Add column type mapping and enhance validation

Introduced a backend mapping for column expected types, improving validation and error handling. Updated UI to highlight default and corrected values, with additional detailed validation for data collection parameters.

backend/app/services/spreadsheet_service.py

@@ -62,30 +62,22 @@ class SampleSpreadsheetImporter:
         return column_type_mapping.get(column_name, str)
 
     def _clean_value(self, value, expected_type=None):
-        """Clean value by converting it to the expected type and handle edge cases."""
         if value is None:
             return None
         if expected_type == str:
-            # Ensure value is converted to string and stripped of whitespace
             return str(value).strip()
         if expected_type in [float, int]:
             try:
                 return expected_type(value)
-            except (ValueError, TypeError):
-                # If conversion fails, return None
-                return None
-        if isinstance(value, str):
-            try:
-                # Handle numeric strings
-                if "." in value:
-                    return float(value)
-                else:
-                    return int(value)
-            except ValueError:
-                pass
-            # In case of failure, return the stripped string
-            return value.strip()
-        # If no expected type or value type match, return the original value
+            except (ValueError, TypeError) as e:
+                logger.error(
+                    f"Failed to cast value '{value}' to {expected_type}. Error: {e}"
+                )
+                raise ValueError(
+                    f"Invalid value: '{value}'. Expected type: {expected_type}."
+                )
+        # Fallback for unhandled types
+        logger.warning(f"Unhandled type for value: '{value}'. Returning as-is.")
         return value
 
     def import_spreadsheet(self, file):