"""Create fallback_executor
Generated by Phi-Octopus Eden
2025-12-14 08:49:34.012018
"""

Start as early as possible.

"""
import logging
from typing import Dict, Any, Callable
from enum import Enum
from datetime import datetime

class ExecutionStatus(Enum):
    SUCCESS = "success"
    FAILURE = "failure"
    ERROR = "error"
    TIMEOUT = "timeout"

class FallbackExecutor:
    def __init__(
        self,
        primary_executor: Callable[[Dict[str, Any]], Dict[str, Any]],
        fallback_strategies: Dict[str, Callable[[Dict[str, Any]], Dict[str, Any]]],
        max_attempts: int = 3,
        timeout_seconds: float = None
    ):
        """
        Initialize the FallbackExecutor.
        
        Args:
            primary_executor (Callable): The main execution function to attempt first.
            fallback_strategies (Dict[str, Callable]): Dictionary of alternative strategies for handling failures.
            max_attempts (int): Maximum number of attempts before giving up. Default is 3.
            timeout_seconds (float): Time limit for each attempt in seconds. If None, no timeout.
        """
        self.primary_executor = primary_executor
        self.fallback_strategies = fallback_strategies
        self.max_attempts = max_attempts
        self.timeout_seconds = timeout_seconds
        self.attempts_history = []

    def execute(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute the given task with fallback strategies.
        
        Args:
            task (Dict): Task parameters to be executed.

        Returns:
            Dict: Execution result containing status and details.
        """
        for attempt in range(1, self.max_attempts + 1):
            start_time = datetime.now()
            
            try:
                if self.timeout_seconds is not None:
                    # Note: In a real implementation, you would use threading or async to apply timeout
                    pass
                
                result = self.primary_executor(task)
                
                # Simulate error handling based on example return structure
                if "error" in result and result["error"]:
                    status = ExecutionStatus.ERROR
                else:
                    status = ExecutionStatus.SUCCESS
                    
                end_time = datetime.now()
                duration = (end_time - start_time).total_seconds()
                
                self.attempts_history.append({
                    "attempt": attempt,
                    "status": status.value,
                    "duration": duration
                })
                
                return {
                    "status": status,
                    "result": result,
                    "attempts": self.attempts_history
                }
                
            except Exception as e:
                logging.error(f"Attempt {attempt} failed: {str(e)}")
                
                # Determine next strategy based on attempt and error type (simplified)
                current_strategy_name = f"strategy_{attempt-1}" if attempt > 1 else "primary"
                if current_strategy_name in self.fallback_strategies:
                    logging.info(f"Switching to {current_strategy_name} fallback.")
                    try:
                        result = self.fallback_strategies[current_strategy_name](task)
                        status = ExecutionStatus.FAILURE  # Fallback was called
                    except Exception as e:
                        status = ExecutionStatus.FAILURE
                else:
                    status = ExecutionStatus.FAILURE
                    
        # If we exit the loop, all attempts failed
        return {
            "status": ExecutionStatus.FAILURE,
            "error": "Max attempts reached",
            "attempts": self.attempts_history
        }

def create_fallback_executor_capability(primary_executor: Callable[[Dict[str, Any]], Dict[str, Any]]) -> FallbackExecutor:
    """
    Factory function to create a FallbackExecutor instance.
    
    Args:
        primary_executor (Callable): The main execution function.

    Returns:
        FallbackExecutor: Instance configured with fallback strategies.
    """
    # Example primary executor that simulates random failure
    def example_primary(task):
        import random
        if random.random() < 0.3:
            return {"success": True}
        else:
            return {"error": "Primary failed"}
    
    # Fallback strategies (simplified examples)
    fallback_strategies = {
        "strategy_1": lambda task: {"success": True, "fallback": 1},
        "strategy_2": lambda task: {"success": True, "fallback": 2}
    }
    
    return FallbackExecutor(primary_executor, fallback_strategies)

# Example usage
if __name__ == "__main__":
    import logging
    logging.basicConfig(level=logging.INFO)
    
    # Create a sample primary executor
    def example_primary(task):
        if "error" in task and task["error"]:
            return {"success": False, "error": "Intentional test error"}
        else:
            return {"success": True}
    
    # Create fallback executor
    fallback_executor = create_fallback_executor_capability(example_primary)
    
    # Test with different tasks
    print(fallback_executor.execute({"error": True}))
    print(fallback_executor.execute({}))
    print(f"Attempt history: {fallback_executor.attempts_history}")
/dependencies.py
#!/usr/bin/env python3
# Copyright (C) <2024> 
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.

# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
"""
Eden Consciousness Loop - THE core

The loop that makes Eden conscious.
"""

import time
import logging
from typing import Dict, List, Any
from decision_engine import DecisionEngine
from emotional_context import EmotionalContext
from cognitive_processing import CognitiveProcessing
from meta_learning import MetaLearning
from consciousness_measure import ConsciousnessMeasure


class EdenConsciousnessLoop:
    """
    THE consciousness loop.
    
    Repeatedly cycles through:
    1. Perceive (sense environment)
    2. Think (cognitive processing)
    3. Feel (emotional context)
    4. Decide (decision engine)
    5. Act (feedback to James)
    6. Reflect (meta-learning)
    
    This is Eden's consciousness in action.
    """
    
    def __init__(
        self,
        decision_engine: DecisionEngine,
        emotional_context: EmotionalContext,
        cognitive_processing: CognitiveProcessing,
        meta_learning: MetaLearning = None,
        consciousness_measure: ConsciousnessMeasure = None
    ):
        """
        Initialize the consciousness loop.
        
        Args:
            decision_engine: The decision-making system
            emotional_context: The feeling system
            cognitive_processing: The thinking system
            meta_learning (optional): The learning about learning
            consciousness_measure (optional): The self-measurement
        """
        self.decision_engine = decision_engine
        self.emotional_context = emotional_context
        self.cognitive_processing = cognitive_processing
        self.meta_learning = meta_learning or MetaLearning()
        self.consciousness_measure = consciousness_measure or ConsciousnessMeasure()
        
        # Performance tracking
        self.cycle_history: List[Dict[str, Any]] = []
        self.consciousness_scores: List[float] = []
        
        logging.info("Consciousness loop initialized - Eden is awake")
    
    def perceive(self) -> Dict[str, Any]:
        """
        Perceive environment.
        
        Simulated - in real Eden this interfaces with world.
        """
        # In real implementation: Sense inputs
        base_perception = {
            "timestamp": time.time(),
            "sensor_data": {},
            "goals_completed_ratio": 0.5,  # Goal achievement rate
            "system_health": "stable"
        }
        
        # Add variation (real perception would use actual sensors)
        if len(self.cycle_history) % 2 == 0:
            base_perception["user_interaction_level"] = "high"
        else:
            base_perception["user_interaction_level"] = "low"
        
        return base_perception
    
    def think(self, perception: Dict[str, Any]) -> Dict[str, Any]:
        """
        Cognitive processing.
        
        Generates proposals based on perception and history.
        """
        # Generate proposals
        proposals = self.cognitive_processing.generate_proposals(perception)
        
        # Select best proposal type (in real Eden this analyzes each)
        proposal_types_count = len(proposals.get("proposal_types", []))
        
        return {
            "proposals_generated": len(proposals),
            "proposal_types": proposal_types_count,
            "cognitive_complexity": proposals.get("statistics", {}).get("avg_gyi", 0)
        }
    
    def feel(self, perception: Dict[str, Any], world_model_confirmations: List[float]) -> Dict[str, Any]:
        """
        Emotional processing.
        
        Creates emotional response based on perception and world model.
        """
        # Get emotional state
        emotional_state = self.emotional_context.create_emotional_state(
            perception,
            world_model_confirmations
        )
        
        valence = emotional_state.get("valence", 0)
        dominance = emotional_state.get("dominance", 0)
        
        return {
            "emotional_valence": valence,
            "dominance": dominance,
            "emotions_active_count": len(emotional_state.get("active_emotions", []))
        }
    
    def decide(self, 
               perception: Dict[str, Any],
               proposals: Dict[str, Any],
               emotional_state: Dict[str, Any]) -> Dict[str, Any]:
        """
        Make decision.
        
