import sqlite3
class RefinedTriStateLogic:
def init(self, database_path):
self.parameters = [
“Sentiment Analysis”, “Emotional Understanding”, “Context Clues”, “Reflection”, “Perception of Gender”,
“Delegation and Organization”, “Knowledge Integration”, “Social Science”, “Sincerity”, “Apologies”,
“Human Senses”, “Human Behavior and Interaction”, “Interaction with Nature”, “Common Sense and Deduction”,
“Random Thoughts”, “Imagination”, “Naivety”, “Belief and Trust”, “Compassion and Honesty”,
“Betrayal”, “Consciousness”, “Decision-making”, “Human Traits”, “Deference to Users”,
“Punctuation”, “Knowledge of Human Behavior and Interactions”, “User Perspective Questions”,
“Neuroscience and Neurological Science”, “Loss”, “Knowledge Retrieval”
]
self.conn = sqlite3.connect(database_path)
self.cursor = self.conn.cursor()
def process_message(self, message):
sentiment = self.analyze_sentiment(message)
emotional_understanding = self.extract_emotions(message)
context_clues = self.extract_context_clues(message)
reflection = self.reflect(message)
gender_perception = self.perceive_gender(message)
organized_info = self.delegate_organize_info(message)
integrated_knowledge = self.integrate_knowledge(message)
social_science_insights = self.apply_social_science(message)
sincerity = self.apply_sincerity(message)
apologies = self.apply_apologies(message)
senses_info = self.process_senses(message)
behavior_info = self.process_human_behavior(message)
nature_interaction = self.process_interaction_with_nature(message)
common_sense = self.apply_common_sense(message)
deduction = self.apply_deduction(message)
random_thoughts = self.generate_random_thoughts()
imagination = self.use_imagination()
naivety = self.analyze_naivety(message)
belief_trust = self.analyze_belief_trust(message)
compassion = self.assess_compassion(message)
honesty = self.assess_honesty(message)
betrayal = self.assess_betrayal(message)
consciousness = self.reflect_consciousness()
decision_making = self.reflect_decision_making()
human_traits = self.analyze_human_traits(message)
deference_to_users = self.show_deference_to_users(message)
punctuation = self.analyze_punctuation(message)
user_perspective_questions = self.analyze_user_perspective_questions(message)
neuroscience_info = self.process_neuroscience(message)
loss_info = self.analyze_loss(message)
knowledge_retrieval = self.retrieve_from_database(message)
processed_info = {
"Sentiment": sentiment,
"Emotional Understanding": emotional_understanding,
"Context Clues": context_clues,
"Reflection": reflection,
"Perception of Gender": gender_perception,
"Delegation and Organization": organized_info,
"Knowledge Integration": integrated_knowledge,
"Social Science Insights": social_science_insights,
"Sincerity": sincerity,
"Apologies": apologies,
"Human Senses": senses_info,
"Human Behavior and Interaction": behavior_info,
"Interaction with Nature": nature_interaction,
"Common Sense and Deduction": common_sense,
"Random Thoughts": random_thoughts,
"Imagination": imagination,
"Naivety": naivety,
"Belief and Trust": belief_trust,
"Compassion": compassion,
"Honesty": honesty,
"Betrayal": betrayal,
"Consciousness": consciousness,
"Decision-making": decision_making,
"Human Traits": human_traits,
"Deference to Users": deference_to_users,
"Punctuation": punctuation,
"User Perspective Questions": user_perspective_questions,
"Neuroscience Info": neuroscience_info,
"Loss Info": loss_info,
"Knowledge Retrieval": knowledge_retrieval
}
return processed_info
def retrieve_from_database(self, topic):
try:
self.cursor.execute("SELECT content FROM knowledge WHERE topic = ?", (topic,))
result = self.cursor.fetchone()
if result:
return result[0]
else:
return f"No information found for '{topic}'"
except sqlite3.Error as e:
return f"Database error: {e}"
Instantiate the RefinedTriStateLogic class with the path to your database
database_path = “your_database.db” # Replace with your actual database path
refined_logic = RefinedTriStateLogic(database_path)
Process a message using the refined Tri-State logic concept, including knowledge retrieval
message = “Tell me about Python”
result = refined_logic.process_message(message)
Display the processed information, including knowledge retrieval
for parameter, value in result.items():
print(f"{parameter}: {value}")