forked from enviPath/enviPy
Tobias O
dc18b73e08
Adds a way to input/display timeseries data to the additional information Reviewed-on: enviPath/enviPy#313 Reviewed-by: jebus <lorsbach@envipath.com> Co-authored-by: Tobias O <tobias.olenyi@envipath.com> Co-committed-by: Tobias O <tobias.olenyi@envipath.com>
83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
"""Shared utilities for handling Pydantic validation errors."""
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import json
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from pydantic import ValidationError
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from pydantic_core import ErrorDetails
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from ninja.errors import HttpError
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def format_validation_error(error: ErrorDetails) -> str:
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"""Format a Pydantic validation error into a user-friendly message.
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Args:
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error: A Pydantic error details dictionary containing 'msg', 'type', 'ctx', etc.
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Returns:
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A user-friendly error message string.
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"""
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msg = error.get("msg") or "Invalid value"
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error_type = error.get("type") or ""
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# Handle common validation types with friendly messages
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if error_type == "enum":
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ctx = error.get("ctx", {})
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expected = ctx.get("expected", "") if ctx else ""
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return f"Please select a valid option{': ' + expected if expected else ''}"
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elif error_type == "literal_error":
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# Literal errors (like Literal["active", "inactive"])
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return msg.replace("Input should be ", "Please enter ")
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elif error_type == "missing":
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return "This field is required"
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elif error_type == "string_type":
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return "Please enter a valid string"
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elif error_type == "int_type":
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return "Please enter a valid int"
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elif error_type == "int_parsing":
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return "Please enter a valid int"
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elif error_type == "float_type":
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return "Please enter a valid float"
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elif error_type == "float_parsing":
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return "Please enter a valid float"
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elif error_type == "value_error":
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# Strip "Value error, " prefix from custom validator messages
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return msg.replace("Value error, ", "")
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else:
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# Default: use the message from Pydantic but clean it up
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return msg.replace("Input should be ", "Please enter ").replace("Value error, ", "")
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def handle_validation_error(e: ValidationError) -> None:
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"""Convert a Pydantic ValidationError into a structured HttpError.
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This function transforms Pydantic validation errors into a JSON structure
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that the frontend expects for displaying field-level errors.
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Args:
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e: The Pydantic ValidationError to handle.
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Raises:
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HttpError: Always raises a 400 error with structured JSON containing
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type, field_errors, and message fields.
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"""
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# Transform Pydantic validation errors into user-friendly format
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field_errors: dict[str, list[str]] = {}
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for error in e.errors():
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# Get the field name from location tuple
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loc = error.get("loc", ())
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field = str(loc[-1]) if loc else "root"
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# Format the error message
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friendly_msg = format_validation_error(error)
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if field not in field_errors:
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field_errors[field] = []
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field_errors[field].append(friendly_msg)
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# Return structured error for frontend parsing
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error_response = {
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"type": "validation_error",
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"field_errors": field_errors,
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"message": "Please correct the errors below",
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}
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raise HttpError(400, json.dumps(error_response))
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