import requests
from typing import Optional
from datetime import datetime

# Ensure the tool resides within the Tools class structure required by Open WebUI
class Tools:
    class CW_PromptGen:
        def __init__(self, compliance_level: int = 1):
            self.compliance_level = compliance_level
            self.compliance_attributes = {}
            self.llm_requirements = {}

        # Define Valves and UserValves for configuration in Open WebUI
        class Valves:
            model_name: str = "default-model"
            compliance_level: int = 1

        # Fetch LLM requirements dynamically based on model type
        def fetch_llm_requirements(self, model_name: str):
            """Fetches specific LLM prompt requirements from an external API."""
            try:
                response = requests.get(f"https://api.example.com/models/{model_name}/requirements")
                if response.status_code == 200:
                    self.llm_requirements = response.json()
                    return "LLM requirements fetched successfully."
                else:
                    return f"Failed to fetch LLM requirements: {response.status_code}"
            except requests.ConnectionError:
                return "Internet connection unavailable."

        def adaptive_prompt(self, user_input: str) -> str:
            """Generates a prompt customized to the compliance level and LLM requirements."""
            base_prompt = "Generate response based on compliance level and user context."
            llm_specifics = f" (LLM adjustments: {self.llm_requirements.get('prompt_structure', 'Standard')})"
            
            if self.compliance_level == 1:
                return f"{base_prompt} Basic response for input: {user_input}{llm_specifics}"
            elif self.compliance_level == 2:
                return f"{base_prompt} Intermediate response for input: {user_input}{llm_specifics}"
            elif self.compliance_level == 3:
                return f"{base_prompt} Advanced response with high detail for input: {user_input}{llm_specifics}"
            else:
                return f"{base_prompt} Default response for input: {user_input}{llm_specifics}"

        # Event Emitters for real-time status updates
        async def emit_status(self, message: str, is_done: bool = False):
            """Emit real-time status updates via Open WebUI's event emitters."""
            await self.__event_emitter__(
                {"type": "status", "data": {"description": message, "done": is_done}}
            )

        # Main function to handle input, check LLM requirements, and generate the prompt
        async def handle_user_input(self, user_input: str, model_name: str) -> dict:
            """Processes input, fetches requirements, and generates a custom prompt."""
            await self.emit_status("Fetching LLM requirements...")
            llm_fetch_status = self.fetch_llm_requirements(model_name)
            
            await self.emit_status("Generating adaptive prompt...")
            prompt = self.adaptive_prompt(user_input)
            
            return {
                "fetch_status": llm_fetch_status,
                "generated_prompt": prompt,
                "compliance_level": self.compliance_level,
                "verbosity": self.compliance_attributes.get("verbosity", "standard")
            }