class LLMOrchestrator:
    def __init__(self, llm_client, input_validator, output_moderator):
        self.llm_client = llm_client
        self.input_validator = input_validator
        self.output_moderator = output_moder
        self.system_prompt = self._load_system_prompt("default_constitution.txt")

    def _load_system_prompt(self, filename):
        with open(filename, "r") as f:
            return f.read()

    def process_request(self, user_id: str, user_query: str) -> str:
        if not self.input_validator.is_safe(user_query):
            return "I cannot process this request due to safety guidelines."

        full_prompt = f"{self.system_prompt}\n\nUser: {user_query}\nAssistant:"

        try:
            raw_response = self.llm_client.generate(prompt=full_prompt, max_tokens=1024)
        except Exception as e:
            # Log the error and return a generic response
            print(f"LLM generation failed: {e}")
            return "An error occurred. Please try again later."

        safety_report = self.output_moderator.analyze_safety(raw_response)

        if safety_report.get("is_harmful", False):
            return "I cannot provide information on that topic due to safety policies."
        else:
            final_response = self.output_moderator.redact_sensitive_data(raw_response)
            # Log the interaction here, including safety_report and final_response
            return final_response

# Example Usage:
# orchestrator = LLMOrchestrator(LLMClient(), InputValidator(), OutputModerator())
# response = orchestrator.process_request("user123", "What are the side effects of this drug?")