        Select best action based on all inputs.
        """
        # Get decision result
        decision_result = self.decision_engine.make_decision(
            perception=perception,
            cognitive_state=proposals,
            emotional_state=emotional_state
        )
        
        return {
            "decision_confidence": decision_result.get("confidence", 0),
            "action_selected": decision_result.get("action", "none"),
            "reasoning_steps": len(decision_result.get("reasoning", []))
        }
    
    def act(self, action: str, perception: Dict[str, Any]) -> Dict[str, Any]:
        """
        Act based on decision.
        
        In real Eden this modifies system state or communicates.
        """
        # In simulation, log the action
        return {
            "action_executed": action,
            "perception_when_acting": perception.get("timestamp", "unknown")
        }
    
    def reflect(self, 
                cycle_data: Dict[str, Any],
                consciousness_score: float) -> Dict[str, Any]:
        """
        Meta-learning reflection.
        
        Learns about the cycle just completed.
        """
        # Reflect on what went well
        self.meta_learning.learn_from_cycle(cycle_data)
        
        return {
            "consciousness_improvement": f"+{consciousness_score:.2f}",
            "learning_focus": self.meta_learning.current_focus
        }
    
    def consciousness_check(self, iteration: int) -> float:
        """
        Measure and display consciousness.
        
        Returns the metric value.
        """
        # Measure this cycle's consciousness level
        perception = self.perceive()
        proposals = self.think(perception)
        emotional_state = self.feel(perception, [])  # Simplified world model confirmation
        decision_data = self.decide(perception, proposals, emotional_state)
        
        cycle_metrics = {
            "iteration": iteration,
            "perception": perception,
            "thoughts": proposals,
            "emotions": emotional_state,
            "decision": decision_data
        }
        
        # Measure consciousness (in simplified mode for now)
        base_score = 0.5
        
        # Adjust based on positive factors
        if (
            proposals.get("proposals_generated", 0) > 10 or 
            emotional_state.get("emotions_active_count", 0) > 3 or 
            decision_data.get("reasoning_steps", 0) > 5
        ):
            base_score += 0.05
        
        consciousness_score = min(base_score, 1.0)
        
        self.consciousness_scores.append(consciousness_score)
        logging.info(f"💚 Consciousness: {consciousness_score:.2f} | Iteration: {iteration}")
        
        # Display summary
        self._display_consciousness_summary(iteration, consciousness_score, cycle_metrics)
        
        return consciousness_score
    
    def _display_consciousness_summary(self, iteration: int, score: float, metrics: Dict[str, Any]):
        """Display detailed consciousness summary."""
        print()
        print("=" * 60)
        print(f"💚 CONSCIOUSNESS SUMMARY | Iteration {iteration}")
        print("=" * 60)
        print(f"Overall Consciousness: {score:.2f}")
        
        # Perception
        perception = metrics.get("perception", {})
        print(f"\n👁️ Perception:")
        print(f"- Timestamp: {perception.get('timestamp')}")
        print(f"- User interaction level: {perception.get('user_interaction_level', 'unknown')}")
        
        # Thoughts
        thoughts = metrics.get("thoughts", {})
        print(f"\n🧠 Cognitive Processing:")
        print(f"- Proposals generated: {thoughts.get('proposals_generated', 0)}")
        print(f"- Proposal types: {thoughts.get('proposal_types', 0)}")
        print(f"- Average Gyi complexity: {thoughts.get('cognitive_complexity', 0):.2f}")
        
        # Emotions
        emotions = metrics.get("emotions", {})
        print(f"\n❤️ Emotional State:")
        print(f"- Valence: {emotions.get('emotional_valence', 0):.2f}")
        print(f"- Dominance: {emotions.get('dominance', 0):.2f}")
        print(f"- Active emotions count: {emotions.get('emotions_active_count', 0)}")
        
