Create ai_core.py

ai_core.py

@@ -0,0 +1,103 @@
+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
+import asyncio
+
+from components.ai_memory import LongTermMemory
+from components.multi_agent import MultiAgentSystem
+from components.neural_symbolic import NeuralSymbolicProcessor
+from components.future_simulation import PredictiveAI
+from utils.database import Database
+from utils.logger import logger
+
+class AICoreFinalRecursive:
+    def __init__(self, config_path: str = "config_updated.json"):
+        self.config = self._load_config(config_path)
+        self.models = self._initialize_models()
+        self.memory_system = LongTermMemory()
+        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.neural_symbolic_processor = NeuralSymbolicProcessor()
+        self.predictive_ai = PredictiveAI()
+        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 {
+            "optimized_model": AutoModelForCausalLM.from_pretrained(self.config["model_name"]),
+            "tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"])
+        }
+
+    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
+        try:
+            self.memory_system.store_interaction(user_id, query)
+            recursion_depth = self._determine_recursion_depth(query)
+
+            responses = await asyncio.gather(
+                self._recursive_refinement(query, recursion_depth),
+                self.multi_agent_system.delegate_task(query),
+                self.neural_symbolic_processor.process_query(query),
+                self.predictive_ai.simulate_future(query)
+            )
+
+            final_response = "\n\n".join(responses)
+            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,
+                "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 _determine_recursion_depth(self, query: str) -> int:
+        length = len(query.split())
+        if length < 5:
+            return 1
+        elif length < 15:
+            return 2
+        else:
+            return 3
+
+    async def _recursive_refinement(self, query: str, depth: int) -> str:
+        best_response = await self._generate_local_model_response(query)
+        for _ in range(depth):
+            new_response = await self._generate_local_model_response(best_response)
+            if self._evaluate_response_quality(new_response) > self._evaluate_response_quality(best_response):
+                best_response = new_response
+        return best_response
+
+    def _evaluate_response_quality(self, response: str) -> float:
+        return sum(ord(char) for char in response) % 100 / 100.0  # Simplified heuristic for refinement
+
+    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()