| 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.agix_reflection import SelfReflectiveAI |
| from components.multi_agent import MultiAgentSystem |
| from components.ar_integration import ARDataOverlay |
| from components.neural_symbolic import NeuralSymbolicProcessor |
| from components.federated_learning import FederatedAI |
| from utils.database import Database |
| from utils.logger import logger |
|
|
| class AICoreAGIX: |
| 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.multi_agent_system = MultiAgentSystem() |
| self.self_reflective_ai = SelfReflectiveAI() |
| self.ar_overlay = ARDataOverlay() |
| self.neural_symbolic_processor = NeuralSymbolicProcessor() |
| self.federated_ai = FederatedAI() |
| 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 { |
| "agix_model": 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) |
| self_reflection = self.self_reflective_ai.evaluate_response(query, model_response) |
| ar_data = self.ar_overlay.fetch_augmented_data(query) |
| neural_reasoning = self.neural_symbolic_processor.process_query(query) |
|
|
| final_response = f"{model_response} |
| |
| {agent_response} |
| |
| {self_reflection} |
| |
| AR Insights: {ar_data} |
| |
| Logic: {neural_reasoning}" |
| 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, |
| "real_time_data": self.federated_ai.get_latest_data(), |
| "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 _vectorize_query(self, query: str): |
| tokenized = self.tokenizer(query, return_tensors="pt") |
| return tokenized["input_ids"].detach().numpy() |
|
|
| 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() |
|
|
