Hi everyone, I’m new to the forums, but I find inspiration in everything I see. I was inspired to create this code from conversations around this forum. I took from a Fibonacci sequence, threw in some AGI theory, added some recursive thought, and used real-world practicality. This is what I made. Your feedback would be amazing as I am still learning the ins and outs of AI and have only built a few models. Here is the code:
import os
import json
import asyncio
import logging
import psutil
import re
import sqlite3
import matplotlib.pyplot as plt
from cryptography.fernet import Fernet
from typing import Dict, List, Any
from sklearn.ensemble import IsolationForest
from openai import AsyncOpenAI
import aiohttp
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import torch
import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import plotly.graph_objects as go
# Secure API Key Handling
class SecureConfig:
"""Manages encrypted API keys and configuration settings"""
def __init__(self, config_file: str = "secure_config.json"):
self.config_file = config_file
self.key = os.getenv("ENCRYPTION_KEY", Fernet.generate_key())
self.fernet = Fernet(self.key)
self.config = self._load_config()
def _load_config(self) -> Dict[str, Any]:
try:
with open(self.config_file, 'r') as f:
encrypted_data = f.read()
decrypted_data = self.fernet.decrypt(encrypted_data.encode()).decode()
return json.loads(decrypted_data)
except (FileNotFoundError, json.JSONDecodeError, Exception):
return {}
def save_config(self, config: Dict[str, Any]):
encrypted_data = self.fernet.encrypt(json.dumps(config).encode()).decode()
with open(self.config_file, 'w') as f:
f.write(encrypted_data)
# Initialize Secure Config
secure_config = SecureConfig()
# OpenAI API Initialization with Secure Storage
try:
api_key = secure_config.config.get("OPENAI_API_KEY", os.getenv("OPENAI_API_KEY"))
if not api_key:
raise ValueError("No API key provided")
aclient = AsyncOpenAI(api_key=api_key)
except Exception as e:
logging.error(f"Failed to initialize OpenAI client: {e}")
aclient = None
# Enhanced Configuration Manager for AI Systems
class EnhancedAIConfig:
_DEFAULTS = {
"model": "gpt-4-turbo",
"safety_thresholds": {
"memory": 85,
"cpu": 90,
"response_time": 2.0
},
"defense_strategies": ["evasion", "adaptability", "barrier"],
"cognitive_modes": ["scientific", "creative", "emotional"]
}
def __init__(self, config_path: str = "ai_config.json"):
self.config = self._load_config(config_path)
self._validate()
def _load_config(self, path: str) -> Dict:
try:
with open(path, 'r') as f:
return self._merge_configs(json.load(f))
except (FileNotFoundError, json.JSONDecodeError) as e:
print(f"Error loading config file: {e}. Using default configuration.")
return self._DEFAULTS
def _merge_configs(self, user_config: Dict) -> Dict:
merged = self._DEFAULTS.copy()
for key, value in user_config.items():
if isinstance(value, dict) and key in merged:
merged[key].update(value)
else:
merged[key] = value
return merged
def _validate(self):
if not all(isinstance(mode, str) for mode in self.config["cognitive_modes"]):
raise ValueError("Invalid cognitive mode configuration")
# Dynamic Knowledge Acquisition and Retrieval System
class DynamicLearner:
def __init__(self):
self.knowledge_base = {}
def learn_from_file(self, file_path: str):
"""Learn from the contents of the provided file"""
with open(file_path, 'r') as file:
data = json.load(file)
for key, value in data.items():
self.knowledge_base[key] = value
def get_knowledge(self, key: str) -> Any:
"""Retrieve knowledge based on the key"""
return self.knowledge_base.get(key)
# DeepSeeks LLM Model Initialization
class DeepSeeksLLM:
"""Integrates DeepSeeks AI models with transformer-based reasoning."""
def __init__(self, model_name: str = "deepseeks/deepseeks-llm"):
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
def generate_response(self, prompt: str) -> str:
"""Generate a response based on the given prompt."""
inputs = self.tokenizer(prompt, return_tensors="pt")
outputs = self.model.generate(**inputs, max_length=512)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# AI Query Processing with O3 Theoretic Reasoning
class AppliedAI:
"""Processes user queries using DeepSeeks LLM, O3 reasoning, and real-world application frameworks."""
COGNITIVE_MODES = {
"scientific": lambda q: f"Scientific Analysis: {q} follows fundamental principles.",
"economic": lambda q: f"Economic Insight: {q} has implications for market behavior and global finance.",
"technological": lambda q: f"Technological Projection: {q} aligns with emerging innovations.",
"futuristic": lambda q: f"Future Forecasting: {q} leads to probable scenarios based on current trends."
}
def __init__(self):
self.llm = DeepSeeksLLM()
def detect_mode(self, query: str) -> List[str]:
"""Dynamically determines which cognitive perspectives apply to a given query."""
modes = []
if re.search(r'why|how|principle|theory', query, re.IGNORECASE):
modes.append("scientific")
if re.search(r'market|economy|finance|trade', query, re.IGNORECASE):
modes.append("economic")
if re.search(r'tech|AI|automation|cyber|quantum', query, re.IGNORECASE):
modes.append("technological")
if re.search(r'future|prediction|trend|forecast', query, re.IGNORECASE):
modes.append("futuristic")
return modes or ["scientific"] # Default to scientific mode
def generate_insights(self, query: str) -> List[str]:
"""Generates insights using detected cognitive modes and DeepSeeks LLM reasoning."""
modes = self.detect_mode(query)
insights = [self.COGNITIVE_MODES[m](query) for m in modes]
deepseeks_response = self.llm.generate_response(query)
return insights + [f"DeepSeeks LLM Response: {deepseeks_response}"]
# Frontend Interactive Dashboard
app = dash.Dash(__name__)
app.layout = html.Div([
dcc.Input(id='query-input', type='text', placeholder='Enter your query...'),
html.Button('Submit', id='submit-button', n_clicks=0),
html.Div(id='query-output')
])
@app.callback(
Output('query-output', 'children'),
Input('submit-button', 'n_clicks'),
Input('query-input', 'value')
)
def update_output(n_clicks, query):
if not query:
return "Enter a query to analyze."
ai_system = AppliedAI()
insights = ai_system.generate_insights(query)
return html.Div([html.P(insight) for insight in insights])
if __name__ == '__main__':
app.run_server(debug=True)
