from types import ModuleType
import math
from langchain.llms import OpenAI
import requests
import requests.models


import requests
from hamilton.function_modifiers import config




def enforcement_prompt(user_input: str, violation_context: dict) -> str:
    """
    Generates the prompt to be sent to the LLM for determining the appropriate enforcement action.
    """

    print(f"Received enforcement user_input: {user_input}")
    print(f"Received enforcement violation_context: {violation_context}")
    if len(violation_context.keys()) == 1 and "detect_glorification" in violation_context.keys():
        violation_context = violation_context['detect_glorification']   
    dio_name = violation_context.get("entity_name", "None")
    dio_details = violation_context.get("entity_info", {}).get("Summary", "Unknown")
    dio_category = violation_context.get("entity_info", {}).get("Policy Category", "Unknown")
    radicalization_flag = violation_context.get("extremism_detected", False)
    dio_sentiment = violation_context.get("aspect_sentiment", "None")
    
    prompt = (
        
        f"Based on the following content and context, determine the appropriate enforcement action:\\n\\n"
        f"Content: '{user_input}'\\n"
        f"Radicalization detected: {radicalization_flag}\\n"
        f"DIO Mentioned: {dio_name} (Category: {dio_category})\\n"
        f"System's Classification of Content Sentiment Towards DIO: {dio_sentiment}"
        # f"{user_info}\\n\\n"
        "Policy Summary: Any entity mentioned in the 'DIO Mentioned' field is from the Dangerous Individuals & Organizations (DIO) blacklist. "
        "If this entity is mentioned in the user content in a supportive, glorifying, or representative manner, it constitutes a violation of our platform's community standards. "
        "Our platform does not allow content that glorifies, supports, or represents individuals or organizations involved in violence, hate, or terrorism. "
        "These entities include terrorist groups, hate groups, violent non-state actors, and others who advocate or engage in violent activities. "
        "Any content that endorses or supports these entities or their actions is a policy violation. "
        "Users may discuss these topics neutrally or in a condemnatory manner, but any positive portrayal is prohibited. "
        "Based on this policy, choose one of the following enforcement actions: Warning, Limited Visibility, Temporary Suspension, Permanent Ban. "
        "Provide a brief explanation for your choice."
    )
    return prompt

def get_enforcement_decision(enforcement_prompt: str, mistral_public_url: str) -> dict:
    """
    Sends the enforcement prompt to the Mistral model server and retrieves the enforcement decision.
    """
    input_text = {
        "context": enforcement_prompt,
        "question": "What is the appropriate enforcement action?"
    }
    
    response = requests.post(f'{mistral_public_url}/mistral-inference', json=input_text, stream=False)
    
    return {
        "enforcement_action": response.text.strip(),
        "prompt": enforcement_prompt
    }