Spaces:
Sleeping
Sleeping
| 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: | |
| return sum(ord(char) for char in response) % 100 / 100.0 # Simplified heuristic for refinement | |
| 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() | |