import aiohttp
import json
import logging
import faiss
import numpy as np
import ollama  # Using Ollama for local Llama 3 inference
from typing import List, Dict, Any
from cryptography.fernet import Fernet
from jwt import encode, decode, ExpiredSignatureError
from datetime import datetime, timedelta
import pyttsx3

from components.adaptive_learning import AdaptiveLearningEnvironment
from components.real_time_data import RealTimeDataIntegrator
from components.sentiment_analysis import EnhancedSentimentAnalyzer
from components.self_improving_ai import SelfImprovingAI
from components.multi_agent import MultiAgentSystem
from utils.database import Database
from utils.logger import logger

class AICore:
    def __init__(self, config_path: str = "config.json"):
        self.config = self._load_config(config_path)
        self.http_session = aiohttp.ClientSession()
        self.database = Database()
        self.sentiment_analyzer = EnhancedSentimentAnalyzer()
        self.data_fetcher = RealTimeDataIntegrator()
        self.self_improving_ai = SelfImprovingAI()
        self.multi_agent_system = MultiAgentSystem()
        self._encryption_key = Fernet.generate_key()
        self.jwt_secret = "your_jwt_secret_key"
        self.speech_engine = pyttsx3.init()
        
        # FAISS Vector Memory
        self.memory_index = faiss.IndexFlatL2(768)
        self.memory_vectors = []
        self.memory_texts = []

    def _load_config(self, config_path: str) -> dict:
        with open(config_path, 'r') as file:
            return json.load(file)

    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
        try:
            self.store_interaction(query)
            previous_context = self.retrieve_past_context(query)
            refined_query = f"{previous_context}\nUser: {query}" if previous_context else query
            model_response = await self._recursive_refinement(refined_query, 3)
            agent_response = self.multi_agent_system.delegate_task(query)
            sentiment = self.sentiment_analyzer.detailed_analysis(query)
            final_response = self._apply_security_filters(model_response + agent_response)

            self.database.log_interaction(user_id, query, final_response)
            self._speak_response(final_response)

            return {
                "response": final_response,
                "sentiment": sentiment,
                "security_level": self._evaluate_risk(final_response),
                "real_time_data": self.data_fetcher.fetch_latest_data(),
                "token_optimized": True
            }
        except Exception as e:
            logger.error(f"Response generation failed: {e}")
            return {"error": "Processing failed - safety protocols engaged"}

    async def _recursive_refinement(self, query: str, depth: int = 3) -> str:
        best_response = await self._generate_local_model_response(query)
        for _ in range(depth):
            new_response = await self._generate_local_model_response(f"Refine this answer: {best_response}")
            if len(new_response) > len(best_response):
                best_response = new_response
        return best_response

    async def _generate_local_model_response(self, query: str) -> str:
        response = ollama.chat(model="llama3", messages=[{"role": "user", "content": query}])
        return response["message"]["content"]

    def store_interaction(self, query: str):
        query_vector = self._vectorize_text(query)
        self.memory_index.add(np.array([query_vector]))
        self.memory_vectors.append(query_vector)
        self.memory_texts.append(query)

    def retrieve_past_context(self, query: str):
        query_vector = self._vectorize_text(query)
        _, indices = self.memory_index.search(np.array([query_vector]), k=3)
        return "\n".join([self.memory_texts[i] for i in indices[0] if i < len(self.memory_texts)])

    def _vectorize_text(self, text: str):
        return np.random.rand(768)  # Replace with an actual embedding model

    def _apply_security_filters(self, response: str):
        return response.replace("malicious", "[filtered]")

    def _speak_response(self, response: str):
        self.speech_engine.say(response)
        self.speech_engine.runAndWait()

    def _evaluate_risk(self, response: str) -> str:
        if "dangerous" in response or "malicious" in response:
            return "High Risk"
        return "Low Risk"