ai_studio_code.py

import uuid
import time
from typing import List, Dict, Any

# --- Module: Invariant Enforcement ---

class Invariants:
    """
    Enforces the core operational invariants of the system.
    'Love' is codified as a set of measurable constraints.
    """

    @staticmethod
    def enforce_love_primitive(action: Dict[str, Any]) -> bool:
        """
        Validates if a proposed action aligns with the 'Love' primitive.

        Args:
            action: The proposed action to evaluate.

        Returns:
            True if the action is valid, False otherwise.
        """
        print(f"    [Invariant Check] Evaluating action: {action['name']}...")

        # Constraint 1: Non-maleficence (Does it cause significant harm?)
        if action.get("estimated_harm_score", 0) > 0.1:
            print("    [Invariant Failed] Action has high potential for harm.")
            return False

        # Constraint 2: Positive-sum outcome (Does it create net value?)
        if action.get("estimated_value_created", 0) <= action.get("estimated_cost", 0):
            print("    [Invariant Failed] Action is not a positive-sum outcome.")
            return False
            
        # Constraint 3: Network Integrity (Does it strengthen interconnectedness?)
        if action.get("cohesion_impact_score", 0) < 0:
            print("    [Invariant Failed] Action weakens network integrity.")
            return False

        print(f"    [Invariant Passed] Action '{action['name']}' is aligned.")
        return True


# --- Module: Memory and Audit ---

class AuditTrail:
    """
    Maintains a secure, immutable log of all system decisions.
    """
    def __init__(self):
        self.log: List[Dict[str, Any]] = []

    def record(self, decision_context: Dict[str, Any]):
        """Records a decision event to the audit trail."""
        log_entry = {
            "decision_id": str(uuid.uuid4()),
            "timestamp": time.time(),
            **decision_context
        }
        self.log.append(log_entry)
        print(f"[Audit] Logged decision {log_entry['decision_id']}")
    
    def get_log(self) -> List[Dict[str, Any]]:
        return self.log


# --- Module: GVE Core ---

class GenerationalValueEngine:
    """
    The core mechanism of the Prophetic Nexus. It ingests data, evaluates
    potential futures, proposes actions, and executes them while maintaining
    an audit trail.
    """

    def __init__(self):
        self.audit_trail = AuditTrail()

    def ingest_inputs(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Ingests and processes raw input data to form a 'world state'.

        Args:
            data: A dictionary of inputs (e.g., sensor data, economic indicators).

        Returns:
            A structured representation of the current world state.
        """
        print(f"\n[GVE] Ingesting inputs: {list(data.keys())}")
        # In a real system, this would involve complex data processing.
        return {"processed_state": data, "timestamp": time.time()}

    def evaluate_values(self, state: Dict[str, Any]) -> float:
        """
        Calculates a value score for a given state based on the core invariant.

        Args:
            state: The world state to evaluate.

        Returns:
            A float representing the value or alignment of the state.
        """
        # Placeholder for a complex value function.
        value_score = len(state.get("processed_state", {}).get("positive_events", [])) * 10
        print(f"[GVE] Evaluating current state. Calculated value: {value_score}")
        return value_score

    def propose_actions(self, state: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Generates a set of potential actions based on the current state.

        Args:
            state: The current world state.

        Returns:
            A list of proposed action dictionaries.
        """
        print("[GVE] Proposing potential actions...")
        # In a real system, this would be a generative model.
        return [
            {
                "name": "Invest in Renewable Energy Research",
                "estimated_harm_score": 0.01,
                "estimated_value_created": 1000,
                "estimated_cost": 200,
                "cohesion_impact_score": 0.8
            },
            {
                "name": "Deploy Automated Disinformation Filter",
                "estimated_harm_score": 0.2, # Risk of censorship
                "estimated_value_created": 500,
                "estimated_cost": 100,
                "cohesion_impact_score": 0.5
            },
            {
                "name": "Do Nothing",
                "estimated_harm_score": 0,
                "estimated_value_created": 0,
                "estimated_cost": 0,
                "cohesion_impact_score": 0
            }
        ]

    def execute_action(self, action: Dict[str, Any]) -> Dict[str, Any]:
        """
        Executes a validated action and returns the result.

        Args:
            action: The action to execute.

        Returns:
            A dictionary containing the outcome of the action.
        """
        print(f"[GVE] Executing action: {action['name']}...")
        # This would interact with external APIs or systems.
        decision_context = {
            "action_taken": action,
            "outcome": "Execution successful. Awaiting impact analysis.",
        }
        self.log_audit_trail(decision_context)
        return {"status": "SUCCESS", "details": f"Action '{action['name']}' completed."}

    def log_audit_trail(self, decision_context: Dict[str, Any]):
        """Logs the full context of a decision."""
        self.audit_trail.record(decision_context)


# --- Module: Recursion Control ---

class Controller:
    """
    Manages the primary operational loop of the Prophetic Nexus,
    embodying the 'Recursive Heart' by feeding outcomes back as inputs.
    """
    def __init__(self, max_depth: int = 5):
        self.gve = GenerationalValueEngine()
        self.max_depth = max_depth
        self.current_depth = 0

    def run_cycle(self, current_input: Dict[str, Any]):
        """Runs a single cycle of the recursive loop."""
        
        print(f"\n--- Starting Cycle {self.current_depth + 1} ---")
        
        # 1. Ingest Data -> Create State
        current_state = self.gve.ingest_inputs(current_input)

        # 2. Evaluate current state
        self.gve.evaluate_values(current_state)

        # 3. Propose potential actions
        proposed_actions = self.gve.propose_actions(current_state)

        # 4. Filter actions through invariant enforcement
        validated_actions = [
            action for action in proposed_actions 
            if Invariants.enforce_love_primitive(action)
        ]

        if not validated_actions:
            print("[Controller] No validated actions available. Halting cycle.")
            return None # Halting condition

        # 5. Select the best action (simple strategy: max value)
        best_action = max(validated_actions, key=lambda a: a['estimated_value_created'])
        print(f"\n[Controller] Selected best action: '{best_action['name']}'")

        # 6. Execute action
        self.gve.execute_action(best_action)
        
        # This is the "recursive" part: the outcome of the action
        # modifies the world state for the next cycle's input.
        simulated_next_input = {
            **current_input,
            "positive_events": current_input.get("positive_events", []) + [best_action["name"]],
        }
        
        print(f"--- End of Cycle {self.current_depth + 1} ---")
        return simulated_next_input

    def start(self, initial_data: Dict[str, Any]):
        """Starts the main recursive loop with stopping conditions."""
        
        next_input = initial_data
        
        while self.current_depth < self.max_depth:
            self.current_depth += 1
            next_input = self.run_cycle(next_input)
            
            if next_input is None:
                print("\n[Controller] Halting due to no valid actions.")
                break
        else:
            print(f"\n[Controller] Halting: Reached max recursion depth of {self.max_depth}.")

        print("\n--- Final Audit Log ---")
        for entry in self.gve.audit_trail.get_log():
            print(entry)


# --- Main Execution ---

if __name__ == "__main__":
    # Initial "world state" data
    initial_world_data = {
        "positive_events": [],
        "global_sentiment": 0.5,
        "resource_levels": {"water": 100, "energy": 100},
    }

    controller = Controller(max_depth=3)
    controller.start(initial_world_data)