import aiohttp import json import logging import torch import faiss import numpy as np from transformers import AutoModelForCausalLM, AutoTokenizer from typing import List, Dict, Any from cryptography.fernet import Fernet from jwt import encode, decode, ExpiredSignatureError from datetime import datetime, timedelta import blockchain_module import speech_recognition as sr 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.models = self._initialize_models() self.context_memory = self._initialize_vector_memory() self.tokenizer = AutoTokenizer.from_pretrained(self.config["model_name"]) self.model = AutoModelForCausalLM.from_pretrained(self.config["model_name"]) 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() def _load_config(self, config_path: str) -> dict: with open(config_path, 'r') as file: return json.load(file) def _initialize_models(self): return { "mistralai": AutoModelForCausalLM.from_pretrained(self.config["model_name"]), "tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"]) } def _initialize_vector_memory(self): return faiss.IndexFlatL2(768) async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]: try: vectorized_query = self._vectorize_query(query) self.context_memory.add(np.array([vectorized_query])) model_response = await self._generate_local_model_response(query) 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) blockchain_module.store_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"} def _vectorize_query(self, query: str): tokenized = self.tokenizer(query, return_tensors="pt") return tokenized["input_ids"].detach().numpy() def _apply_security_filters(self, response: str): return response.replace("malicious", "[filtered]") async def _generate_local_model_response(self, query: str) -> str: inputs = self.tokenizer(query, return_tensors="pt") outputs = self.model.generate(**inputs) return self.tokenizer.decode(outputs[0], skip_special_tokens=True) def generate_jwt(self, user_id: int): payload = { "user_id": user_id, "exp": datetime.utcnow() + timedelta(hours=1) } return encode(payload, self.jwt_secret, algorithm="HS256") def verify_jwt(self, token: str): try: return decode(token, self.jwt_secret, algorithms=["HS256"]) except ExpiredSignatureError: return None def _speak_response(self, response: str): self.speech_engine.say(response) self.speech_engine.runAndWait()