AI backed processing

backend/llm_service.py

@@ -0,0 +1,54 @@
+import ollama
+import json
+import base64
+
+def extract_data(text: str = None, image_path: str = None, model_type: str = "text") -> dict:
+    """
+    Extracts structured data using either Text (Gemma) or Vision (Qwen) models.
+    """
+    
+    prompt = """
+    You are an expert data extraction assistant. 
+    Extract the following fields from the provided document and return them as a SINGLE VALID JSON OBJECT:
+    - invoice_number (string)
+    - date (string)
+    - vendor_name (string)
+    - total_amount (string)
+    - currency (string)
+    - line_items (list of objects with: description, quantity, unit_price, total)
+
+    IMPORTANT: 
+    - Return ONLY the JSON. No markdown formatting, no explanations. 
+    - If a field is not found, use null.
+    """
+    
+    messages = [{'role': 'user', 'content': prompt}]
+    model = 'gemma:2b'
+    
+    if model_type == 'vision':
+        if not image_path:
+            return {"error": "Image path required for vision mode"}
+        
+        # Qwen-VL handles images passed in the message
+        model = 'qwen2.5vl:7b' # Using the installed model ID
+        messages[0]['images'] = [image_path]
+        messages[0]['content'] = "Analyze this image. " + prompt
+    else:
+        # Text Mode
+        if not text:
+            return {"error": "Text required for text mode"}
+        messages[0]['content'] += f"\n\n---\n{text}\n---"
+
+    try:
+        response = ollama.chat(model=model, messages=messages)
+        content = response['message']['content']
+        
+        # Clean up markdown
+        content = content.replace("```json", "").replace("```", "").strip()
+        
+        return json.loads(content)
+
+    except Exception as e:
+        print(f"LLM Extraction Error ({model_type}): {e}")
+        return {"error": str(e), "raw_output": content if 'content' in locals() else ""}
+