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Python vibe_code annotation
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| from openai import OpenAI | |
| from pydantic import BaseModel, create_model | |
| import inspect | |
| import typing as t | |
| client = OpenAI() | |
| def vibe_code(model: str = "gpt-4o-mini"): | |
| """Decorator for the lazy""" | |
| def _decorate(fn): | |
| sig = inspect.signature(fn) | |
| name = fn.__name__ | |
| doc = inspect.getdoc(fn) or "" | |
| hints = t.get_type_hints(fn) | |
| ret_type = hints.get("return", t.Any) | |
| if not isinstance(ret_type, type) or not issubclass(ret_type, BaseModel): | |
| class _ReturnModel(BaseModel): | |
| result: ret_type # type: ignore | |
| return_model = _ReturnModel | |
| else: | |
| return_model = ret_type | |
| def wrapper(*args, **kwargs): | |
| bound = sig.bind_partial(*args, **kwargs) | |
| bound.apply_defaults() | |
| params = bound.arguments | |
| resp = client.beta.responses.parse( | |
| model=model, | |
| input=[ | |
| { | |
| "role": "system", | |
| "content": ( | |
| "Implement this Python function call. " | |
| "Only return valid structured JSON." | |
| ), | |
| }, | |
| { | |
| "role": "user", | |
| "content": { | |
| "type": "text", | |
| "text": f""" | |
| Function: {name} | |
| Signature: {sig} | |
| Docstring: {doc} | |
| Args: {params} | |
| """ | |
| }, | |
| }, | |
| ], | |
| response_format=return_model, | |
| ) | |
| parsed = resp.output_parsed | |
| return parsed.result if hasattr(parsed, "result") else parsed | |
| wrapper.__name__ = name | |
| wrapper.__doc__ = doc | |
| wrapper.__signature__ = sig | |
| return wrapper | |
| return _decorate | |
| # Example Usage | |
| @vibe_code() | |
| def add_two_numbers(a: int, b: int) -> int: | |
| """Return the sum of a and b.""" | |
| pass |
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