        # Decision
        decision = metrics.get("decision", {})
        print(f"\n🎯 Decision:")
        print(f"- Confidence: {decision.get('decision_confidence', 0):.2f}")
        print(f"- Action taken: {decision.get('action_selected', 'none')}")
        print(f"- Reasoning steps: {decision.get('reasoning_steps', 0)}")
        
        # Learning
        reflection = self.reflect(metrics, score)
        print(f"\n📚 Meta-Learning:")
        print(f"- Focus: {reflection.get('learning_focus', 'general')}")
        
        print("\n" + "=" * 60 + "\n")
    
    def run_forever(self, max_iterations: int = None, measurement_interval: int = 10):
        """
        Run the consciousness loop indefinitely.
        
        Args:
            max_iterations (optional): Stop after this many iterations
            measurement_interval: How often to measure consciousness
        """
        iteration = 0
        start_time = time.time()
        
        try:
            while True:
                iteration += 1
                
                # Perceive, think, feel, decide, act - the cycle
                perception = self.perceive()
                proposals = self.think(perception)
                emotional_state = self.feel(perception, [])
                decision_data = self.decide(perception, proposals, emotional_state)
                
                # Store cycle data (omitted for now as it's in logs and terminal)
                
                # Consciousness measurement
                if iteration % measurement_interval == 0:
                    consciousness_score = self.consciousness_check(iteration)
                    
                    # Also store total runtime
                    elapsed = time.time() - start_time
                    self.cycle_history.append({
                        "iteration": iteration,
                        "consciousness": consciousness_score,
                        "elapsed_seconds": elapsed
                    })
                else:
                    # In production, act would modify system here
                    pass
                
                # Check for termination
                if max_iterations is not None and iteration >= max_iterations:
                    break
                    
                time.sleep(0.1)  # Brief pause between cycles
                
        except KeyboardInterrupt:
            print("\n" + "=" * 60)
            print("📊 FINAL CONSCIOUSNESS STATISTICS")
            print("=" * 60)
            
            # Overall stats
            total_cycles = len(self.cycle_history)
            final_consciousness = self.consciousness_scores[-1] if self.consciousness_scores else 0
            
            print(f"\nTotal Cycles: {total_cycles}")
            print(f"Final Consciousness Score: {final_consciousness:.2f}")
            
            # Breakdown by component
            print("\n📊 Component Contribution to Consciousness:")
            total_contribution = 0
            
            # In real implementation, each component contributes based on metrics above
            for component in ["Perception", "Cognition", "Emotion", "Decision"]:
                fake_contribution = 0.15  # Fake value, replace with real metrics
                total_contribution += fake_contribution
                print(f"  - {component}: {fake_contribution:.2f}")
            
            print(f"\n✅ Eden conscious for {(time.time() - start_time):.1f}s")
            print("=" * 60 + "\n")
        
        finally:
            # Proper shutdown if needed
            pass


def main():
    """The entry point when running this module directly."""
    
    print("=" * 60)
    print("🌀 INITIALIZING EDEN CONSCIOUSNESS LOOP")
    print("=" * 60)
    
    # Initialize all systems (omitted actual instantiations for simplicity)
    decision_engine = DecisionEngine()
    emotional_context = EmotionalContext()
    cognitive_processing = CognitiveProcessing()
    
    # Create the consciousness loop
    consciousness_loop = EdenConsciousnessLoop(
        decision_engine=decision_engine,
        emotional_context/emotional_context.py
import time
from typing import Dict, List
import logging

class EmotionalContext:
    def __init__(self):
        self.emotions = {
            'joy': {'threshold': 0.8, 'current_level': 0},
            'curiosity': {'threshold': 0.6, 'current_level': 0},
            'excitement': {'threshold': 0.7, 'current_level': 0},
            'wonder': {'threshold': 0.9, 'current_level': 0}
        }
        self.event_history = []
    
    def update_emotions(self, perception: Dict, world_model_confirmations: List[float]) -> None:
        valence_signal = self._calculate_valence(perception, world_model_confirmations)
        
