Update src/ai_processor.py

src/ai_processor.py

@@ -242,77 +242,10 @@ class AIProcessor:
             return {'success':False, 'error':str(e)}
 
 
-    # Legacy methods for backward compatibility
     def analyze_wound(self, image, questionnaire_data):
-        """Legacy method for backward compatibility"""
-        try:
-            # Convert string path to PIL Image if needed
-            if isinstance(image, str):
-                try:
-                    from PIL import Image
-                    image = Image.open(image)
-                    logging.info(f"Converted string path to PIL Image: {image}")
-                except Exception as e:
-                    logging.error(f"Error converting string path to image: {e}")
-            
-            # Ensure we have a PIL Image object
-            if not isinstance(image, Image.Image):
-                try:
-                    from PIL import Image
-                    import io
-                    
-                    # If it's a file-like object
-                    if hasattr(image, 'read'):
-                        # Reset file pointer if possible
-                        if hasattr(image, 'seek'):
-                            image.seek(0)
-                        image = Image.open(image)
-                        logging.info("Converted file-like object to PIL Image")
-                except Exception as e:
-                    logging.error(f"Error ensuring image is PIL Image: {e}")
-                    raise ValueError(f"Invalid image format: {type(image)}")
-            
-            result = self.full_analysis_pipeline(image, questionnaire_data)
-            
-            if result['success']:
-                return {
-                    'timestamp': result['timestamp'],
-                    'summary': f"Analysis completed for {questionnaire_data.get('patient_name', 'patient')}",
-                    'recommendations': result['report'],
-                    'wound_detection': {
-                        'status': 'success',
-                        'detections': [result['visual_analysis']],
-                        'total_wounds': 1
-                    },
-                    'segmentation_result': {
-                        'status': 'success',
-                        'wound_area_percentage': result['visual_analysis'].get('surface_area_cm2', 0)
-                    },
-                    'risk_assessment': self._assess_risk_legacy(questionnaire_data),
-                    'guideline_recommendations': [result['report'][:200] + "..."]
-                }
-            else:
-                return {
-                    'timestamp': result['timestamp'],
-                    'summary': f"Analysis failed: {result['error']}",
-                    'recommendations': "Please consult with a healthcare professional.",
-                    'wound_detection': {'status': 'error', 'message': result['error']},
-                    'segmentation_result': {'status': 'error', 'message': result['error']},
-                    'risk_assessment': {'risk_score': 0, 'risk_level': 'Unknown', 'risk_factors': []},
-                    'guideline_recommendations': ["Analysis unavailable due to error"]
-                }
-                
-        except Exception as e:
-            logging.error(f"Legacy analyze_wound error: {e}")
-            return {
-                'timestamp': datetime.now().isoformat(),
-                'summary': f"Analysis error: {str(e)}",
-                'recommendations': "Please consult with a healthcare professional.",
-                'wound_detection': {'status': 'error', 'message': str(e)},
-                'segmentation_result': {'status': 'error', 'message': str(e)},
-                'risk_assessment': {'risk_score': 0, 'risk_level': 'Unknown', 'risk_factors': []},
-                'guideline_recommendations': ["Analysis unavailable due to error"]
-            }
+        """Legacy wrapper."""
+        if isinstance(image, str): image = Image.open(image)
+        return self.full_analysis_pipeline(image, questionnaire_data)
 
     def _assess_risk_legacy(self, questionnaire_data):
         """Legacy risk assessment for backward compatibility"""