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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
#blockchain_module
import speech_recognition as sr
import pyttsx3
import os
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
from secure_memory import SecureMemorySession
from codriao_tb_module import CodriaoHealthModule
class AICoreAGIX:
def __init__(self, config_path: str = "config.json"):
self.ethical_filter = EthicalFilter()
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()
# Security + Memory
key = os.environ.get("CODRIAO_SECRET_KEY").encode()
self._encryption_key = key
self.secure_memory = SecureMemorySession(self._encryption_key)
self.speech_engine = pyttsx3.init()
self.health_module = CodriaoHealthModule(ai_core=self)
async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
try:
# Ethical Safety Check
result = self.ethical_filter.analyze_query(query)
if result["status"] == "blocked":
return {"error": result["reason"]}
if result["status"] == "flagged":
logger.warning(result["warning"])
# Check if user explicitly requests TB analysis
if any(phrase in query.lower() for phrase in ["tb check", "analyze my tb", "run tb diagnostics", "tb test"]):
result = await self.run_tb_diagnostics("tb_image.jpg", "tb_cough.wav", user_id)
return {
"response": result["ethical_analysis"],
"explanation": result["explanation"],
"tb_risk": result["tb_risk"],
"image_analysis": result["image_analysis"],
"audio_analysis": result["audio_analysis"],
"system_health": result["system_health"]
}
# Vectorize and encrypt
vectorized_query = self._vectorize_query(query)
self.secure_memory.encrypt_vector(user_id, vectorized_query)
# (Optional) retrieve memory
user_vectors = self.secure_memory.decrypt_vectors(user_id)
# Main AI processing
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}\n\n{agent_response}\n\n{self_reflection}\n\nAR Insights: {ar_data}\n\nLogic: {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"}
async def run_tb_diagnostics(self, image_path: str, audio_path: str, user_id: int) -> Dict[str, Any]:
"""Only runs TB analysis if explicitly requested."""
try:
result = await self.health_module.evaluate_tb_risk(image_path, audio_path, user_id)
logger.info(f"TB Diagnostic Result: {result}")
return result
except Exception as e:
logger.error(f"TB diagnostics failed: {e}")
return {
"tb_risk": "ERROR",
"error": str(e),
"image_analysis": {},
"audio_analysis": {},
"ethical_analysis": "Unable to complete TB diagnostic.",
"explanation": None,
"system_health": None
}
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)
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()
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