def run_piranha()

in experimental/piranha_playground/rule_inference/piranha_agent.py [0:0]

• 25 lines of code
• 5 McCabe index (conditional complexity)

    def run_piranha(self, toml_dict) -> str:
        """
        Runs the inferred rule graph by applying it to the source code using Piranha.

        :param toml_dict: Inferred rules in TOML format
        :type toml_dict: dict
        :return: Refactored code as a result of the rule application
        :rtype: str
        """
        rules = toml_dict.get("rules", [])
        if not rules:
            raise PiranhaAgentError("TOML does not include any rule specifications.")
        try:
            raw_graph = RawRuleGraph.from_toml(toml_dict)
            logger.debug(f"Raw graph: {raw_graph.to_toml()}")

            res, success = run_piranha_with_timeout(
                self.source_code, self.language, raw_graph, timeout=5
            )

            if not success:
                if "QueryError" in res:
                    raise PiranhaAgentError(
                        f"One of the provided queries is not valid {res}. "
                        f"Do not use nodes you cannot see in the tree representation. "
                        f"Make sure you parenthesis are balanced."
                    )
                raise PiranhaAgentError(f"Piranha failed to execute: {res}.")
            return res

        except multiprocessing.context.TimeoutError:
            raise PiranhaAgentError(
                "Piranha in infinite loop. Please add a filter or constraint the query. "
                "Remember you can only constraint queries with #eq, #not-eq, #match. "
                "Otherwise you need to use a [[rules.filters]] with contains or not_contains."
            )