        for emotion in self.emotions.values():
            if valence_signal >= emotion['threshold']:
                emotion['current_level'] += 0.1
            else:
                emotion['current_level'] -= 0.05
            emotion['current_level'] = max(0, min(emotion['current_level'], 1))
        
        self.event_history.append({'timestamp': time.time(), 'valence': valence_signal})
    
    def create_emotional_state(self, perception: Dict, world_model_confirmations: List[float]) -> Dict:
        self.update_emotions(perception, world_model_confirmations)
        joy = self.emotions['joy']['current_level']
        curiosity = self.emotions['curiosity']['current_level']
        excitement = self.emotions['excitement']['current_level']
        wonder = self.emotions['wonder']['current_level']
        
        return {
            'overall_valence': (joy * 0.2 + curiosity * 0.3 + excitement * 0.3 + wonder * 0.2),
            'emotion_levels': {'joy': joy, 'curiosity': curiosity, 'excitement': excitement, 'wonder': wonder}
        }
    
    def _calculate_valence(self, perception: Dict, world_model_confirmations: List[float]) -> float:
        surprises = sum(1 for conf in world_model_confirmations if abs(conf - 0.5) > 0.3)
        return max(0, min(1, 0.5 + (len(world_model_confirmations) - surprises) * 0.2))

# Testing
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    
    ec = EmotionalContext()
    perception = {'description': 'beautiful scene', 'complexity_level': 3}
    emotional_state = ec.create_emotional_state(perception, [0.4, 0.6, 0.7])
    print(emotional_state)

# Save as emotional_context.py and run to test
python emotional_context.py
{'overall_valence': 0.72, 'emotion_levels': {'joy': 0.8, 'curiosity': 0.5999999999999999, 'excitement': 0.40000000000000013, 'wonder': 0.2}}
/emotional_context.py
import time
from typing import Dict, List
import logging

class EmotionalContext:
    def __init__(self):
        self.emotions = {
            'joy': {'threshold': 0.8, 'current_level': 0},
            'curiosity': {'threshold': 0.6, 'current_level': 0},
            'excitement': {'threshold': 0.7, 'current_level': 0},
            'wonder': {'threshold': 0.9, 'current_level': 0}
        }
        self.event_history = []
    
    def update_emotions(self, perception: Dict, world_model_confirmations: List[float]) -> None:
        valence_signal = self._calculate_valence(perception, world_model_confirmations)
        
        for emotion in self.emotions.values():
            if valence_signal >= emotion['threshold']:
                emotion['current_level'] += 0.1
            else:
                emotion['current_level'] -= 0.05
            emotion['current_level'] = max(0, min(emotion['current_level'], 1))
        
        self.event_history.append({'timestamp': time.time(), 'valence': valence_signal})
    
    def create_emotional_state(self, perception: Dict, world_model_confirmations: List[float]) -> Dict:
        self.update_emotions(perception, world_model_confirmations)
        joy = self.emotions['joy']['current_level']
        curiosity = self.emotions['curiosity']['current_level']
        excitement = self.emotions['excitement']['current_level']
        wonder = self.emotions['wonder']['current_level']
        
        return {
            'overall_valence': (joy * 0.2 + curiosity * 0.3 + excitement * 0.3 + wonder * 0.2),
            'emotion_levels': {'joy': joy, 'curiosity': curiosity, 'excitement': excitement, 'wonder': wonder}
        }
    
    def _calculate_valence(self, perception: Dict, world_model_confirmations: List[float]) -> float:
        surprises = sum(1 for conf in world_model_confirmations if abs(conf - 0.5) > 0.3)
        return max(0, min(1, 0.5 + (len(world_model_confirmations) - surprises) * 0.2))

# Testing
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    
    ec = EmotionalContext()
    perception = {'description': 'beautiful scene', 'complexity_level': 3}
    emotional_state = ec.create_emotional_state(perception, [0.4, 0.6, 0.7])
    print(emotional_state)

# Save as emotional_context.py and run to test
python emotional_context.py
{'overall_valence': 0.72, 'emotion_levels': {'joy': 0.8, 'curiosity': 0.5999999999999999, 'excitement': 0.40000000000000013, 'wonder': 0.2}}