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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
import asyncio
from components.ai_memory import LongTermMemory
from components.multi_agent import MultiAgentSystem
from components.neural_symbolic import NeuralSymbolicProcessor
from components.future_simulation import PredictiveAI
from utils.database import Database
from utils.logger import logger
class AICoreFinalRecursive:
def __init__(self, config_path: str = "config_updated.json"):
self.config = self._load_config(config_path)
self.models = self._initialize_models()
self.memory_system = LongTermMemory()
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.multi_agent_system = MultiAgentSystem()
self.neural_symbolic_processor = NeuralSymbolicProcessor()
self.predictive_ai = PredictiveAI()
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 {
"optimized_model": AutoModelForCausalLM.from_pretrained(self.config["model_name"]),
"tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"])
}
async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
try:
self.memory_system.store_interaction(user_id, query)
recursion_depth = self._determine_recursion_depth(query)
responses = await asyncio.gather(
self._recursive_refinement(query, recursion_depth),
self.multi_agent_system.delegate_task(query),
self.neural_symbolic_processor.process_query(query),
self.predictive_ai.simulate_future(query)
)
final_response = "\n\n".join(responses)
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,
"context_enhanced": True,
"security_status": "Fully Secure"
}
except Exception as e:
logger.error(f"Response generation failed: {e}")
return {"error": "Processing failed - safety protocols engaged"}
def _determine_recursion_depth(self, query: str) -> int:
length = len(query.split())
if length < 5:
return 1
elif length < 15:
return 2
else:
return 3
async def _recursive_refinement(self, query: str, depth: int) -> str:
best_response = await self._generate_local_model_response(query)
for _ in range(depth):
new_response = await self._generate_local_model_response(best_response)
if self._evaluate_response_quality(new_response) > self._evaluate_response_quality(best_response):
best_response = new_response
return best_response
def _evaluate_response_quality(self, response: str) -> float:
# Simplified heuristic for refinement
return sum(ord(char) for char in response) % 100 / 100.0
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 _speak_response(self, response: str):
self.speech_engine.say(response)
self.speech_engine.runAndWait()
# Main function to initialize and run the AI Core
async def main():
try:
logging.info("Initializing AI Core...")
ai_core = AICoreFinalRecursive(config_path="config_updated.json")
query = "What is the latest in AI advancements?"
logging.info(f"Processing query: {query}")
response = await ai_core.generate_response(query, user_id=1)
logging.info("Response received successfully.")
print("AI Response:", response)
await ai_core.http_session.close()
logging.info("Closed AI Core session.")
except Exception as e:
logging.error(f"An error occurred: {e}")
if __name__ == "__main__":
asyncio.run(main()) |