Spaces:
Sleeping
Sleeping
File size: 7,086 Bytes
93917f2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
codette_demo_script = """
import logging
import time
from datetime import datetime
import sympy as sp
import numpy as np
import pymc as pm
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('codette_demo.log'),
logging.StreamHandler()
]
)
class Codette:
def __init__(self, user_name="User", demo_mode=False):
self.user_name = user_name
self.demo_mode = demo_mode
self.memory = []
self.session_start = datetime.now()
self.response_count = 0
if demo_mode:
logging.info("CODETTE AI DEMO MODE ACTIVATED")
logging.info("=" * 50)
self.audit_log("Codette initialized", system=True)
self.audit_log(f"Session started at {self.session_start}", system=True)
def audit_log(self, message, system=False):
source = "SYSTEM" if system else self.user_name
timestamp = datetime.now().strftime("%H:%M:%S.%f")[:-3]
if self.demo_mode:
log_message = f"[{timestamp}] {source}: {message}"
logging.info(log_message)
if "perspectives" in message.lower():
logging.info("BRAIN " + "-" * 40)
elif "quantum" in message.lower():
logging.info("QUANTUM " + "-" * 40)
elif "security" in message.lower():
logging.info("SECURITY " + "-" * 40)
else:
logging.info(f"{source}: {message}")
safe_message = message[:100] + "..." if len(message) > 100 else message
print(f"[Log] {source}: {safe_message}")
def analyze_sentiment(self, text):
positive_words = ['good', 'great', 'excellent', 'amazing', 'wonderful']
negative_words = ['bad', 'terrible', 'awful', 'horrible', 'disappointing']
text_lower = text.lower()
pos_count = sum(1 for word in positive_words if word in text_lower)
neg_count = sum(1 for word in negative_words if word in text_lower)
compound = (pos_count - neg_count) / max(len(text.split()), 1)
score = {
'compound': compound,
'pos': pos_count / max(len(text.split()), 1),
'neg': neg_count / max(len(text.split()), 1),
'neu': 1 - abs(compound)
}
self.audit_log(f"Sentiment analysis: {score}")
return score
def respond(self, prompt):
self.response_count += 1
start_time = time.perf_counter()
processing_start = datetime.now()
sentiment = self.analyze_sentiment(prompt)
self.memory.append({"prompt": prompt, "sentiment": sentiment, "timestamp": processing_start})
modules = [
self.neuralNetworkPerspective,
self.newtonianLogic,
self.daVinciSynthesis,
self.resilientKindness,
self.quantumLogicPerspective,
self.philosophicalInquiry,
self.copilotAgent,
self.mathematicalRigor,
self.symbolicReasoning
]
responses = []
active_modules = []
for module in modules:
try:
result = module(prompt)
responses.append(result)
active_modules.append(module.__name__)
except Exception as e:
responses.append(f"[Error] {module.__name__} failed: {e}")
processing_time = time.perf_counter() - start_time
self.audit_log(f"Perspectives used: {active_modules}")
return "\\n\\n".join(responses)
def neuralNetworkPerspective(self, text):
return "[NeuralNet] Based on historical patterns, adaptability and ethical alignment drive trustworthiness."
def newtonianLogic(self, text):
return "[Reason] If openness increases verifiability, and trust depends on verifiability, then openness implies higher trust."
def daVinciSynthesis(self, text):
return "[Dream] Imagine systems as ecosystems � where open elements evolve harmoniously under sunlight, while closed ones fester in shadow."
def resilientKindness(self, text):
return "[Ethics] Your concern reflects deep care. Let's anchor this response in compassion for both users and developers."
def quantumLogicPerspective(self, text):
prior_open = 0.7 if "open-source" in text.lower() else 0.5
prior_prop = 1 - prior_open
try:
with pm.Model() as model:
trust_open = pm.Beta("trust_open", alpha=prior_open * 10, beta=(1 - prior_open) * 10)
trust_prop = pm.Beta("trust_prop", alpha=prior_prop * 10, beta=(1 - prior_prop) * 10)
better = pm.Deterministic("better", trust_open > trust_prop)
trace = pm.sample(draws=1000, chains=2, progressbar=False, random_seed=42)
prob = float(np.mean(trace.posterior["better"].values))
return f"[Quantum] Bayesian estimate: There is a {prob*100:.2f}% probability that open-source is more trustworthy in this context."
except:
return "[Quantum] Quantum processing indicates multiple probability states favor transparency and openness."
def philosophicalInquiry(self, text):
return "[Philosophy] From a deontological lens, openness respects autonomy and truth. From a utilitarian view, it maximizes communal knowledge. Both suggest a moral edge for openness."
def copilotAgent(self, text):
return "[Copilot] I can interface with APIs or code tools to test claims, retrieve documentation, or automate analysis. (Simulated here)"
def mathematicalRigor(self, text):
try:
expr = sp.sympify("2*x + 1")
solved = sp.solve(expr - 5)
return f"[Math] For example, solving 2x + 1 = 5 gives x = {solved[0]} � demonstrating symbolic logic at work."
except:
return "[Math] Mathematical analysis confirms logical consistency in the reasoning chain."
def symbolicReasoning(self, text):
if "transparency" in text.lower():
rule = "If a system is transparent, then it is more auditable. If it is more auditable, then it is more trustworthy."
return f"[Symbolic] Rule chain:\\n{rule}\\nThus, transparency ? trust."
else:
return "[Symbolic] No rule matched. Default: Trust is linked to observable accountability."
def get_session_stats(self):
session_duration = (datetime.now() - self.session_start).total_seconds()
return {
"session_duration": session_duration,
"responses_generated": self.response_count,
"memory_entries": len(self.memory),
"avg_response_time": session_duration / max(1, self.response_count)
}
"""
# Write the script
with open(codette_script_path, "w") as f:
f.write(codette_demo_script)
codette_script_path.as_posix() |