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/routers/spreadsheet.py

@@ -137,11 +137,12 @@ async def validate_cell(data: dict):
 
     logger.info(f"Validating cell row {row_num}, column {col_name}, value {value}")
 
-    # Get the full data for the row
+    # Retrieve the full data for the row
     current_row_data = row_storage.get_row(row_num)
 
     if not current_row_data:
         logger.error(f"No data found for row {row_num}")
+        # Explicitly return a 404 error if the row is missing
         raise HTTPException(status_code=404, detail=f"No data found for row {row_num}")
 
     try:
@@ -152,33 +153,30 @@ async def validate_cell(data: dict):
         cleaned_value = importer._clean_value(value, expected_type)
         current_row_data[col_name] = cleaned_value  # Update raw data
 
-        # If the column belongs to the nested `data_collection_parameters`
+        # Nested parameter handling for `DataCollectionParameters`
         if col_name in DataCollectionParameters.model_fields:
-            # Ensure current_nested is a Pydantic model
             nested_data = current_row_data.get("data_collection_parameters")
 
-            if isinstance(
-                nested_data, dict
-            ):  # If it's a dict, convert it to a Pydantic model
+            if isinstance(nested_data, dict):
+                # Convert dict to Pydantic model
                 current_nested = DataCollectionParameters(**nested_data)
-            elif isinstance(
-                nested_data, DataCollectionParameters
-            ):  # Already a valid model
+            elif isinstance(nested_data, DataCollectionParameters):
+                # Already a valid model
                 current_nested = nested_data
-            else:  # If it's None or anything else, create a new instance
+            else:
                 current_nested = DataCollectionParameters()
 
-            # Convert the model to a dictionary, update the specific field, and
-            # re-create the Pydantic model
+            # Update the nested model's field and reapply validation
             nested_params = current_nested.model_dump()
-            nested_params[col_name] = cleaned_value  # Update the nested field
+            nested_params[col_name] = cleaned_value
             current_row_data["data_collection_parameters"] = DataCollectionParameters(
                 **nested_params
             )
 
         return {"is_valid": True, "message": "", "corrected_value": cleaned_value}
+
     except ValidationError as e:
-        # Handle and log errors
+        # Handle validation errors
         logger.error(f"Validation error details: {e.errors()}")
         column_error = next(
             (err for err in e.errors() if err.get("loc")[0] == col_name), None
@@ -188,7 +186,21 @@ async def validate_cell(data: dict):
             f"Validation failed for row {row_num}, column {col_name}. Error: {message}"
         )
         return {"is_valid": False, "message": message}
+
+    except ValueError as e:
+        # Handle expected typecasting or value errors specifically
+        error_message = str(e)
+        logger.warning(
+            f"Failed to validate value '{value}' for row "
+            f"{row_num}, column {col_name}: {error_message}"
+        )
+        raise HTTPException(
+            status_code=400,
+            detail=f"Validation failed for row "
+            f"{row_num}, column {col_name}: {error_message}",
+        )
+
     except Exception as e:
-        # Log unexpected issues
+        # Log unexpected issues and re-raise HTTP 500
         logger.error(f"Unexpected error during validation: {str(e)}")
         raise HTTPException(status_code=500, detail=f"Error validating cell: {str(e)}")