Create codrioagix

codrioagix

@@ -0,0 +1,104 @@
+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 ethical_filter import EthicalFilter
+
+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.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()
+        self._encryption_key = Fernet.generate_key()
+        self.jwt_secret = "your_jwt_secret_key"
+        self.speech_engine = pyttsx3.init()
+
+    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"])
+
+            # Continue with safe processing
+            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}\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"}
+
+    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()