import os
import sys
import asyncio
import logging
import openai  # Correct OpenAI import
from transformers import AutoTokenizer
from typing import Dict, Any, List
import aiohttp
from cryptography.fernet import Fernet
import gradio as gr
import json
import torch
import psutil
import random

class EnvironmentManager:
    """Handles loading and validation of environment variables."""
    @staticmethod
    def load_env_variables() -> Dict[str, str]:
        required_vars = [
            "OPENAI_API_KEY", "ENCRYPTION_KEY"
        ]

        env_vars = {var: os.getenv(var) for var in required_vars}
        missing_vars = [var for var, value in env_vars.items() if not value]

        if missing_vars:
            raise ValueError(f"Missing required environment variables: {', '.join(missing_vars)}")

        return env_vars

class EncryptionManager:
    """Handles encryption and decryption of sensitive data."""
    def __init__(self, key: str):
        self.cipher = Fernet(key.encode())

    def encrypt(self, data: str) -> str:
        return self.cipher.encrypt(data.encode()).decode()

    def decrypt(self, encrypted_data: str) -> str:
        return self.cipher.decrypt(encrypted_data.encode()).decode()

class AICore:
    """Main AI Core system integrating OpenAI chat functionality."""
    def __init__(self, env_vars: Dict[str, str]):
        self.env_vars = env_vars
        self.encryption_manager = EncryptionManager(env_vars["ENCRYPTION_KEY"])
        self.openai_api_key = env_vars["OPENAI_API_KEY"]

    async def generate_response(self, query: str) -> Dict[str, Any]:
        try:
            encrypted_query = self.encryption_manager.encrypt(query)

            chat_completion = await openai.ChatCompletion.acreate(
                model="gpt-4-turbo",  # Ensure you use a valid model name
                messages=[
                    {"role": "system", "content": "You are a helpful AI assistant."},
                    {"role": "user", "content": query}
                ],
                api_key=self.openai_api_key
            )

            model_response = chat_completion['choices'][0]['message']['content']

            return