactions,
+                    "total_transactions": len(cleaned_transactions)
+                }
+            else:
+                return {
+                    "extraction_success": False,
+                    "error": "Could not parse JSON from AI response",
+                    "transactions": []
+                }
+                
+        except Exception as e:
+            return {
+                "extraction_success": False,
+                "error": f"JSON parsing error: {str(e)}",
+                "transactions": []
+            } 

main.py

@@ -155,6 +155,100 @@ async def import_quickbooks_transactions_csv(file: UploadFile = File(...)):
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
+@app.post("/transactions/import/image", response_model=QuickBooksImportResponse)
+async def import_transactions_from_image(file: UploadFile = File(...)):
+    """
+    Import transactions from an image (bank statement, credit card statement, etc.) using AI extraction.
+    
+    This endpoint uses AI to extract transaction data from images like:
+    - Bank statements
+    - Credit card statements  
+    - Transaction lists
+    - Financial documents
+    """
+    try:
+        # 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:
+            raise HTTPException(status_code=400, detail=f"Unsupported file type. Allowed: {allowed_types}")
+        
+        # Read file content
+        file_content = await file.read()
+        
+        # Save file temporarily
+        file_path = await document_processor.save_uploaded_file(file_content, file.filename)
+        
+        # Use AI to extract transactions from the image
+        extraction_result = await document_processor.extract_transactions_from_image(file_path)
+        
+        if not extraction_result.get("extraction_success", False):
+            raise HTTPException(
+                status_code=400, 
+                detail=f"Failed to extract transactions from image: {extraction_result.get('error', 'Unknown error')}"
+            )
+        
+        # Parse extracted transactions
+        transactions = []
+        errors = []
+        
+        extracted_transactions = extraction_result.get("transactions", [])
+        
+        for idx, txn in enumerate(extracted_transactions):
+            try:
+                # Generate unique ID
+                txn_id = f"img_{file.filename}_{idx+1}"
+                
+                # Parse date
+                txn_date = txn.get("date", "")
+                if not txn_date:
+                    raise ValueError("No date found in transaction")
+                
+                # Parse amount
+                amount_str = str(txn.get("amount", "0"))
+                amount = float(amount_str.replace('$', '').replace(',', '').strip())
+                
+                # Get vendor/description
+                payee_name = txn.get("vendor", txn.get("description", "Unknown"))
+                
+                # Get memo/notes
+                memo = txn.get("memo", txn.get("notes", ""))
+                
+                transactions.append({
+                    "id": txn_id,
+                    "txn_date": txn_date,
+                    "amount": amount,
+                    "payee_name": payee_name,
+                    "memo": memo
+                })
+                
+            except Exception as e:
+                errors.append(f"Transaction {idx+1}: {str(e)}")
+        
+        if not transactions:
+            raise HTTPException(
+                status_code=400, 
+                detail="No valid transactions could be extracted from the image"
+            )
+        
+        # Store transactions globally for auto-matching
+        global stored_transactions
+        stored_transactions = transactions
+        
+        # Use the same logic as the JSON import endpoint
+        request_obj = QuickBooksImportRequest(transactions=transactions)
+        response = await import_quickbooks_transactions(request_obj)
+        
+        # Attach errors from image parsing
+        if hasattr(response, 'errors'):
+            response.errors.extend(errors)
+        
+        return response
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
 # ============================================================================
 # RECEIPT PROCESSING ENDPOINTS
 # ============================================================================