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.
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GotthardG
parent
92306fcfa6
commit
35369fd13c
@ -137,11 +137,12 @@ async def validate_cell(data: dict):
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logger.info(f"Validating cell row {row_num}, column {col_name}, value {value}")
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# Get the full data for the row
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# Retrieve the full data for the row
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current_row_data = row_storage.get_row(row_num)
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if not current_row_data:
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logger.error(f"No data found for row {row_num}")
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# Explicitly return a 404 error if the row is missing
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raise HTTPException(status_code=404, detail=f"No data found for row {row_num}")
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try:
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@ -152,33 +153,30 @@ async def validate_cell(data: dict):
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cleaned_value = importer._clean_value(value, expected_type)
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current_row_data[col_name] = cleaned_value # Update raw data
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# If the column belongs to the nested `data_collection_parameters`
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# Nested parameter handling for `DataCollectionParameters`
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if col_name in DataCollectionParameters.model_fields:
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# Ensure current_nested is a Pydantic model
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nested_data = current_row_data.get("data_collection_parameters")
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if isinstance(
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nested_data, dict
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): # If it's a dict, convert it to a Pydantic model
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if isinstance(nested_data, dict):
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# Convert dict to Pydantic model
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current_nested = DataCollectionParameters(**nested_data)
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elif isinstance(
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nested_data, DataCollectionParameters
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): # Already a valid model
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elif isinstance(nested_data, DataCollectionParameters):
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# Already a valid model
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current_nested = nested_data
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else: # If it's None or anything else, create a new instance
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else:
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current_nested = DataCollectionParameters()
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# Convert the model to a dictionary, update the specific field, and
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# re-create the Pydantic model
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# Update the nested model's field and reapply validation
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nested_params = current_nested.model_dump()
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nested_params[col_name] = cleaned_value # Update the nested field
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nested_params[col_name] = cleaned_value
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current_row_data["data_collection_parameters"] = DataCollectionParameters(
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**nested_params
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)
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return {"is_valid": True, "message": "", "corrected_value": cleaned_value}
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except ValidationError as e:
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# Handle and log errors
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# Handle validation errors
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logger.error(f"Validation error details: {e.errors()}")
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column_error = next(
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(err for err in e.errors() if err.get("loc")[0] == col_name), None
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@ -188,7 +186,21 @@ async def validate_cell(data: dict):
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f"Validation failed for row {row_num}, column {col_name}. Error: {message}"
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)
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return {"is_valid": False, "message": message}
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except ValueError as e:
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# Handle expected typecasting or value errors specifically
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error_message = str(e)
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logger.warning(
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f"Failed to validate value '{value}' for row "
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f"{row_num}, column {col_name}: {error_message}"
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)
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raise HTTPException(
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status_code=400,
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detail=f"Validation failed for row "
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f"{row_num}, column {col_name}: {error_message}",
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)
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except Exception as e:
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# Log unexpected issues
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# Log unexpected issues and re-raise HTTP 500
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logger.error(f"Unexpected error during validation: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Error validating cell: {str(e)}")
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@ -163,24 +163,19 @@ class DataCollectionParameters(BaseModel):
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) from e
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return v
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@field_validator("oscillation", "targetresolution", mode="before")
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@field_validator("oscillation", mode="before")
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@classmethod
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def positive_float_validator(cls, v):
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logger.debug(f"Running positive_float_validator for value: {v}")
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if v is not None:
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try:
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v = float(v)
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if v <= 0:
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logger.error(f"Validation failed: '{v}' is not greater than 0.")
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raise ValueError(
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f"'{v}' is not valid. Value must be a positive float."
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)
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except (ValueError, TypeError) as e:
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logger.error(f"Validation failed: '{v}' caused error {str(e)}")
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raise ValueError(
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f"'{v}' is not valid. Value must be a positive float."
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) from e
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logger.debug(f"Validation succeeded for value: {v}")
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if v is None:
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return None
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try:
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v = float(v)
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if v <= 0:
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raise ValueError(f"'{v}' is not valid. Value must be a positive float.")
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except (ValueError, TypeError) as e:
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raise ValueError(
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f"'{v}' is not valid. Value must be a positive float."
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) from e
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return v
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@field_validator("exposure", mode="before")
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@ -62,30 +62,22 @@ class SampleSpreadsheetImporter:
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return column_type_mapping.get(column_name, str)
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def _clean_value(self, value, expected_type=None):
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"""Clean value by converting it to the expected type and handle edge cases."""
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if value is None:
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return None
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if expected_type == str:
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# Ensure value is converted to string and stripped of whitespace
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return str(value).strip()
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if expected_type in [float, int]:
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try:
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return expected_type(value)
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except (ValueError, TypeError):
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# If conversion fails, return None
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return None
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if isinstance(value, str):
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try:
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# Handle numeric strings
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if "." in value:
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return float(value)
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else:
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return int(value)
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except ValueError:
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pass
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# In case of failure, return the stripped string
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return value.strip()
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# If no expected type or value type match, return the original value
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except (ValueError, TypeError) as e:
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logger.error(
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f"Failed to cast value '{value}' to {expected_type}. Error: {e}"
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)
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raise ValueError(
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f"Invalid value: '{value}'. Expected type: {expected_type}."
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)
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# Fallback for unhandled types
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logger.warning(f"Unhandled type for value: '{value}'. Returning as-is.")
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return value
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def import_spreadsheet(self, file):
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@ -122,23 +122,22 @@ const SpreadsheetTable = ({
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if (response && response.is_valid !== undefined) {
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if (response.is_valid) {
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// Handle validation success (remove error)
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// If valid, update the value (and use corrected_value if returned)
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const correctedValue = response.corrected_value ?? newValue;
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currentRow.data[colIndex] = correctedValue;
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updatedRawData[rowIndex] = currentRow;
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setRawData(updatedRawData); // Update table data
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setRawData(updatedRawData);
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// Remove error associated with this cell
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// Remove the error and mark as non-editable
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const updatedErrors = localErrors.filter(
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(error) => !(error.row === currentRow.row_num && error.cell === colIndex)
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);
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setLocalErrors(updatedErrors);
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setLocalErrors(updatedErrors); // Update error list
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// Update non-editable state
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setNonEditableCells((prev) => new Set([...prev, `${rowIndex}-${colIndex}`]));
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} else {
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// Handle validation failure (add error)
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// If not valid, don't add to nonEditableCells and update the error list
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const errorMessage = response.message || "Invalid value.";
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const newError = {
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row: currentRow.row_num,
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@ -147,10 +146,18 @@ const SpreadsheetTable = ({
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};
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const updatedErrors = [
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...localErrors.filter((error) => !(error.row === newError.row && error.cell === newError.cell)), // Avoid duplicates
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...localErrors.filter(
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(error) => !(error.row === newError.row && error.cell === newError.cell)
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),
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newError,
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];
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setLocalErrors(updatedErrors);
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setNonEditableCells((prev) => {
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const updatedSet = new Set(prev);
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updatedSet.delete(`${rowIndex}-${colIndex}`); // Ensure it stays editable
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return updatedSet;
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});
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}
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} else {
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console.error("Unexpected response from backend:", response);
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