Create CodriaoCore.py

CodriaoCore.py

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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 os
+import speech_recognition as sr
+import pyttsx3
+
+# 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_agent import MultiAgentSystem
+
+# Codriao's enhanced modules
+from codriao_tb_module import CodriaoHealthModule
+from secure_memory import SecureMemorySession
+from ethical_filter import EthicalFilter
+from results_store import save_result
+
+# Utilities
+from utils.database import Database
+from utils.logger import logger
+
+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:
+            # Ethical Safety
+            check = self.ethical_filter.analyze_query(query)
+            if check["status"] == "blocked":
+                return {"error": check["reason"]}
+            if check["status"] == "flagged":
+                logger.warning(check["warning"])
+
+            # Optional: Trigger TB diagnostics by user request
+            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}
+
+{agent_response}
+
+{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."}
+
+    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)
+
+    async def run_tb_diagnostics(self, image_path: str, audio_path: str, user_id: int) -> 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}"
+        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"]:
+            self.speech_engine.say(response)
+            self.speech_engine.runAndWait()
+
+    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