made new changes

main.py

@@ -188,21 +188,33 @@ def sanitize_search_query(query: str) -> str:
 def safe_create_disease_info(class_index: str, disease_data: Optional[Dict[str, Any]] = None) -> DiseaseInfo:
     """Safely create DiseaseInfo object with proper validation and defaults"""
     try:
+        # Set up base defaults to always match DiseaseInfo model
+        base_defaults = {
+            'disease_name': None,
+            'common_names': [],
+            'crop': "Unknown",
+            'description': f"This appears to be a healthy plant or an unrecognized condition for class {class_index}",
+            'symptoms': [],
+            'cause': None,
+            'treatment': [],
+            'image_urls': [],
+            'prevention': [],
+            'management_tips': "",
+            'risk_level': "Unknown",
+            'sprayer_intervals': "",
+            'localized_tips': "",
+            'type': "Healthy/Unknown",
+            'external_resources': [],
+            'is_healthy': True
+        }
+
         if not disease_data:
-            # This is likely a healthy plant or unknown disease
-            return DiseaseInfo(
-                disease_name=None,
-                crop="Unknown",
-                description=f"This appears to be a healthy plant or an unrecognized condition for class {class_index}",
-                is_healthy=True,
-                type="Healthy/Unknown"
-            )
-        
-        # Create a copy of the data to avoid modifying the original
+            return DiseaseInfo(**base_defaults)
+
+        # Use defaults but override with any provided disease data
         safe_data = disease_data.copy()
-        
-        # Ensure all required fields have proper defaults
-        defaults = {
+
+        final_data = {
             'disease_name': safe_data.get('disease_name'),
             'common_names': safe_data.get('common_names', []),
             'crop': safe_data.get('crop', 'Unknown'),
@@ -217,36 +229,48 @@ def safe_create_disease_info(class_index: str, disease_data: Optional[Dict[str,
             'sprayer_intervals': safe_data.get('sprayer_intervals', ''),
             'localized_tips': safe_data.get('localized_tips', ''),
             'type': safe_data.get('type', 'Unknown'),
-            'external_resources': safe_data.get('external_resources', []),
+            'external_resources': [],
             'is_healthy': False
         }
-        
-        # Validate and clean external_resources
-        if defaults['external_resources']:
-            validated_resources = []
-            for resource in defaults['external_resources']:
-                if isinstance(resource, dict):
-                    validated_resources.append({
-                        'title': str(resource.get('title', '')),
-                        'url': str(resource.get('url', ''))
-                    })
-            defaults['external_resources'] = validated_resources
-        
-        return DiseaseInfo(**defaults)
-        
+
+        # Validate and normalize external_resources
+        external_resources = safe_data.get('external_resources', [])
+        if isinstance(external_resources, list):
+            final_data['external_resources'] = [
+                {
+                    'title': str(res.get('title', '')),
+                    'url': str(res.get('url', ''))
+                }
+                for res in external_resources if isinstance(res, dict)
+            ]
+
+        return DiseaseInfo(**final_data)
+
     except Exception as e:
         logger.error(f"Error creating DiseaseInfo for class {class_index}: {str(e)}")
         logger.error(f"Data causing error: {disease_data}")
-        
-        # Return a minimal valid DiseaseInfo object
+
+        # Return a safe fallback object with all required fields
         return DiseaseInfo(
             disease_name="Unknown",
+            common_names=[],
             crop="Unknown",
             description=f"Error loading disease information for class {class_index}",
+            symptoms=[],
             cause="Unknown",
+            treatment=[],
+            image_urls=[],
+            prevention=[],
+            management_tips="",
+            risk_level="Unknown",
+            sprayer_intervals="",
+            localized_tips="",
+            type="Unknown",
+            external_resources=[],
             is_healthy=False
         )
 
+
 def get_recommendations(class_index: str, confidence: float, disease_info: DiseaseInfo) -> List[str]:
     """Generate actionable recommendations based on prediction using treatment and prevention from JSON"""
     recommendations = []
@@ -451,9 +475,9 @@ def predict_image(image_bytes: bytes) -> PredictionResponse:
         # Final structured response
         return PredictionResponse(
             success=True,
-            predicted_class=disease_info['disease_name'] if disease_info.disease_name else "Unknown",
+            predicted_class=clean_name,
             predicted_class_index=predicted_class_idx,
-            clean_class_name=disease_info['disease_name'] if disease_info.disease_name else "Unknown",
+            clean_class_name= clean_name,
             message="Prediction successful",
             all_predictions=all_predictions,
             class_id=class_id,