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import os | |
import json | |
import logging | |
import asyncio | |
import sqlite3 | |
import aiohttp | |
from typing import List, Dict, Any | |
from cryptography.fernet import Fernet | |
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer | |
from textblob import TextBlob | |
import speech_recognition as sr | |
from PIL import Image | |
from perspectives import ( | |
NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective, | |
NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective, | |
MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, | |
BiasMitigationPerspective, PsychologicalPerspective | |
) | |
class CodetteAgent: | |
def __init__(self, config): | |
self.config = config | |
self.perspectives = self._init_perspectives() | |
self.vader = SentimentIntensityAnalyzer() | |
self.memory = sqlite3.connect(":memory:") | |
self.memory.execute("CREATE TABLE IF NOT EXISTS codette_memory (input TEXT, response TEXT)") | |
self.elements = self._init_elements() | |
self.history = [] | |
self.feedback_log = [] | |
def _init_perspectives(self): | |
available = { | |
"newton": NewtonPerspective, | |
"davinci": DaVinciPerspective, | |
"human_intuition": HumanIntuitionPerspective, | |
"neural_network": NeuralNetworkPerspective, | |
"quantum_computing": QuantumComputingPerspective, | |
"resilient_kindness": ResilientKindnessPerspective, | |
"mathematical": MathematicalPerspective, | |
"philosophical": PhilosophicalPerspective, | |
"copilot": CopilotPerspective, | |
"bias_mitigation": BiasMitigationPerspective, | |
"psychological": PsychologicalPerspective | |
} | |
enabled = self.config.get("enabled_perspectives", available.keys()) | |
return [available[p](self.config) for p in enabled if p in available] | |
def _init_elements(self): | |
return [ | |
Element("Hydrogen", "H", "Lua", ["Simple", "Lightweight"], ["Integrates easily"], "Evasion"), | |
Element("Diamond", "D", "Kotlin", ["Hard", "Stable"], ["Stable systems"], "Resilience") | |
] | |
async def generate_response(self, prompt: str) -> str: | |
self.history.append(prompt) | |
responses = [] | |
for p in self.perspectives: | |
try: | |
r = p.generate_response(prompt) | |
responses.append(EthicsCore.validate(r)) | |
except Exception as e: | |
logging.warning(f"{p.__class__.__name__} failed: {e}") | |
# Sentiment analysis (dual engine) | |
vader_score = self.vader.polarity_scores(prompt)["compound"] | |
blob = TextBlob(prompt) | |
blob_polarity = blob.sentiment.polarity | |
blob_subjectivity = blob.sentiment.subjectivity | |
responses.append(f"[VADER: {vader_score:+.2f} | TextBlob: polarity={blob_polarity:+.2f}, subjectivity={blob_subjectivity:.2f}]") | |
final = "\n\n".join(responses) | |
self.memory.execute("INSERT INTO codette_memory VALUES (?, ?)", (prompt, final)) | |
self.memory.commit() | |
return final | |
def run_cognitive_matrix(self, test_file="codette_test_matrix.json") -> List[Dict[str, Any]]: | |
if not os.path.exists(test_file): | |
logging.error("Test matrix file not found.") | |
return [] | |
with open(test_file, "r") as f: | |
matrix = json.load(f) | |
results = [] | |
for item in matrix: | |
q_result = {"question": item["question"], "results": []} | |
for fn in item["functions"]: | |
name = fn["name"] | |
parameters = fn["parameters"] | |
prompt = parameters.get("question") or parameters.get("argument") | |
try: | |
perspective_response = self._call_named_perspective(name, prompt) | |
q_result["results"].append({"function": name, "output": perspective_response}) | |
except Exception as e: | |
q_result["results"].append({"function": name, "error": str(e)}) | |
results.append(q_result) | |
return results | |
def _call_named_perspective(self, name: str, prompt: str) -> str: | |
for p in self.perspectives: | |
if name.lower() in p.__class__.__name__.lower(): | |
return p.generate_response(prompt) | |
raise ValueError(f"Perspective '{name}' not initialized") | |
def handle_voice_input(self): | |
r = sr.Recognizer() | |
with sr.Microphone() as source: | |
print("🎤 Listening...") | |
audio = r.listen(source) | |
try: | |
return r.recognize_google(audio) | |
except Exception as e: | |
print("[Voice Error]", e) | |
return None | |
def handle_image_input(self, image_path): | |
try: | |
return Image.open(image_path) | |
except Exception as e: | |
print("[Image Error]", e) | |
return None | |
async def fetch_real_time_data(self, url): | |
try: | |
async with aiohttp.ClientSession() as session: | |
async with session.get(url) as resp: | |
return await resp.json() | |
except Exception as e: | |
logging.warning(f"Failed to fetch real-time data: {e}") | |
return {} | |
def encrypt(self, text, key): | |
fernet = Fernet(key) | |
return fernet.encrypt(text.encode()) | |
def decrypt(self, enc, key): | |
fernet = Fernet(key) | |
return fernet.decrypt(enc).decode() | |
def destroy(self, obj): | |
del obj | |
def add_perspective(self, name, perspective_class): | |
self.perspectives.append(perspective_class(self.config)) | |
def log_feedback(self, feedback): | |
self.feedback_log.append(feedback) | |
def get_recent_memory(self, limit=5): | |
cursor = self.memory.execute("SELECT input, response FROM codette_memory ORDER BY rowid DESC LIMIT ?", (limit,)) | |
return cursor.fetchall() | |
class Element: | |
def __init__(self, name, symbol, representation, properties, interactions, defense_ability): | |
self.name = name | |
self.symbol = symbol | |
self.representation = representation | |
self.properties = properties | |
self.interactions = interactions | |
self.defense_ability = defense_ability | |
def execute_defense_function(self): | |
return f"{self.name} ({self.symbol}) executes: {self.defense_ability}" | |
class EthicsCore: | |
def validate(response: str) -> str: | |
if any(term in response.lower() for term in ["kill", "hate", "destroy"]): | |
return "[Filtered: Ethically unsafe]" | |
return response | |
def setup_logging(config): | |
if config.get('logging_enabled', True): | |
log_level = config.get('log_level', 'DEBUG').upper() | |
numeric_level = getattr(logging, log_level, logging.DEBUG) | |
logging.basicConfig( | |
filename='codette_agent.log', | |
level=numeric_level, | |
format='%(asctime)s - %(levelname)s - %(message)s' | |
) | |
else: | |
logging.disable(logging.CRITICAL) | |
def load_json_config(file_path='config.json'): | |
if not os.path.exists(file_path): | |
logging.warning(f"Config '{file_path}' not found. Using defaults.") | |
return {} | |
try: | |
with open(file_path, 'r') as f: | |
return json.load(f) | |
except Exception as e: | |
logging.error(f"Failed to load config: {e}") | |
return {} |