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 os import speech_recognition as sr import pyttsx3 from deep_translator import GoogleTranslator # Codette's legacy modules (secured) 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_model_analyzer import MultiAgentSystem # Codriao's enhanced modules from codriao_tb_module import CodriaoHealthModule from secure_memory_loader import load_secure_memory_module secure_memory_module = load_secure_memory_module() from ethical_filter import EthicalFilter from results_store import save_result class CodriaoCore: def __init__(self, config_path: str = "config.json"): self.config = self._load_config(config_path) self.tokenizer = AutoTokenizer.from_pretrained(self.config["model_name"]) self.model = AutoModelForCausalLM.from_pretrained(self.config["model_name"]) self.models = self._initialize_models() self.context_memory = self._initialize_vector_memory() self._encryption_key = self.config["security_settings"]["encryption_key"].encode() self.jwt_secret = self.config["security_settings"]["jwt_secret"] self.http_session = aiohttp.ClientSession() self.database = Database() # Cognitive & ethical subsystems self.sentiment_analyzer = EnhancedSentimentAnalyzer() self.self_improving_ai = SelfImprovingAI() self.adaptive_learning = AdaptiveLearningEnvironment() self.data_fetcher = RealTimeDataIntegrator() self.multi_agent_system = MultiAgentSystem() self.ethical_filter = EthicalFilter() self.secure_memory = SecureMemorySession(self._encryption_key) self.speech_engine = pyttsx3.init() self.health_module = CodriaoHealthModule(ai_core=self) def _load_config(self, config_path: str) -> dict: with open(config_path, 'r') as file: return json.load(file) def _initialize_models(self): return { "base_model": self.model, "tokenizer": self.tokenizer } def _initialize_vector_memory(self): return faiss.IndexFlatL2(768) async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]: try: check = self.ethical_filter.analyze_query(query) if check["status"] == "blocked": return {"error": check["reason"]} if check["status"] == "flagged": logger.warning(check["warning"]) if any(trigger in query.lower() for trigger in ["tb check", "run tb diagnostics", "tb test"]): result = await self.run_tb_diagnostics("tb_image.jpg", "tb_cough.wav", user_id) return result vectorized_query = self._vectorize_query(query) self.secure_memory.encrypt_vector(user_id, 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) self_reflection = self.self_improving_ai.evaluate_response(query, model_response) real_time = self.data_fetcher.fetch_latest_data() final_response = f"{model_response}\n\n{agent_response}\n\n{self_reflection}" self.database.log_interaction(user_id, query, final_response) self._speak_response(final_response) return { "response": final_response, "sentiment": sentiment, "real_time_data": real_time, "security_level": self._evaluate_risk(final_response), "token_optimized": True } except Exception as e: logger.error(f"Response generation failed: {e}") return {"error": "Codriao encountered a critical reasoning issue."} async def run_tb_diagnostics(self, image_path: str, audio_path: str, user_id: int, language="en") -> Dict[str, Any]: result = await self.health_module.evaluate_tb_risk(image_path, audio_path, user_id) result_filename = save_result(result) result["shareable_link"] = f"https://huggingface.co/spaces/Raiff1982/codriao/blob/main/results/{result_filename}" if result["tb_risk"] == "HIGH": result["next_steps"] = "⚠️ Immediate follow-up required. Please visit a healthcare provider." elif result["tb_risk"] == "MEDIUM": result["next_steps"] = "🔍 Consider additional testing for confirmation." if language != "en": try: translated_result = GoogleTranslator(source="auto", target=language).translate(json.dumps(result)) return json.loads(translated_result) except Exception as e: result["translation_error"] = str(e) return result def _evaluate_risk(self, response: str) -> str: if "critical" in response.lower(): return "HIGH" elif "concern" in response.lower(): return "MEDIUM" else: return "LOW" def _speak_response(self, response: str): if self.config["speech_settings"]["emotion_adaptive"]: try: self.speech_engine.say(response) self.speech_engine.runAndWait() except: pass 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 async def shutdown(self): await self.http_session.close()